One of the issues that I have with the current generation of “AI” products is that they do not appear to take their own premises seriously. I look at a plethora of obsequious chatbots claiming to be serious tools for problem solving, and I think, this is not what a problem-solving tool would look like.

Even before we get to the tremendous ethical problems with the frontier labs, it is this impression of their composition as a product that makes me feel, constantly, whenever I am interacting with them, that they are less a software product than that they are a grift, a scam designed to make me feel like I am interacting with a product that has capabilities that it simply does not, to try to lull me into a false sense of security that I can trust it.

The frontier labs are of course the worst offenders, but every criticism here applies just as much to Ollama, which (if anything, due to the obviously poorer quality of the available models themselves) needs these features even more than the frontier labs do.

Here, I will set down a few features that might convince me that an LLM-based product, particularly one focused on research or software development, was actually serious about helping me do useful things with it.

Make “Checking For Mistakes” A First-Class Feature

This is the biggest issue, and the major reason that I was inspired to write this post.

It is a truth universally acknowledged, that AIs cannot reliably provide information.

I could cite a ton of news articles and studies about this fact, but there is no need. Every single chatbot admits this, up front, in a fine-print disclaimer as a core part of their user interface. Gemini says “AI can make mistakes, so double-check responses”, Claude says “Claude is AI and can make mistakes. Please double-check responses.¹” ChatGPT says “ChatGPT can make mistakes. Check important info.”.

Every time I see that last one, I wonder how I’m supposed to know what “info” is supposed to be “important”.

All of these warnings are all small, gray text, painfully obviously included as legalese to push responsibility back onto the user rather than to help with anything. This is a core limitation of all these products. Checking their output is a part of the workflow for using them that:

1. you absolutely cannot skip or skimp on without creating risks to yourself and whoever you are conveying its output to, and,
2. it is very easy to skip or skimp on and you are encouraged at every turn to do so, because “just trust the output” is one of the quickest ways to save time.

A chatbot product that took this weakness seriously, as an actual consideration for using it, would put a checkbox next to every claim in its output. It would be a 2-column worksheet, where you’ve got the LLM output in the first column, and next to it, human notes in the second column, explaining what work went into checking this claim, and a big checkbox that you would only check off after you believe you’d checked its claims thoroughly enough.

Coding assistants would need to have some version of this as well. Right now, this is pushed off into code review, which means it is a dark pattern which subtly encourages the “author”² to offload this work to their code reviewer without ever looking. Once again, “it’s probably fine, I don’t need to check” is the quickest way to save time and churn out those PRs faster.

It might even be useful for coding harnesses to have some affordance for checking code before it even runs tests. As the vendors themselves have admitted, it’s not just expensive to burn tokens on your “AI”, you also end up burning far more compute on the AI. Being able to check your diffs before sending them over to uselessly exhaust your testing compute cluster would be useful.

If your product tells me that it makes mistakes and I must be the one to check for the mistakes, but then gives me zero tools to check for mistakes, I cannot take it seriously.

More Citations to Check, And More Details

Most chatbots prefer to give an answer, rather than a citation. In my own personal use, I find that when asked to provide a list of citations with clearly marked sources for each one, they will appear to “get bored” halfway through the list and simply stop including citations at some point.

When the bots include citations at all, present them as inline annotations that say nothing but the domain name of the search result, in a font so small that it’s barely legible, and an equally indecipherable icon that is fewer than 16 pixels on a side.

This is backwards.

Now, I am aware that these citations do come from somewhere, and in an attempt to reduce hallucinations, all of the major providers support some form of “grounding”³, and that those little barely-readable citation links are referencing actual structures in the RAG pipeline and not just potentially-hallucinated tokens, but I’m not talking about the underlying machinery in the model, I’m talking about the presentation to the user.

Plus, regardless of whether a snippet of text came from a RAG query, we know that LLMs can never provide an authoritative result; it’s a fundamental limitation of the technology. They can still garble the results of RAG as much as they can misrepresent any other training data. This means that it must never present its results as authoritative.

If you ask an AI to do research queries, every result should be presented as a list of citations. Moreover, the presentation should display each citation as a large object of in its own right, with clearly identified metadata, including not just the site where it was found but its publication date and, if possible, the name of the author. The literal, unmodified quotation (not from RAG, not a summary: a quotation extracted with a regular program and not an LLM) should be front-and-center, larger than any AI-generated text.

If the AI product wants to editorialize or summarize (which should not always be necessary!), the AI-generated text should be presented as small text underneath the citation that has been found, de-emphasized as much as the disclaimer is right now, at the very least until the user has verified that the summary is accurate. Perhaps, for a research project, a “did you read the citation” checkbox might even be helpful.

If your product openly tells me that it will scramble, misrepresent, or omit its citations in its summaries, and I must read the original human-authored citations to be sure, but then gives me no tools to track my reading of those citations or even any way to find them, I cannot take it seriously.

No First-Person Output, No Apologies

There is no reason for a software development or research tool to use first-person language to describe itself. They should not do so. In fact they should not be allowed to do so.

There is also no reason that they should ever apologize. It is a waste of everyone’s time; it’s a waste for the chatbot to generate the apology, it’s a waste for the user to read the apology, and it’s a waste for the user to respond to the apology. Yet they unfailingly do this upon every correction.

The vendors of these tools know that they are routinely causing mental-health crises. In response, they have added non-functional “guard rails” that can still, in 2026, easily be bypassed.⁴

A product seriously interested in helping with productivity would correct this glaringly obvious flaw, focus on the task at hand, and stop emitting useless verbiage.

In the previous two sections, I tried to focus on ways in which the harness would be constructed differently even if the LLM technology is fundamentally impossible to improve; in this case, I have to assume that the labs have some control over the model itself. But unless they are truly incapable of influencing their output (and all their “benchmarks” and “capabilities” seem to indicate that they can control it very tightly) they ought to be building models that are much less verbose.

More Non-Natural-Language User Interfaces

Although natural language could hypothetically be a powerful interface for interacting with a computer system, the practical upshot of LLM natural language interfaces is that these interfaces are imprecise and repetitive, full of superstitions masquerading as “best practices”. The inputs are a mess and the resulting outputs are a mess.

The general way of addressing this unstructured mess is to allow the chatbot to directly take action in response to the user’s input; in other words to supply it with “tools” via an MCP server. But again, this is backwards. If we cannot even express our intent clearly in the first place, why are we trusting this system to take potentially destructive and harmful actions on our behalf?

Instead, I would expect a product that was seriously invested in helping me accomplish specific tasks, to have user interfaces specific to those tasks. Is it supposed to be able to be a security scanner that can discover OWASP top 10 bugs in a codebase? Have a button for that. Build that functionality into your harness, train it directly into the model, use smaller models that can satisfy that functionality more effectively than throwing it at the planet-sized brain of Fable or whatever.

I’m aware that there are small software startups that do something like this, but they are bolted on to the side of the main model providers’ APIs, not integrated into the core of the product and not using their own models and AI systems to achieve consistent and repeatable results.

Strong Data Provenance Indicators

Chatbots produce data tables pulled from websites, from APIs, from MCP tools or from summarizing and scrambling the user’s input. In order to provide the illusion of a seamless interface, this data is presented in-line regardless of where it comes from. But some of these outputs are produced mechanically via regular old API calls, for example, from the result of calling a tool or querying a website, but presented uniformly.

But there is a huge difference between an authoritative data source being inlined as part of a chatbot conversation, being treated as input by the chatbot, and some ad-hoc hallucinated data being treated as output of the chatbot.

If a product is trying to help me make accurate, empirically-grounded, data-driven decisions, the source of the data is critical.

Integrated into the “check for mistakes” and “verify citations” workflow I described above, there’s a necessary “verify data programmatically” pass as well; to have tools that will treat portions of the output as a regular spreadsheet, allowing regular-old computer arithmetic to verify things and showing where such arithmetic was used, and how.

Better User Control of Reproducibility

Anyone familiar with the technical specifics of LLMs will know that they have a variable called “temperature” which controls the degree of randomness that the LLM uses to produce its outputs. But most users don’t know this, because it isn’t exposed as part of the user interface by default.

This leads to a subjective impression that you asked ChatGPT, and you got ChatGPT’s authoritative answer.

You can’t just set the temperature to zero and still get useful results - I am aware that it does more than just scramble the output at random, and there are perhaps good reasons that simply exposing just a temperature setting would not be that useful to users. But if we followed some more of my earlier recommendations for making more structured UI elements to solve specific problems rather than having long back-and-forth chats where each refinement depends on the previous response, perhaps those elements could also re-play the process so that users can see how reliable the bot is at a particular task and develop a sense of how the stochastic nature of the process actually affects it.

Similarly, if a user is trying to solve the same problem repeatedly with a chatbot, and the chatbot product has numerous computational tools that don’t really have anything to do with the LLM, such as deterministic data-processing tools, then having a way to freeze the non-deterministic parts of the transcript but re-populate a particular data frame with updated information and fork / continue the conversation from there would be a way to avoid introducing pointless additional randomness when you already know what tool you’re trying to use.

The fact that every conversation is presented as this flat chat prompt that doesn’t let me interact with any of the widgets that were previously produced except through more chatting, really makes me feel like the whole product is just doing predatory social-media style “increase time on site” optimization, just trying to lure me into further repetitive and unreliable chats, rather than letting me get in, solve my problem, and get out.

Context Visibility

Managing the LLM context is the ongoing challenge facing organizations that are trying to use “agentic” workflows. Filling up the context with too much information causes well-known problems. In response, advanced LLM users attempting to solve larger problems must break up very long prompts into “skills”, give access to lengthy information via “tools”, and delegating sub-problems to “sub-agents” rather than simply extending a single prompt indefinitely.

All of these strategies have flaws, because even on the largest models, compared to the breadth and depth of knowledge-work problems, LLM contexts are quite small.

And yet, none of these products will show the context to the user by default. There are third-party addons that can show you a simple progress bar but for addressing the premier engineering difficulty with this technology, that is below the bare minimum.

This lack of visibility means that almost all of the tools for extending the context are flying blind. Rather than responding meaningfully to a full context, everyone just kind of guesses how much state they need by guessing and trying over and over again with progressively more elaborate skill and sub-agent layouts. Even managing context compaction ends up being an advanced API-driven workflow⁵.

A serious product that was trying to help the user understand would not only show “available context” but explain the impact of context compactions, make it easier to see harness-generated prompts, and so on. This would be a first-class feature, combined with the aforementioned reproducibility / replay tools, would allow users to do real experiments to develop an understanding about how to make good use of the context window.

A Sandbox That Actually Works

I’ve been focused on the chatbot interface here because it is the most immediately egregious upon looking at the UI. But the “agentic loop” tools used for coding are equally dangerous, if not more so. Coding tools keep destroying everyone’s data, over the course of years.

These catastrophic incidents that become front-page news are relatively rare compared to the amount of coding-agent use out there. But they also aren’t the only kind of sandbox violation. Coding models will so routinely edit test code instead of the system under test that there are “pro tips” articles all over the web giving you the flawed advice to simply ask the agent not to cheat. News write-ups of the catastrophic incidents themselves will also offer glib and wrong advice, like “use a docker container”. That might prevent it from literally deleting your operating system, but it won’t prevent it from destroying all the local work you have in your codebase (it needs access to a checkout, after all!)

There is a flurry of activity in the infosec space where people are rushing to plug the gaps left by these coding harnesses. Everyone’s got their own version of an MCP approval gateway where you can optionally place a proxy between your agent and your production infrastructure.

In the best case, though, all these mitigations and proxies and prompts simply turn the user into an auto-approval automaton, hitting Y, Y, Y, Y over and over again, until you finally are driven mad and hit “yes to all”, turn on full-auto mode and submit yourself to the void. With nothing between your personal vigilance and disaster, there are no workflows left beyond decrementing your own vigilance until there’s nothing left and then hoping the disaster never arrives.

The fact that some mitigations exist that can be deployed by extra-cautious users does not change the fact that “agentic coding” is an unsafe-by-default technology deployed without concern or guidance. Every frontier lab has tied a spring-loaded shotgun to a dog; the fact that dog owners can publish thoughtful blog posts explaining how you can teach your dog the basics of gun safety or how you can have your dogs play in a bullet-proof room does not mitigate the fact that the product should not have been allowed in the first place, nor should it continue to exist without VERY strong security controls.

I might believe that a frontier lab were seriously interested in providing developers with a useful tool if they shipped something that had safety built-in.

That means tools in the harness, detached from any LLM, independent of the prompt, that could:

• sandbox all filesystem operations and strictly limit ANY deletions outside of specified scopes, regardless of operating system,
• enforce snapshotting of the entire repo on every operation for easy rollbacks and minimal lost work,
• remove the disaster of “auto mode” (not to mention nonsense like --dangerously-skip-permissions) entirely, and
• carefully consider a structure for presenting plans to the user where, rather than provoking immediate alert fatigue by asking for checks on every action, make structured plans which can be submitted to the user as a group of actions and reviewed and approved as a batch.

In the same way that I suggested above that research-based tasks should have a way of re-issuing prompts to determine how reproducible a result is, or whether other sources might be found, agent-based tasks should have a way of being executed against mock services for popular APIs, so that the verification can match both on the front-end (review the plan for making the API calls before they’re executed) and the back end (review the API calls that were issued to the mock service and verify that they matched).

Instead, the frontier labs provide us products that are disasters out of the box, give us “best practices” to build massive and elaborate, as well as incomplete and error-prone, security perimeters of our own design. Then they blame “operator error” when it inevitably goes wrong. I cannot believe that these design choices are intended to help us be productive.

Bonus: Human Processes

Organizations deploying AI also frequently come across as unserious, for similar reasons. In 2023, naive exuberance could perhaps be forgiven. But today, as we near the close of 2026, there are several well-known problems, that have been extremely well-covered in the press. None of these things should be surprising, but most orgs deploying these tools are still just letting them rip and hoping it all works out.

Organizations deploying these tools would need at least three kinds of major modifications to their internal processes, if they wanted to be serious about using them safely:

1. Shift Rotations to Prevent Vigilance Decrement

There have been several high-profile incidents where software developers’ gradual acquiescence to accepting LLM output have lead to serious economic consequences for the companies deploying them, perhaps best typified by Amazon’s “millions of lost orders” due to a gradual decay of their engineering processes from LLM use.

These outages, and other AI-related failures, are due to the difficulty of maintaining focus on the same problems. In other words, as I described above, vigilance decrement is a constant problem, because AI outputs are most often correct, but continue to be incorrect in surprising and non-intuitive ways. As I have previously written, you cannot trust yourself to catch every bug with code review, and LLM output.

Aviation, for example, has very strict rules around rest requirements. There is also a specific rule that “No certificate holder may operate an aircraft without a second in command if that aircraft has a passenger seating configuration, excluding any pilot seat, of ten seats or more.”. Other safety-critical professions have similar rules.

And yet, even in the age of the supposed “AI revolution”, most software teams are still assigning every engineer a full feature load, not planning for any rest, and telling people to review code whenever they happen to have some “free time”.

Maintenance of vigilance has to be your top priority. Regular, scheduled, inviolable rest periods where people do work without AI assistance, and are not exposed to any AI output for review or otherwise, would be crucial in order to stay mentally sharp enough.

The tools themselves should have this sort of thing built in. The mistake-review process described above should have a periodic spot-check mode where a second reviewer periodically reviews a chatbot log, doing their own independent verification of claims, to see if they spot the same errors. This could provide a feedback loop to determine how much rest is necessary to maintain continuous attention and actually spot hallucinations.

2. Skill Practice To Prevent Skill Loss

It is also well-known that AI use leads to AI reliance, and AI reliance leads to skill loss.

I like to use the analogy to dockworkers at a seaport⁶ adopting automation.

If you employ dockworkers to load and unload ships all day long, they are going to be getting tons of exercise. They will be able to lift heavy objects on demand, whenever. They might have plenty of health problems and injuries from this type of work, but “lack of exercise” will not be a problem.

With the development of standardized container ships and mechanized cranes, you are going to be changing their job description substantially: now they mostly spend all day sitting in a small cubicle moving a control lever back and forth, not lifting heavy stuff. They will get worse at lifting heavy objects.

In this analogy however, the cranes are not all that reliable. We know they break, and they drop their payloads sometimes, and the stuff needs to be manually moved. But this only happens a few times a week, at most. If you need whoever is driving the crane to be able to jump out at any moment and still move stuff around manually, then you need to make an affordance for that. You need to give them time to go to the gym and do some lifting for practice, or every crane failure is going to be a major emergency.

An organization doing an AI transformation would also need a massive increase to learning & development budget, both in terms of resources and in terms of schedule. If your people are going to lose skills because they’ve lost regular practice in the incidental course of doing their duties, then they are going to need deliberate, intentional, non-incidental practice of those skills to keep them sharp.

But rather than trying to accommodate new workflows and give time for people to adjust, most AI mandates are simply dropped on workers like a ton of bricks, with no time to adapt and no affordance for maintaining their skills. Operate the crane and stay fit and healthy and ready to switch back to manual lifting at any time and then get back in the crane cockpit right afterwards. Don’t mess up.

Then an accident happens and everyone is surprised, as if this process weren’t practically designed to produce a terrible result.

3. Mental Health Resources to Deal with Mental Health Risks

AI psychosis often begins with practical problem-solving, and beyond that, it can start specifically at work. Not to mention the more pedestrian condition of “AI brain fry”.

If you are mandating your employees to use a hazardous tool that may seriously and directly damage their mental health, you need trainings and resources. You need in-house therapists and you need to be making sure to check in with people actively to make sure that this is not happening.

Again, the tool itself ought to have some way of dealing with this. An occasional “take a break” popup is easily dismissed; they need a user-visible AI personal dosimeter so you can see your cumulative usage over time.

I don’t even know if “usage over time” is a sufficient metric to gauge risk. Maybe if your work chatbot start to talk about resonance too much, unless you literally work as an acoustic engineer, that should be flagged for someone.

We are, again, years into dealing with these tools, and we know these risks exist. Yet no serious mitigations are provided. Not even any way of measuring the risk exposure.

And More

There are also many other risks associated with the technology. There are intellectual property risks with the foundation models, due to recklessness with their training data. There are existential financial risks associated with the infrastructure build-out. The extent to which most “open” models are simply derivatives of frontier models is an open question.

What I Think

If any one of these things were regularly overlooked by AI vendors or users, that would be a totally normal product oversight. Room for improvement for the next version, but nothing catastrophic.

Shipping without any of them doesn’t seem like lean product management, it seems like a careless attitude towards risk and a product design philosophy oriented entirely towards short-term demos, with no regard for how to realize actual productivity gains.

Furthermore, being available for years without anything like these features, despite hundreds of incidents demonstrating the risks, with hundreds of billions of dollars of funding, makes it seem to me like if they were to add all the features that would make their product actually safe and hypothetically useful, these features would reveal that it is actually not an improvement to productivity.

In the year since I first wrote about measuring the cost/benefit ratio of AI, I have heard from numerous people who have shown this to management to try to illustrate why their AI initiatives — like almost all AI initiatives — were either failing or burning out their engineers.

I’ve also heard from lots of people that have told me that it’s obviously useful and they don’t need to measure so carefully, because they are getting lots of work done that they couldn’t have otherwise.⁷

I have yet to hear from a single person who has said “yeah, we measured according to your methodology⁸, and it turns out that our AI work is going great and that our ratio is 0.75”.

Obviously, I cannot say for sure why this is; absence of evidence is not evidence of absence. But at this point I think the null hypothesis is that AI tools provide, in aggregate, zero value. They make mistakes too often, and the externalities they produce are so bad and so difficult to control that even before we get to the places where they are just physically poisoning people, even the negative effects on their direct users end up cancelling out whatever benefit to they provide to their organizations.

If I were wrong, then including tools to measure an AI’s effectiveness at the tasks their users are actually trying to accomplish, rather than meaningless benchmarks, would show big productivity gains. The frontier labs would be champing at the bit to add such features, and crowing about their fantastic results.

I think the labs know that if they did that, it would present a grim picture to their users. Such tools would let their users see that it’s making mistakes much more often than they realized, that they’re spending much more time with it than they want to be, and that it’s just generally not fit for purpose.

If they prove me wrong by adding in all of these safety mechanisms, and in the process, they make all of their AI technology less harmful, I’ll be thrilled to be debunked.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Open source is, at least in part, about you, where “you” refers to the user.

Open Source Is Not About “Open Source Is Not About You”

In other words: Rich Hickey was wrong when he wrote “Open Source Is Not About You” and I’m tired of pretending otherwise.

Of course he’s not completely wrong, or his famous post would not have resonated quite so much in the first place. Obnoxious users who demand their personal use-cases be immediately addressed by volunteer maintainers for free should indeed be viewed as the pariahs that they are. Similarly, corporate users who want free support from the community that supplies their infrastructure to lower their costs. As should those who profit from this type of externalization by their own customers.

But the exchange of “open source” (or even “free software”) is not as simple as “I have prepared some software for you, please enjoy it, you have no right to complain”, and maintainers ought to have a precise understanding of the costs and benefits — as well as the ethical implications — of that exchange.

Right now we barely even articulate that the exchange exists, let alone that it establishes a long-term, subtle, and implicit relationship between maintainer and user.

Let’s fix that.

What Are We Doing When We Do An Open Source?

Hickey, and his many acolytes in the years since his fateful post, asserts that the process of “open source” goes like this:

1. Maintainer makes a thing, and makes it available to users as a gift.
   1. Maintainer may “love working with the team”.
   2. Maintainer may be “proud of the work we do”.
2. Users accept the gift, and extract utility from it.
   1. (Users MUST be grateful for this.)
3. A tiny fraction of users reciprocally contribute to the thing.
   1. (Maintainers may be grateful for this.)

He makes various oblique references to the specific activities of his company, which does things vaguely related to his projects for money¹. These activities are exclusively characterized as for “customers”, however, a subset of the aforementioned users so tiny (“fewer than 1%”) as to nearly be an entirely distinct group.

Breezing past this process in an essay about obnoxious users demanding things they are not entitled to, one might nod along, as this sounds mostly sensible. Giving gifts is nice. I too love working with good teams and taking pride in things.

Examined more closely, however, it starts to logically fall apart. If you have consulting clients and that’s where all of your money is coming from, why are you bothering (as he repeatedly insists) “doing [things] for the community”? What was the point of releasing this code in the first place? You could love working with your team and be proud of the work that you do in a lot of different contexts; why bother implicating this horde of entitled and obnoxious people, if that’s all you’re getting out of it? What’s in it for you?

If we’ve left out something as fundamental as “why is the maintainer doing this”, perhaps this story leaves out some other important bits as well.

Why Are You Doing This?

There are many possible motivations for releasing and maintaining open source software. They are often subtle, often overlapping, and rarely clearly stated. Maintainers are not a monolith and not everyone does it for similar reasons. But let’s review a few reasons that someone might want to contribute.

Reputation

One reason that you might want to release some open source software is advertising. The most common form of this is self-promotion; if you are a visible, prominent contributor to an open source project, it stands to reason that you will have an easier time finding work in the domain of that project.

If you operate a consultancy, as Rich Hickey did at the time of his famous rant, then this reputational currency translates into advertising for your services. It’s a practical demonstration of the skills of your team.

The trade in this benefit is most like the traditional “gift economy” that open source has been compared to. You give the code to your users, which has some value, but the users give you back some reputation, in the form of their attention, their esteem, and possibly even their money if they become customers or employers.

Influence

Infrastructure is the most popular type of open source for a good reason. Programmers working on a problem are often hemmed in by sclerotic architectural choices which prevent them from solving problems in the way that they’d prefer to solve them. Major infrastructural investments are difficult to justify in a planning process, as their benefits are hard to prove. Sometimes the benefits are highly personal; different engineers have different aesthetic preferences about what types of equally-valid solutions they’d prefer to work with.

If you can develop your preferred type of solution and release it as open source, then you can influence how everyone else solves this type of problem. As an individual, such a position of influence can allow you to have some transferable expertise between employers. You know how to use the tool you developed, so you can be very quick and effective with it, and you can shape it to your ongoing taste over time.

If you’re an employer, and you can get everyone else to use your open source thing², this can reduce both your hiring and training costs. Potential employees can read the code, see that it’s good, and want to work at a place that produces good code like that. They can also read the code and become familiar with it in advance of coming to work for you, which means that you have a ready supply of developers who already know how your internal systems work.

The trade in this benefit is more like “soft power” than a gift economy. You give the code to your users, which has some value, but the users give you back the ability to dictate their technological agenda. You gain both the ability to influence their initial direction, and, as part of ongoing maintenance, to dictate their behavior over time.

Improvement

As an engineer, you might want to improve your own skills. Writing something proprietary and commercial cuts against this in two ways.

First, you will want to build something that already exists within your skill set, so that it will attract commercial interest and actually be competitive. Within the context of a larger team, you will want to personally be able to be immediately effective for similar reasons. But you still need a way to learn new things.

Second, you will want to build something somewhat secretively, so that the value you are producing is captured rather than released to the community. This means that you will be cut off from external sources of expert feedback.

As an organization, you might want to build the skills of your staff in similar ways.

The trade in this benefit is code for knowledge. You release the code or changes, and in return you expect your users to provide you good bug reports, and to induce at least some of them to become co-developers.

Outsourcing

As an engineer, you can only do so much on your own. Perhaps you want to have some influence over your infrastructure so you want to write it, but you also want to have a communal place to keep your infrastructure such that you can make a change to something to suit your needs, but you know that even if you walk away, someone else will maintain that change and keep it working across years or even decades of changes to underlying platforms, hardware, etc.

This sort of communal maintenance effort can be shared among all interested participants; if a thousand companies all need the same tool, if even a few dozen can share it, that reduces even their own load massively, let alone everyone else’s.

The trade in this benefit is more complex, since there’s less symmetry between the main maintainer and peripheral community members who also contribute code. The main maintainer is actually trading a namespace, a central place for people to contribute, coordinate, and release changes, rather than the code. They are a sort of market maker where then all the other contributors trade code for code within that market-ish structure.

In practice, this motivation produces a game theory problem where, when maintenance drops below a critical threshold, it creates a big enough crisis that at least some freeloading stakeholders will be forced to start making contributions.

Ultimately, however, this saves all involved parties a ton on maintenance, more eager volunteers who do not freeload in the first place get all the other benefits mentioned above as well.

The Things that Aren’t Supposed To Be Benefits

These categories could be made as explicit, rational trade-offs, even if they are often implicit and subtle in practice. They are transactions where the maintainer gets something and the user gets something.

However, not everything that you are getting as a maintainer is something you are actually supposed to use to your own benefit. Being given trust in service of a responsibility is not a transaction.

“Oops, All Root Shells”

Open source code is code. In our modern world of absolutely pathetic sandboxing, installing code from somebody else gives them control over your system, even if it is somewhat indirect.

There is an unwritten rule that if I create an open source library, and you use it, it probably shouldn’t have a backdoor in it that gives me the credentials to your bank account. There is a trust relationship between the user and the maintainer, and here, we see the first obligation that the maintainer has. The maintainer is obligated not to use the user’s computer for their own gain.

This rule might seem obvious and straightforward. It might even seem unfair to you that I call the rule “unwritten”, because the rule is, in fact, written down in a few places: for example, in the npm Acceptable Content Policy, it says right there:

A few examples of unacceptable content:

…

3. Content containing malicious computer code, such as computer viruses, computer worms, rootkits, back doors, or spyware. This includes content submitted for research purposes. Tools designed and documented explicitly to assist in security research are acceptable, but exploits and malware that use the npm registry as a deployment or delivery vector are not.

I think we can all agree that a script which steals your bank credentials and sends them to me to buy a totally sick jet ski would qualify as “malware”, so clearly that is forbidden.

There is also an enormous gray area here. npm also explicitly allows “Information on how to pay, donate to, and otherwise support Package development”, but then goes on to explicitly forbid “Packages that display ads at runtime, on installation, or at other stages of the software development lifecycle, such as via npm scripts.”⁴ How are the lines drawn around these gray areas? “npm will continue to apply its judgment when deciding what content is acceptable.”

But also... this is forbidden by npm, not by the transcendental nature of “open source”. I could give away code that displays all kinds of ads to its users as a “gift” on my website. The exact structure of this policy is not uncommon, but it also isn’t exactly the same as other such sites. PyPI, for example, explicitly bans “cryptocurrency mining”, which NPM does not. Is cryptocurrency mining “not open source”? A lot of judgement calls are happening here about what is allowable in these “gifts” that you are giving to your users.

But I digress.

My point is that policy-making around this concept is not clear, there are lots of little disagreements around the edges, but there is a very strong consensus that while the user is giving you their trust here, that is not a trade. The deal is not “you give the user some code, the user gives you unlimited compute and access to all their financial accounts”. The user has made themselves vulnerable to your code on the strength of your reputation.

This creates an obligation for you to not do anything evil with that code, either intentionally or through negligence.

Security Updates Are Just Command And Control In A Funny Hat

All of this is just about the initial download of some code, and that is the way that Rich Hickey describes it, as if you just grabbed some code off a web page and put it in a folder that you like on your desktop. But that is not how open source relationships work today, if indeed it ever was.

The way it works today is that you add a dependency to your pyproject.toml or your package.json or your Cargo.toml and now your users are vulnerable not just to whatever you happened to upload in the first place, but to whoever happens to have your package index credentials.

This creates an obligation to maintain an operational security posture that protects your users from malicious updates.

The Roadmap Is Someone’s Life

Another kind of trust that the user is placing in you is the trust that you are going to have at least some kind of regard for their usage of your software.

In a perfect world, the user’s expectations could be clearly circumscribed. Whatever ongoing maintenance you commit to perform would be encapsulated in clear policies that you’d write up in advance, about exactly what kind of security response policy you have, how you will communicate when you no longer have the resources for maintenance, and so on.

But anyone who has been involved in any project at anything but the most extreme tier of operational maturity knows that 99% of the ecosystem relies on a set of loose conventions around how all that stuff works. We expect that maintainers will generally be around, that they’ll use existing tools like an issue tracker for triaging user bugs, GHSA and CVEs for security reporting, that they will mark the project as “archived” and maybe do a final release before abandoning it, that they will maintain a ChangeLog explaining at least a little bit of what is going on.

Users assume that those conventions will be followed when there are any gaps in explicit policy, or indeed if policy is lacking entirely. This assumption is reasonable, because otherwise nobody could ever use any open source without a stack of service contracts that nobody has any time to write.

The strongest such convention is that an actively maintained program will, at least, more or less keep doing what it does as time goes on. A user who has elected to use a bit of open source software has made themselves vulnerable to changes and breakages in that software by the mere fact of using it. In the time that they have used it and invested in it, they have not invested in:

• creating alternative software to meet their needs,
• maintaining data in formats that other software can read, or
• learning how to use existing alternative software.

This can, and does, go badly wrong, when those expectations are mismatched.

How It Goes Wrong

Let’s say a maintainer creates an open source paint program, OpenPaint.

An artist, known for their unique style of making blended collages, switches from their previous app, ProprietaryPaint, to this new OpenPaint to make these culturally significant works of art. However, the maintainer decides that the ‘blend’ tool is kind of a pain to maintain, and they remove it in OpenPaint 2.

A few months later, the artist’s operating system vendor issues a security update that breaks OpenPaint, because older versions of OpenPaint were unknowingly abusing some platform API.

The maintainer releases a new OpenPaint 2.0.1 that addresses this incompatibility, but doesn’t care about version 1.x any more so they don’t bother to update that one.

This places the artist in an impossible situation. They can stay on an old version of their operating system, putting all their personal data at risk. Or they can upgrade to the new operating system, effectively either cutting off access to their livelihood, or forcing them to change their art style entirely.

Now, proprietary software can place users in similarly untenable positions (and in fact, it is more often proprietary software that does). But does the openness completely remove any obligation for this consideration? Should the OpenPaint team have to at least communicate the reasons for doing this, to give the artist some recourse?⁵

The only thing that “open source” does is that it allows the artist to pay a prohibitive amount of money to a new maintenance team to create a fork. This is rarely the kind of thing that individuals can manage.

This creates an obligation to at least consider how your users might be relying on you.

This is the most complex obligation of the bunch. Obviously it does not entitle every single user to infinite work from the maintainer, but it also shouldn’t entitle the user to nothing for having trusted these subtle implied claims that the maintainer is making by making their work public.

It is a nuanced and ongoing negotiation and I do not think we have a clear moral intuition about how it should work out. But we do need to figure out a way to work it out.

It also raises a clarifying question.

Why Are We Even Doing This, and Who Are We Doing It For?

People generally like to do things for more than one reason. We live in an economy where people need to make money, but we mostly prefer to make that money doing things that are useful, and that make other people happy.

So, yes, we create open source for self-interested reasons to improve our reputations, to improve our skills, to increase our influence and to share our maintenance burdens. In so doing we take on some level of obligation to not abuse the trust that is placed in us, even if that level of obligation is not clear.

But if we are not doing it to serve those users at least a little bit, then those motivations are going to quickly ring hollow. We will not increase our reputation with a person if we respond to their every request by telling them that we owe them nothing and that their opinions are worthless. We will not gain influence over a community if we ignore their desires.

Many interactions with open source maintainers are unnecessarily adversarial. This is of course partially the fault of those users, who should calibrate their expectations appropriately.

Still: maintainers could do a better job of listening before these interactions become toxic. There’s no reason that “open source users” should be an especially toxic group of people. At this point in history, that group is basically just … people with computers.

It’s like that old truism. If you meet one person who is a jerk to you, that’s their problem. But if everyone you meet, everywhere you go, is constantly abrasive to you and treats you like you’re doing something wrong, maybe it’s time to look inward.

If all open source users are entitled assholes, maybe it’s time to look for a structural problem.

Surprise, It’s About AI Again

Sigh.⁶

Users hate slop.

I know, dear AI-positive reader, your AI outputs are different from everyone else’s, you aren’t pushing thoughtless slop into your code, just because everyone else is and it is the inevitable terminus of using those tools. You aren’t “lazy vibe coding” with Claude, you’re doing “responsible agentic engineering”, which is different because you’re just built different.

Still, humor me, for a moment. Your users don’t know that. They know what it looks like when products that they like adopt slop. They know that they will start leaking data. Developers know that it will make them personally less secure. They know that they can expect more outages and that your code will inexorably decline in quality.

In other words, your users are going to assume that this means you are violating that final obligation that the software should keep working.

Your users are going to tell you to stop, and they are probably going to get mad. Maybe you, or a plurality of your team, also want to stop, maybe you disagree with them, but in any case you need some way to have that conversation in a way that does not immediately overflow into every adjacent discussion forum. Users need to feel welcome in some space so they can have the discussion in that space, and not explode out into a thousand different group chats and social media threads.

This post was inspired by yet another prominent open source community discourse where a ton of angry users showed up to yell at developers to stop accepting LLM-generated code. I’m not going to link to any of these, because we don’t need any more fuel for the discourse fire. But there is more than one such case and the pattern is becoming familiar.

On social media - usually BlueSky or Mastodon, but sometimes a user group forum - users become aware of some AI-adjacent policy. They show up in a horde to the developer forum or mailing list. They loudly start demanding the project take a hard stand⁷ against AI. This pressure is simultaneous, but uncoordinated; extremely repetitive, very diverse, often inconsistent, and pretty stressful, especially if you’re a burnt-out maintainer with other things to be doing who may not even like AI yourself in the first place.

Believe me, I get it. It can be very unpleasant to deal with.

Like most problems that AI is causing, though, it’s not really an “AI” problem as much as it is a pre-existing dumpster fire that “AI” is pouring gasoline onto. In this case, an online mob is the language of the unheard⁸.

If Users Are Mad It’s Probably Already Too Late (But Maybe You Can Get Ready For Next Time)

One day, all of a sudden, you’re getting feedback from a bunch of users that are using inappropriate channels to complain. But did they already have appropriate channels to use?

Did you have a place for people to congregate and discuss your project? To make orderly complaints in a way that will be legible to you? Or do you just have a GitHub Issues page, which non-technical users have no idea how to interact with, and a forum for developers, where users don’t know the norms and any arriving brigade of pissed-off users will be seen as disruptive and inappropriate?

I don’t want to be throwing any stones from within my particular glass house. Setting up such a place has gotten harder over the years. I don’t really have one, either.

Could I have one, though? IRC has been slowly dying, mailing lists are unpopular and present increasingly annoying moderation challenges, forum software is expensive to operate and keep maintained, Discord is a confusing mess and the upshot of all of this is every community needs community management and forum moderation. Which means that for my own small solo projects, I couldn’t possibly have such infrastructure because such infrastructure requires a dedicated second person to maintain it, and until someone volunteers for that, it’s not really feasible. Even for my larger projects you’d be surprised how slim of a skeleton crew we are getting by with, and we definitely don’t have a whole spare maintainer to go manage this, especially as we are under attack from the slopocalypse ourselves.

The nature of open source community is that most communities start too small to need such a thing, grow incrementally until one day they are suddenly way too big and needed one yesterday, and then suddenly they are too small again when interest wanes even a little bit. Even as we need it more and more, building and maintaining community infrastructure remains a challenge.

Even so, having a dedicated place for users — not maintainers — to converse amongst themselves, be an actual community, and present feedback to the developers, is fast becoming a necessary component of a successful community and not a nice-to-have.

In Conclusion

As trying as it can be sometimes, we maintainers all do get something out of open source, and it is good to be honest with your users — and with yourself — exactly what you want to get out of it. In order to know whether the juice is worth the squeeze, we must know both what the juice is, and what the squeeze is.

Part of the metaphorical squeeze is a set of obligations, and those are the most poorly defined of all. We should try to be clear about what those are too. Both about exactly what we believe we are signing up for, and also, about how we are willing to let our users hold us to account for them. Codes of conduct are a start here, but only the absolute barest bare minimum; “do not harass your colleagues or your users” is not a standard of excellence to aspire to, it’s just basic manners.

I can’t tell you exactly what your obligations are, only try to gesture at my idea of the outlines of the fuzzy moral intuition we’ve all been implicitly sharing up until now.

Drawing this line is not just for the benefit of the users, either. Maintainers already feel pressure, we already feel obligations. We resent that feeling of obligation. While there are a diverse array of reasons for that resentment, one big one is that it’s not clear, even to ourselves where the obligations end. Lashing out by saying “I promised nothing and I owe you nothing!” followed by some choice expletives feels cathartic, but it doesn’t really solve the problem, because we clearly don’t really believe that’s where the line is, or we would have already stopped there. We wouldn’t feel the need to say it.

It is going to be a very big collective endeavor to figure out exactly where that line is. The best time to have gotten started on that endeavor was 50 years ago.

But the second best time is today.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!⁹

I get asked this rhetorical question a lot, in various forms:

Sure, datacenters might use a lot of energy, but you don’t have to use a hosted frontier model to do software development. What if I just run a local open-weights model to do some coding, with an open-source coding agent? Video games also use my GPU. Is local model development any worse than playing a video game?

So I want to write down my comprehensive answer to this: Yes, using an LLM to write some code is worse than playing a video game, for a few reasons.

Video Games Are Interactive, LLMs Are Batch Jobs

Video games use compute to respond to human input. You are using your GPU while you are looking at a screen, displaying an image. When you are done playing, you shut off the game, and your computer goes back to idle. It’s much less energy. By contrast, agentic loops with evals (the only kind of “AI” that is meaningfully any good at coding) are running hot, for days. To use the most recent example of such a thing, a very rough first sketch of an implementation of a Windows graphics API backend to help port a paint program to other platforms, it took 3 weeks of Claude time, “day and night”. Do you play a lot of video games for 500 hours to make it past the tutorial level, while also using other computers for other things, as well as the rest of your carbon footprint?

Video Games Need Development, LLMs Need Training

Video games use compute to respond to human input during development, too. Your game has to be made, but your LLM has to be trained. LLMs use a historically extreme amount of power, probably using more than the entire Internet, but it’s kind of hard to say. Still, it seems a reasonable estimate to within several orders of magnitude that even over a multi-year project with hundreds of developers, the power used to develop an individual video game is nowhere close to training even a small LLM.

This is true even for local models. OpenAI has openly claimed that DeepSeek “stole its intellectual property”, and I have heard grumblings that none of the open-weights generalist models could realistically exist without the massive lift that the frontier labs are doing with their training, in various other ways too. Secrecy throughout the industry makes this kind of impossible to understand rigorously, but it seems fair to say that you are partially culpable for all that famously energy-intensive frontier lab training if you’re using a local model.

And They Keep Needing Training

You also can’t dismiss this as a sunk cost, because in order to stay current with industry developments, models need to be updated with new information from the rest of the world, which means that you need to keep training them. Beyond the energy for your own use, if you want a real-life agentic workflow that actually does useful stuff, practically speaking you would still need to update your local models over and over again, at least once every few months, which means you would be incentivizing continued energy consumption by whoever was doing that training for you, including the energy cost of scraping.

Let’s Be Real Here, You Aren’t Actually Using A Local Model

This question is a hypothetical thought experiment. Despite synthetic benchmarks that keep showing there isn’t much difference between open weight and frontier models, nobody’s actually using local models for much of anything beyond sharing those talking points. Depending on which benchmark you’re looking at, maybe it’s good enough or maybe it’s worse.

As an inveterate AI hater, all these systems seem pretty bad to me, but it seems that people who find them useful tend to subjectively believe the frontier models are worth the premium, and that’s what they’re actually using. Once you have accepted that it is OK to use LLMs for coding at all, it seems like a very quick slippery slope on down to “we’ll go ahead and use the frontier models for now anyway, but we could be ethically better in the future by switching to an open weights one, that option is always available”.

There’s A Reason We Have Data Centers

Devolving power usage to local LLMs might be good to make users responsible for their costs and decrease the impacts to communities that are physically next to huge concentrations of power utilization, not to mention generation. However, there’s a reason that it makes sense for the providers to build these giant facilities: economies of scale reduce total power consumption, they don’t increase it. If you do all the same stuff with a local model that they have to do in hosted environments, it will probably take more power, even though you will be incentivized to do different stuff. This incentive to “do different stuff” is why although local models can hypothetically hold their own against the frontier labs for some tasks, when people or businesses take their inference costs in-house they often find that it’s too painful and move back to hosted LLMs.

There Are Problems Other Than Power

These are subjects for a different post, but you have to consider a lot of other externalities: AI psychosis, de-skilling, comprehension debt, cultivating a dependency, introducing security defects, limiting your design space based on what LLMs can understand, context rot, wasting time on invalid solutions, introducing unpredictability into your workflows. You still have to consider the total cost benefit ratio.

To Sum Up

Local LLMs might alleviate some of the harms from using the hosted frontier providers. There are fewer privacy concerns, you can measure your power utilization and be more directly responsible for it, you can build interfaces with affordances that are less oriented towards addiction and dependency than the major frontier labs’ harnesses.

But they’re not automatically “the same as playing a video game” just because they can use the same GPU.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Starting with 0.3.0, Astral’s uv brought many great features, including support for cross-platform lock files uv.lock. Together with subsequent fixes, it has become Python’s finest workflow tool for my (non-scientific) use cases. Here’s how I build production-ready containers, as fast as possible.

As I have discussed in previous posts, “AIs” can make mistakes. In fact, they do make mistakes, and their mistake-making patterns are such that where and how they will make mistakes is both uncertain and constantly changing.

Thus, in any scenario where you want to attempt to make “productive” use of “AI”, you must have a system in place for checking every result. Not checking some results; checking every result. If each result might have a consequence for you (and if it didn’t have a consequence, why bother automating it?) and you cannot predict in advance which kinds of results will need verification, then verification is always required.

The verification often ends up being just as expensive as doing the work in the first place, which means that if you want your usage of “AI” to be personally profitable, you have to find someone else to externalize the cost of verification onto. This person becomes your adversary, and, if you are successful, your “AI’s” victim.

The Ladder-Climber And Their Reverse-Centaur Rungs

One way that this constellation of facts can straightforwardly assemble themselves into a dystopian nightmare is the phenomenon, described by Cory Doctorow, of the reverse centaur. This is when your employer non-consensually turns you into the verification system. The “AI” does the fun part of initially performing the work, and then you do the boring part where you check if the robot is right and clean up its messes, even if everyone already knows that it would, in aggregate, be cheaper for you to do the work in the first place.

Reverse centaurs can be made from any automation, not only “AI” automation. I think that there is a reason that this term happens to have emerged in the “age of AI”, though, and not with earlier automation technologies (even those which were considerably more viscerally horrific). That reason is: the wrongness of “AI” output is not merely a technical feature that must be compensated for, it is a generalized externality.

As I mentioned above, if you are responsible for the entirety of the work, both extruding the “AI” output and checking it, it’s usually cheaper to have humans do the entirety of the work to begin with. When humans do the writing directly, we can check as we go, and thus verification doesn’t need to be as comprehensive.

When “AI” coding advocates say “code review is the bottleneck”, what they are observing is that the LLM is still rolling the dice for each PR, and a human is still necessary to verify that each of those rolls is a winner. But calling this process “code review” is a bit of a misnomer; it’s not really “code review” in the traditional sense, it’s human understanding.

Before the advent of “AI”, the human understanding was implicit in the process of writing the code in the first place¹, and the code review was a way of diffusing and extending that understanding. Now that the code can be authored with no initial understanding taking place, that cost has not gone away, it has moved.

Human understanding was always the bottleneck.

However, this is taking a collaborative view of a software project, where satisfying the needs and solving the problems of your customers are the goals. We can see that “AI” is a bad tool to satisfy those goals, because all it’s doing is converting the first half of the work, that of understanding the code as you write it, to understanding the agent’s output as you read it.

What if, instead, we were to take the view that every software company is a Hobbesian nightmare, red in tooth and claw? In this view, the only goal of a software project is for the individual developers to make their promo cycles and get their bonuses. Given that there is only a certain amount of money to go around, this is a zero-sum game where each programmer wants to look more productive than their colleagues.

Pretty much every organization finds it easy to reward “productivity” as expressed by lines of code emitted, but the benefits of doing thorough and thoughtful design, analysis, and code review very difficult to reward. In this world, an LLM is an invaluable tool for the sociopathic ladder-climber, particularly if your legacy organization is still structuring their workflows as if the person prompting the bot is “writing” the code, and then they get to foist off the act of “reviewing” the code onto someone else.

Here, the prompter effectively externalizes the cost of the LLM’s failures but internalizes any benefits. The prompter will vibe-code a big feature, so large that the assigned reviewer can’t possibly comprehend it all effectively. When this happens, the reviewer will, eventually, be pressured to approve it, even if they can try to spot a few problems along the way. The reviewer has their own work to get back to, after all, the obligation to review the prompter’s (read: the bot’s) code is a drain on their time that they are not going to get rewarded for.

If this feature is a big success, the prompter gets a promotion. If it causes a big issue, well, the reviewer must not have been careful enough.

This is why LLMs are “good for coding”, and also why their biggest promoters keep having outages.

The Generative Gish Galloper

Coding is the biggest “success story” of this type of adversarial communication, but it is by far not the only instance of such a thing. LLMs create a new form of leverage that can turn Brandolini’s law from a linear advantage into an exponential one. If you are engaged in a political debate where you want to overwhelm the other side in nonsense, an LLM can generate bullshit faster than it is physically possible for a human being to type, let alone respond thoughtfully. There is an asymmetry to the utility of this weapon as well: only one side of the political spectrum wants to flood the zone and destroy trust in institutions and the concept of truth. There’s a good reason that the fascists love it.

Straightforward Spam and Fraud

This is kind of obvious, but LLMs can generate lightly-customized, plausible-looking text much more quickly than any human being. This facilitates their use in fraud, spam, and scams. In a spamming or fraudulent interaction, once again, the costs are externalized onto the victim: the recipient of a spam message has to do all the work of “checking” the LLM’s output. Spammers already expect very low hit rates from boilerplate, and if the LLM can increase those percentages from 1% to 5% the technology will pay for itself; they don’t need anything like reliable accuracy.

Customer “Support”

If you have any kind of commercial relationship with a company, I probably don’t even need to mention this: customer “support” bots are a misery. Everybody knows it at this point. But customer support is usually conceptualized by businesses as an adversarial interaction, because it is a cost center. They maintain internal metrics on time-to-resolution and try to optimize them. Implicitly, this creates a dynamic where the goal of the customer service agent’s job is not to solve your problem, but to emit noise that will cause you to think your problem is resolved, or to give up, as fast as possible. Unsurprisingly, LLMs can emit this noise faster than humans can, getting those customers off the phone. But those customers will remember those interactions, and the story outside the TTR metrics is horrible.

Similarly to the situation in software development, LLMs can look very good on paper for customer support, but mostly what they are doing is illuminating the problems with the industry’s existing metrics, by turning “winning the metrics battle against the customer” into a more obvious and immediate defeat for the company’s long term reputation.

“Education”

In 2026 it is sadly a fact of life that students cheat all the time using “AI”, and that this cheating is very successful, in that the teachers find it very hard to detect.

LLMs are great for cheating on schoolwork because the student is externalizing the work of the checking onto the teachers, who are often starting at a disadvantage to begin with, at least in the US.

My view is that this is happening because of a divergence in the way that students vs. teachers (or, more accurately, “the broader educational system”) view grading.

When a student is asked to write an essay, the teachers see the effort as both intrinsically worthwhile for the student, as well as useful as a pedagogical tool to evaluate and react to the student’s progress. The student, by contrast, sees a stumbling block designed to knock them off the path to success and into a permanent underclass. It is no wonder that the student sees “AI” as useful to their own goals and has no compunction about deploying it.

There is a bitter irony that the ability to understand the inherent value of actually writing the essay on their own is the sort of thing that students can really only learn by writing a bunch of essays. There’s no way that I can think of which makes the benefit legible as long as a shortcut is available.

The net effect here is a downward spiral, where the already-wobbling educational system is sustaining an attack that it doesn’t have the resources to recover from. The individual students’ attacks against their teachers and their schools’ grading systems might appear to momentarily succeed, but they will win the battle and lose the war.

Spamming “For Good”?

Usually when we talk about someone unilaterally choosing to enter into an adversarial relationship, that’s an “attack” and for good reasons we have a negative impression of the attacker. However, I would be remiss if I did not point out that there are some cases where the relationship was already adversarial; just because you’re the attacker doesn’t mean that you are evil.

For example we might imagine use-cases like automatically filing appeals for prior authorizations against health insurance. It’s relatively well-known at this point that the main way for-profit insurers maintain their margins is by denying claims right up to the line of the policies themselves being fraud, so using a spamming tool to fight them might be entirely justifiable² in that case.

Similarly, using an LLM could be justified in a fight against a company refusing to honor a warranty. One could imagine using an LLM to immediately generate replies and escalations.

However, even in imagined cases like these, the underlying problem is that the insurers and the vendors already have a tremendous amount of structural power, so it is more likely that they will have the advantage in deploying a communications weapon like an LLM, as well as enacting policies to simply ignore any LLM-based communication that you might submit. Worse, if these strategies were to become widespread, they might provide an excuse to reject any communications by feeding them into an unreliable “LLM detector” and issuing an automated “computer says no” even to hand-written correspondence.

It is also worth stressing that these cases are imagined, as compared to the very real coworker-abuse, spam, scam, fraud, and disinformation campaigns being waged in real life today.

Therefore, while legitimate uses might exist, it’s hard to imagine that there’s anywhere they would be genuinely valuable and sustainable. In the best case “AI” will provide a temporary advantage for underdogs that will provoke an arms race which the resource-advantaged adversaries will win in the long run, in the worst case the arms race itself will cement permanent structural change that will make things worse.

“Search” By Stealing

Most of the adversarial utility of “AI” is on the “write” side, since write-amplification is more obviously aggressive than reading. But the “read” side of LLMs — summarization and question-answering — can be a form of attack as well.

To begin with, the act of reading itself is currently enormously destructive, but that’s arguably not a fundamental aspect of this technology. They could set reasonable rate-limits and respect things like robots.txt, as search engines have for decades now. They could also refrain from committing criminal levels of copyright infringement. But, today, using “AI” tools does suborn this sort of out-of-control crawling.

More insidiously, consider the scenario described in this YouTube video. The LTT Bros decided to try Linux again, and in the course of so doing, they had problems. When trying to solve these problems, they were faced with a choice: they could consult Reddit, or they could ask an LLM. Asking an LLM would “gaslight the heck out of” them, but they still found it preferable, because they would at least get an answer without getting yelled at.

Initially this sounds great. But it also means that you want to extract knowledge from a community, while mechanically eliding any values or norms that the community may want to impart as part of offering that knowledge. As someone who spent many years in a community tech support role, this is worrying. Many requests for support are people asking how to do things that will momentarily solve a superficial problem but create a long-term reliability problem or even an immediate security risk, that the question-asker doesn’t want to hear about. Consider the question “I’m tired of entering my password so much, how do I make it so my laptop unlocks automatically”. An obsequious chatbot will helpfully tell you how to do this without pushback.

But, this is also a sort of ethically murky area. The Linux community is somewhat famously, for many years now, a toxic cesspool of general hostility, misogyny, etc. It is certainly a good thing that people can get access to this knowledge without subjecting themselves to abuse. But it also means that the people with the power and the privilege to change the community for the better can just quietly withdraw, rather than fixing the problems. It also means that the positive elements of culture cannot be transmitted, and people will have no opportunity to learn about unknown unknowns.

In this case, the “adversarial” communication is with society. The thing that using an LLM for search lets you do is withdraw from society and avoid forming any personal connections. There are some personal connections which are painful and annoying, and so that can feel like a momentary balm. But the need to make connections in general is, like, the concept of society itself.

Who Am I Hurting?

LLMs are good at adversarial communication. They are so good at it, relative to their other benefits, that they will tend to make communications adversarial if you are not remaining vigilant about the possibility that it might do so. My request to you, dear reader, if you are going to use such tools, is to always ask yourself, “who might I be hurting, if I use an LLM for this?”

If you’re using an “AI”, who is its adversary? If you haven’t given it one yet, who might the “AI” turn into an adversary? Who might you overwhelm with an asymmetric amount of output, or, if you’re receiving information and not sending it, who are you taking that information from without consulting?

Figure out the answers to these questions and conduct yourself accordingly; the answer might be “yourself”.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Codecov’s unreliability breaking CI on my open source projects has been a constant source of frustration for me for years. I have found a way to enforce coverage over a whole GitHub Actions build matrix that doesn’t rely on third-party services.

Let’s say you’re writing a Python library.

In this library, you have some collection of state that represents “options” or “configuration” for a bunch of operations. Such a set of options is a bundle of potentially ever-increasing complexity. Thus, you will want it to have an extremely minimal compatibility surface, with a very carefully chosen public interface, that is either small, or perhaps nothing at all. Such an object conveys state and might have some private behavior, but all you want consumers to be able to do is build it in very constrained, specific ways, and then pass it along as a parameter to your own APIs.

By way of example, imagine that you’re wrapping a library that handles shipping physical packages.

There are a zillion ways to do it ship a package. There are different carriers who can ship it for you. There’s air freight, and ground freight, and sea freight. There’s overnight shipping. There’s the option to require a signature. There’s package tracking and certified mail. Suffice it to say, lots of stuff.

If you are starting out to implement such a library, you might need an object called something like ShippingOptions that encapsulates some of this. At the core of your library you might have a function like this:

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async def shipPackage(
        how: ShippingOptions,
        where: Address,
    ) -> ShippingStatus:
    ...

If you are starting out implementing such a library, you know that you’re going to get the initial implementation of ShippingOptions wrong; or, at the very least, if not “wrong”, then “incomplete”. You should not want to commit to an expansive public API with a ton of different attributes until you really understand the problem domain pretty well.

Yet, ShippingOptions is absolutely vital to the rest of your library. You’ll need to construct it and pass it to various methods like estimateShippingCost and shipPackage. So you’re not going to want a ton of complexity and churn as you evolve it to be more complex.

Worse yet, this object has to hold a ton of state. It’s got attributes, maybe even quite complex internal attributes that relate to different shipping services.

Right now, today, you need to add something so you can have “no rush”, “standard” and “expedited” options. You can’t just put off implementing that indefinitely until you can come up with the perfect shape. What to do?

The tool you want here is the opaque data type design pattern. C is lousy with such things (FILE, pthread_*_t, fd_set, etc). A typedef in a header file can easily achieve this.

But in Python, if you expose a dataclass — or any class, really — even if you keep all your fields private, the constructor is still, inherently, public. You can make it raise an exception or something, but your type checker still won’t help your users; it’ll still look like it’s a normal class.

Luckily, Python typing provides a tool for this: typing.NewType.

Let’s review our requirements:

1. We need a type that our client code can use in its type annotations; it needs to be public.
2. They need to be able to consruct it somehow, even if they shouldn’t be able to see its attributes or its internal constructor arguments.
3. To express high-level things (like “ship fast”) that should stay supported as we add more nuanced and complex configurations in the future (like “ship with the fastest possible option provided by the lowest-cost carrier that supports signature verification”).

In order to solve these problems respectively, we will use:

1. a public NewType, which gives us our public name...
2. which wraps a private class with entirely private attributes, to give us an actual data structure, while not exposing the constructor,
3. a set of public constructor functions, which returns our NewType.

When we put that all together, it looks like this:

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from dataclasses import dataclass
from typing import Literal, NewType

@dataclass
class _RealShipOpts:
    _speed: Literal["fast", "normal", "slow"]

ShippingOptions = NewType("ShippingOptions", _RealShipOpts)

def shipFast() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts("fast"))

def shipNormal() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts("normal"))

def shipSlow() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts("slow"))

As a snapshot in time, this is not all that interesting; we could have just exposed _RealShipOpts as a public class and saved ourselves some time. The fact that this exposes a constructor that takes a string is not a big deal for the present moment. For an initial quick and dirty implementation, we can just do checks like if options._speed == "fast" in our shipping and estimation code.

However, the main thing we are doing here is preserving our flexibility to evolve the related APIs into the future, so let’s see how we might do that. For example, let’s allow the shipping options to contain a concrete and specific carrier and freight method:

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from dataclasses import dataclass
from enum import Enum, auto
from typing import NewType

class Carrier(Enum):
    FedEx = auto()
    USPS = auto()
    DHL = auto()
    UPS = auto()

class Conveyance(Enum):
    air = auto()
    truck = auto()
    train = auto()

@dataclass
class _RealShipOpts:
    _carrier: Carrier
    _freight: Conveyance

ShippingOptions = NewType("ShippingOptions", _RealShipOpts)

def shipFast() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(Carrier.FedEx, Conveyance.air))

def shipNormal() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(Carrier.UPS, Conveyance.truck))

def shipSlow() -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(Carrier.USPS, Conveyance.train))

def shippingDetailed(
    carrier: Carrier, conveyance: Conveyance
) -> ShippingOptions:
    return ShippingOptions(_RealShipOpts(carrier, conveyance))

As a NewType, our public ShippingOptions type doesn’t have a constructor. Since _RealShipOpts is private, and all its attributes are private, we can completely remove the old versions.

Anything within our shipping library can still access the private variables on ShippingOptions; as a NewType, it’s the same type as its base at runtime, so it presents minimal¹ overhead.

Clients outside our shipping library can still call all of our public constructors: shipFast, shipNormal, and shipSlow all still work with the same (as far as calling code knows) signature and behavior.

If you need to build and convey some state within your public API, while avoiding breakages associated with compatibility churn, hopefully this technique can help you do that!

Acknowledgments

Thanks for reading, and thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor.

Humans Are Bad At Perceiving

Humans are not particularly good at catching bugs. For one thing, we get tired easily. There is some science on this, indicating that humans can’t even maintain enough concentration to review more than about 400 lines of code at a time..

We have existing terms of art, in various fields, for the ways in which the human perceptual system fails to register stimuli. Perception fails when humans are distracted, tired, overloaded, or merely improperly engaged.

Each of these has implications for the fundamental limitations of code review as an engineering practice:

• Inattentional Blindness: you won’t be able to reliably find bugs that you’re not looking for.

• Repetition Blindness: you won’t be able to reliably find bugs that you are looking for, if they keep occurring.

• Vigilance Fatigue: you won’t be able to reliably find either kind of bugs, if you have to keep being alert to the presence of bugs all the time.

• and, of course, the distinct but related Alert Fatigue: you won’t even be able to reliably evaluate reports of possible bugs, if there are too many false positives.

Never Send A Human To Do A Machine’s Job

When you need to catch a category of error in your code reliably, you will need a deterministic tool to evaluate — and, thanks to our old friend “alert fatigue” above — ideally, to also remedy that type of error. These tools will relieve the need for a human to make the same repetitive checks over and over. None of them are perfect, but:

• to catch logical errors, use automated tests.
• to catch formatting errors, use autoformatters.
• to catch common mistakes, use linters.
• to catch common security problems, use a security scanner.

Don’t blame reviewers for missing these things.

Code review should not be how you catch bugs.

What Is Code Review For, Then?

Code review is for three things.

First, code review is for catching process failures. If a reviewer has noticed a few bugs of the same type in code review, that’s a sign that that type of bug is probably getting through review more often than it’s getting caught. Which means it’s time to figure out a way to deploy a tool or a test into CI that will reliably prevent that class of error, without requiring reviewers to be vigilant to it any more.

Second — and this is actually its more important purpose — code review is a tool for acculturation. Even if you already have good tools, good processes, and good documentation, new members of the team won’t necessarily know about those things. Code review is an opportunity for older members of the team to introduce newer ones to existing tools, patterns, or areas of responsibility. If you’re building an observer pattern, you might not realize that the codebase you’re working in already has an existing idiom for doing that, so you wouldn’t even think to search for it, but someone else who has worked more with the code might know about it and help you avoid repetition.

You will notice that I carefully avoided saying “junior” or “senior” in that paragraph. Sometimes the newer team member is actually more senior. But also, the acculturation goes both ways. This is the third thing that code review is for: disrupting your team’s culture and avoiding stagnation. If you have new talent, a fresh perspective can also be an extremely valuable tool for building a healthy culture. If you’re new to a team and trying to build something with an observer pattern, and this codebase has no tools for that, but your last job did, and it used one from an open source library, that is a good thing to point out in a review as well. It’s an opportunity to spot areas for improvement to culture, as much as it is to spot areas for improvement to process.

Thus, code review should be as hierarchically flat as possible. If the goal of code review were to spot bugs, it would make sense to reserve the ability to review code to only the most senior, detail-oriented, rigorous engineers in the organization. But most teams already know that that’s a recipe for brittleness, stagnation and bottlenecks. Thus, even though we know that not everyone on the team will be equally good at spotting bugs, it is very common in most teams to allow anyone past some fairly low minimum seniority bar to do reviews, often as low as “everyone on the team who has finished onboarding”.

Oops, Surprise, This Post Is Actually About LLMs Again

Sigh. I’m as disappointed as you are, but there are no two ways about it: LLM code generators are everywhere now, and we need to talk about how to deal with them. Thus, an important corollary of this understanding that code review is a social activity, is that LLMs are not social actors, thus you cannot rely on code review to inspect their output.

My own personal preference would be to eschew their use entirely, but in the spirit of harm reduction, if you’re going to use LLMs to generate code, you need to remember the ways in which LLMs are not like human beings.

When you relate to a human colleague, you will expect that:

1. you can make decisions about what to focus on based on their level of experience and areas of expertise to know what problems to focus on; from a late-career colleague you might be looking for bad habits held over from legacy programming languages; from an earlier-career colleague you might be focused more on logical test-coverage gaps,
2. and, they will learn from repeated interactions so that you can gradually focus less on a specific type of problem once you have seen that they’ve learned how to address it,

With an LLM, by contrast, while errors can certainly be biased a bit by the prompt from the engineer and pre-prompts that might exist in the repository, the types of errors that the LLM will make are somewhat more uniformly distributed across the experience range.

You will still find supposedly extremely sophisticated LLMs making extremely common mistakes, specifically because they are common, and thus appear frequently in the training data.

The LLM also can’t really learn. An intuitive response to this problem is to simply continue adding more and more instructions to its pre-prompt, treating that text file as its “memory”, but that just doesn’t work, and probably never will. The problem — “context rot” is somewhat fundamental to the nature of the technology.

Thus, code-generators must be treated more adversarially than you would a human code review partner. When you notice it making errors, you always have to add tests to a mechanical, deterministic harness that will evaluates the code, because the LLM cannot meaningfully learn from its mistakes outside a very small context window in the way that a human would, so giving it feedback is unhelpful. Asking it to just generate the code again still requires you to review it all again, and as we have previously learned, you, a human, cannot review more than 400 lines at once.

To Sum Up

Code review is a social process, and you should treat it as such. When you’re reviewing code from humans, share knowledge and encouragement as much as you share bugs or unmet technical requirements.

If you must reviewing code from an LLM, strengthen your automated code-quality verification tooling and make sure that its agentic loop will fail on its own when those quality checks fail immediately next time. Do not fall into the trap of appealing to its feelings, knowledge, or experience, because it doesn’t have any of those things.

But for both humans and LLMs, do not fall into the trap of thinking that your code review process is catching your bugs. That’s not its job.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

As you already know if you’ve read any of this blog in the last few years, I am a somewhat reluctant — but nevertheless quite staunch — critic of LLMs. This means that I have enthusiasts of varying degrees sometimes taking issue with my stance.

It seems that I am not going to get away from discussions, and, let’s be honest, pretty intense arguments about “AI” any time soon. These arguments are starting to make me quite upset. So it might be time to set some rules of engagement.

I’ve written about all of these before at greater length, but this is a short post because it’s not about the technology or making a broader point, it’s about me. These are rules for engaging with me, personally, on this topic. Others are welcome to adopt these rules if they so wish but I am not encouraging anyone to do so.

Thus, I’ve made this post as short as I can so everyone interested in engaging can read the whole thing. If you can’t make it through to the end, then please just follow Rule Zero.

Rule Zero: Maybe Don’t

You are welcome to ignore me. You can think my take is stupid and I can think yours is. We don’t have to get into an Internet Fight about it; we can even remain friends. You do not need to instigate an argument with me at all, if you think that my analysis is so bad that it doesn’t require rebutting.

Rule One: No ‘Just’

As I explained in a post with perhaps the least-predictive title I’ve ever written, “I Think I’m Done Thinking About genAI For Now”, I’ve already heard a bunch of bad arguments. Don’t tell me to ‘just’ use a better model, use an agentic tool, use a more recent version, or use some prompting trick that you personally believe works better. If you skim my work and think that I must not have deeply researched anything or read about it because you don’t like my conclusion, that is wrong.

Rule Two: No ‘Look At This Cool Thing’

Purely as a productivity tool, I have had a terrible experience with genAI. Perhaps you have had a great one. Neat. That’s great for you. As I explained at great length in “The Futzing Fraction”, my concern with generative AI is that I believe it is probably a net negative impact on productivity, based on both my experience and plenty of citations. Go check out the copious footnotes if you’re interested in more detail.

Therefore, I have already acknowledged that you can get an LLM to do various impressive, cool things, sometimes. If I tell you that you will, on average, lose money betting on a slot machine, a picture of a slot machine hitting a jackpot is not evidence against my position.

Rule Two And A Half: Engage In Metacognition

I specifically didn’t title the previous rule “no anecdotes” because data beyond anecdotes may be extremely expensive to produce. I don’t want to say you can never talk to me unless you’re doing a randomized controlled trial. However, if you are going to tell me an anecdote about the way that you’re using an LLM, I am interested in hearing how you are compensating for the well-documented biases that LLM use tends to induce. Try to measure what you can.

Rule Three: Do Not Cite The Deep Magic To Me

As I explained in “A Grand Unified Theory of the AI Hype Cycle”, I already know quite a bit of history of the “AI” label. If you are tempted to tell me something about how “AI” is really such a broad field, and it doesn’t just mean LLMs, especially if you are trying to launder the reputation of LLMs under the banner of jumbling them together with other things that have been called “AI”, I assure you that this will not be convincing to me.

Rule Four: Ethics Are Not Optional

I have made several arguments in my previous writing: there are ethical arguments, efficacy arguments, structuralist arguments, efficiency arguments and aesthetic arguments.

I am happy to, for the purposes of a good-faith discussion, focus on a specific set of concerns or an individual point that you want to make where you think I got something wrong. If you convince me that I am entirely incorrect about the effectiveness or predictability of LLMs in general or as specific LLM product, you don’t need to make a comprehensive argument about whether one should use the technology overall. I will even assume that you have your own ethical arguments.

However, if you scoff at the idea that one should have any ethical boundaries at all, and think that there’s no reason to care about the overall utilitarian impact of this technology, that it’s worth using no matter what else it does as long as it makes you 5% better at your job, that’s sociopath behavior.

This includes extreme whataboutism regarding things like the water use of datacenters, other elements of the surveillance technology stack, and so on.

Consequences

These are rules, once again, just for engaging with me. I have no particular power to enact broader sanctions upon you, nor would I be inclined to do so if I could. However, if you can’t stay within these basic parameters and you insist upon continuing to direct messages to me about this topic, I will summarily block you with no warning, on mastodon, email, GitHub, IRC, or wherever else you’re choosing to do that. This is for your benefit as well: such a discussion will not be a productive use of either of our time.

The dawning of a new year is an opportune moment to contemplate what has transpired in the old year, and consider what is likely to happen in the new one.

Today, I’d like to contemplate that contemplation itself.

The 20th century was an era characterized by rapidly accelerating change in technology and industry, creating shorter and shorter cultural cycles of changes in lifestyles. Thus far, the 21st century seems to be following that trend, at least in its recently concluded first quarter.

The early half of the twentieth century saw the massive disruption caused by electrification, radio, motion pictures, and then television.

In 1971, Intel poured gasoline on that fire by releasing the 4004, a microchip generally recognized as the first general-purpose microprocessor. Popular innovations rapidly followed: the computerized cash register, the personal computer, credit cards, cellular phones, text messaging, the Internet, the web, online games, mass surveillance, app stores, social media.

These innovations have arrived faster than previous generations, but also, they have crossed a crucial threshold: that of the human lifespan.

While the entire second millennium A.D. has been characterized by a gradually accelerating rate of technological and social change — the printing press and the industrial revolution were no slouches, in terms of changing society, and those predate the 20th century — most of those changes had the benefit of unfolding throughout the course of a generation or so.

Which means that any individual person in any given century up to the 20th might remember one major world-altering social shift within their lifetime, not five to ten of them. The diversity of human experience is vast, but most people would not expect that the defining technology of their lifetime was merely the latest in a progression of predictable civilization-shattering marvels.

Along with each of these successive generations of technology, we minted a new generation of industry titans. Westinghouse, Carnegie, Sarnoff, Edison, Ford, Hughes, Gates, Jobs, Zuckerberg, Musk. Not just individual rich people, but entire new classes of rich people that did not exist before. “Radio DJ”, “Movie Star”, “Rock Star”, “Dot Com Founder”, were all new paths to wealth opened (and closed) by specific technologies. While most of these people did come from at least some level of generational wealth, they no longer came from a literal hereditary aristocracy.

To describe this new feeling of constant acceleration, a new phrase was coined: “The Next Big Thing”. In addition to denoting that some Thing was coming and that it would be Big (i.e.: that it would change a lot about our lives), this phrase also carries the strong implication that such a Thing would be a product. Not a development in social relationships or a shift in cultural values, but some new and amazing form of conveying salted meat paste or what-have-you, that would make whatever lucky tinkerer who stumbled into it into a billionaire — along with any friends and family lucky enough to believe in their vision and get in on the ground floor with an investment.

In the latter part of the 20th century, our entire model of capital allocation shifted to account for this widespread belief. No longer were mega-businesses built by bank loans, stock issuances, and reinvestment of profit, the new model was “Venture Capital”. Venture capital is a model of capital allocation explicitly predicated on the idea that carefully considering each bet on a likely-to-succeed business and reducing one’s risk was a waste of time, because the return on the equity from the Next Big Thing would be so disproportionately huge — 10x, 100x, 1000x – that one could afford to make at least 10 bad bets for each good one, and still come out ahead.

The biggest risk was in missing the deal, not in giving a bunch of money to a scam. Thus, value investing and focus on fundamentals have been broadly disregarded in favor of the pursuit of the Next Big Thing.

If Americans of the twentieth century were temporarily embarrassed millionaires, those of the twenty-first are all temporarily embarrassed FAANG CEOs.

The predicament that this tendency leaves us in today is that the world is increasingly run by generations — GenX and Millennials — with the shared experience that the computer industry, either hardware or software, would produce some radical innovation every few years. We assume that to be true.

But all things change, even change itself, and that industry is beginning to slow down. Physically, transistor density is starting to brush up against physical limits. Economically, most people are drowning in more compute power than they know what to do with anyway. Users already have most of what they need from the Internet.

The big new feature in every operating system is a bunch of useless junk nobody really wants and is seeing remarkably little uptake. Social media and smartphones changed the world, true, but… those are both innovations from 2008. They’re just not new any more.

So we are all — collectively, culturally — looking for the Next Big Thing, and we keep not finding it.

It wasn’t 3D printing. It wasn’t crowdfunding. It wasn’t smart watches. It wasn’t VR. It wasn’t the Metaverse, it wasn’t Bitcoin, it wasn’t NFTs¹.

It’s also not AI, but this is why so many people assume that it will be AI. Because it’s got to be something, right? If it’s got to be something then AI is as good a guess as anything else right now.

The fact is, our lifetimes have been an extreme anomaly. Things like the Internet used to come along every thousand years or so, and while we might expect that the pace will stay a bit higher than that, it is not reasonable to expect that something new like “personal computers” or “the Internet”³ will arrive again.

We are not going to get rich by getting in on the ground floor of the next Apple or the next Google because the next Apple and the next Google are Apple and Google. The industry is maturing. Software technology, computer technology, and internet technology are all maturing.

There Will Be Next Things

Research and development is happening in all fields all the time. Amazing new developments quietly and regularly occur in pharmaceuticals and in materials science. But these are not predictable. They do not inhabit the public consciousness until they’ve already happened, and they are rarely so profound and transformative that they change everybody’s life.

There will even be new things in the computer industry, both software and hardware. Foldable phones do address a real problem (I wish the screen were even bigger but I don’t want to carry around such a big device), and would probably be more popular if they got the costs under control. One day somebody’s going to crack the problem of volumetric displays, probably. Some VR product will probably, eventually, hit a more realistic price/performance ratio where the niche will expand at least a little more.

Maybe there will even be something genuinely useful, which is recognizably adjacent to the current “AI” fad, but if it is, it will be some new development that we haven’t seen yet. If current AI technology were sufficient to drive some interesting product, it would already be doing it, not using marketing disguised as science to conceal diminishing returns on current investments.

But They Will Not Be Big

The impulse to find the One Big Thing that will dominate the next five years is a fool’s errand. Incremental gains are diminishing across the board. The markets for time and attention² are largely saturated. There’s no need for another streaming service if 100% of your leisure time is already committed to TikTok, YouTube and Netflix; famously, Netflix has already considered sleep its primary competitor for close to a decade - years before the pandemic.

Those rare tech markets which aren’t saturated are suffering from pedestrian economic problems like wealth inequality, not technological bottlenecks.

For example, the thing preventing the development of a robot that can do your laundry and your dishes without your input is not necessarily that we couldn’t build something like that, but that most households just can’t afford it without wage growth catching up to productivity growth. It doesn’t make sense for anyone to commit to the substantial R&D investment that such a thing would take, if the market doesn’t exist because the average worker isn’t paid enough to afford it on top of all the other tech which is already required to exist in society.

The projected income from the tiny, wealthy sliver of the population who could pay for the hardware, cannot justify an investment in the software past a fake version remotely operated by workers in the global south, only made possible by Internet wage arbitrage, i.e. a more palatable, modern version of indentured servitude.

Even if we were to accept the premise of an actually-“AI” version of this, that is still just a wish that ChatGPT could somehow improve enough behind the scenes to replace that worker, not any substantive investment in a novel, proprietary-to-the-chores-robot software system which could reliably perform specific functions.

What, Then?

The expectation for, and lack of, a “big thing” is a big problem. There are others who could describe its economic, political, and financial dimensions better than I can. So then let me speak to my expertise and my audience: open source software developers.

When I began my own involvement with open source, a big part of the draw for me was participating in a low-cost (to the corporate developer) but high-value (to society at large) positive externality. None of my employers would ever have cared about many of the applications for which Twisted forms a core bit of infrastructure; nor would I have been able to predict those applications’ existence. Yet, it is nice to have contributed to their development, even a little bit.

However, it’s not actually a positive externality if the public at large can’t directly benefit from it.

When real world-changing, disruptive developments are occurring, the bean-counters are not watching positive externalities too closely. As we discovered with many of the other benefits that temporarily accrued to labor in the tech economy, Open Source that is usable by individuals and small companies may have been a ZIRP. If you know you’re gonna make a billion dollars you’re not going to worry about giving away a few hundred thousand here and there.

When gains are smaller and harder to realize, and margins are starting to get squeezed, it’s harder to justify the investment in vaguely good vibes.

But this, itself, is not a call to action. I doubt very much that anyone reading this can do anything about the macroeconomic reality of higher interest rates. The technological reality of “development is happening slower” is inherently something that you can’t change on purpose.

However, what we can do is to be aware of this trend in our own work.

Fight Scale Creep

It seems to me that more and more open source infrastructure projects are tools for hyper-scale application development, only relevant to massive cloud companies. This is just a subjective assessment on my part — I’m not sure what tools even exist today to measure this empirically — but I remember a big part of the open source community when I was younger being things like Inkscape, Themes.Org and Slashdot, not React, Docker Hub and Hacker News.

This is not to say that the hobbyist world no longer exists. There is of course a ton of stuff going on with Raspberry Pi, Home Assistant, OwnCloud, and so on. If anything there’s a bit of a resurgence of self-hosting. But the interests of self-hosters and corporate developers are growing apart; there seems to be far less of a beneficial overflow from corporate infrastructure projects into these enthusiast or prosumer communities.

This is the concrete call to action: if you are employed in any capacity as an open source maintainer, dedicate more energy to medium- or small-scale open source projects.

If your assumption is that you will eventually reach a hyper-scale inflection point, then mimicking Facebook and Netflix is likely to be a good idea. However, if we can all admit to ourselves that we’re not going to achieve a trillion-dollar valuation and a hundred thousand engineer headcount, we can begin to consider ways to make our Next Thing a bit smaller, and to accommodate the world as it is rather than as we wish it would be.

Be Prepared to Scale Down

Here are some design guidelines you might consider, for just about any open source project, particularly infrastructure ones:

1. Don’t assume that your software can sustain an arbitrarily large fixed overhead because “you just pay that cost once” and you’re going to be running a billion instances so it will always amortize; maybe you’re only going to be running ten.

2. Remember that such fixed overhead includes not just CPU, RAM, and filesystem storage, but also the learning curve for developers. Front-loading a massive amount of conceptual complexity to accommodate the problems of hyper-scalers is a common mistake. Try to smooth out these complexities and introduce them only when necessary.

3. Test your code on edge devices. This means supporting Windows and macOS, and even Android and iOS. If you want your tool to help empower individual users, you will need to meet them where they are, which is not on an EC2 instance.

4. This includes considering Desktop Linux as a platform, as opposed to Server Linux as a platform, which (while they certainly have plenty in common) they are also distinct in some details. Consider the highly specific example of secret storage: if you are writing something that intends to live in a cloud environment, and you need to configure it with a secret, you will probably want to provide it via a text file or an environment variable. By contrast, if you want this same code to run on a desktop system, your users will expect you to support the Secret Service. This will likely only require a few lines of code to accommodate, but it is a massive difference to the user experience.

5. Don’t rely on LLMs remaining cheap or free. If you have LLM-related features⁴, make sure that they are sufficiently severable from the rest of your offering that if ChatGPT starts costing $1000 a month, your tool doesn’t break completely. Similarly, do not require that your users have easy access to half a terabyte of VRAM and a rack full of 5090s in order to run a local model.

Even if you were going to scale up to infinity, the ability to scale down and consider smaller deployments means that you can run more comfortably on, for example, a developer’s laptop. So even if you can’t convince your employer that this is where the economy and the future of technology in our lifetimes is going, it can be easy enough to justify this sort of design shift, particularly as individual choices. Make your onboarding cheaper, your development feedback loops tighter, and your systems generally more resilient to economic headwinds.

So, please design your open source libraries, applications, and services to run on smaller devices, with less complexity. It will be worth your time as well as your users’.

But if you can fix the whole wealth inequality thing, do that first.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Tell me if you’ve heard this one before.

You’re working on an application. Let’s call it “FooApp”. FooApp has a dependency on an open source library, let’s call it “LibBar”. You find a bug in LibBar that affects FooApp.

To envisage the best possible version of this scenario, let’s say you actively like LibBar, both technically and socially. You’ve contributed to it in the past. But this bug is causing production issues in FooApp today, and LibBar’s release schedule is quarterly. FooApp is your job; LibBar is (at best) your hobby. Blocking on the full upstream contribution cycle and waiting for a release is an absolute non-starter.

What do you do?

There are a few common reactions to this type of scenario, all of which are bad options.

I will enumerate them specifically here, because I suspect that some of them may resonate with many readers:

1. Find an alternative to LibBar, and switch to it.

   This is a bad idea because a transition to a core infrastructure component could be extremely expensive.

2. Vendor LibBar into your codebase and fix your vendored version.

   This is a bad idea because carrying this one fix now requires you to maintain all the tooling associated with a monorepo¹: you have to be able to start pulling in new versions from LibBar regularly, reconcile your changes even though you now have a separate version history on your imported version, and so on.

3. Monkey-patch LibBar to include your fix.

   This is a bad idea because you are now extremely tightly coupled to a specific version of LibBar. By modifying LibBar internally like this, you’re inherently violating its compatibility contract, in a way which is going to be extremely difficult to test. You can test this change, of course, but as LibBar changes, you will need to replicate any relevant portions of its test suite (which may be its entire test suite) in FooApp. Lots of potential duplication of effort there.

4. Implement a workaround in your own code, rather than fixing it.

   This is a bad idea because you are distorting the responsibility for correct behavior. LibBar is supposed to do LibBar’s job, and unless you have a full wrapper for it in your own codebase, other engineers (including “yourself, personally”) might later forget to go through the alternate, workaround codepath, and invoke the buggy LibBar behavior again in some new place.

5. Implement the fix upstream in LibBar anyway, because that’s the Right Thing To Do, and burn credibility with management while you anxiously wait for a release with the bug in production.

   This is a bad idea because you are betraying your users — by allowing the buggy behavior to persist — for the workflow convenience of your dependency providers. Your users are probably giving you money, and trusting you with their data. This means you have both ethical and economic obligations to consider their interests.

   As much as it’s nice to participate in the open source community and take on an appropriate level of burden to maintain the commons, this cannot sustainably be at the explicit expense of the population you serve directly.

   Even if we only care about the open source maintainers here, there’s still a problem: as you are likely to come under immediate pressure to ship your changes, you will inevitably relay at least a bit of that stress to the maintainers. Even if you try to be exceedingly polite, the maintainers will know that you are coming under fire for not having shipped the fix yet, and are likely to feel an even greater burden of obligation to ship your code fast.

   Much as it’s good to contribute the fix, it’s not great to put this on the maintainers.

The respective incentive structures of software development — specifically, of corporate application development and open source infrastructure development — make options 1-4 very common.

On the corporate / application side, these issues are:

• it’s difficult for corporate developers to get clearance to spend even small amounts of their work hours on upstream open source projects, but clearance to spend time on the project they actually work on is implicit. If it takes 3 hours of wrangling with Legal² and 3 hours of implementation work to fix the issue in LibBar, but 0 hours of wrangling with Legal and 40 hours of implementation work in FooApp, a FooApp developer will often perceive it as “easier” to fix the issue downstream.

• it’s difficult for corporate developers to get clearance from management to spend even small amounts of money sponsoring upstream reviewers, so even if they can find the time to contribute the fix, chances are high that it will remain stuck in review unless they are personally well-integrated members of the LibBar development team already.

• even assuming there’s zero pressure whatsoever to avoid open sourcing the upstream changes, there’s still the fact inherent to any development team that FooApp’s developers will be more familiar with FooApp’s codebase and development processes than they are with LibBar’s. It’s just easier to work there, even if all other things are equal.

• systems for tracking risk from open source dependencies often lack visibility into vendoring, particularly if you’re doing a hybrid approach and only vendoring a few things to address work in progress, rather than a comprehensive and disciplined approach to a monorepo. If you fully absorb a vendored dependency and then modify it, Dependabot isn’t going to tell you that a new version is available any more, because it won’t be present in your dependency list. Organizationally this is bad of course but from the perspective of an individual developer this manifests mostly as fewer annoying emails.

But there are problems on the open source side as well. Those problems are all derived from one big issue: because we’re often working with relatively small sums of money, it’s hard for upstream open source developers to consume either money or patches from application developers. It’s nice to say that you should contribute money to your dependencies, and you absolutely should, but the cost-benefit function is discontinuous. Before a project reaches the fiscal threshold where it can be at least one person’s full-time job to worry about this stuff, there’s often no-one responsible in the first place. Developers will therefore gravitate to the issues that are either fun, or relevant to their own job.

These mutually-reinforcing incentive structures are a big reason that users of open source infrastructure, even teams who work at corporate users with zillions of dollars, don’t reliably contribute back.

The Answer We Want

All those options are bad. If we had a good option, what would it look like?

It is both practically necessary³ and morally required⁴ for you to have a way to temporarily rely on a modified version of an open source dependency, without permanently diverging.

Below, I will describe a desirable abstract workflow for achieving this goal.

Step 0: Report the Problem

Before you get started with any of these other steps, write up a clear description of the problem and report it to the project as an issue; specifically, in contrast to writing it up as a pull request. Describe the problem before submitting a solution.

You may not be able to wait for a volunteer-run open source project to respond to your request, but you should at least tell the project what you’re planning on doing.

If you don’t hear back from them at all, you will have at least made sure to comprehensively describe your issue and strategy beforehand, which will provide some clarity and focus to your changes.

If you do hear back from them, in the worst case scenario, you may discover that a hard fork will be necessary because they don’t consider your issue valid, but even that information will save you time, if you know it before you get started. In the best case, you may get a reply from the project telling you that you’ve misunderstood its functionality and that there is already a configuration parameter or usage pattern that will resolve your problems with no new code. But in all cases, you will benefit from early coordination on what needs fixing before you get to how to fix it.

Step 1: Source Code and CI Setup

Fork the source code for your upstream dependency to a writable location where it can live at least for the duration of this one bug-fix, and possibly for the duration of your application’s use of the dependency. After all, you might want to fix more than one bug in LibBar.

You want to have a place where you can put your edits, that will be version controlled and code reviewed according to your normal development process. This probably means you’ll need to have your own main branch that diverges from your upstream’s main branch.

Remember: you’re going to need to deploy this to your production, so testing gates that your upstream only applies to final releases of LibBar will need to be applied to every commit here.

Depending on your LibBar’s own development process, this may result in slightly unusual configurations where, for example, your fixes are written against the last LibBar release tag, rather than its current⁵ main; if the project has a branch-freshness requirement, you might need two branches, one for your upstream PR (based on main) and one for your own use (based on the release branch with your changes).

Ideally for projects with really good CI and a strong “keep main release-ready at all times” policy, you can deploy straight from a development branch, but it’s good to take a moment to consider this before you get started. It’s usually easier to rebase changes from an older HEAD onto a newer one than it is to go backwards.

Speaking of CI, you will want to have your own CI system. The fact that GitHub Actions has become a de-facto lingua franca of continuous integration means that this step may be quite simple, and your forked repo can just run its own instance.

Optional Bonus Step 1a: Artifact Management

If you have an in-house artifact repository, you should set that up for your dependency too, and upload your own build artifacts to it. You can often treat your modified dependency as an extension of your own source tree and install from a GitHub URL, but if you’ve already gone to the trouble of having an in-house package repository, you can pretend you’ve taken over maintenance of the upstream package temporarily (which you kind of have) and leverage those workflows for caching and build-time savings as you would with any other internal repo.

Step 2: Do The Fix

Now that you’ve got somewhere to edit LibBar’s code, you will want to actually fix the bug.

Step 2a: Local Filesystem Setup

Before you have a production version on your own deployed branch, you’ll want to test locally, which means having both repositories in a single integrated development environment.

At this point, you will want to have a local filesystem reference to your LibBar dependency, so that you can make real-time edits, without going through a slow cycle of pushing to a branch in your LibBar fork, pushing to a FooApp branch, and waiting for all of CI to run on both.

This is useful in both directions: as you prepare the FooApp branch that makes any necessary updates on that end, you’ll want to make sure that FooApp can exercise the LibBar fix in any integration tests. As you work on the LibBar fix itself, you’ll also want to be able to use FooApp to exercise the code and see if you’ve missed anything - and this, you wouldn’t get in CI, since LibBar can’t depend on FooApp itself.

In short, you want to be able to treat both projects as an integrated development environment, with support from your usual testing and debugging tools, just as much as you want your deployment output to be an integrated artifact.

Step 2b: Branch Setup for PR

However, for continuous integration to work, you will also need to have a remote resource reference of some kind from FooApp’s branch to LibBar. You will need 2 pull requests: the first to land your LibBar changes to your internal LibBar fork and make sure it’s passing its own tests, and then a second PR to switch your LibBar dependency from the public repository to your internal fork.

At this step it is very important to ensure that there is an issue filed on your own internal backlog to drop your LibBar fork. You do not want to lose track of this work; it is technical debt that must be addressed.

Until it’s addressed, automated tools like Dependabot will not be able to apply security updates to LibBar for you; you’re going to need to manually integrate every upstream change. This type of work is itself very easy to drop or lose track of, so you might just end up stuck on a vulnerable version.

Step 3: Deploy Internally

Now that you’re confident that the fix will work, and that your temporarily-internally-maintained version of LibBar isn’t going to break anything on your site, it’s time to deploy.

Some deployment heritage should help to provide some evidence that your fix is ready to land in LibBar, but at the next step, please remember that your production environment isn’t necessarily emblematic of that of all LibBar users.

Step 4: Propose Externally

You’ve got the fix, you’ve tested the fix, you’ve got the fix in your own production, you’ve told upstream you want to send them some changes. Now, it’s time to make the pull request.

You’re likely going to get some feedback on the PR, even if you think it’s already ready to go; as I said, despite having been proven in your production environment, you may get feedback about additional concerns from other users that you’ll need to address before LibBar’s maintainers can land it.

As you process the feedback, make sure that each new iteration of your branch gets re-deployed to your own production. It would be a huge bummer to go through all this trouble, and then end up unable to deploy the next publicly released version of LibBar within FooApp because you forgot to test that your responses to feedback still worked on your own environment.

Step 4a: Hurry Up And Wait

If you’re lucky, upstream will land your changes to LibBar. But, there’s still no release version available. Here, you’ll have to stay in a holding pattern until upstream can finalize the release on their end.

Depending on some particulars, it might make sense at this point to archive your internal LibBar repository and move your pinned release version to a git hash of the LibBar version where your fix landed, in their repository.

Before you do this, check in with the LibBar core team and make sure that they understand that’s what you’re doing and they don’t have any wacky workflows which may involve rebasing or eliding that commit as part of their release process.

Step 5: Unwind Everything

Finally, you eventually want to stop carrying any patches and move back to an official released version that integrates your fix.

You want to do this because this is what the upstream will expect when you are reporting bugs. Part of the benefit of using open source is benefiting from the collective work to do bug-fixes and such, so you don’t want to be stuck off on a pinned git hash that the developers do not support for anyone else.

As I said in step 2b⁶, make sure to maintain a tracking task for doing this work, because leaving this sort of relatively easy-to-clean-up technical debt lying around is something that can potentially create a lot of aggravation for no particular benefit. Make sure to put your internal LibBar repository into an appropriate state at this point as well.

Up Next

This is part 1 of a 2-part series. In part 2, I will explore in depth how to execute this workflow specifically for Python packages, using some popular tools. I’ll discuss my own workflow, standards like PEP 517 and pyproject.toml, and of course, by the popular demand that I just know will come, uv.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

The most optimistic vision of generative AI¹ is that it will relieve us of the tedious, repetitive elements of knowledge work so that we can get to work on the really interesting problems that such tedium stands in the way of. Even if you fully believe in this vision, it’s hard to deny that today, some tedium is associated with the process of using generative AI itself.

Generative AI also isn’t free, and so, as responsible consumers, we need to ask: is it worth it? What’s the ROI of genAI, and how can we tell? In this post, I’d like to explore a logical framework for evaluating genAI expenditures, to determine if your organization is getting its money’s worth.

Perpetually Proffering Permuted Prompts

I think most LLM users would agree with me that a typical workflow with an LLM rarely involves prompting it only one time and getting a perfectly useful answer that solves the whole problem.

Generative AI best practices, even from the most optimistic vendors all suggest that you should continuously evaluate everything. ChatGPT, which is really the only genAI product with significantly scaled adoption, still says at the bottom of every interaction:

ChatGPT can make mistakes. Check important info.

If we have to “check important info” on every interaction, it stands to reason that even if we think it’s useful, some of those checks will find an error. Again, if we think it’s useful, presumably the next thing to do is to perturb our prompt somehow, and issue it again, in the hopes that the next invocation will, by dint of either:

1. better luck this time with the stochastic aspect of the inference process,
2. enhanced application of our skill to engineer a better prompt based on the deficiencies of the current inference, or
3. better performance of the model by populating additional context in subsequent chained prompts.

Unfortunately, given the relative lack of reliable methods to re-generate the prompt and receive a better answer², checking the output and re-prompting the model can feel like just kinda futzing around with it. You try, you get a wrong answer, you try a few more times, eventually you get the right answer that you wanted in the first place. It’s a somewhat unsatisfying process, but if you get the right answer eventually, it does feel like progress, and you didn’t need to use up another human’s time.

In fact, the hottest buzzword of the last hype cycle is “agentic”. While I have my own feelings about this particular word³, its current practical definition is “a generative AI system which automates the process of re-prompting itself, by having a deterministic program evaluate its outputs for correctness”.

A better term for an “agentic” system would be a “self-futzing system”.

However, the ability to automate some level of checking and re-prompting does not mean that you can fully delegate tasks to an agentic tool, either. It is, plainly put, not safe. If you leave the AI on its own, you will get terrible results that will at best make for a funny story⁴⁵ and at worst might end up causing serious damage⁶⁷.

Taken together, this all means that for any consequential task that you want to accomplish with genAI, you need an expert human in the loop. The human must be capable of independently doing the job that the genAI system is being asked to accomplish.

When the genAI guesses correctly and produces usable output, some of the human’s time will be saved. When the genAI guesses wrong and produces hallucinatory gibberish or even “correct” output that nevertheless fails to account for some unstated but necessary property such as security or scale, some of the human’s time will be wasted evaluating it and re-trying it.

Income from Investment in Inference

Let’s evaluate an abstract, hypothetical genAI system that can automate some work for our organization. To avoid implicating any specific vendor, let’s call the system “Mallory”.

Is Mallory worth the money? How can we know?

Logically, there are only two outcomes that might result from using Mallory to do our work.

1. We prompt Mallory to do some work; we check its work, it is correct, and some time is saved.
2. We prompt Mallory to do some work; we check its work, it fails, and we futz around with the result; this time is wasted.

As a logical framework, this makes sense, but ROI is an arithmetical concept, not a logical one. So let’s translate this into some terms.

In order to evaluate Mallory, let’s define the Futzing Fraction, “ FF ”, in terms of the following variables:

H

the average amount of time a Human worker would take to do a task, unaided by Mallory

I

the amount of time that Mallory takes to run one Inference⁸

C

the amount of time that a human has to spend Checking Mallory’s output for each inference

P

the Probability that Mallory will produce a correct inference for each prompt

W

the average amount of time that it takes for a human to Write one prompt for Mallory

E

since we are normalizing everything to time, rather than money, we do also have to account for the dollar of Mallory as as a product, so we will include the Equivalent amount of human time we could purchase for the marginal cost of one⁹ inference.

As in last week’s example of simple ROI arithmetic, we will put our costs in the numerator, and our benefits in the denominator.

The idea here is that for each prompt, the minimum amount of time-equivalent cost possible is W+I+C+E. The user must, at least once, write a prompt, wait for inference to run, then check the output; and, of course, pay any costs to Mallory’s vendor.

If the probability of a correct answer is P=13, then they will do this entire process 3 times¹⁰, so we put P in the denominator. Finally, we divide everything by H, because we are trying to determine if we are actually saving any time or money, versus just letting our existing human, who has to be driving this process anyway, do the whole thing.

If the Futzing Fraction evaluates to a number greater than 1, as previously discussed, you are a bozo; you’re spending more time futzing with Mallory than getting value out of it.

Figuring out the Fraction is Frustrating

In order to even evaluate the value of the Futzing Fraction though, you have to have a sound method to even get a vague sense of all the terms.

If you are a business leader, a lot of this is relatively easy to measure. You vaguely know what H is, because you know what your payroll costs, and similarly, you can figure out E with some pretty trivial arithmetic based on Mallory’s pricing table. There are endless YouTube channels, spec sheets and benchmarks to give you I. W is probably going to be so small compared to H that it hardly merits consideration¹¹.

But, are you measuring C? If your employees are not checking the outputs of the AI, you’re on a path to catastrophe that no ROI calculation can capture, so it had better be greater than zero.

Are you measuring P? How often does the AI get it right on the first try?

Challenges to Computing Checking Costs

In the fraction defined above, the term C is going to be large. Larger than you think.

Measuring P and C with a high degree of precision is probably going to be very hard; possibly unreasonably so, or too expensive¹² to bother with in practice. So you will undoubtedly need to work with estimates and proxy metrics. But you have to be aware that this is a problem domain where your normal method of estimating is going to be extremely vulnerable to inherent cognitive bias, and find ways to measure.

Margins, Money, and Metacognition

First let’s discuss cognitive and metacognitive bias.

My favorite cognitive bias is the availability heuristic and a close second is its cousin salience bias. Humans are empirically predisposed towards noticing and remembering things that are more striking, and to overestimate their frequency.

If you are estimating the variables above based on the vibe that you’re getting from the experience of using an LLM, you may be overestimating its utility.

Consider a slot machine.

If you put a dollar in to a slot machine, and you lose that dollar, this is an unremarkable event. Expected, even. It doesn’t seem interesting. You can repeat this over and over again, a thousand times, and each time it will seem equally unremarkable. If you do it a thousand times, you will probably get gradually more anxious as your sense of your dwindling bank account becomes slowly more salient, but losing one more dollar still seems unremarkable.

If you put a dollar in a slot machine and it gives you a thousand dollars, that will probably seem pretty cool. Interesting. Memorable. You might tell a story about this happening, but you definitely wouldn’t really remember any particular time you lost one dollar.

Luckily, when you arrive at a casino with slot machines, you probably know well enough to set a hard budget in the form of some amount of physical currency you will have available to you. The odds are against you, you’ll probably lose it all, but any responsible gambler will have an immediate, physical representation of their balance in front of them, so when they have lost it all, they can see that their hands are empty, and can try to resist the “just one more pull” temptation, after hitting that limit.

Now, consider Mallory.

If you put ten minutes into writing a prompt, and Mallory gives a completely off-the-rails, useless answer, and you lose ten minutes, well, that’s just what using a computer is like sometimes. Mallory malfunctioned, or hallucinated, but it does that sometimes, everybody knows that. You only wasted ten minutes. It’s fine. Not a big deal. Let’s try it a few more times. Just ten more minutes. It’ll probably work this time.

If you put ten minutes into writing a prompt, and it completes a task that would have otherwise taken you 4 hours, that feels amazing. Like the computer is magic! An absolute endorphin rush.

Very memorable. When it happens, it feels like P=1.

But... did you have a time budget before you started? Did you have a specified N such that “I will give up on Mallory as soon as I have spent N minutes attempting to solve this problem with it”? When the jackpot finally pays out that 4 hours, did you notice that you put 6 hours worth of 10-minute prompt coins into it in?

If you are attempting to use the same sort of heuristic intuition that probably works pretty well for other business leadership decisions, Mallory’s slot-machine chat-prompt user interface is practically designed to subvert those sensibilities. Most business activities do not have nearly such an emotionally variable, intermittent reward schedule. They’re not going to trick you with this sort of cognitive illusion.

Thus far we have been talking about cognitive bias, but there is a metacognitive bias at play too: while Dunning-Kruger, everybody’s favorite metacognitive bias does have some problems with it, the main underlying metacognitive bias is that we tend to believe our own thoughts and perceptions, and it requires active effort to distance ourselves from them, even if we know they might be wrong.

This means you must assume any intuitive estimate of C is going to be biased low; similarly P is going to be biased high. You will forget the time you spent checking, and you will underestimate the number of times you had to re-check.

To avoid this, you will need to decide on a Ulysses pact to provide some inputs to a calculation for these factors that you will not be able to able to fudge if they seem wrong to you.

Problematically Plausible Presentation

Another nasty little cognitive-bias landmine for you to watch out for is the authority bias, for two reasons:

1. People will tend to see Mallory as an unbiased, external authority, and thereby see it as more of an authority than a similarly-situated human¹³.
2. Being an LLM, Mallory will be overconfident in its answers¹⁴.

The nature of LLM training is also such that commonly co-occurring tokens in the training corpus produce higher likelihood of co-occurring in the output; they’re just going to be closer together in the vector-space of the weights; that’s, like, what training a model is, establishing those relationships.

If you’ve ever used an heuristic to informally evaluate someone’s credibility by listening for industry-specific shibboleths or ways of describing a particular issue, that skill is now useless. Having ingested every industry’s expert literature, commonly-occurring phrases will always be present in Mallory’s output. Mallory will usually sound like an expert, but then make mistakes at random.¹⁵.

While you might intuitively estimate C by thinking “well, if I asked a person, how could I check that they were correct, and how long would that take?” that estimate will be extremely optimistic, because the heuristic techniques you would use to quickly evaluate incorrect information from other humans will fail with Mallory. You need to go all the way back to primary sources and actually fully verify the output every time, or you will likely fall into one of these traps.

Mallory Mangling Mentorship

So far, I’ve been describing the effect Mallory will have in the context of an individual attempting to get some work done. If we are considering organization-wide adoption of Mallory, however, we must also consider the impact on team dynamics. There are a number of possible potential side effects that one might consider when looking at, but here I will focus on just one that I have observed.

I have a cohort of friends in the software industry, most of whom are individual contributors. I’m a programmer who likes programming, so are most of my friends, and we are also (sigh), charitably, pretty solidly middle-aged at this point, so we tend to have a lot of experience.

As such, we are often the folks that the team — or, in my case, the community — goes to when less-experienced folks need answers.

On its own, this is actually pretty great. Answering questions from more junior folks is one of the best parts of a software development job. It’s an opportunity to be helpful, mostly just by knowing a thing we already knew. And it’s an opportunity to help someone else improve their own agency by giving them knowledge that they can use in the future.

However, generative AI throws a bit of a wrench into the mix.

Let’s imagine a scenario where we have 2 developers: Alice, a staff engineer who has a good understanding of the system being built, and Bob, a relatively junior engineer who is still onboarding.

The traditional interaction between Alice and Bob, when Bob has a question, goes like this:

1. Bob gets confused about something in the system being developed, because Bob’s understanding of the system is incorrect.
2. Bob formulates a question based on this confusion.
3. Bob asks Alice that question.
4. Alice knows the system, so she gives an answer which accurately reflects the state of the system to Bob.
5. Bob’s understanding of the system improves, and thus he will have fewer and better-informed questions going forward.

You can imagine how repeating this simple 5-step process will eventually transform Bob into a senior developer, and then he can start answering questions on his own. Making sufficient time for regularly iterating this loop is the heart of any good mentorship process.

Now, though, with Mallory in the mix, the process now has a new decision point, changing it from a linear sequence to a flow chart.

We begin the same way, with steps 1 and 2. Bob’s confused, Bob formulates a question, but then:

3. Bob asks Mallory that question.

Here, our path then diverges into a “happy” path, a “meh” path, and a “sad” path.

The “happy” path proceeds like so:

4. Mallory happens to formulate a correct answer.
5. Bob’s understanding of the system improves, and thus he will have fewer and better-informed questions going forward.

Great. Problem solved. We just saved some of Alice’s time. But as we learned earlier,

Mallory can make mistakes. When that happens, we will need to check important info. So let’s get checking:

4. Mallory happens to formulate an incorrect answer.
5. Bob investigates this answer.
6. Bob realizes that this answer is incorrect because it is inconsistent with some of his prior, correct knowledge of the system, or his investigation.
7. Bob asks Alice the same question; GOTO traditional interaction step 4.

On this path, Bob spent a while futzing around with Mallory, to no particular benefit. This wastes some of Bob’s time, but then again, Bob could have ended up on the happy path, so perhaps it was worth the risk; at least Bob wasn’t wasting any of Alice’s much more valuable time in the process.¹⁶

Notice that beginning at the start of step 4, we must begin allocating all of Bob’s time to C, so C already starts getting a bit bigger than if it were just Bob checking Mallory’s output specifically on tasks that Bob is doing.

That brings us to the “sad” path.

4. Mallory happens to formulate an incorrect answer.
5. Bob investigates this answer.
6. Bob does not realize that this answer is incorrect because he is unable to recognize any inconsistencies with his existing, incomplete knowledge of the system.
7. Bob integrates Mallory’s incorrect information of the system into his mental model.
8. Bob proceeds to make a larger and larger mess of his work, based on an incorrect mental model.
9. Eventually, Bob asks Alice a new, worse question, based on this incorrect understanding.
10. Sadly we cannot return to the happy path at this point, because now Alice must unravel the complex series of confusing misunderstandings that Mallory has unfortunately conveyed to Bob at this point. In the really sad case, Bob actually doesn’t believe Alice for a while, because Mallory seems unbiased¹⁷, and Alice has to waste even more time convincing Bob before she can simply explain to him.

Now, we have wasted some of Bob’s time, and some of Alice’s time. Everything from step 5-10 is C, and as soon as Alice gets involved, we are now adding to C at double real-time. If more team members are pulled in to the investigation, you are now multiplying C by the number of investigators, potentially running at triple or quadruple real time.

But That’s Not All

Here I’ve presented a brief selection reasons why C will be both large, and larger than you expect. To review:

1. Gambling-style mechanics of the user interface will interfere with your own self-monitoring and developing a good estimate.
2. You can’t use human heuristics for quickly spotting bad answers.
3. Wrong answers given to junior people who can’t evaluate them will waste more time from your more senior employees.

But this is a small selection of ways that Mallory’s output can cost you money and time. It’s harder to simplistically model second-order effects like this, but there’s also a broad range of possibilities for ways that, rather than simply checking and catching errors, an error slips through and starts doing damage. Or ways in which the output isn’t exactly wrong, but still sub-optimal in ways which can be difficult to notice in the short term.

For example, you might successfully vibe-code your way to launch a series of applications, successfully “checking” the output along the way, but then discover that the resulting code is unmaintainable garbage that prevents future feature delivery, and needs to be re-written¹⁸. But this kind of intellectual debt isn’t even specific to technical debt while coding; it can even affect such apparently genAI-amenable fields as LinkedIn content marketing¹⁹.

Problems with the Prediction of P

C isn’t the only challenging term though. P, is just as, if not more important, and just as hard to measure.

LLM marketing materials love to phrase their accuracy in terms of a percentage. Accuracy claims for LLMs in general tend to hover around 70%²⁰. But these scores vary per field, and when you aggregate them across multiple topic areas, they start to trend down. This is exactly why “agentic” approaches for more immediately-verifiable LLM outputs (with checks like “did the code work”) got popular in the first place: you need to try more than once.

Independently measured claims about accuracy tend to be quite a bit lower²¹. The field of AI benchmarks is exploding, but it probably goes without saying that LLM vendors game those benchmarks²², because of course every incentive would encourage them to do that. Regardless of what their arbitrary scoring on some benchmark might say, all that matters to your business is whether it is accurate for the problems you are solving, for the way that you use it. Which is not necessarily going to correspond to any benchmark. You will need to measure it for yourself.

With that goal in mind, our formulation of P must be a somewhat harsher standard than “accuracy”. It’s not merely “was the factual information contained in any generated output accurate”, but, “is the output good enough that some given real knowledge-work task is done and the human does not need to issue another prompt”?

Surprisingly Small Space for Slip-Ups

The problem with reporting these things as percentages at all, however, is that our actual definition for P is 1attempts, where attempts for any given attempt, at least, must be an integer greater than or equal to 1.

Taken in aggregate, if we succeed on the first prompt more often than not, we could end up with a P>12, but combined with the previous observation that you almost always have to prompt it more than once, the practical reality is that P will start at 50% and go down from there.

If we plug in some numbers, trying to be as extremely optimistic as we can, and say that we have a uniform stream of tasks, every one of which can be addressed by Mallory, every one of which:

• we can measure perfectly, with no overhead
• would take a human 45 minutes
• takes Mallory only a single minute to generate a response
• Mallory will require only 1 re-prompt, so “good enough” half the time
• takes a human only 5 minutes to write a prompt for
• takes a human only 5 minutes to check the result of
• has a per-prompt cost of the equivalent of a single second of a human’s time

Thought experiments are a dicey basis for reasoning in the face of disagreements, so I have tried to formulate something here that is absolutely, comically, over-the-top stacked in favor of the AI optimist here.

Would that be a profitable? It sure seems like it, given that we are trading off 45 minutes of human time for 1 minute of Mallory-time and 10 minutes of human time. If we ask Python:

1
2
3
4
5

>>> def FF(H, I, C, P, W, E):
...     return (W + I + C + E) / (P * H)
... FF(H=45.0, I=1.0, C=5.0, P=1/2, W=5.0, E=0.01)
...
0.48933333333333334

We get a futzing fraction of about 0.4893. Not bad! Sounds like, at least under these conditions, it would indeed be cost-effective to deploy Mallory. But… realistically, do you reliably get useful, done-with-the-task quality output on the second prompt? Let’s bump up the denominator on P just a little bit there, and see how we fare:

1
2

>>> FF(H=45.0, I=1.0, C=5.0, P=1/3, W=5.0, E=0.01)
0.734

Oof. Still cost-effective at 0.734, but not quite as good. Where do we cap out, exactly?

1
2
3
4
5
6
7
8
9

>>> from itertools import count
... for A in count(start=4):
...     print(A, result := FF(H=45.0, I=1.0, C=5.0, P=1 / A, W=5.0, E=1/60.))
...     if result > 1:
...         break
...
4 0.9792592592592594
5 1.224074074074074
>>>

With this little test, we can see that at our next iteration we are already at 0.9792, and by 5 tries per prompt, even in this absolute fever-dream of an over-optimistic scenario, with a futzing fraction of 1.2240, Mallory is now a net detriment to our bottom line.

Harm to the Humans

We are treating H as functionally constant so far, an average around some hypothetical Gaussian distribution, but the distribution itself can also change over time.

Formally speaking, an increase to H would be good for our fraction. Maybe it would even be a good thing; it could mean we’re taking on harder and harder tasks due to the superpowers that Mallory has given us.

But an observed increase to H would probably not be good. An increase could also mean your humans are getting worse at solving problems, because using Mallory has atrophied their skills²³ and sabotaged learning opportunities²⁴²⁵. It could also go up because your senior, experienced people now hate their jobs²⁶.

For some more vulnerable folks, Mallory might just take a shortcut to all these complex interactions and drive them completely insane²⁷ directly. Employees experiencing an intense psychotic episode are famously less productive than those who are not.

This could all be very bad, if our futzing fraction eventually does head north of 1 and you need to reconsider introducing human-only workflows, without Mallory.

Abridging the Artificial Arithmetic (Alliteratively)

To reiterate, I have proposed this fraction:

which shows us positive ROI when FF is less than 1, and negative ROI when it is more than 1.

This model is heavily simplified. A comprehensive measurement program that tests the efficacy of any technology, let alone one as complex and rapidly changing as LLMs, is more complex than could be captured in a single blog post.

Real-world work might be insufficiently uniform to fit into a closed-form solution like this. Perhaps an iterated simulation with variables based on the range of values seem from your team’s metrics would give better results.

However, in this post, I want to illustrate that if you are going to try to evaluate an LLM-based tool, you need to at least include some representation of each of these terms somewhere. They are all fundamental to the way the technology works, and if you’re not measuring them somehow, then you are flying blind into the genAI storm.

I also hope to show that a lot of existing assumptions about how benefits might be demonstrated, for example with user surveys about general impressions, or by evaluating artificial benchmark scores, are deeply flawed.

Even making what I consider to be wildly, unrealistically optimistic assumptions about these measurements, I hope I’ve shown:

1. in the numerator, C might be a lot higher than you expect,
2. in the denominator, P might be a lot lower than you expect,
3. repeated use of an LLM might make H go up, but despite the fact that it's in the denominator, that will ultimately be quite bad for your business.

Personally, I don’t have all that many concerns about E and I. E is still seeing significant loss-leader pricing, and I might not be coming down as fast as vendors would like us to believe, if the other numbers work out I don’t think they make a huge difference. However, there might still be surprises lurking in there, and if you want to rationally evaluate the effectiveness of a model, you need to be able to measure them and incorporate them as well.

In particular, I really want to stress the importance of the influence of LLMs on your team dynamic, as that can cause massive, hidden increases to C. LLMs present opportunities for junior employees to generate an endless stream of chaff that will simultaneously:

• wreck your performance review process by making them look much more productive than they are,
• increase stress and load on senior employees who need to clean up unforeseen messes created by their LLM output,
• and ruin their own opportunities for career development by skipping over learning opportunities.

If you’ve already deployed LLM tooling without measuring these things and without updating your performance management processes to account for the strange distortions that these tools make possible, your Futzing Fraction may be much, much greater than 1, creating hidden costs and technical debt that your organization will not notice until a lot of damage has already been done.

If you got all the way here, particularly if you’re someone who is enthusiastic about these technologies, thank you for reading. I appreciate your attention and I am hopeful that if we can start paying attention to these details, perhaps we can all stop futzing around so much with this stuff and get back to doing real work.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

My father, also known as “R0ML” once described a methodology for evaluating volume purchases that I think needs to be more popular.

If you are a hardcore fan, you might know that he has already described this concept publicly in a talk at OSCON in 2005, among other places, but it has never found its way to the public Internet, so I’m giving it a home here, and in the process, appropriating some of his words.¹

Let’s say you’re running a circus. The circus has many clowns. Ten thousand clowns, to be precise. They require bright red clown noses. Therefore, you must acquire a significant volume of clown noses. An enterprise licensing agreement for clown noses, if you will.

If the nose plays, it can really make the act. In order to make sure you’re getting quality noses, you go with a quality vendor. You select a vendor who can supply noses for $100 each, at retail.

Do you want to buy retail? Ten thousand clowns, ten thousand noses, one hundred dollars: that’s a million bucks worth of noses, so it’s worth your while to get a good deal.

As a conscientious executive, you go to the golf course with your favorite clown accessories vendor and negotiate yourself a 50% discount, with a commitment to buy all ten thousand noses.

Is this a good deal? Should you take it?

To determine this, we will use an analytical tool called R0ML’s Ratio (RR).

The ratio has 2 terms:

1. the Full Undiscounted Retail List Price of Units Used (FURLPoUU), which can of course be computed by the individual retail list price of a single unit (in our case, $100) multiplied by the number of units used
2. the Total Price of the Entire Enterprise Volume Licensing Agreement (TPotEEVLA), which in our case is $500,000.

It is expressed as:

Crucially, you must be able to compute the number of units used in order to complete this ratio. If, as expected, every single clown wears their nose at least once during the period of the license agreement, then our Units Used is 10,000, our FURLPoUU is $1,000,000 and our TPotEEVLA is $500,000, which makes our RR 0.5.

Congratulations. If R0ML’s Ratio is less than 1, it’s a good deal. Proceed.

But… maybe the nose doesn’t play. Not every clown’s costume is an exact clone of the traditional, stereotypical image of a clown. Many are avant-garde. Perhaps this plentiful proboscis pledge was premature. Here, I must quote the originator of this theoretical framework directly:

What if the wheeze doesn’t please?

What if the schnozz gives some pause?

In other words: what if some clowns don’t wear their noses?

If we were to do this deal, and then ask around afterwards to find out that only 200 of our 10,000 clowns were to use their noses, then FURLPoUU comes out to 200 * $100, for a total of $20,000. In that scenario, RR is 25, which you may observe is substantially greater than 1.

If you do a deal where R0ML’s ratio is greater than 1, then you are the bozo.

I apologize if I have belabored this point. As R0ML expressed in the email we exchanged about this many years ago,

I do not mind if you blog about it — and I don't mind getting the credit — although one would think it would be obvious.

And yeah, one would think this would be obvious? But I have belabored it because many discounted enterprise volume purchasing agreements still fail the R0ML’s Ratio Bozo Test.²

In the case of clown noses, if you pay the discounted price, at least you get to keep the nose; maybe lightly-used clown noses have some resale value. But in software licensing or SaaS deals, once you’ve purchased the “discounted” software or service, once you have provisioned the “seats”, the money is gone, and if your employees don’t use it, then no value for your organization will ever result.

Measuring number of units used is very important. Without this number, you have no idea if you are a bozo or not.

It is often better to give your individual employees a corporate card and allow them to make arbitrary individual purchases of software licenses and SaaS tools, with minimal expense-reporting overhead; this will always keep R0ML’s Ratio at 1.0, and thus, you will never be a bozo.

It is always better to do that the first time you are purchasing a new software tool, because the first time making such a purchase you (almost by definition) have no information about “units used” yet. You have no idea — you cannot have any idea — if you are a bozo or not.

If you don’t know who the bozo is, it’s probably you.

Acknowledgments

Thank you for reading, and especially thank you to my patrons who are supporting my writing on this blog. Of course, extra thanks to dad for, like, having this idea and doing most of the work here beyond my transcription. If you like my dad’s ideas and you’d like to post more of them, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

What’s a good maximum line length for your coding standard?

This is, of course, a trick question. By posing it as a question, I have created the misleading impression that it is a question, but Black has selected the correct number for you; it’s 90 characters or so, about 10% wider than the width of a standard VT100 hardware terminal.

Thanks for reading my blog.

OK, OK. Clearly, there’s more to it than that. This is an age-old debate on the level of “tabs versus spaces”. So contentious, in fact, that even the famously opinionated Black does in fact let you change it.

Ancient History

One argument that certain silly people¹ like to make is “why are we wrapping at 80 characters like we are using 80 character teletypes, it’s the 2020s! I have an ultrawide monitor!”. The implication here is that the width of 80-character terminals is an antiquated relic, based entirely around the hardware limitations of a bygone era, and modern displays can put tons of stuff on one line, so why not use that capability?

This feels intuitively true, given the huge disparity between ancient times and now: on my own display, I can comfortably fit about 350 characters on a line. What a shame, to have so much room for so many characters in each line, and to waste it all on blank space!

But... is that true?

I stretched out my editor window all the way to measure that ‘350’ number, but I did not continue editing at that window width. In order to have a more comfortable editing experience, I switched back into writeroom mode, a mode which emulates a considerably more writerly application, which limits each line length to 92 characters, regardless of frame width.

You’ve probably noticed this too. Almost all sites that display prose of any kind limit their width, even on very wide screens.

As silly as that tiny little ribbon of text running down the middle of your monitor might look with a full-screened stereotypical news site or blog, if you full-screen a site that doesn’t set that width-limit, although it makes sense that you can now use all that space up, it will look extremely, almost unreadably bad.

Blogging software does not set a column width limit on your text because of some 80-character-wide accident of history in the form of a hardware terminal.

Similarly, if you really try to use that screen real estate to its fullest for coding, and start editing 200-300 character lines, you’ll quickly notice it starts to feel just a bit weird and confusing. It gets surprisingly easy to lose your place. Rhetorically the “80 characters is just because of dinosaur technology! Use all those ultrawide pixels!” talking point is quite popular, but practically people usually just want a few more characters worth of breathing room, maxing out at 100 characters, far narrower than even the most svelte widescreen.

So maybe those 80 character terminals are holding us back a little bit, but... wait a second. Why were the terminals 80 characters wide in the first place?

Ancienter History

As this lovely Software Engineering Stack Exchange post summarizes, terminals were probably 80 characters because teletypes were 80 characters, and teletypes were probably 80 characters because punch cards were 80 characters, and punch cards were probably 80 characters because that’s just about how many typewritten characters fit onto one line of a US-Letter piece of paper.

Even before typewriters, consider the average newspaper: why do we call a regularly-occurring featured article in a newspaper a “column”? Because broadsheet papers were too wide to have only a single column; they would always be broken into multiple! Far more aggressive than 80 characters, columns in newspapers typically have 30 characters per line.

The first newspaper printing machines were custom designed and could have used whatever width they wanted, so why standardize on something so narrow?³

Science!

There has been a surprising amount of scientific research around this issue, but in brief, there’s a reason here rooted in human physiology: when you read a block of text, you are not consciously moving your eyes from word to word like you’re dragging a mouse cursor, repositioning continuously. Human eyes reading text move in quick bursts of rotation called “saccades”. In order to quickly and accurately move from one line of text to another, the start of the next line needs to be clearly visible in the reader’s peripheral vision in order for them to accurately target it. This limits the angle of rotation that the reader can perform in a single saccade, and, thus, the length of a line that they can comfortably read without hunting around for the start of the next line every time they get to the end.

So, 80 (or 80 plus about 10%) characters isn’t too unreasonable for a limit. It’s longer than 30 characters, that’s for sure!

But, surely that’s not all, or this wouldn’t be so contentious in the first place?

Caveats

The screen is wide, though.

The ultrawide aficionados do have a point, even if it’s not really the simple one about “old terminals” they originally thought. Our modern wide-screen displays are criminally underutilized, particularly for text. Even adding in the big chunky file, class, and method tree browser over on the left and the source code preview on the right, a brief survey of a Google Image search for “vs code” shows a lot of editors open with huge, blank areas on the right side of the window.

Big screens are super useful as they allow us to leverage our spatial memories to keep more relevant code around and simply glance around as we think, rather than navigate interactively. But it only works if you remember to do it.

Newspapers allowed us to read a ton of information in one sitting with minimum shuffling by packing in as much as 6 columns of text. You could read a column to the bottom of the page, back to the top, and down again, several times.

Similarly, books fill both of their opposed pages with text at the same time, doubling the amount of stuff you can read at once before needing to turn the page.

You may notice that reading text in a book, even in an ebook app, is more comfortable than reading a ton of text by scrolling around in a web browser. That’s because our eyes are built for saccades, and repeatedly tracking the continuous smooth motion of the page as it scrolls to a stop, then re-targeting the new fixed location to start saccading around from, is literally more physically strenuous on your eye’s muscles!

There’s a reason that the codex was a big technological innovation over the scroll. This is a regression!

Today, the right thing to do here is to make use of horizontally split panes in your text editor or IDE, and just make a bit of conscious effort to set up the appropriate code on screen for the problem you’re working on. However, this is a potential area for different IDEs to really differentiate themselves, and build multi-column continuous-code-reading layouts that allow for buffers to wrap and be navigable newspaper-style.

Similar, modern CSS has shockingly good support for multi-column layouts, and it’s a shame that true multi-column, page-turning layouts are so rare. If I ever figure out a way to deploy this here that isn’t horribly clunky and fighting modern platform conventions like “scrolling horizontally is substantially more annoying and inconsistent than scrolling vertically” maybe I will experiment with such a layout on this blog one day. Until then… just make the browser window narrower so other useful stuff can be in the other parts of the screen, I guess.

Code Isn’t Prose

But, I digress. While I think that columnar layouts for reading prose are an interesting thing more people should experiment with, code isn’t prose.

The metric used for ideal line width, which you may have noticed if you clicked through some of those Wikipedia links earlier, is not “character cells in your editor window”, it is characters per line, or “CPL”.

With an optimal CPL somewhere between 45 and 95, a code-line-width of somewhere around 90 might actually be the best idea, because whitespace uses up your line-width budget. In a typical object-oriented Python program², most of your code ends up indented by at least 8 spaces: 4 for the class scope, 4 for the method scope. Most likely a lot of it is 12, because any interesting code will have at least one conditional or loop. So, by the time you’re done wasting all that horizontal space, a max line length of 90 actually looks more like a maximum of 78... right about that sweet spot from the US-Letter page in the typewriter that we started with.

What about soft-wrap?

In principle, source code is structured information, whose presentation could be fully decoupled from its serialized representation. Everyone could configure their preferred line width appropriate to their custom preferences and the specific physiological characteristics of their eyes, and the code could be formatted according to the language it was expressed in, and “hard wrapping” could be a silly antiquated thing.

The problem with this argument is the same as the argument against “but tabs are semantic indentation”, to wit: nope, no it isn’t. What “in principle” means in the previous paragraph is actually “in a fantasy world which we do not inhabit”. I’d love it if editors treated code this way and we had a rich history and tradition of structured manipulations rather than typing in strings of symbols to construct source code textually. But that is not the world we live in. Hard wrapping is unfortunately necessary to integrate with diff tools.

So what’s the optimal line width?

The exact, specific number here is still ultimately a matter of personal preference.

Hopefully, understanding the long history, science, and underlying physical constraints can lead you to select a contextually appropriate value for your own purposes that will balance ease of reading, integration with the relevant tools in your ecosystem, diff size, presentation in the editors and IDEs that your contributors tend to use, reasonable display in web contexts, on presentation slides, and so on.

But — and this is important — counterpoint:

No it isn’t, you don’t need to select an optimal width, because it’s already been selected for you. It is the full width of a standard VT100 terminal plus 10%.

Acknowledgments

Thank you for reading, and especially thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

The Problem

Like many other self-styled thinky programmer guys, I like to imagine myself as a sort of Holmesian genius, making trenchant observations, collecting them, and then synergizing them into brilliant deductions with the keen application of my powerful mind.

However, several years ago, I had an epiphany in my self-concept. I finally understood that, to the extent that I am usefully clever, it is less in a Holmesian idiom, and more, shall we say, Monkesque.

For those unfamiliar with either of the respective franchises:

• Holmes is a towering intellect honed by years of training, who catalogues intentional, systematic observations and deduces logical, factual conclusions from those observations.
• Monk, on the other hand, while also a reasonably intelligent guy, is highly neurotic, wracked by unresolved trauma and profound grief. As both a consulting job and a coping mechanism, he makes a habit of erratically wandering into crime scenes, and, driven by a carefully managed jenga tower of mental illnesses, leverages his dual inabilities to solve crimes. First, he is unable to filter out apparently inconsequential details, building up a mental rat’s nest of trivia about the problem; second, he is unable to let go of any minor incongruity, obsessively ruminating on the collection of facts until they all make sense in a consistent timeline.

Perhaps surprisingly, this tendency serves both this fictional wretch of a detective, and myself, reasonably well. I find annoying incongruities in abstractions and I fidget and fiddle with them until I end up building something that a lot of people like, or perhaps something that a smaller number of people get really excited about. At worst, at least I eventually understand what’s going on. This is a self-soothing activity but it turns out that, managed properly, it can very effectively soothe others as well.

All that brings us to today’s topic, which is an incongruity I cannot smooth out or fit into a logical framework to make sense. I am, somewhat reluctantly, a genAI skeptic. However, I am, even more reluctantly, exposed to genAI Discourse every damn minute of every damn day. It is relentless, inescapable, and exhausting.

This preamble about personality should hopefully help you, dear reader, to understand how I usually address problematical ideas by thinking and thinking and fidgeting with them until I manage to write some words — or perhaps a new open source package — that logically orders the ideas around it in a way which allows my brain to calm down and let it go, and how that process is important to me.

In this particular instance, however, genAI has defeated me. I cannot make it make sense, but I need to stop thinking about it anyway. It is too much and I need to give up.

My goal with this post is not to convince anyone of anything in particular — and we’ll get to why that is a bit later — but rather:

1. to set out my current understanding in one place, including all the various negative feelings which are still bothering me, so I can stop repeating it elsewhere,
2. to explain why I cannot build a case that I think should be particularly convincing to anyone else, particularly to someone who actively disagrees with me,
3. in so doing, to illustrate why I think the discourse is so fractious and unresolvable, and finally
4. to give myself, and hopefully by proxy to give others in the same situation, permission to just peace out of this nightmare quagmire corner of the noosphere.

But first, just because I can’t prove that my interlocutors are Wrong On The Internet, doesn’t mean I won’t explain why I feel like they are wrong.

The Anti-Antis

Most recently, at time of writing, there have been a spate of “the genAI discourse is bad” articles, almost exclusively written from the perspective of, not boosters exactly, but pragmatically minded (albeit concerned) genAI users, wishing for the skeptics to be more pointed and accurate in our critiques. This is anti-anti-genAI content.

I am not going to link to any of these, because, as part of their self-fulfilling prophecy about the “genAI discourse”, they’re also all bad.

Mostly, however, they had very little worthwhile to respond to because they were straw-manning their erstwhile interlocutors. They are all getting annoyed at “bad genAI criticism” while failing to engage with — and often failing to even mention — most of the actual substance of any serious genAI criticism. At least, any of the criticism that I’ve personally read.

I understand wanting to avoid a callout or Gish-gallop culture and just express your own ideas. So, I understand that they didn’t link directly to particular sources or go point-by-point on anyone else’s writing. Obviously I get it, since that’s exactly what this post is doing too.

But if you’re going to talk about how bad the genAI conversation is, without even mentioning huge categories of problem like “climate impact” or “disinformation”¹ even once, I honestly don’t know what conversation you’re even talking about. This is peak “make up a guy to get mad at” behavior, which is especially confusing in this circumstance, because there’s an absolutely huge crowd of actual people that you could already be mad at.

The people writing these pieces have historically seemed very thoughtful to me. Some of them I know personally. It is worrying to me that their critical thinking skills appear to have substantially degraded specifically after spending a bunch of time intensely using this technology which I believe has a scary risk of degrading one’s critical thinking skills. Correlation is not causation or whatever, and sure, from a rhetorical perspective this is “post hoc ergo propter hoc” and maybe a little “ad hominem” for good measure, but correlation can still be concerning.

Yet, I cannot effectively respond to these folks, because they are making a practical argument that I cannot, despite my best efforts, find compelling evidence to refute categorically. My experiences of genAI are all extremely bad, but that is barely even anecdata. Their experiences are neutral-to-positive. Little scientific data exists. How to resolve this?²

The Aesthetics

As I begin to state my own position, let me lead with this: my factual analysis of genAI is hopelessly negatively biased. I find the vast majority of the aesthetic properties of genAI to be intensely unpleasant.

I have been trying very hard to correct for this bias, to try to pay attention to the facts and to have a clear-eyed view of these systems’ capabilities. But the feelings are visceral, and the effort to compensate is tiring. It is, in fact, the desire to stop making this particular kind of effort that has me writing up this piece and trying to take an intentional break from the subject, despite its intense relevance.

When I say its “aesthetic qualities” are unpleasant, I don’t just mean the aesthetic elements of output of genAIs themselves. The aesthetic quality of genAI writing, visual design, animation and so on, while mostly atrocious, is also highly variable. There are cherry-picked examples which look… fine. Maybe even good. For years now, there have been, famously, literally award-winning aesthetic outputs of genAI³.

While I am ideologically predisposed to see any “good” genAI art as accruing the benefits of either a survivorship bias from thousands of terrible outputs or simple plagiarism rather than its own inherent quality, I cannot deny that in many cases it is “good”.

However, I am not just talking about the product, but the process; the aesthetic experience of interfacing with the genAI system itself, rather than the aesthetic experience of the outputs of that system.

I am not a visual artist and I am not really a writer⁴, particularly not a writer of fiction or anything else whose experience is primarily aesthetic. So I will speak directly to the experience of software development.

I have seen very few successful examples of using genAI to produce whole, working systems. There are no shortage of highly public miserable failures, particularly from the vendors of these systems themselves, where the outputs are confused, self-contradictory, full of subtle errors and generally unusable. While few studies exist, it sure looks like this is an automated way of producing a Net Negative Productivity Programmer, throwing out chaff to slow down the rest of the team.⁵

Juxtapose this with my aforementioned psychological motivations, to wit, I want to have everything in the computer be orderly and make sense, I’m sure most of you would have no trouble imagining that sitting through this sort of practice would make me extremely unhappy.

Despite this plethora of negative experiences, executives are aggressively mandating the use of AI⁶. It looks like without such mandates, most people will not bother to use such tools, so the executives will need muscular policies to enforce its use.⁷

Being forced to sit and argue with a robot while it struggles and fails to produce a working output, while you have to rewrite the code at the end anyway, is incredibly demoralizing. This is the kind of activity that activates every single major cause of burnout at once.

But, at least in that scenario, the thing ultimately doesn’t work, so there’s a hope that after a very stressful six month pilot program, you can go to management with a pile of meticulously collected evidence, and shut the whole thing down.

I am inclined to believe that, in fact, it doesn’t work well enough to be used this way, and that we are going to see a big crash. But that is not the most aesthetically distressing thing. The most distressing thing is that maybe it does work; if not well enough to actually do the work, at least ambiguously enough to fool the executives long-term.

This project, in particular, stood out to me as an example. Its author, a self-professed “AI skeptic” who “thought LLMs were glorified Markov chain generators that didn’t actually understand code and couldn’t produce anything novel”, did a green-field project to test this hypothesis.

Now, this particular project is not totally inconsistent with a world in which LLMs cannot produce anything novel. One could imagine that, out in the world of open source, perhaps there is enough “OAuth provider written in TypeScript” blended up into the slurry of “borrowed⁸” training data that the minor constraint of “make it work on Cloudflare Workers” is a small tweak⁹. It is not fully dispositive of the question of the viability of “genAI coding”.

But it is a data point related to that question, and thus it did make me contend with what might happen if it were actually a fully demonstrative example. I reviewed the commit history, as the author suggested. For the sake of argument, I tried to ask myself if I would like working this way. Just for clarity on this question, I wanted to suspend judgement about everything else; assuming:

• the model could be created with ethically, legally, voluntarily sourced training data
• its usage involved consent from labor rather than authoritarian mandates
• sensible levels of energy expenditure, with minimal CO2 impact
• it is substantially more efficient to work this way than to just write the code yourself

and so on, and so on… would I like to use this magic robot that could mostly just emit working code for me? Would I use it if it were free, in all senses of the word?

No. I absolutely would not.

I found the experience of reading this commit history and imagining myself using such a tool — without exaggeration — nauseating.

Unlike many programmers, I love code review. I find that it is one of the best parts of the process of programming. I can help people learn, and develop their skills, and learn from them, and appreciate the decisions they made, develop an impression of a fellow programmer’s style. It’s a great way to build a mutual theory of mind.

Of course, it can still be really annoying; people make mistakes, often can’t see things I find obvious, and in particular when you’re reviewing a lot of code from a lot of different people, you often end up having to repeat explanations of the same mistakes. So I can see why many programmers, particularly those more introverted than I am, hate it.

But, ultimately, when I review their code and work hard to provide clear and actionable feedback, people learn and grow and it’s worth that investment in inconvenience.

The process of coding with an “agentic” LLM appears to be the process of carefully distilling all the worst parts of code review, and removing and discarding all of its benefits.

The lazy, dumb, lying robot asshole keeps making the same mistakes over and over again, never improving, never genuinely reacting, always obsequiously pretending to take your feedback on board.

Even when it “does” actually “understand” and manages to load your instructions into its context window, 200K tokens later it will slide cleanly out of its memory and you will have to say it again.

All the while, it is attempting to trick you. It gets most things right, but it consistently makes mistakes in the places that you are least likely to notice. In places where a person wouldn’t make a mistake. Your brain keeps trying to develop a theory of mind to predict its behavior but there’s no mind there, so it always behaves infuriatingly randomly.

I don’t think I am the only one who feels this way.

The Affordances

Whatever our environments afford, we tend to do more of. Whatever they resist, we tend to do less of. So in a world where we were all writing all of our code and emails and blog posts and texts to each other with LLMs, what do they afford that existing tools do not?

As a weirdo who enjoys code review, I also enjoy process engineering. The central question of almost all process engineering is to continuously ask: how shall we shape our tools, to better shape ourselves?

LLMs are an affordance for producing more text, faster. How is that going to shape us?

Again arguing in the alternative here, assuming the text is free from errors and hallucinations and whatever, it’s all correct and fit for purpose, that means it reduces the pain of circumstances where you have to repeat yourself. Less pain! Sounds great; I don’t like pain.

Every codebase has places where you need boilerplate. Every organization has defects in its information architecture that require repetition of certain information rather than a link back to the authoritative source of truth. Often, these problems persist for a very long time, because it is difficult to overcome the institutional inertia required to make real progress rather than going along with the status quo. But this is often where the highest-value projects can be found. Where there’s muck, there’s brass.

The process-engineering function of an LLM, therefore, is to prevent fundamental problems from ever getting fixed, to reward the rapid-fire overwhelm of infrastructure teams with an immediate, catastrophic cascade of legacy code that is now much harder to delete than it is to write.

There is a scene in Game of Thrones where Khal Drogo kills himself. He does so by replacing a stinging, burning, therapeutic antiseptic wound dressing with some cool, soothing mud. The mud felt nice, addressed the immediate pain, removed the discomfort of the antiseptic, and immediately gave him a lethal infection.

The pleasing feeling of immediate progress when one prompts an LLM to solve some problem feels like cool mud on my brain.

The Economics

We are in the middle of a mania around this technology. As I have written about before, I believe the mania will end. There will then be a crash, and a “winter”. But, as I may not have stressed sufficiently, this crash will be the biggest of its kind — so big, that it is arguably not of a kind at all. The level of investment in these technologies is bananas and the possibility that the investors will recoup their investment seems close to zero. Meanwhile, that cost keeps going up, and up, and up.

Others have reported on this in detail¹⁰, and I will not reiterate that all here, but in addition to being a looming and scary industry-wide (if we are lucky; more likely it’s probably “world-wide”) economic threat, it is also going to drive some panicked behavior from management.

Panicky behavior from management stressed that their idea is not panning out is, famously, the cause of much human misery. I expect that even in the “good” scenario, where some profit is ultimately achieved, will still involve mass layoffs rocking the industry, panicked re-hiring, destruction of large amounts of wealth.

It feels bad to think about this.

The Energy Usage

For a long time I believed that the energy impact was overstated. I am even on record, about a year ago, saying I didn’t think the energy usage was a big deal. I think I was wrong about that.

It initially seemed like it was letting regular old data centers off the hook. But recently I have learned that, while the numbers are incomplete because the vendors aren’t sharing information, they’re also extremely bad.¹¹

I think there’s probably a version of this technology that isn’t a climate emergency nightmare, but that’s not the version that the general public has access to today.

The Educational Impact

LLMs are making academic cheating incredibly rampant.¹²

Not only is it so common as to be nearly universal, it’s also extremely harmful to learning.¹³

For learning, genAI is a forklift at the gym.

To some extent, LLMs are simply revealing a structural rot within education and academia that has been building for decades if not centuries. But it was within those inefficiencies and the inconveniences of the academic experience that real learning was, against all odds, still happening in schools.

LLMs produce a frictionless, streamlined process where students can effortlessly glide through the entire credential, learning nothing. Once again, they dull the pain without regard to its cause.

This is not good.

The Invasion of Privacy

This is obviously only a problem with the big cloud models, but then, the big cloud models are the only ones that people actually use. If you are having conversations about anything private with ChatGPT, you are sending all of that private information directly to Sam Altman, to do with as he wishes.

Even if you don’t think he is a particularly bad guy, maybe he won’t even create the privacy nightmare on purpose. Maybe he will be forced to do so as a result of some bizarre kafkaesque accident.¹⁴

Imagine the scenario, for example, where a woman is tracking her cycle and uploading the logs to ChatGPT so she can chat with it about a health concern. Except, surprise, you don’t have to imagine, you can just search for it, as I have personally, organically, seen three separate women on YouTube, at least one of whom lives in Texas, not only do this on camera but recommend doing this to their audiences.

Citation links withheld on this particular claim for hopefully obvious reasons.

I assure you that I am neither particularly interested in menstrual products nor genAI content, and if I am seeing this more than once, it is probably a distressingly large trend.

The Stealing

The training data for LLMs is stolen. I don’t mean like “pirated” in the sense where someone illicitly shares a copy they obtained legitimately; I mean their scrapers are ignoring both norms¹⁵ and laws¹⁶ to obtain copies under false pretenses, destroying other people’s infrastructure¹⁷ in the process.

The Fatigue

I have provided references to numerous articles outlining rhetorical and sometimes data-driven cases for the existence of certain properties and consequences of genAI tools. But I can’t prove any of these properties, either at a point in time or as a durable ongoing problem.

The LLMs themselves are simply too large to model with the usual kind of heuristics one would use to think about software. I’d sooner be able to predict the physics of dice in a casino than a 2 trillion parameter neural network. They resist scientific understanding, not just because of their size and complexity, but because unlike a natural phenomenon (which could of course be considerably larger and more complex) they resist experimentation.

The first form of genAI resistance to experiment is that every discussion is a motte-and-bailey. If I use a free model and get a bad result I’m told it’s because I should have used the paid model. If I get a bad result with ChatGPT I should have used Claude. If I get a bad result with a chatbot I need to start using an agentic tool. If an agentic tool deletes my hard drive by putting os.system(“rm -rf ~/”) into sitecustomize.py then I guess I should have built my own MCP integration with a completely novel heretofore never even considered security sandbox or something?

What configuration, exactly, would let me make a categorical claim about these things? What specific methodological approach should I stick to, to get reliably adequate prompts?

For the record though, if the idea of the free models is that they are going to be provocative demonstrations of the impressive capabilities of the commercial models, and the results are consistently dogshit, I am finding it increasingly hard to care how much better the paid ones are supposed to be, especially since the “better”-ness cannot really be quantified in any meaningful way.

The motte-and-bailey doesn’t stop there though. It’s a war on all fronts. Concerned about energy usage? That’s OK, you can use a local model. Concerned about infringement? That’s okay, somewhere, somebody, maybe, has figured out how to train models consensually¹⁸. Worried about the politics of enriching the richest monsters in the world? Don’t worry, you can always download an “open source” model from Hugging Face. It doesn’t matter that many of these properties are mutually exclusive and attempting to fix one breaks two others; there’s always an answer, the field is so abuzz with so many people trying to pull in so many directions at once that it is legitimately difficult to understand what’s going on.

Even here though, I can see that characterizing everything this way is unfair to a hypothetical sort of person. If there is someone working at one of these thousands of AI companies that have been springing up like toadstools after a rain, and they really are solving one of these extremely difficult problems, how can I handwave that away? We need people working on problems, that’s like, the whole point of having an economy. And I really don’t like shitting on other people’s earnest efforts, so I try not to dismiss whole fields. Given how AI has gotten into everything, in a way that e.g. cryptocurrency never did, painting with that broad a brush inevitably ends up tarring a bunch of stuff that isn’t even really AI at all.

The second form of genAI resistance to experiment is the inherent obfuscation of productization. The models themselves are already complicated enough, but the products that are built around the models are evolving extremely rapidly. ChatGPT is not just a “model”, and with the rapid¹⁹ deployment of Model Context Protocol tools, the edges of all these things will blur even further. Every LLM is now just an enormous unbounded soup of arbitrary software doing arbitrary whatever. How could I possibly get my arms around that to understand it?

The Challenge

I have woefully little experience with these tools.

I’ve tried them out a little bit, and almost every single time the result has been a disaster that has not made me curious to push further. Yet, I keep hearing from all over the industry that I should.

To some extent, I feel like the motte-and-bailey characterization above is fair; if the technology itself can really do real software development, it ought to be able to do it in multiple modalities, and there’s nothing anyone can articulate to me about GPT-4o which puts it in a fundamentally different class than GPT-3.5.

But, also, I consistently hear that the subjective experience of using the premium versions of the tools is actually good, and the free ones are actually bad.

I keep struggling to find ways to try them “the right way”, the way that people I know and otherwise respect claim to be using them, but I haven’t managed to do so in any meaningful way yet.

I do not want to be using the cloud versions of these models with their potentially hideous energy demands; I’d like to use a local model. But there is obviously not a nicely composed way to use local models like this.

Since there are apparently zero models with ethically-sourced training data, and litigation is ongoing²⁰ to determine the legal relationships of training data and outputs, even if I can be comfortable with some level of plagiarism on a project, I don’t feel that I can introduce the existential legal risk into other people’s infrastructure, so I would need to make a new project.

Others have differing opinions of course, including some within my dependency chain, which does worry me, but I still don’t feel like I can freely contribute further to the problem; it’s going to be bad enough to unwind any impact upstream. Even just for my own sake, I don’t want to make it worse.

This especially presents a problem because I have way too much stuff going on already. A new project is not practical.

Finally, even if I did manage to satisfy all of my quirky²¹ constraints, would this experiment really be worth anything? The models and tools that people are raving about are the big, expensive, harmful ones. If I proved to myself yet again that a small model with bad tools was unpleasant to use, I wouldn’t really be addressing my opponents’ views.

I’m stuck.

The Surrender

I am writing this piece to make my peace with giving up on this topic, at least for a while. While I do idly hope that some folks might find bits of it convincing, and perhaps find ways to be more mindful with their own usage of genAI tools, and consider the harm they may be causing, that’s not actually the goal. And that is not the goal because it is just so much goddamn work to prove.

Here, I must return to my philosophical hobbyhorse of sprachspiel. In this case, specifically to use it as an analytical tool, not just to understand what I am trying to say, but what the purpose for my speech is.

The concept of sprachspiel is most frequently deployed to describe the goal of the language game being played, but in game theory, that’s only half the story. Speech — particularly rigorously justified speech — has a cost, as well as a benefit. I can make shit up pretty easily, but if I want to do anything remotely like scientific or academic rigor, that cost can be astronomical. In the case of developing an abstract understanding of LLMs, the cost is just too high.

So what is my goal, then? To be king Canute, standing astride the shore of “tech”, whatever that is, commanding the LLM tide not to rise? This is a multi-trillion dollar juggernaut.

Even the rump, loser, also-ran fragment of it has the power to literally suffocate us in our homes²² if they so choose, completely insulated from any consequence. If the power curve starts there, imagine what the winners in this industry are going to be capable of, irrespective of the technology they’re building - just with the resources they have to hand. Am I going to write a blog post that can rival their propaganda apparatus? Doubtful.

Instead, I will just have to concede that maybe I’m wrong. I don’t have the skill, or the knowledge, or the energy, to demonstrate with any level of rigor that LLMs are generally, in fact, hot garbage. Intellectually, I will have to acknowledge that maybe the boosters are right. Maybe it’ll be OK.

Maybe the carbon emissions aren’t so bad. Maybe everybody is keeping them secret in ways that they don’t for other types of datacenter for perfectly legitimate reasons. Maybe the tools really can write novel and correct code, and with a little more tweaking, it won’t be so difficult to get them to do it. Maybe by the time they become a mandatory condition of access to developer tools, they won’t be miserable.

Sure, I even sincerely agree, intellectual property really has been a pretty bad idea from the beginning. Maybe it’s OK that we’ve made an exception to those rules. The rules were stupid anyway, so what does it matter if we let a few billionaires break them? Really, everybody should be able to break them (although of course, regular people can’t, because we can’t afford the lawyers to fight off the MPAA and RIAA, but that’s a problem with the legal system, not tech).

I come not to praise “AI skepticism”, but to bury it.

Maybe it really is all going to be fine. Perhaps I am simply catastrophizing; I have been known to do that from time to time. I can even sort of believe it, in my head. Still, even after writing all this out, I can’t quite manage to believe it in the pit of my stomach.

Unfortunately, that feeling is not something that you, or I, can argue with.

Acknowledgments

Thank you to my patrons. Normally, I would say, “who are supporting my writing on this blog”, but in the case of this piece, I feel more like I should apologize to them for this than to thank them; these thoughts have been preventing me from thinking more productive, useful things that I actually have relevant skill and expertise in; this felt more like a creative blockage that I just needed to expel than a deliberately written article. If you like what you’ve read here and you’d like to read more of it, well, too bad; I am sincerely determined to stop writing about this topic. But, if you’d like to read more stuff like other things I have written, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

The History

Before dataclasses were added to Python in version 3.7 — in June of 2018 — the __init__ special method had an important use. If you had a class representing a data structure — for example a 2DCoordinate, with x and y attributes — you would want to be able to construct it as 2DCoordinate(x=1, y=2), which would require you to add an __init__ method with x and y parameters.

The other options available at the time all had pretty bad problems:

1. You could remove 2DCoordinate from your public API and instead expose a make_2d_coordinate function and make it non-importable, but then how would you document your return or parameter types?
2. You could document the x and y attributes and make the user assign each one themselves, but then 2DCoordinate() would return an invalid object.
3. You could default your coordinates to 0 with class attributes, and while that would fix the problem with option 2, this would now require all 2DCoordinate objects to be not just mutable, but mutated at every call site.
4. You could fix the problems with option 1 by adding a new abstract class that you could expose in your public API, but this would explode the complexity of every new public class, no matter how simple. To make matters worse, typing.Protocol didn’t even arrive until Python 3.8, so, in the pre-3.7 world this would condemn you to using concrete inheritance and declaring multiple classes even for the most basic data structure imaginable.

Also, an __init__ method that does nothing but assign a few attributes doesn’t have any significant problems, so it is an obvious choice in this case. Given all the problems that I just described with the alternatives, it makes sense that it became the obvious default choice, in most cases.

However, by accepting “define a custom __init__” as the default way to allow users to create your objects, we make a habit of beginning every class with a pile of arbitrary code that gets executed every time it is instantiated.

Wherever there is arbitrary code, there are arbitrary problems.

The Problems

Let’s consider a data structure more complex than one that simply holds a couple of attributes. We will create one that represents a reference to some I/O in the external world: a FileReader.

Of course Python has its own open-file object abstraction, but I will be ignoring that for the purposes of the example.

Let’s assume a world where we have the following functions, in an imaginary fileio module:

• open(path: str) -> int
• read(fileno: int, length: int)
• close(fileno: int)

Our hypothetical fileio.open returns an integer representing a file descriptor¹, fileio.read allows us to read length bytes from an open file descriptor, and fileio.close closes that file descriptor, invalidating it for future use.

With the habit that we have built from writing thousands of __init__ methods, we might want to write our FileReader class like this:

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class FileReader:
    def __init__(self, path: str) -> None:
        self._fd = fileio.open(path)
    def read(self, length: int) -> bytes:
        return fileio.read(self._fd, length)
    def close(self) -> None:
        fileio.close(self._fd)

For our initial use-case, this is fine. Client code creates a FileReader by doing something like FileReader("./config.json"), which always creates a FileReader that maintains its file descriptor int internally as private state. This is as it should be; we don’t want user code to see or mess with _fd, as that might violate FileReader’s invariants. All the necessary work to construct a valid FileReader — i.e. the call to open — is always taken care of for you by FileReader.__init__.

However, additional requirements will creep in, and as they do, FileReader.__init__ becomes increasingly awkward.

Initially we only care about fileio.open, but later, we may have to deal with a library that has its own reasons for managing the call to fileio.open by itself, and wants to give us an int that we use as our _fd, we now have to resort to weird workarounds like:

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def reader_from_fd(fd: int) -> FileReader:
    fr = object.__new__(FileReader)
    fr._fd = fd
    return fr

Now, all those nice properties that we got from trying to force object construction to give us a valid object are gone. reader_from_fd’s type signature, which takes a plain int, has no way of even suggesting to client code how to ensure that it has passed in the right kind of int.

Testing is much more of a hassle, because we have to patch in our own copy of fileio.open any time we want an instance of a FileReader in a test without doing any real-life file I/O, even if we could (for example) share a single file descriptor among many FileReader s for testing purposes.

All of this also assumes a fileio.open that is synchronous. Although for literal file I/O this is more of a hypothetical concern, there are many types of networked resource which are really only available via an asynchronous (and thus: potentially slow, potentially error-prone) API. If you’ve ever found yourself wanting to type async def __init__(self): ... then you have seen this limitation in practice.

Comprehensively describing all the possible problems with this approach would end up being a book-length treatise on a philosophy of object oriented design, so I will sum up by saying that the cause of all these problems is the same: we are inextricably linking the act of creating a data structure with whatever side-effects are most often associated with that data structure. If they are “often” associated with it, then by definition they are not “always” associated with it, and all the cases where they aren’t associated become unweildy and potentially broken.

Defining an __init__ is an anti-pattern, and we need a replacement for it.

The Solutions

I believe this tripartite assemblage of design techniques will address the problems raised above:

• using dataclass to define attributes,
• replacing behavior that previously would have previously been in __init__ with a new classmethod that does the same thing, and
• using precise types to describe what a valid instance looks like.

Using dataclass attributes to create an __init__ for you

To begin, let’s refactor FileReader into a dataclass. This does get us an __init__ method, but it won’t be one an arbitrary one we define ourselves; it will get the useful constraint enforced on it that it will just assign attributes.

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@dataclass
class FileReader:
    _fd: int
    def read(self, length: int) -> bytes:
        return fileio.read(self._fd, length)
    def close(self) -> None:
        fileio.close(self._fd)

Except... oops. In fixing the problems that we created with our custom __init__ that calls fileio.open, we have re-introduced several problems that it solved:

1. We have removed all the convenience of FileReader("path"). Now the user needs to import the low-level fileio.open again, making the most common type of construction both more verbose and less discoverable; if we want users to know how to build a FileReader in a practical scenario, we will have to add something in our documentation to point at a separate module entirely.
2. There’s no enforcement of the validity of _fd as a file descriptor; it’s just some integer, which the user could easily pass an incorrect instance of, with no error.

In isolation, dataclass by itself can’t solve all our problems, so let’s add in the second technique.

Using classmethod factories to create objects

We don’t want to require any additional imports, or require users to go looking at any other modules — or indeed anything other than FileReader itself — to figure out how to create a FileReader for its intended usage.

Luckily we have a tool that can easily address all of these concerns at once: @classmethod. Let’s define a FileReader.open class method:

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from typing import Self
@dataclass
class FileReader:
    _fd: int
    @classmethod
    def open(cls, path: str) -> Self:
        return cls(fileio.open(path))

Now, your callers can replace FileReader("path") with FileReader.open("path"), and get all the same benefits.

Additionally, if we needed to await fileio.open(...), and thus we needed its signature to be @classmethod async def open, we are freed from the constraint of __init__ as a special method. There is nothing that would prevent a @classmethod from being async, or indeed, from having any other modification to its return value, such as returning a tuple of related values rather than just the object being constructed.

Using NewType to address object validity

Next, let’s address the slightly trickier issue of enforcing object validity.

Our type signature calls this thing an int, and indeed, that is unfortunately what the lower-level fileio.open gives us, and that’s beyond our control. But for our own purposes, we can be more precise in our definitions, using NewType:

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from typing import NewType
FileDescriptor = NewType("FileDescriptor", int)

There are a few different ways to address the underlying library, but for the sake of brevity and to illustrate that this can be done with zero run-time overhead, let’s just insist to Mypy that we have versions of fileio.open, fileio.read, and fileio.write which actually already take FileDescriptor integers rather than regular ones.

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from typing import Callable
_open: Callable[[str], FileDescriptor] = fileio.open  # type:ignore[assignment]
_read: Callable[[FileDescriptor, int], bytes] = fileio.read
_close: Callable[[FileDescriptor], None] = fileio.close

We do of course have to slightly adjust FileReader, too, but the changes are very small. Putting it all together, we get:

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from typing import Self
@dataclass
class FileReader:
    _fd: FileDescriptor
    @classmethod
    def open(cls, path: str) -> Self:
        return cls(_open(path))
    def read(self, length: int) -> bytes:
        return _read(self._fd, length)
    def close(self) -> None:
        _close(self._fd)

Note that the main technique here is not necessarily using NewType specifically, but rather aligning an instance’s property of “has all attributes set” as closely as possible with an instance’s property of “fully valid instance of its class”; NewType is just a handy tool to enforce any necessary constraints on the places where you need to use a primitive type like int, str or bytes.

In Summary - The New Best Practice

From now on, when you’re defining a new Python class:

• Make it a dataclass².
• Use its default __init__ method³.
• Add @classmethods to provide your users convenient and discoverable ways to build your objects.
• Require that all dependencies be satisfied by attributes, so you always start with a valid object.
• Use typing.NewType to enforce any constraints on primitive data types (like int and str) which might have magical external attributes, like needing to come from a particular library, needing to be random, and so on.

If you define all your classes this way, you will get all the benefits of a custom __init__ method:

• All consumers of your data structures will receive valid objects, because an object with all its attributes populated correctly is inherently valid.
• Users of your library will be presented with convenient ways to create your objects that do as much work as is necessary to make them easy to use, and they can discover these just by looking at the methods on your class itself.

Along with some nice new benefits:

• You will be future-proofed against new requirements for different ways that users may need to construct your object.
• If there are already multiple ways to instantiate your class, you can now give each of them a meaningful name; no need to have monstrosities like def __init__(self, maybe_a_filename: int | str | None = None):
• Your test suite can always construct an object by satisfying all its dependencies; no need to monkey-patch anything when you can always call the type and never do any I/O or generate any side effects.

Before dataclasses, it was always a bit weird that such a basic feature of the Python language — giving data to a data structure to make it valid — required overriding a method with 4 underscores in its name. __init__ stuck out like a sore thumb. Other such methods like __add__ or even __repr__ were inherently customizing esoteric attributes of classes.

For many years now, that historical language wart has been resolved. @dataclass, @classmethod, and NewType give you everything you need to build classes which are convenient, idiomatic, flexible, testable, and robust.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! I am also available for consulting work if you think your organization could benefit from expertise on topics like “but what is a ‘class’, really?”.

A Database File

When I was 10 years old, and going through a fairly difficult time, I was lucky enough to come into the possession of a piece of software called Claris FileMaker Pro™.

FileMaker allowed its users to construct arbitrary databases, and to associate their tables with a customized visual presentation. FileMaker also had a rudimentary scripting language, which would allow users to imbue these databases with behavior.

As a mentally ill pre-teen, lacking a sense of control over anything or anyone in my own life, including myself, I began building a personalized database to catalogue the various objects and people in my immediate vicinity. If one were inclined to be generous, one might assess this behavior and say I was systematically taxonomizing the objects in my life and recording schematized information about them.

As I saw it at the time, if I collected the information, I could always use it later, to answer questions that I might have. If I didn’t collect it, then what if I needed it? Surely I would regret it! Thus I developed a categorical imperative to spend as much of my time as possible collecting and entering data about everything that I could reasonably arrange into a common schema.

Having thus summoned this specter of regret for all lost data-entry opportunities, it was hard to dismiss. We might label it “Claris’s Basilisk”, for obvious reasons.

Therefore, a less-generous (or more clinically-minded) observer might have replaced the word “systematically” with “obsessively” in the assessment above.

I also began writing what scripts were within my marginal programming abilities at the time, just because I could: things like computing the sum of every street number of every person in my address book. Why was this useful? Wrong question: the right question is “was it possible” to which my answer was “yes”.

If I was obliged to collect all the information which I could observe — in case it later became interesting — I was similarly obliged to write and run every program I could. It might, after all, emit some other interesting information.

I was an avid reader of science fiction as well.

I had this vague sense that computers could kind of think. This resulted in a chain of reasoning that went something like this:

1. human brains are kinda like computers,
2. the software running in the human brain is very complex,
3. I could only write simple computer programs, but,
4. when you really think about it, a “complex” program is just a collection of simpler programs

Therefore: if I just kept collecting data, collecting smaller programs that could solve specific problems, and connecting them all together in one big file, eventually the database as a whole would become self-aware and could solve whatever problem I wanted. I just needed to be patient; to “keep grinding” as the kids would put it today.

I still feel like this is an understandable way to think — if you are a highly depressed and anxious 10-year-old in 1990.

Anyway.

35 Years Later

OpenAI is a company that produces transformer architecture machine learning generative AI models; their current generation was trained on about 10 trillion words, obtained in a variety of different ways from a large variety of different, unrelated sources.

A few days ago, on March 26, 2025 at 8:41 AM Pacific Time, Sam Altman took to “X™, The Everything App™,” and described the trajectory of his career of the last decade at OpenAI as, and I quote, a “grind for a decade trying to help make super-intelligence to cure cancer or whatever” (emphasis mine).

I really, really don’t want to become a full-time AI skeptic, and I am not an expert here, but I feel like I can identify a logically flawed premise when I see one.

This is not a system-design strategy. It is a trauma response.

You can’t cure cancer “or whatever”. If you want to build a computer system that does some thing, you actually need to hire experts in that thing, and have them work to both design and validate that the system is fit for the purpose of that thing.

Aside: But... are they, though?

I am not an oncologist; I do not particularly want to be writing about the specifics here, but, if I am going to make a claim like “you can’t cure cancer this way” I need to back it up.

My first argument — and possibly my strongest — is that cancer is not cured.

QED.

But I guess, to Sam’s credit, there is at least one other company partnering with OpenAI to do things that are specifically related to cancer. However, that company is still in a self-described “initial phase” and it’s not entirely clear that it is going to work out very well.

Almost everything I can find about it online was from a PR push in the middle of last year, so it all reads like a press release. I can’t easily find any independently-verified information.

A lot of AI hype is like this. A promising demo is delivered; claims are made that surely if the technology can solve this small part of the problem now, within 5 years surely it will be able to solve everything else as well!

But even the light-on-content puff-pieces tend to hedge quite a lot. For example, as the Wall Street Journal quoted one of the users initially testing it (emphasis mine):

The most promising use of AI in healthcare right now is automating “mundane” tasks like paperwork and physician note-taking, he said. The tendency for AI models to “hallucinate” and contain bias presents serious risks for using AI to replace doctors. Both Color’s Laraki and OpenAI’s Lightcap are adamant that doctors be involved in any clinical decisions.

I would probably not personally characterize “‘mundane’ tasks like paperwork and … note-taking” as “curing cancer”. Maybe an oncologist could use some code I developed too; even if it helped them, I wouldn’t be stealing valor from them on the curing-cancer part of their job.

Even fully giving it the benefit of the doubt that it works great, and improves patient outcomes significantly, this is medical back-office software. It is not super-intelligence.

It would not even matter if it were “super-intelligence”, whatever that means, because “intelligence” is not how you do medical care or medical research. It’s called “lab work” not “lab think”.

To put a fine point on it: biomedical research fundamentally cannot be done entirely by reading papers or processing existing information. It cannot even be done by testing drugs in computer simulations.

Biological systems are enormously complex, and medical research on new therapies inherently requires careful, repeated empirical testing to validate the correspondence of existing research with reality. Not “an experiment”, but a series of coordinated experiments that all test the same theoretical model. The data (which, in an LLM context, is “training data”) might just be wrong; it may not reflect reality, and the only way to tell is to continuously verify it against reality.

Previous observations can be tainted by methodological errors, by data fraud, and by operational mistakes by practitioners. If there were a way to do verifiable development of new disease therapies without the extremely expensive ladder going from cell cultures to animal models to human trials, we would already be doing it, and “AI” would just be an improvement to efficiency of that process. But there is no way to do that and nothing about the technologies involved in LLMs is going to change that fact.

Knowing Things

The practice of science — indeed any practice of the collection of meaningful information — must be done by intentionally and carefully selecting inclusion criteria, methodically and repeatedly curating our data, building a model that operates according to rules we understand and can verify, and verifying the data itself with repeated tests against nature. We cannot just hoover up whatever information happens to be conveniently available with no human intervention and hope it resolves to a correct model of reality by accident. We need to look where the keys are, not where the light is.

Piling up more and more information in a haphazard and increasingly precarious pile will not allow us to climb to the top of that pile, all the way to heaven, so that we can attack and dethrone God.

Eventually, we’ll just run out of disk space, and then lose the database file when the family gets a new computer anyway.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! Special thanks also to Itamar Turner-Trauring and Thomas Grainger for pre-publication feedback on this article; any errors of course remain my own.

Today on stream, I updated PINPal to fix the memorization algorithm.

If you haven’t heard of PINPal before, it is a vault password memorization tool. For more detail on what that means, you can check it out the README, and why not give it a ⭐ while you’re at it.

As I started writing up an update post I realized that I wanted to contextualize it a bit more, because it’s a tool I really wish were more popular. It solves one of those small security problems that you can mostly ignore, right up until the point where it’s a huge problem and it’s too late to do anything about it.

In brief, PINPal helps you memorize new secure passcodes for things you actually have to remember and can’t simply put into your password manager, like the password to your password manager, your PC user account login, your email account¹, or the PIN code to your phone or debit card.

Too often, even if you’re properly using a good password manager for your passwords, you’ll be protecting it with a password optimized for memorability, which is to say, one that isn’t random and thus isn’t secure. But I have also seen folks veer too far in the other direction, trying to make a really secure password that they then forget right after switching to a password manager. Forgetting your vault password can also be a really big deal, making you do password resets across every app you’ve loaded into it so far, so having an opportunity to practice it periodically is important.

PINPal uses spaced repetition to ensure that you remember the codes it generates.

While periodic forced password resets are a bad idea, if (and only if!) you can actually remember the new password, it is a good idea to get rid of old passwords eventually — like, let’s say, when you get a new computer or phone. Doing so reduces the risk that a password stored somewhere on a very old hard drive or darkweb data dump is still floating around out there, forever haunting your current security posture. If you do a reset every 2 years or so, you know you’ve never got more than 2 years of history to worry about.

PINPal is also particularly secure in the way it incrementally generates your password; the computer you install it on only ever stores the entire password in memory when you type it in. It stores even the partial fragments that you are in the process of memorizing using the secure keyring module, avoiding plain-text whenever possible.

I’ve been using PINPal to generate and memorize new codes for a while, just in case², and the change I made today was because encountered a recurring problem. The problem was, I’d forget a token after it had been hidden, and there was never any going back. The moment that a token was hidden from the user, it was removed from storage, so you could never get a reminder. While I’ve successfully memorized about 10 different passwords with it so far, I’ve had to delete 3 or 4.

So, in the updated algorithm, the visual presentation now hides tokens in the prompt several memorizations before they’re removed. Previously, if the password you were generating was ‘hello world’, you’d see hello world 5 times or so, times, then •••• world; if you ever got it wrong past that point, too bad, start over. Now, you’ll see hello world, then °°°° world, then after you have gotten the prompt right without seeing the token a few times, you’ll see •••• world after the backend has locked it in and it’s properly erased from your computer.

If you get the prompt wrong, breaking your streak reveals the recently-hidden token until you get it right again. I also did a new release on that same livestream, so if this update sounds like it might make the memorization process more appealing, check it out via pip install pinpal today.

Right now this tool is still only extremely for a specific type of nerd — it’s command-line only, and you probably need to hand-customize your shell prompt to invoke it periodically. But I’m working on making it more accessible to a broader audience. It’s open source, of course, so you can feel free to contribute your own code!

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more things like it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Have you ever written some Python code that looks like this?

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from enum import Enum, auto

class SomeNumber(Enum):
    one = auto()
    two = auto()
    three = auto()

def behavior(number: SomeNumber) -> int:
    match number:
        case SomeNumber.one:
            print("one!")
            return 1
        case SomeNumber.two:
            print("two!")
            return 2
        case SomeNumber.three:
            print("three!")
            return 3

That is to say, have you written code that:

1. defined an enum with several members
2. associated custom behavior, or custom values, with each member of that enum,
3. needed one or more match / case statements (or, if you’ve been programming in Python for more than a few weeks, probably a big if/elif/elif/else tree) to do that association?

In this post, I’d like to submit that this is an antipattern; let’s call it the “passive enum” antipattern.

For those of you having a generally positive experience organizing your discrete values with enums, it may seem odd to call this an “antipattern”, so let me first make something clear: the path to a passive enum is going in the correct direction.

Typically - particularly in legacy code that predates Python 3.4 - one begins with a value that is a bare int constant, or maybe a str with some associated values sitting beside in a few global dicts.

Starting from there, collecting all of your values into an enum at all is a great first step. Having an explicit listing of all valid values and verifying against them is great.

But, it is a mistake to stop there. There are problems with passive enums, too:

1. The behavior can be defined somewhere far away from the data, making it difficult to:
   1. maintain an inventory of everywhere it’s used,
   2. update all the consumers of the data when the list of enum values changes, and
   3. learn about the different usages as a consumer of the API
2. Logic may be defined procedurally (via if/elif or match) or declaratively (via e.g. a dict whose keys are your enum and whose values are the required associated value).
   1. If it’s defined procedurally, it can be difficult to build tools to interrogate it, because you need to parse the AST of your Python program. So it can be difficult to build interactive tools that look at the associated data without just calling the relevant functions.
   2. If it’s defined declaratively, it can be difficult for existing tools that do know how to interrogate ASTs (mypy, flake8, Pyright, ruff, et. al.) to make meaningful assertions about it. Does your linter know how to check that a dict whose keys should be every value of your enum is complete?

To refactor this, I would propose a further step towards organizing one’s enum-oriented code: the active enum.

An active enum is one which contains all the logic associated with the first-party provider of the enum itself.

You may recognize this as a more generalized restatement of the object-oriented lens on the principle of “separation of concerns”. The responsibilities of a class ought to be implemented as methods on that class, so that you can send messages to that class via method calls, and it’s up to the class internally to implement things. Enums are no different.

More specifically, you might notice it as a riff on the Active Nothing pattern described in this excellent talk by Sandi Metz, and, yeah, it’s the same thing.

The first refactoring that we can make is, thus, to mechanically move the method from an external function living anywhere, to a method on SomeNumber . At least like this, we present an API to consumers externally that shows that SomeNumber has a behavior method that can be invoked.

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from enum import Enum, auto

class SomeNumber(Enum):
    one = auto()
    two = auto()
    three = auto()

    def behavior(self) -> int:
        match self:
            case SomeNumber.one:
                print("one!")
                return 1
            case SomeNumber.two:
                print("two!")
                return 2
            case SomeNumber.three:
                print("three!")
                return 3

However, this still leaves us with a match statement that repeats all the values that we just defined, with no particular guarantee of completeness. To continue the refactoring, what we can do is change the value of the enum itself into a simple dataclass to structurally, by definition, contain all the fields we need:

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from dataclasses import dataclass
from enum import Enum
from typing import Callable

@dataclass(frozen=True)
class NumberValue:
    result: int
    effect: Callable[[], None]

class SomeNumber(Enum):
    one = NumberValue(1, lambda: print("one!"))
    two = NumberValue(2, lambda: print("two!"))
    three = NumberValue(3, lambda: print("three!"))

    def behavior(self) -> int:
        self.value.effect()
        return self.value.result

Here, we give SomeNumber members a value of NumberValue, a dataclass that requires a result: int and an effect: Callable to be constructed. Mypy will properly notice that if x is a SomeNumber, that x will have the type NumberValue and we will get proper type checking on its result (a static value) and effect (some associated behaviors)¹.

Note that the implementation of behavior method - still conveniently discoverable for callers, and with its signature unchanged - is now vastly simpler.

But what about...

Lookups?

You may be noticing that I have hand-waved over something important to many enum users, which is to say, by-value lookup. enum.auto will have generated int values for one, two, and three already, and by transforming those into NumberValue instances, I can no longer do SomeNumber(1).

For the simple, string-enum case, one where you might do class MyEnum: value = “value” so that you can do name lookups via MyEnum("value"), there’s a simple solution: use square brackets instead of round ones. In this case, with no matching strings in sight, SomeNumber["one"] still works.

But, if we want to do integer lookups with our dataclass version here, there’s a simple one-liner that will get them back for you; and, moreover, will let you do lookups on whatever attribute you want:

1

by_result = {each.value.result: each for each in SomeNumber}

enum.Flag?

You can do this with Flag more or less unchanged, but in the same way that you can’t expect all your list[T] behaviors to be defined on T, the lack of a 1-to-1 correspondence between Flag instances and their values makes it more complex and out of scope for this pattern specifically.

3rd-party usage?

Sometimes an enum is defined in library L and used in application A, where L provides the data and A provides the behavior. If this is the case, then some amount of version shear is unavoidable; this is a situation where the data and behavior have different vendors, and this means that other means of abstraction are required to keep them in sync. Object-oriented modeling methods are for consolidating the responsibility for maintenance within a single vendor’s scope of responsibility. Once you’re not responsible for the entire model, you can’t do the modeling over all of it, and that is perfectly normal and to be expected.

The goal of the Active Enum pattern is to avoid creating the additional complexity of that shear when it does not serve a purpose, not to ban it entirely.

A Case Study

I was inspired to make this post by a recent refactoring I did from a more obscure and magical² version of this pattern into the version that I am presenting here, but if I am going to call passive enums an “antipattern” I feel like it behooves me to point at an example outside of my own solo work.

So, for a more realistic example, let’s consider a package that all Python developers will recognize from their day-to-day work, python-hearthstone, the Python library for parsing the data files associated with Blizzard’s popular computerized collectible card game Hearthstone.

As I’m sure you already know, there are a lot of enums in this library, but for one small case study, let’s look a few of the methods in hearthstone.enums.GameType.

GameType has already taken the “step 1” in the direction of an active enum, as I described above: as_bnet is an instancemethod on GameType itself, making it at least easy to see by looking at the class definition what operations it supports. However, in the implementation of that method (among many others) we can see the worst of both worlds:

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class GameType(IntEnum):
    def as_bnet(self, format: FormatType = FormatType.FT_STANDARD):
        if self == GameType.GT_RANKED:
            if format == FormatType.FT_WILD:
                return BnetGameType.BGT_RANKED_WILD
            elif format == FormatType.FT_STANDARD:
                return BnetGameType.BGT_RANKED_STANDARD
            # ...
            else:
                raise ValueError()
        # ...
        return {
            GameType.GT_UNKNOWN: BnetGameType.BGT_UNKNOWN,
            # ...
            GameType.GT_BATTLEGROUNDS_DUO_FRIENDLY: BnetGameType.BGT_BATTLEGROUNDS_DUO_FRIENDLY,
        }[self]

We have procedural code mixed with a data lookup table; raise ValueError mixed together with value returns. Overall, it looks like this might be hard to maintain this going forward, or to see what’s going on without a comprehensive understanding of the game being modeled. Of course for most python programmers that understanding can be assumed, but, still.

If GameType were refactored in the manner above³, you’d be able to look at the member definition for GT_RANKED and see a mapping of FormatType to BnetGameType, or GT_BATTLEGROUNDS_DUO_FRIENDLY to see an unconditional value of BGT_BATTLEGROUNDS_DUO_FRIENDLY. Given that this enum has 40 elements, with several renamed or removed, it seems reasonable to expect that more will be added and removed as the game is developed.

Conclusion

If you have large enums that change over time, consider placing the responsibility for the behavior of the values alongside the values directly, and any logic for processing the values as methods of the enum. This will allow you to quickly validate that you have full coverage of any data that is required among all the different members of the enum, and it will allow API clients a convenient surface to discover the capabilities associated with that enum.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Over the last decade, it has become a common experience to be using a social media app, and to perceive that app as saying something specific to you. This manifests in statements like “Twitter thinks Rudy Giuliani has lost his mind”, “Facebook is up in arms about DEI”, “Instagram is going crazy for this new water bottle”, “BlueSky loves this bigoted substack”, or “Mastodon can’t stop talking about Linux”. Sometimes this will even be expressed with “the Internet” as a metonym for the speaker’s preferred social media: “the Internet thinks that Kate Middleton is missing”.

However, even the smallest of these networks comprises literal millions of human beings, speaking dozens of different languages, many of whom never interact with each other at all. The hot takes that you see from a certain excitable sub-community, on your particular timeline or “for you” page, are not necessarily representative of “the Internet” — at this point, a group that represents a significant majority of the entire human population.

If I may coin a phrase, I will refer to these as “Diffuse, Amorphous, Nebulous, Generalized Internet Takes”, or DANGITs, which handily evokes the frustrating feeling of arguing against them.

A DANGIT is not really a new “internet” phenomenon: it is a specific expression of the availability heuristic.

If we look at our device and see a bunch of comments in our inbox, particularly if those comments have high salience via being recent, emotive, and repeated, we will naturally think that this is what The Internet thinks. However, just because we will naturally think this does not mean that we will accurately think it.

It is worth keeping this concept in mind when participating in public discourse because it leads to a specific type of communication breakdown. If you are arguing with a DANGIT, you will feel like you are arguing with someone with incredibly inconsistent, hypocritical, and sometimes even totally self-contradictory views. But to be self-contradictory, one needs to have a self. And if you are arguing with 9 different people from 3 different ideological factions, all making completely different points and not even taking time to agree on the facts beforehand, of course it’s going to sound like cacophonous nonsense. You’re arguing with the cacophony, it’s just presented to you in a way that deceives you into thinking that it’s one group.

There are subtle variations on this breakdown; for example, it can also make people’s taste seem incoherent. If it seems like one week the Interior Designer internet loves stark Scandinavian minimalism, and the next week baroque Rococo styles are making a comeback, it might seem like The Internet has no coherent sense of taste, and these things don’t go together. That’s because it doesn’t! Why would you expect it to?

Most likely, you are simply seeing some posts from minimalists, and then, separately, some posts from Rococo aficionados. Any particular person’s feed may be dedicated to a specific, internally coherent viewpoint, aesthetic, or ideology, but if you dump them all into a blender to separate them from their context, of course they will look jumbled together.

This is what social media does. It is context collapse as a service. Even if you eliminate engagement-maximizing algorithms and view everything perfectly chronologically, even if you have the world’s best trust & safety team making sure that there is nothing harmful and no disinformation, social media — like email — inherently remains that context-collapsing blender. There’s no way for it not to be; if two people you follow, who do not follow and are not aware of each other, are both posting unrelated things at the same time, you’re going to see them at around the same time.

Do not argue with a DANGIT. Discussions are the internet are famously Pyrrhic battles to begin with, but if you argue with a DANGIT it’s not that you will achieve a Pyrrhic victory, you cannot possibly achieve any victory, because you are shadowboxing an imagined consensus where none exits.

You can’t win against something that isn’t there.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more things like it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Kamala Harris lost. Here we are. So it goes.

Are you sad? Are you scared?

I am very sad. I am very scared.

But, like everyone else in this position, most of all, I want to know what to do next.

A Mission For Progress

I believe that we should set up a missionary organization for progressive and liberal values.

In 2017, Kayla Chadwick wrote the now-classic article, “I Don’t Know How To Explain To You That You Should Care About Other People”. It resonated with millions of people, myself included. It expresses an exasperation with a populace that seems ignorant of economics, history, politics, and indeed unable to read the news. It is understandable to be frustrated with people who are exercising their electoral power callously and irresponsibly.

But I think in 2024, we need to reckon with the fact that we do, in fact, need to explain to a large swathe of the population that they should care about other people.

We had better figure out how to explain it soon.

Shared Values — A Basis for Hope

The first question that arises when we start considering outreach to the conservative-leaning or undecided independent population is, “are these people available to be convinced?”.

To that, I must answer an unqualified “yes”.

I know that some of you are already objecting. For those of us with an understanding of history and the mechanics of bigotry in the United States, it might initially seem like the answer is “no”.

As the Nazis came to power in the 1920s, they were campaigning openly on a platform of antisemitic violence. Everyone knew what the debate was. It was hard to claim that you didn’t, in spite of some breathtakingly cowardly contemporaneous journalism, they weren’t fooling anyone.

It feels ridiculous to say this, but Hitler did not have support among Jews.

Yet, after campaigning on a platform of defaming immigrants, and Mexican immigrants specifically for a decade, a large part of what drove his victory is that Trump enjoyed a shockingly huge surge of support among the Hispanic population. Even some undocumented migrants — the ones most likely to be herded into concentration camps starting in January — are supporting him.

I believe that this is possible because, in order to maintain support of the multi-ethnic working-class coalition that Trump has built, the Republicans must maintain plausible deniability. They have to say “we are not racist”, “we are not xenophobic”. Incredibly, his supporters even say “I don’t hate trans people” with startling regularity.

Most voters must continue to believe that hateful policies with devastating impacts are actually race-neutral, and are simply going to get rid of “bad” people. Even the ones motivated by racial resentment are mostly motivated by factually incorrect beliefs about racialized minorities receiving special treatment and resources which they are not in fact receiving.

They are victims of a disinformation machine. One that has rendered reality incomprehensible.

If you listen to conservative messaging, you can hear them referencing this all the time. Remember when JD Vance made that comment about Democrats calling Diet Mountain Dew racist?

Many publications wrote about this joke “bombing”¹, but the kernel of truth within it is this: understanding structural bigotry in the United States is difficult. When we progressives talk about it, people who don’t understand it think that our explanations sound ridiculous and incoherent.

There’s a reason that the real version of critical race theory is a graduate-level philosophy-of-law course, and not a couple of catch phrases.

If, without context, someone says that “municipal zoning laws are racist”, this makes about as much sense as “Diet Mountain Dew is racist” to someone who doesn’t already know what “redlining” is.

Conservatives prey upon this confusion to their benefit. But they prey on this because they must do so. They must do so because, despite everything, hate is not actually popular among the American electorate. Even now, they have to be deceived into it.

The good news is that all we need to do is stop the deception.

Politics Matter

If I have sold you on the idea that a substantial plurality of voters are available to be persuaded, the next question is: can we persuade them? Do we, as progressives, have the resources and means to do so? We did lose, after all, and it might seem like nothing we did had much of an impact.

Let’s analyze that assumption.

Across the country, Trump’s margins increased. However, in the swing states, where Harris spent money on campaigning, his margins increased less than elsewhere. At time of writing, we project that the safe-state margin shift will be 3.55% towards trump, and the swing-state margin shift will be 1.69%.

This margin was, sadly, too small for a victory, but it does show that the work mattered. Perhaps given more time, or more resources, it would have mattered just a little bit more, and that would have been decisive.

This is to say, in the places where campaign dollars were spent, even against the similar spending of the Trump campaign, we pushed the margin of support 1.86% higher within 107 days. So yes: campaigning matters. Which parts and how much are not straightforward, but it definitely matters.

This is a bit of a nonsensical comparison for a whole host of reasons², but just for a ballpark figure, if we kept this pressure up continuously during the next 4 years, we could increase support for a democratic candidate by 25%.

We Can Teach, Not Sell

Political junkies tend to overestimate the knowledge of the average voter. Even when we are trying to compensate for it, we tend to vastly overestimate how much the average voter knows about politics and policy. I suspect that you, dear reader, are a political junkie even if you don’t think of yourself as one.

To give you a sense of what I mean, across the country, on Election day and the day after, there was a huge spike in interest for the Google query, “did Joe Biden drop out”.

Consistently over the last decade, democratic policies are more popular than their opponents. Even deep red states, such as Kansas, often vote for policies supported by democrats and opposed by Republicans.

This confusion about policy is not organic; it is not voters’ fault. It is because Republicans constantly lie.

All this ignorance might seem discouraging, but it presents an opportunity: people will not sign up to be persuaded, but people do like being informed. Rather than proselytizing via a hard sales pitch, it should be possible to offer to explain how policy connects to elections. And this is made so much the easier if so many of these folks already generally like our policies.

The Challenge Is Enormous

I’ve listed some reasons for optimism, but that does not mean that this will be easy.

Republicans have a tremendously powerful, decentralized media apparatus that reinforces their culture-war messaging all the time.

After some of the post-election analysis, “The Left Needs Its Own Joe Rogan” is on track to become a cliché within the week.³ While I am deeply sympathetic to that argument, the right-wing media’s success is not organic; it is funded by petrochemical billionaires.

We cannot compete via billionaire financing, and as such, we have to have a way to introduce voters to progressive and liberal media. Which means more voters need social connections to liberals and progressives.

Good Works

The democratic presidential campaign alone spent a billion and a half dollars. And, as shown above, this can be persuasive, but it’s just the persuasion itself.

Better than spending all this money on telling people what good stuff we would do for them if we were in power, we could just show them, by doing good stuff. We should live our values, not just endlessly reiterate them.

A billion dollars is a significant amount of power in its own right.

For historical precedent, consider the Black Panthers’ Free Breakfast For Children program. This program absolutely scared the shit out of the conservative power structure, to the point that Nixon’s FBI literally raided them for giving out free food to children.

Religious missionaries, who are famously annoying, often offset their annoying-ness by doing charitable work in the communities they are trying to reach. A lot of the country that we need to reach are religious people, and nominally both Christians and leftists share a concern for helping those in need, so we should find some cultural common ground there.

We can leverage that overlap in values by partnering with churches. This immediately makes such work culturally legible to many who we most need to reach.

Jobs Jobs Jobs

When I raised this idea with Philip James, he had been mulling over similar ideas for a long time, but with a slightly different tack: free career skills workshops from folks who are obviously “non-traditional” with respect to the average rural voter’s cultural expectations. Recruit trans folks, black folks, women, and non-white immigrants from our tech networks.

Run the trainings over remote video conferencing to make volunteering more accessible. Run those workshops through churches as a distribution network.

There is good evidence that this sort of prolonged contact and direct exposure to outgroups, to help people see others as human beings, very effective politically.

However, job skills training is by no means the only benefit we could bring. There are lots of other services we could offer remotely, particularly with the skills that we in the tech community could offer. I offer this as an initial suggestion; if you have more ideas I’d love to hear them. I think the best ideas are ones where folks can opt in, things that feel like bettering oneself rather than receiving charity; nobody likes getting handouts, particularly from the outgroup, but getting help to improve your own skills feels more participatory.

I do think that free breakfast for children, specifically, might be something to start with because people are far more willing to accept gifts to benefit others (particularly their children, or the elderly!) rather than themselves.

Take Credit

Doing good works in the community isn’t enough. We need to do visible good works. Attributable good works.

We don’t want to be assholes about it, but we do want to make sure that these benefits are clearly labeled. We do not want to attach an obligation to any charitable project, but we do want to attach something to indicate where it came from.

I don’t know what that “something” should be. The most important thing is that whatever “something” is appeals to set of partially-overlapping cultures that I am not really a part of — Midwestern, rural, southern, exurban, working class, “red state” — and thus, I would want to hear from people from those cultures about what works best.

But it’s got to be something.

Maybe it’s a little sticker, “brought to you by progressives and liberals. we care about you!”. Maybe it’s a subtle piece of consistent branding or graphic design, like a stylized blue stripe. Maybe we need to avoid the word “democrats”, or even “progressive” or “liberal”, and need some independent brand for such a thing, that is clearly tenuously connected but not directly; like the Coalition of Liberal and Leftist Helpful Neighbors or something.

Famously, when Trump sent everybody a check from the government, he put his name on it. Joe Biden did the same thing, and Democrats seem to think it’s a good thing that he didn’t take credit because it “wasn’t about advancing politics”, even though this obviously backfired. Republicans constantly take credit for the benefits of Democratic policies, which is one reason why voters don’t know they’re democratic policies.

Our broad left-liberal coalition is attempting to improve people’s material conditions. Part of that is, and must be, advancing a political agenda. It’s no good if we provide job trainings and free lunches to a community if that community is just going to be reduced to ruin by economically catastrophic tariffs and mass deportations.

We cannot do this work just for the credit, but getting credit is important.

Let’s You And Me — Yes YOU — Get Started

I think this is a good idea, but I am not the right person to lead it.

For one thing, building this type of organization requires a lot of organizational and leadership skills that are not really my forte. Even the idea of filing the paperwork for a new 501(c)3 right now sounds like rolling Sisyphus’s rock up the hill to me.

For another, we need folks who are connected to this culture, in ways that I am not. I would be happy to be involved — I do have some relevant technical skills to help with infrastructure, and I could always participate in some of the job-training stuff, and I can definitely donate a bit of money to a nonprofit, but I don’t think I can be in charge.

You can definitely help too, and we will need a wide variety of skills to begin with, and it will definitely need money. Maybe you can help me figure out who should be in charge.

This project will be weaker without your support. Thus: I need to hear from you.

You can email me, or, if you’d prefer a more secure channel, feel free to reach out over Signal, where my introduction code is glyph.99 . Please start the message with “good works:” so I can easily identify conversations about this.

If I receive any interest at all, I plan to organize some form of meeting within the next 30 days to figure out concrete next steps.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more things like it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! My aspirations for this support are more in the directions of software development than activism, but needs must, when the devil drives. Thanks especially to Philip James for both refining the idea and helping to edit this post, and to Marley Myrianthopoulos for assistance with the data analysis.

It’s the weekend, and I have some Thoughts about federated social media. So, buckle up, I guess, it’s time to start some fights.

Recently there has been some discourse about Bluesky’s latest fundraising round. I’ve been participating in conversations about this on Mastodon, and I think I might sometimes come across as a Mastodon partisan, but my feelings are complex and I really don’t want to be boosting the ActivityPub Fediverse without qualification.

So here are some qualifications.

Bluesky Is Evil

To the extent that I am an ActivityPub partisan in the discourse between ActivityPub and ATProtocol, it is because I do not believe that Bluesky is a meaningfully decentralized social network. It is a social network, run by a company, which has a public API with some elements that might, one day, make it possible for it to be decentralized. But today, it is not, either practically or theoretically.

The Bluesky developers are putting in a ton of effort to maybe make it decentralized, hypothetically, someday. A lot of people think they will succeed. But ActivityPub (and, of course, Mastodon specifically) are already, today, meaningfully decentralized, as you can see on FediDB, there are instances with hundreds of thousands of people on them, before we even get to esoterica like the integrations Threads, Wordpress, Flipboard, and Ghost are doing.

The inciting incident for this post — that a lot of people are also angry about Bluesky raising millions of dollars from Evil Guys Doing Evil Stuff Capital — is indeed a serious concern. It lights the fuse that burns towards their eventual, inevitable incredible journey. ATProtocol is just an API, and that API will get shut off one day, whenever their funders get bored of the pretense of their network being “decentralized”.

At time of writing, it is also interesting that 3 of the 4 times that the CEO of Bluesky has even skeeted the word “blockchain” is to say “no blockchain”, to reassure users that the scam magnet of “Blockchain” is not actually near their product or protocol, which is a much harder position to maintain when your lead investor is “Blockchain Capital”.

I think these are all valid criticisms of Bluesky. But I also think that the actual engineers working on the product are aware of these issues, and are making a significant effort to address them or mitigate them in any way they can. All that work can still be easily incinerated by a slow quarter in terms of user growth numbers or a missed revenue forecast when the VCs are getting impatient, but it’s not nothing, it is a life’s work.

Really, who among us could not have our life’s ambitions trivially destroyed in an afternoon, simply because a billionaire decided that they should be? If you feel like you are safe from this, I have some bad news about how money works. So we are all doing our best in an imperfect system and maybe Bluesky is on to something here. That’s eminently possible. They’re certainly putting forth an earnest effort.

Mastodon Is Stupid

Meanwhile, not nearly as much has been made recently of Mastodon refusing funding from a variety of sources, when all indications are that funding is low, and plummeting, far below the level required to actually sustain the site, and they haven’t done a financial transparency report for over a year, and that report was already nearly a year late.

Mastodon and the fediverse are not nearly in a position to claim moral superiority over Bluesky. Sure, taking blockchain VC money might seem like a rookie mistake, but going out of business because you are spurning every possible source of funding is not that wise either.

Some might think that, sure, Mastodon the company might die but at least the Fediverse as a whole will keep going strong, right? Lots of people run their own instances! I even find elements of this argument convincing, and I think there is probably some truth to it. But to really believe this argument as claimed, that it’s a fait accompli that the fediverse will survive in some form, that all those self-run servers will be a robust network that will self-repair, requires believing some obviously false stuff. It is frankly unprofitable to run a Fediverse instance. Realistically, if you want to operate a mastodon server for yourself, it is going to cost at least $100/year once you include stuff like having a domain name, and managing the infrastructure costs is a complex problem that keeps getting harder to manage as the software itself gets slower.

Cory Doctorow has recently argued that this is all worth it, because at least on Mastodon, you’re in control, not at the whims of centralized website operators like Bluesky. In his words,

On Mastodon (and other services based on Activitypub), you can easily leave one server and go to another, and everyone you follow and everyone who follows you will move over to the new server. If the person who runs your server turns out to be imperfect in a way that you can’t endure, you can find another server, spend five minutes moving your account over, and you’re back up and running on the new server

He concludes:

Any system where users can leave without pain is a system whose owners have high switching costs and whose users have none

(Emphasis mine).

This is a beautiful vision. It is, however, an incorrect assessment of the state of the Fediverse as it stands today. It’s not true in two important ways:

First, if you look at any account of a user’s fediverse account migration, like this one from Steve Bate or this one from the Ente project or this one from Erin Kissane, you will see that it is “painful for the foreseeable future” or “wasn’t as seamless as advertised”, and that “the best time to […] migrate instances […] is never”. This language does not presage a pleasant experience, as Doctorow puts it, “without pain”.

Second, migration is an active process that requires engagement from the instance that hosts you. If you have been blocked or banned, or had your account terminated, you are just out of luck. You do not have control over your data or agency over your online identity unless you’ve shelled out the relatively exorbitant amount of money to actually operate your own instance.

In short, ActivityPub is no panacea. A federated system is not really a “decentralized” system, as much as it is a bunch of smaller centralized systems that all talk to each other. You still need to know, and care, about your social and financial relationship to the operators of your instance. There is probably no getting away from this, like, just generally on the Internet, no matter how much peer-to-peer software we deploy, but there certainly isn’t in the incomplete mess that is ActivityPub.

JOIN, or DIE.

Neither Mastodon (or ActivityPub) nor Bluesky (or ATProtocol) has a comprehensive solution to the problem of decentralized social media. These companies, and these protocols, are both deeply flawed and if everything keeps bumping along as it is, I believe both are likely to fail. At different times, on different timelines, and for different reasons, but fail nonetheless.

However, these networks are both small and growing, and we are not yet in the phase of enshittification where margins are shrinking and audiences are captured and the screws must be tightened to juice revenue. There are stil possibilities. Mastodon is crowdfunded and what they lack in resources they make up for in flexibility and scrappiness. Bluesky has money and while there will eventually be a need to monetize somehow, they have plenty of runway to come up with that answer, and a lot of sophisticated protocol work has been done. Not enough to make a complete circut and allow users true, practical decentralization, but it’s not nothing, either.

Mastodon and Bluesky are both organizations with humans in them, and piles of data that is roughly schema-compatible even if the nuances and details are different. I know that there is a compatible model becuse thanks to both platforms being relatively open, there is a functioning ActivityPub/ATProtocol bridge in the form of Brid.gy Fed. You can use it today, and I highly recommend that you do so, so that “choice of protocol” does not fully define your audience. If you’re on bluesky, follow this account, and if you’re on Mastodon or elsewhere on the Fediverse, search for and follow @bsky.brid.gy@bsky.brid.gy.

The reality that fans of decentralized, independent social media must confront is that we are a tiny audicence right now. Whichever site we are looking at, we are talking about a few million monthly active users at best, in a world where even the pathetic husk of Twitter still has hundreds of millions and Facebook has billions. Interneceine fights are not going to get us anywhere. We need to build bridges and links and connect our networks as densely as possible. If I’m being honest, Bridgy Fed looks like a pretty janky solution, but it’s something, and we need to start doing something soon, so we do not collectively become a permanent minority that mass markets can safely ignore.

As users, we need to set an example, so that the developers of the respective platforms get their shit together and work together directly so that workarounds like Bridgy are not required. Frankly, this is mostly on the ActivityPub and Mastodon devs, as far as I can tell. Unfortunately, not a lot of this seems to be public, or at least I haven’t witnessed a lot of it directly, but I have heard repeatedly that the ActivityPub developers are prickly, and this is one high-profile public example where an ActivityPub partisan is incredibly, pointlessly hostile and borderline harrassing towards someone — Mike Masnick, a long-time staunch advocate for open protocols and open patents, someone with a Mastodon account, and thus as good a prospective ally as the ActivityPub fediverse might reasonably find — explaining some of the relative benefits of Bluesky.

Most of us are technology nerds in one way or another. In that way we can look at signifiers like “ActivityPub” and “ATProtocol”, and feel like these are hard boundaries around different all-encompassing structures for the future, and thus tribes we must join and support.

A better way to look at this, however, is to see social entities like Mastodon gGmbH and Bluesky PBC — or, more to the point, Fosstodon, SFBA Social, Hachyderm (and maybe, one day, even an instance which isn’t fully just for software development nerds), as groups that deploy these protocols to access some data that they publish, just as they might publish their website over HTTP or their newsletters over SMTP. There are technical challenges involved in bridging between mutually unintelligible domain models, but that is, like, network software’s whole deal. Most software is just some kind of translation from one format or context to another. The best possible future for the fediverse is the one where users care as much about the distinction between ATProtocol and ActivityPub as they do about the distinction between POP3 and IMAP.

To both developers and users of these systems, I say: get it together. Be nice to each other. Because the rest of the social media ecosystem is sure as shit not going to be nice to us if we ever see even a hint of success and start to actually cut into their user base.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

One of my first TIL entries was about how you can imitate Node’s node_modules semantics in Python on UNIX-like operating systems. A lot has happened since then (to the better!) and it’s time for an update. direnv still rocks, though.

When you build Python, you can pass various options to ./configure that change aspects of how it is built. There is documentation for all of these options, and they are things like --prefix to tell the build where to install itself, --without-pymalloc if you have some esoteric need for everything to go through a custom memory allocator, or --with-pydebug.

One of these options only matters on macOS, and its effects are generally poorly understood. The official documentation just says “Create a Python.framework rather than a traditional Unix install.” But… do you need a Python.framework? If you’re used to running Python on Linux, then a “traditional Unix install” might sound pretty good; more consistent with what you are used to.

If you use a non-Framework build, most stuff seems to work, so why should anyone care? I have mentioned it as a detail in my previous post about Python on macOS, but even I didn’t really explain why you’d want it, just that it was generally desirable.

The traditional answer to this question is that you need a Framework build “if you want to use a GUI”, but this is demonstrably not true. At first it might not seem so, since the go-to Python GUI test is “run IDLE”; many non-Framework builds also omit Tkinter because they don’t ship a Tk dependency, so IDLE won’t start. But other GUI libraries work fine. For example, uv tool install runsnakerun / runsnake will happily pop open a GUI window, Framework build or not. So it bears some explaining

Wait, what is a “Framework” anyway?

Let’s back up and review an important detail of the mac platform.

On macOS, GUI applications are not just an executable file, they are organized into a bundle, which is a directory with a particular layout, that includes metadata, that launches an executable. A thing that, on Linux, might live in a combination of /bin/foo for its executable and /share/foo/ for its associated data files, is instead on macOS bundled together into Foo.app, and those components live in specified locations within that directory.

A framework is also a bundle, but one that contains a library. Since they are directories, Applications can contain their own Frameworks and Frameworks can contain helper Applications. If /Applications is roughly equivalent to the Unix /bin, then /Library/Frameworks is roughly equivalent to the Unix /lib.

App bundles are contained in a directory with a .app suffix, and frameworks are a directory with a .framework suffix.

So what do you need a Framework for in Python?

The truth about Framework builds is that there is not really one specific thing that you can point to that works or doesn’t work, where you “need” or “don’t need” a Framework build. I was not able to quickly construct an example that trivially fails in a non-framework context for this post, but I didn’t try that many different things, and there are a lot of different things that might fail.

The biggest issue is not actually the Python.framework itself. The metadata on the framework is not used for much outside of a build or linker context. However, Python’s Framework builds also ship with a stub application bundle, which places your Python process into a normal application(-ish) execution context all the time, which allows for various platform APIs like [NSBundle mainBundle] to behave in the normal, predictable ways that all of the numerous, various frameworks included on Apple platforms expect.

Various Apple platform features might want to ask a process questions like “what is your unique bundle identifier?” or “what entitlements are you authorized to access” and even beginning to answer those questions requires information stored in the application’s bundle.

Python does not ship with a wrapper around the core macOS “cocoa” API itself, but we can use pyobjc to interrogate this. After installing pyobjc-framework-cocoa, I can do this

1
2

>>> import AppKit
>>> AppKit.NSBundle.mainBundle()

On a non-Framework build, it might look like this:

1

NSBundle </Users/glyph/example/.venv/bin> (loaded)

But on a Framework build (even in a venv in a similar location), it might look like this:

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NSBundle </Library/Frameworks/Python.framework/Versions/3.12/Resources/Python.app> (loaded)

This is why, at various points in the past, GUI access required a framework build, since connections to the window server would just be rejected for Unix-style executables. But that was an annoying restriction, so it was removed at some point, or at least, the behavior was changed. As far as I can tell, this change was not documented. But other things like user notifications or geolocation might need to identity an application for preferences or permissions purposes, respectively. Even something as basic as “what is your app icon” for what to show in alert dialogs is information contained in the bundle. So if you use a library that wants to make use of any of these features, it might work, or it might behave oddly, or it might silently fail in an undocumented way.

This might seem like undocumented, unnecessary cruft, but it is that way because it’s just basic stuff the platform expects to be there for a lot of different features of the platform.

/etc/ builds

Still, this might seem like a strangely vague description of this feature, so it might be helpful to examine it by a metaphor to something you are more familiar with. If you’re familiar with more Unix style application development, consider a junior developer — let’s call him Jim — asking you if they should use an “/etc build” or not as a basis for their Docker containers.

What is an “/etc build”? Well, base images like ubuntu come with a bunch of files in /etc, and Jim just doesn’t see the point of any of them, so he likes to delete everything in /etc just to make things simpler. It seems to work so far. More experienced Unix engineers that he has asked react negatively and make a face when he tells them this, and seem to think that things will break. But their app seems to work fine, and none of these engineers can demonstrate some simple function breaking, so what’s the problem?

Off the top of your head, can you list all the features that all the files that /etc is needed for? Why not? Jim thinks it’s weird that all this stuff is undocumented, and it must just be unnecessary cruft.

If Jim were to come back to you later with a problem like “it seems like hostname resolution doesn’t work sometimes” or “ls says all my files are owned by 1001 rather than the user name I specified in my Dockerfile” you’d probably say “please, put /etc back, I don’t know exactly what file you need but lots of things just expect it to be there”.

This is what a framework vs. a non-Framework build is like. A Framework build just includes all the pieces of the build that the macOS platform expects to be there. What pieces do what features need? It depends. It changes over time. And the stub that Python’s Framework builds include may not be sufficient for some more esoteric stuff anyway. For example, if you want to use a feature that needs a bundle that has been signed with custom entitlements to access something specific, like the virtualization API, you might need to build your own app bundle. To extend our analogy with Jim, the fact that /etc exists and has the default files in it won’t always be sufficient; sometimes you have to add more files to /etc, with quite specific contents, for some features to work properly. But “don’t get rid of /etc (or your application bundle)” is pretty good advice.

Do you ever want a non-Framework build?

macOS does have a Unix subsystem, and many Unix-y things work, for Unix-y tasks. If you are developing a web application that mostly runs on Linux anyway and never care about using any features that touch the macOS-specific parts of your mac, then you probably don’t have to care all that much about Framework builds. You’re not going to be surprised one day by non-framework builds suddenly being unable to use some basic Unix facility like sockets or files. As long as you are aware of these limitations, it’s fine to install non-Framework builds. I have a dozen or so Pythons on my computer at any given time, and many of them are not Framework builds.

Framework builds do have some small drawbacks. They tend to be larger, they can be a bit more annoying to relocate, they typically want to live in a location like /Library or ~/Library. You can move Python.framework into an application bundle according to certain rules, as any bundling tool for macOS will have to do, but it might not work in random filesystem locations. This may make managing really large number of Python versions more annoying.

Most of all, the main reason to use a non-Framework build is if you are building a tool that manages a fleet of Python installations to perform some automation that needs to know about Python installs, and you want to write one simple tool that does stuff on Linux and on macOS. If you know you don’t need any platform-specific features, don’t want to spend the (not insignificant!) effort to cover those edge cases, and you get a lot of value from that level of consistency (for example, a teaching environment or interdisciplinary development team with a lot of platform diversity) then a non-framework build might be a better option.

Why do I care?

Personally, I think it’s important for Framework builds to be the default for most users, because I think that as much stuff should work out of the box as possible. Any user who sees a neat library that lets them get control of some chunk of data stored on their mac - map data, health data, game center high scores, whatever it is - should be empowered to call into those APIs and deal with that data for themselves.

Apple already makes it hard enough with their thicket of code-signing and notarization requirements for distributing software, aggressive privacy restrictions which prevents API access to some of this data in the first place, all these weird Unix-but-not-Unix filesystem layout idioms, sandboxing that restricts access to various features, and the use of esoteric abstractions like mach ports for communications behind the scenes. We don't need to make it even harder by making the way that you install your Python be a surprise gotcha variable that determines whether or not you can use an API like “show me a user notification when my data analysis is done” or “don’t do a power-hungry data analysis when I’m on battery power”, especially if it kinda-sorta works most of the time, but only fails on certain patch-releases of certain versions of the operating system, becuase an implementation detail of a proprietary framework changed in the meanwhile to require an application bundle where it didn’t before, or vice versa.

More generally, I think that we should care about empowering users with local computation and platform access on all platforms, Linux and Windows included. This just happens to be one particular quirk of how native platform integration works on macOS specifically.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. For this one, thanks especially to long-time patron Hynek who requested it specifically. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! I am also available for consulting work if you think your organization could benefit from expertise on topics like “how can we set up our Mac developers’ laptops with Python”.

Whenever I publish something about my Python Docker workflows, I invariably get challenged about whether it makes sense to use virtual environments in Docker containers. As always, it’s a trade-off, and I err on the side of standards and predictability.

If a high-status member of a community that you participate in is accused of misbehavior, you may want to defend them. You may even write a long essay in their defense.

In that essay, it may seem only natural to begin with a lengthy enumeration of the accused’s positive personal qualities. To extol the quality of their career and their contributions to your community. To talk about how nice they are. To be a character witness in the court of public opinion.

If you do this, you are not defending them. You are proving the point. This is exactly how missing stairs come to exist. People don’t get away with bad behavior if they don’t have high status and a good reputation already.

Sometimes, someone with antisocial inclinations seeks out status, in order to facilitate their bad behavior. Sometimes, a good, but, flawed person does a lot of really good work and thereby accidentally ends up with more status than they were expecting to have, and they don’t know how to handle it. In either case, bad behavior may ensue.

If you truly believe that your fave is being accused or punished unjustly, focus on the facts. What, specifically, has been alleged? How are these allegations substantiated? What verifiable evidence exists to the contrary? If you feel that someone is falsely accusing them to ruin their reputation, is there evidence to support your claim that the accusation is false? Ask yourself the question: what information do you have, that is leading to your correct analysis of the situation, that the people making the accusations do not have, which might be leading them into error?

But, also, maybe just… don’t?

The urge to defend someone like this is much more likely to come from a sense of personal grievance than justice. Consider: does it feel like you are being attacked, when your fave has been attacked? Is there a tightness in your chest, heat rising on your cheeks? Do you feel suddenly defensive?

Do you think that defensiveness is likely to lead to you making good, rational decisions about what steps to take next?

Let your heroes face accountability. If they are really worth your admiration, they might accept responsibility and make amends. Or they might fight the accusations with their own real evidence — evidence that you, someone peripheral to their situation, are unlikely to have — and prove the accusations wrong.

They might not want your defense. Even if they feel like they do want it in the moment — they are human too, after all, and facing accountability does not feel good to us humans — is the intensified feeling that they can’t let down their supporters who believe in them likely to make them feel less defensive and panicked?

In either case, your character defense is unlikely to serve them. At best it helps them stay on an ego trip, at worst it muddies the waters and might confuse the collection of facts that would, if considered dispassionately, properly exonerate them.

Do you think that I am pretending to speak in generalities but really talking about one specific recent event?

Wrong!

Just in this last week, I have read 2 different blog posts about 2 completely different people in completely unrelated communities and both of their authors need to read this. But each of those were already of a type, one that I’ve read dozens of instances of in the past.

It is a very human impulse to perceive a threat to someone we think well of, and to try to defend against that threat. But the consequences of someone’s own actions are not a threat you can defend them from.

Updated 2024-07-04: After some discussion, added an epilogue going into more detail about the value of the distinction between the two types of tokens.

In 2015, Dan McKinley laid out a model for software teams selecting technologies. He proposed that each team have a limited supply of “innovation tokens”, and, when selecting a technology, they can choose boring ones for free but “innovative” ones cost a token. This implies that we all know which technologies are innovative, and we assume that they are inherently costly, so we want to restrict their supply.

That model has become popular to the point that it is now part of the vernacular. In many discussions, it is accepted as received wisdom, or even common sense.

In this post I aim to show you that despite being superficially helpful, this model is wrong, and in fact, may be counterproductive. I believe it is an attractive nuisance in computer programming discourse.

In fairness to Mr. McKinley, the model he described in this post is:

1. nearly a decade old at this point, and
2. much more nuanced in its description of the problem with “innovation” than the subsequent memetic mutation of the concept.

While I will be referencing McKinley’s post, and I do take some issue with it, I am reacting more strongly to the life of its own that this idea has taken on once it escaped its original context. There are a zillion worse posts rehashing this concept, on blogs and LinkedIn, but I won’t be linking to them because the goal is not to call anybody out.

To some extent I am re-raising McKinley’s own caveats and reinforcing them. So I may be arguing with a strawman, but it’s a strawman I have seen deployed with some regularity over the years.

To reduce it to its core, this strawman is “don’t use new or interesting technology, and if you have to, only use a little bit”.

Within the broader culture of programmers, an “innovation token” has become a shorthand to smear any technology perceived — almost always based on vibes, not data — as risky, and the adoption of novel approaches as pretentious and unserious. Speaking of programmer culture though, I do have to acknowledge there is also a pervasive tendency for us to get distracted by novelty and waste time on puzzles rather than problem-solving, so I understand where the reactionary attitude represented by the concept of an innovation token comes from.

But it is reactionary.

At its worst, it borders on anti-intellectualism. I have heard it used on more than one occasion as a thought-terminating cliche to discard a potentially promising new tool. But before I get into that, let me try to give a sympathetic summary of the idea, because the model is not entirely bad.

It has been popular for a long time because it does work okay as an heuristic.

The real problem that McKinley is describing is operational overhead. When programmers make a technology selection, we are often considering how difficult it will make the programming. Innovative technology selections are, by definition, less mature.

That lack of maturity — particularly in the open source world — often means that the project is in a part of its lifecycle where it is concerned with development affordances more than operational ones. Therefore, the stereotypical innovative project, even one which might legitimately be a big improvement to development velocity, will create more operational overhead. That operational overhead creates a hidden cost for the operations team later on.

This is a point I emphatically agree with. When selecting a technology, you should consider its ease of operation more than its ease of development. If your team is successful, they will be operating and maintaining it far longer than they are initially integrating and deploying it.

Furthermore, some operational overhead is inevitable. You will need to hire people to mitigate it. More popular, more mature projects will have a bigger talent pool to hire from, so your training costs will be lower, and those training costs are part of your operational cost too.

Rationing innovation tokens therefore can work as a reasonable heuristic, or proxy metric, for avoiding a mess of complex operational problems associated with dependencies that are expensive to operate and hard to hire for.

There are some minor issues I want to point out before getting to the overarching one.

1. “has a lot of operational overhead” is a stereotype of a new technology, not an inherent property. If you want to reject a technology on the basis of being too high-overhead, at least look into its actual overhead a little bit. Sometimes, especially in 2024 as opposed to 2015, the point of a new, shiny piece of tech is to address operational issues that the more boring, older one had.
2. “hard to learn” is also a stereotype; if “newer” meant “harder” then we would all be using troff rather than Google Docs. Actually ask if the innovativeness is making things harder or easier; don’t assume.
3. You are going to have to train people on your stack no matter what. If a technology is adding a lot of value, it’s absolutely worth hiring for general ability and making a plan to teach people about it. You are going to have to do this with the core technology of your product anyway.

As I said, though, these are minor issues. The big problem with modeling operational overhead as an “innovation token” is that an even bigger concern than selecting an innovative tool is selecting too many tools.

The impulse to select more tools and make your operational environment more complex can be made worse by trying to avoid innovative tools. The important thing is not “less innovation”, but more consistency. To illustrate this, let’s do a simple thought experiment.

Let’s say you’re going to make a web app. There’s a tool in Haskell that you really like for a critical part of your app’s problem domain. You don’t want to spend more than one innovation token though, and everything in Haskell is inherently innovative, so you write a little service that just does that one part and you write the rest of your app in Ruby, calling into that service whenever you need to use that thing. This will appropriately restrict your “innovation token” expenditure.

Does doing this actually reduce your operational overhead, though?

First, you will have to find a team that likes both Ruby and Haskell and sees no problem using both. If you are not familiar with the cultural proclivities of these languages, suffice it to say that this is unlikely. Hiring for Haskell programmers is hard because there are fewer of them than Ruby programmers, but hiring for polyglot Haskell/Ruby programmers who are happy to do either is going to be really hard.

Since you will need to find different people to write in the different languages, even in the best case scenario, you will have two teams: the Haskell team and the Ruby team. Even if you are incredibly disciplined about inter-service responsibilities, there will be some areas where duplication of code is necessary across those services. Disagreements will arise and every one of these disagreements will be a source of social friction and software defects.

Then, you need to set up separate CI pipelines for each language, separate deployment systems, and of course, separate databases. Right away you are effectively doubling your workload.

In the worse, and unfortunately more likely scenario, there will be enormous infighting between these two teams. Operational incidents will be more difficult to manage because rather than learning the Haskell tools for operational visibility and disseminating that institutional knowledge amongst your team, you will be half-learning the lessons from two separate ecosystems and attempting to integrate them. Every on-call engineer will be frantically trying to learn a language ecosystem they don’t use regularly, or you will double the size of your on-call rotation. The Ruby team may start to resent the Haskell team for getting to exclusively work on the fun parts of the problem while they are doing things that look more like rote grunt work.

A better way to think about the problem of managing operational overhead is, rather than “innovation tokens”, consider “boundary tokens”.

That is to say, rather than evaluating the general sense of weird vibes from your architecture, consider the consistency of that architecture. If you’re using Haskell, use Haskell. You should be all-in on Haskell web frameworks, Haskell ORMs, Haskell OAuth integrations, and so on.¹ To cross the boundary out of Haskell, you need to spend a boundary token, and you shouldn’t have many of those.

I submit that the increased operational overhead that you might experience with an all-Haskell tool selection will be dwarfed by the savings that you get by having a team that is aligned with each other, that can communicate easily, and that can share programs with each other without needing to first strategize about a channel for the two pieces of work to establish bidirectional communication. The ability to simply call a function when you need to call it is very powerful, and extremely underrated.

Consistency ought to apply at each layer of the stack; it is perhaps most obvious with programming languages, but it is true of web frameworks, test frameworks, cryptographic libraries, you name it. Make a choice and stick with it, because every deviation from that choice carries a significant cost. Moreover this cost is a hidden cost, in the same way that the operational downsides of an “innovative” tool that hasn’t seen much production use might be hidden.

Discarding a more standard tool in favor of a tool more consistent with your architecture extends even to fairly uncontroversial, ubiquitous tools. For example, one of my favorite architectural patterns is to forego the use of the venerable — and very boring – Cron, the UNIX task-scheduler. Instead of Cron, it can make a lot of sense to have hand-written bespoke code for scheduling tasks within the application. Within the “innovation tokens” model, this is a very silly waste of a token!

Just use Cron! Everybody knows how to use Cron!

Except… does everybody know how to use Cron? Here are some questions to consider, if you’re about to roll out a big dependency on Cron:

1. How do you write a unit test for a scheduling rule with Cron?
2. Can you even remember how to write a cron rule that runs at the times you want?
3. How do you inject secrets and configuration variables into the distinct and somewhat idiosyncratic runtime execution environment of Cron?
4. How do you know that you did that variable-injection properly until the job actually runs, possibly in the middle of the night?
5. How do you deploy your monitoring and error-logging frameworks to observe your scripts run under Cron?

Granted, this architectural choice is less controversial than it once was. Cron used to be ambiently available on whatever servers you happened to be running. As container-based deployments have increased in popularity, this sense that Cron is just kinda around has gone away, and if you need to run a container that just runs Cron, much of the jankiness of its deployment is a lot more immediately visible.

There is friction at the boundary between things. That friction is a cost, but sometimes it’s a cost worth paying.

If there’s a really good library in Haskell and a really good library in Ruby and you really do want to use them both, maybe it makes sense to actually have multiple services. As your team gets larger and more mature, the need to bring in more tools, and the ability to handle the associated overhead, will only increase over time. But the place that the cost comes in the most is at the boundary between tools, not in the operational deficiencies of any one particular tool.

Even in a bog-standard web application with the most boring, least innovative tech stack imaginable (PHP, MySQL, HTML, CSS, JavaScript), many of the annoying points of friction are where different, inconsistent technologies make contact. If you are a programmer working on the web yourself, consider your own impression of the level of controversy of these technologies:

• CSS frameworks that attempt to bridge the gap between CSS and JavaScript, like Bootstrap or Tailwind.
• ORMs that attempt to bridge the gap between SQL and your backend language of choice
• RPC systems that attempt to connect disparate services together using simple abstractions.

Consider that there are far more complex technical tools in terms of required skills to implement them, like computer vision or physics simulation, tools which are also pretty widely used, which consistently generate lower levels of controversy. People do have strong feelings about these things as well, of course, and it’s hard to find things to link to that show “this isn’t controversial”, but, like, search your feelings, you know it to be true.

You can see the benefits of the boundary token approach in programming language design. Many of the most influential and best-loved programming languages had an impact not by bundling together lots of tools, but by making everything into one thing:

• LISP: everything is a list
• Smalltalk: everything is an object
• ML: everything is an algebraic data type
• Forth: everything is a stack

There is a tremendous power in thinking about everything as a single kind of thing, because then you don’t have to juggle lots of different ideas about different kinds of things; you can just think about your problem.

When people complain about programming languages, they’re often complaining about how many different kinds of thing they have to remember in order to use it.

If you keep your boundary-token budget small, and allow your developers to accomplish as much as possible while staying within a solution space delineated by a single, clean cognitive boundary, I promise you can innovate as much as you want and your operational costs will remain manageable.

Epilogue

In subsequent Mastodon discussion of this post on with Matt Campbell and Meejah, I realized that I may not have made it entirely clear why I feel the distinction between “boundary” and “innovation” tokens is important. I do say above that the “innovation token” model can be a useful heuristic, so why bother with a new, but slightly different heuristic? Especially since most experienced engineers - indeed, McKinley himself - would budget “innovation” quite similarly to “boundaries”, and might even consider the use of more “innovative” Haskell tools in my hypothetical scenario to not even be an expenditure of innovation tokens at all.

To answer that, I need to highlight the purpose of having heuristics like this in the first place. These are vague, nebulous guidelines, not hard and fast rules. I cannot give you a token calculator to plug your technical decisions into. The purpose of either token heuristic is to facilitate discussions among a team.

With a team of skilled and experienced engineers, the distinction is meaningless. Senior and staff engineers (at least, the ones who deserve their level) will intuit the goals behind “innovation tokens” and inherently consider things like operational overhead anyway. In practice, a high-performing, well-aligned team discussing innovation tokens and one discussing boundary tokens will look functionally indistinguishable.

The distinction starts to be important when you have management pressures, nervous executives, inexperienced engineers, a fresh team without existing consensus about core technology choices, and so on. That is to say, most teams that exist in the messy, perpetually in medias res world of the software industry.

If you are just getting started on a project and you have a bunch of competent but disagreeable engineers, the words “innovation” and “boundaries” function very differently.

If you ask, “is this an innovation” about a particular technical tool, you are asking your interlocutor to pull in a bunch of their skills and experience to subjectively evaluate the relative industry-wide, or maybe company-wide, or maybe team-wide² newness of the thing being discussed. The discussion of whether it counts as boring or innovative is immediately fraught with a ton of subjective, difficult-to-quantify information about costs of hiring, difficulty of learning, and your impression of the feelings of hundreds or thousands of people outside of your team. And, yes, ultimately you do need to have an estimate of all that stuff, but starting your “is it OK to use this” conversation by simultaneously arguing about all those subjective judgments is setting yourself up for failure.

Instead, if you ask “does this introduce a boundary between two different technologies with different conceptual models”, while that is not a perfectly objective question, it is much easier for your team to answer, with much crisper intermediary factual questions. What are the two technologies? What are the models? How much do they differ? You can just hash out the answers to each one within the team directly, rather than needing to sift through the last few years of Stack Overflow developer surveys to determine relative adoption or popularity of technologies in the world at large.

Restricting your supply of either boundary or innovation tokens is a good idea, but achieving unanimity within your team about what your boundaries are is always going to be easier than deciding what your innovations are.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! I am also available for consulting work if you think your organization could benefit from expertise on topics like “how can we make our architecture more consistent”.

The story of Kubernetes, the leading container orchestration platform, is a tale of mathematical innovation, wartime necessity, and the open-source revolution. It begins, perhaps unexpectedly, with the work of Omar Khayyam, the 11th-century Persian polymath known for his contributions to mathematics, astronomy, and poetry.

Khayyam's work on solving cubic equations laid the foundation for the development of algebraic geometry, which in turn led to the invention of Cartesian coordinates by René Descartes in the 17th century. This "algebrization of geometry" allowed for the mathematical description of physical phenomena, such as planetary motion, and paved the way for Isaac Newton's development of calculus.

However, Newton's calculus, while groundbreaking, lacked a rigorous mathematical foundation. It took the work of 19th-century mathematicians like Augustin-Louis Cauchy and Karl Weierstrass to establish the epsilon-delta definition of limits and place calculus on a solid footing. This development also opened up new questions about infinity, leading to Georg Cantor's work on set theory and the discovery of paradoxes, such as Russell's paradox by Bertrand Russell, that threatened the foundations of mathematics.

The quest to resolve these paradoxes and establish a secure foundation for mathematics led to Kurt Gödel's incompleteness theorems, published in 1931. Gödel's first incompleteness theorem showed that in any consistent axiomatic system that includes arithmetic, there are statements that can neither be proved nor disproved within the system. The second incompleteness theorem demonstrated that such a system cannot prove its own consistency.

Crucially, Gödel's theorems relied on the concept of computability, which he used to construct a formal system representing arithmetic. However, Gödel's definition of computability was not entirely convincing, as it relied on the intuitive notion of a "finite procedure." This left open the possibility that a non-computable axiomatization of number theory, capturing "all that is true about the natural numbers," could exist and potentially sidestep the incompleteness theorems.

It was Alan Turing who took up the challenge of formalizing the concept of computability. In his groundbreaking 1936 paper "On Computable Numbers, with an Application to the Entscheidungsproblem," Turing introduced the Turing machine, a simple yet powerful mathematical model of computation. Turing's work not only provided a more rigorous foundation for Gödel's ideas but also proved that certain problems, such as the halting problem, are undecidable by Turing machines.

Turing's formalization of computability had far-reaching implications beyond the foundations of mathematics. It laid the groundwork for the development of modern computer science and played a crucial role in the birth of the digital age. Turing's work took on new urgency with the outbreak of World War II, as the need to break the German Enigma machine led to the development of early computing machines based on the principles he had established.

After the war, the individuals who had worked on these machines helped to establish the first computing companies, leading to the industrialization of computing and the development of programming languages and operating systems. One notable example is Alan Turing himself, who joined the National Physical Laboratory (NPL) in London, where he worked on the design of the Automatic Computing Engine (ACE), one of the first stored-program computers.

Another key figure was John von Neumann, a mathematician and physicist who made significant contributions to the design of the EDVAC (Electronic Discrete Variable Automatic Computer), an early stored-program computer. Von Neumann's work on the EDVAC and his subsequent report, "First Draft of a Report on the EDVAC," laid the foundation for the von Neumann architecture, which became the standard design for modern computers.

In the United Kingdom, Maurice Wilkes, who had worked on radar systems during the war, led the development of the EDSAC (Electronic Delay Storage Automatic Calculator) at the University of Cambridge. The EDSAC, which became operational in 1949, was the first practical stored-program computer and inspired the development of similar machines in the United States and elsewhere.

In the United States, J. Presper Eckert and John Mauchly, who had worked on the ENIAC (Electronic Numerical Integrator and Computer) during the war, founded the Eckert-Mauchly Computer Corporation in 1946. The company developed the UNIVAC (Universal Automatic Computer), which became the first commercially available general-purpose computer in the United States.

The UNIVAC was followed by the Multiprocessing Automatic Computer (Multivac), developed by IBM in the late 1950s. The Multivac introduced several innovative features, such as multiprogramming and memory protection, which allowed multiple users to share the same machine and provided a degree of isolation between their programs. These features would later inspire both positive and negative lessons for the creators of Unix, the influential operating system developed at Bell Labs in the 1970s.

Due to antitrust pressures, Bell Labs made Unix available to universities, where it became a standard teaching tool. This decision led to the development of Minix, a simplified Unix-like system, and eventually to the creation of Linux by Linus Torvalds.

As Linux grew in popularity, thanks to its open-source nature and ability to run on cheap hardware, it caught the attention of Google, which was looking for an operating system to power its "cloud-native" approach to computing. Google's engineers contributed key features to the Linux kernel, such as cgroups and namespaces, which laid the groundwork for the development of containerization technologies like Docker.

Google, recognizing the potential of containers and the need for a robust orchestration platform, developed Kubernetes as an open-source system based on its experience with Borg and other orchestration tools. By establishing Kubernetes as the standard for container orchestration, Google aimed to reduce the barrier to entry for users looking to switch between cloud providers, challenging the dominance of Amazon Web Services.

Today, Kubernetes has become the de facto standard for managing containerized applications. As in previous improvements, this led to a new open problem: generating and deploying manifests. Tools for generating manifests range from general templating solutions like Bash variable substitution, Sed, and Jinja, through full fledged programming languages, like using Jsonnet and Python, all the way to using dedicated tools like Kustomize and Helm. Meanwhile, deploying the manifests to Kubernetes can be done through continuous integration platforms running "helm upgrade" or "kubectl apply" or using dedicated platforms like ArgoCD or FluxCD. ArgoCD or Flagger also support gradual roll-outs.

From cubic equations to cloud orchestration, the story of Kubernetes is a reminder that the path of progress is rarely straightforward, but rather a winding journey through the realms of mathematics, computer science, and human ingenuity.

The Cycle

The history of AI goes in cycles, each of which looks at least a little bit like this:

1. Scientists do some basic research and develop a promising novel mechanism, N. One important detail is that N has a specific name; it may or may not be carried out under the general umbrella of “AI research” but it is not itself “AI”. N always has a few properties, but the most common and salient one is that it initially tends to require about 3x the specifications of the average computer available to the market at the time; i.e., it requires three times as much RAM, CPU, and secondary storage as is shipped in the average computer.
2. Research and development efforts begin to get funded on the hypothetical potential of N. Because N is so resource intensive, this funding is used to purchase more computing capacity (RAM, CPU, storage) for the researchers, which leads to immediate results, as the technology was previously resource constrained.
3. Initial successes in the refinement of N hint at truly revolutionary possibilities for its deployment. These revolutionary possibilities include a dimension of cognition that has not previously been machine-automated.
4. Leaders in the field of this new development — specifically leaders, like lab administrators, corporate executives, and so on, as opposed to practitioners like engineers and scientists — recognize the sales potential of referring to this newly-“thinking” machine as “Artificial Intelligence”, often speculating about science-fictional levels of societal upheaval (specifically in a period of 5-20 years), now that the “hard problem” of machine cognition has been solved by N.
5. Other technology leaders, in related fields, also recognize the sales potential and begin adopting elements of the novel mechanism to combine with their own areas of interest, also referring to their projects as “AI” in order to access the pool of cash that has become available to that label. In the course of doing so, they incorporate N in increasingly unreasonable ways.
6. The scope of “AI” balloons to include pretty much all of computing technology. Some things that do not even include N start getting labeled this way.
7. There’s a massive economic boom within the field of “AI”, where “the field of AI” means any software development that is plausibly adjacent to N in any pitch deck or grant proposal.
8. Roughly 3 years pass, while those who control the flow of money gradually become skeptical of the overblown claims that recede into the indeterminate future, where N precipitates a robot apocalypse somewhere between 5 and 20 years away. Crucially, because of the aforementioned resource-intensiveness, the gold owners skepticism grows slowly over this period, because their own personal computers or the ones they have access to do not have the requisite resources to actually run the technology in question and it is challenging for them to observe its performance directly. Public critics begin to appear.
9. Competent practitioners — not leaders — who have been successfully using N in research or industry quietly stop calling their tools “AI”, or at least stop emphasizing the “artificial intelligence” aspect of them, and start getting funding under other auspices. Whatever N does that isn’t “thinking” starts getting applied more seriously as its limitations are better understood. Users begin using more specific terms to describe the things they want, rather than calling everything “AI”.
10. Thanks to the relentless march of Moore’s law, the specs of the average computer improve. The CPU, RAM, and disk resources required to actually run the software locally come down in price, and everyone upgrades to a new computer that can actually run the new stuff.
11. The investors and grant funders update their personal computers, and they start personally running the software they’ve been investing in. Products with long development cycles are finally released to customers as well, but they are disappointing. The investors quietly get mad. They’re not going to publicly trash their own investments, but they stop loudly boosting them and they stop writing checks. They pivot to biotech for a while.
12. The field of “AI” becomes increasingly desperate, as it becomes the label applied to uses of N which are not productive, since the productive uses are marketed under their application rather than their mechanism. Funders lose their patience, the polarity of the “AI” money magnet rapidly reverses. Here, the AI winter is finally upon us.
13. The remaining AI researchers who still have funding via mechanisms less vulnerable to hype, who are genuinely thinking about automating aspects of cognition rather than simply N, quietly move on to the next impediment to a truly thinking machine, and in the course of doing so, they discover a new novel mechanism, M. Go to step 1, with M as the new N, and our current N as a thing that is now “not AI”, called by its own, more precise name.

The History

A non-exhaustive list of previous values of N have been:

• Neural networks and symbolic reasoning in the 1950s.
• Theorem provers in the 1960s.
• Expert systems in the 1980s.
• Fuzzy logic and hidden Markov models in the 1990s.
• Deep learning in the 2010s.

Each of these cycles has been larger and lasted longer than the last, and I want to be clear: each cycle has produced genuinely useful technology. It’s just that each follows the progress of a sigmoid curve that everyone mistakes for an exponential one. There is an initial burst of rapid improvement, followed by gradual improvement, followed by a plateau. Initial promises imply or even state outright “if we pour more {compute, RAM, training data, money} into this, we’ll get improvements forever!” The reality is always that these strategies inevitably have a limit, usually one that does not take too long to find.

Where Are We Now?

So where are we in the current hype cycle?

• Here’s a Computerphile video which explains some recent research into LLM performance. I’d highly encourage you to have a look at the paper itself, particularly Figure 2, “Log-linear relationships between concept frequency and CLIP zero-shot performance”.
• Here’s a series of posts by Simon Willison explaining the trajectory of the practicality of actually-useful LLMs on personal devices. He hasn’t written much about it recently because it is now fairly pedestrian for an AI-using software developer to have a bunch of local models, and although we haven’t quite broken through the price floor of the gear-acquisition-syndrome prosumer market in terms of the requirements of doing so, we are getting close.
• The Rabbit R1 and Humane AI Pin were both released; were they disappointments to their customers and investors? I think we all know how that went at this point.
• I hear Karius just raised a series C, and they’re an “emerging unicorn”.
• It does appear that we are all still resolutely calling these things “AI” for now, though, much as I wish, as a semasiology enthusiast, that we would be more precise.

Some Qualifications

History does not repeat itself, but it does rhyme. This hype cycle is unlike any that have come before in various ways. There is more money involved now. It’s much more commercial; I had to phrase things above in very general ways because many previous hype waves have been based on research funding, some really being exclusively a phenomenon at one department in DARPA, and not, like, the entire economy.

I cannot tell you when the current mania will end and this bubble will burst. If I could, you’d be reading this in my $100,000 per month subscribers-only trading strategy newsletter and not a public blog. What I can tell you is that computers cannot think, and that the problems of the current instantation of the nebulously defined field of “AI” will not all be solved within “5 to 20 years”.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. Special thanks also to Ben Chatterton for a brief pre-publication review; any errors remain my own. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! I am also available for consulting work if you think your organization could benefit from expertise on topics like “what are we doing that history will condemn us for”. Or, you know, Python programming.

These tips are not the “right” way to do PyCon, but they are suggestions based on how I try to do PyCon. Consider them reminders to myself, an experienced long-time attendee, which you are welcome to overhear.

See Some Talks

The hallway track is awesome. But the best version of the hallway track is not just bumping into people and chatting; it’s the version where you’ve all recently seen the same thing, and thereby have a shared context of something to react to. If you aren’t going to talks, you aren’t going to get a good hallway track.. Therefore: choose talks that interest you, attend them and pay close attention, then find people to talk to about them.

Given that you will want to see some of the talks, make sure that you have the schedule downloaded and available offline on your mobile device, or printed out on a piece of paper.

Make a list of the talks you think you want to see, but have that schedule with you in case you want to call an audible in the middle of the conference, switching to a different talk you didn’t notice based on some of those “hallway track” conversations.

Participate In Open Spaces

The name “hallway track” itself is antiquated, in a way which is relevant and important to modern conferences. It used to be that conferences were exclusively oriented around their scheduled talks; it was called the “hallway” track because the way to access it was to linger in the hallways, outside the official structure of the conference, and just talk to people.

But however, at PyCon and many other conferences, this unofficial track is now much more of an integrated, official part of the program. In particular, open spaces are not only a more official hallway track, they are considerably better than the historical “hallway” experience, because these ad-hoc gatherings can be convened with a prepared topic and potentially a loose structure to facilitate productive discussion.

With open spaces, sessions can have an agenda and so conversations are easier to start. Rooms are provided, which is more useful than you might think; literally hanging out in a hallway is actually surprisingly disruptive to speakers and attendees at talks; us nerds tend to get pretty loud and can be quite audible even through a slightly-cracked door, so avail yourself of these rooms and don’t be a disruptive jerk outside somebody’s talk.

Consult the open space board, and put up your own proposed sessions. Post them as early as you can, to maximize the chance that they will get noticed. Post them on social media, using the conference's official hashtag, and ask any interested folks you bump into help boost it.¹

Remember that open spaces are not talks. If you want to give a mini-lecture on a topic and you can find interested folks you could do that, but the format lends itself to more peer-to-peer, roundtable-style interactions. Among other things, this means that, unlike proposing a talk, where you should be an expert on the topic that you are proposing, you can suggest open spaces where you are curious — but ignorant — about something, in the hopes that some experts will show up and you can listen to their discussion.

Be prepared for this to fail; there’s a lot going on and it’s always possible that nobody will notice your session. Again, maximize your chances by posting it as early as you can and promoting it, but be prepared to just have a free 30 minutes to check your email. Sometimes that’s just how it goes. The corollary here is not to always balance attending others’ spaces with proposing your own. After all if someone else proposed it you know at least one other person is gonna be there.

Take Care of Your Body

Conferences can be surprisingly high-intensity physical activities. It’s not a marathon, but you will be walking quickly from one end of a large convention center to another, potentially somewhat anxiously.

Hydrate, hydrate, hydrate. Bring a water bottle, and have it with you at all times. It might be helpful to set repeating timers on your phone to drink water, since it can be easy to forget in the middle of engaging conversations. If you take advantage of the hallway track as much as you should, you will talk more than you expect; talking expels water from your body. All that aforementioned walking might make you sweat a bit more than you realize.

Hydrate.

More generally, pay attention to what you are eating and drinking. Conference food isn’t always the best, and in a new city you might be tempted to load up on big meals and junk food. You should enjoy yourself and experience the local cuisine, but do it intentionally. While you enjoy the local fare, do so in whatever moderation works best for you. Similarly for boozy night-time socializing. Nothing stings quite as much as missing a morning of talks because you’ve got a hangover or a migraine.

This is worth emphasizing because in the enthusiasm of an exciting conference experience, it’s easy to lose track and overdo it.

Meet Both New And Old Friends: Plan Your Socializing

A lot of the advice above is mostly for first-time or new-ish conferencegoers, but this one might be more useful for the old heads. As we build up a long-time clique of conference friends, it’s easy to get a bit insular and lose out on one of the bits of magic of such an event: meeting new folks and hearing new perspectives.

While open spaces can address this a little bit, there's one additional thing I've started doing in the last few years: dinners are for old friends, but lunches are for new ones. At least half of the days I'm there, I try to go to a new table with new folks that I haven't seen before, and strike up a conversation. I even have a little canned icebreaker prompt, which I would suggest to others as well, because it’s worked pretty nicely in past years: “what is one fun thing you have done with Python recently?”².

Given that I have a pretty big crowd of old friends at these things, I actually tend to avoid old friends at lunch, since it’s so easy to get into multi-hour conversations, and meeting new folks in a big group can be intimidating. Lunches are the time I carve out to try and meet new folks.

I’ll See You There

I hope some of these tips were helpful, and I am looking forward to seeing some of you at PyCon US 2024!

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor!

Humans are pattern-matching machines. As a species, it is our superpower. To summarize the core of my own epistemic philosophy, here is a brief list of the activities in the core main-loop of a human being:

1. stuff happens to us
2. we look for patterns in the stuff
3. we weave those patterns into narratives
4. we turn the narratives into models of the world
5. we predict what will happen based on those models
6. we do stuff based on those predictions
7. based on the stuff we did, more stuff happens to us; return to step 1

While this ability lets humans do lots of great stuff, like math and physics and agriculture and so on, we can just as easily build bad stories and bad models. We can easily trick ourselves into thinking that our predictive abilities are more powerful than they are.

The existence of magic-seeming levels of prediction in fields like chemistry and physics and statistics, in addition to the practical usefulness of rough estimates and heuristics in daily life, itself easily creates a misleading pattern. “I see all these patterns and make all these predictions and I’m right a lot of the time, so if I just kind of wing it and predict some more stuff, I’ll also be right about that stuff.”

This leaves us very vulnerable to things like mean world syndrome. Mean world syndrome itself is specifically about danger, but I believe it is a manifestation of an even broader phenomenon which I would term “the apophenia of despair”.

Confirmation bias is an inherent part of human cognition, but the internet has turbocharged it. Humans have immediate access to more information than we ever had in the past. In order to cope with that information, we have also built ways to filter that information. Even disregarding things like algorithmic engagement maximization and social media filter bubbles, the simple fact that when you search for things, you are a lot more likely to find the thing that you’re searching for than to find arguments refuting it, can provide a very strong sense that you’re right about whatever you’re researching.

All of this is to say: if you decide that something in the world is getting worse, you can very easily convince yourself that it is getting much, much worse, very rapidly. Especially because there are things which are, unambiguously, getting worse.

However, Pollyanna-ism is just the same phenomenon in reverse and I don’t want to engage in that. The ice sheets really are melting, globally, fascism really is on the rise. I am not here to deny reality or to cherry pick a bunch of statistics to lull people into complacency.

I believe that while dwelling on a negative reality is bad, I also believe that in the face of constant denial, it is sometimes necessary to simply emphasize those realities, however unpleasant they may be. Distinguishing between unhelpful rumination on negativity and repetition of an unfortunate but important truth to correct popular perception is subjective and subtle, but the difference is nevertheless important.

As our ability to acquire information about things getting worse has grown, our ability to affect those things has not. Knowledge is not power; power is power, and most of us don’t have a lot of it, so we need to be strategic in the way that we deploy our limited political capital and personal energy.

Overexposure to negative news can cause symptoms of depression; depressed people have reduced executive function and find it harder to do stuff. One of the most effective interventions against this general feeling of malaise? Hope.. Not “hope” in the sense of wishing. As this article in the American Psychological Association’s “Monitor on Psychology” puts it:

“We often use the word ‘hope’ in place of wishing, like you hope it rains today or you hope someone’s well,” said Chan Hellman, PhD, a professor of psychology and founding director of the Hope Research Center at the University of Oklahoma. “But wishing is passive toward a goal, and hope is about taking action toward it.”

Here, finally, I can get around to my point.

If you have an audience, and you have some negative thoughts about some social trend, talking about it in a way which is vague and non-actionable is potentially quite harmful. If you are doing this, you are engaged in the political project of robbing a large number of people of hope. You are saying that the best should have less conviction, while the worst will surely remain just as full of passionate intensity.

I do not mean to say that it is unacceptable to ever publicly share thoughts of sadness, or even hopelessness. If everyone in public is forced to always put on a plastic smile and pretend that everything is going to be okay if we have grit and determination, then we have an Instagram culture of fake highlight reels where anyone having their own struggles with hopelessness will just feel even worse in their isolation. I certainly posted my way through my fair share of pretty bleak mental health issues during the worst of the pandemic.

But we should recognize that while sadness is a feeling, hopelessness is a problem, a bad reaction to that feeling, one that needs to be addressed if we are going to collectively dig ourselves out of the problem that creates the sadness in the first place. We may not be able to conjure hope all the time, but we should always be trying.

When we try to address these feelings, as I said earlier, Pollyanna-ism doesn’t help. The antidote to hopelessness is not optimism, but curiosity. If you have a strong thought like “people these days just don’t care about other people¹”, yelling “YES THEY DO” at yourself (or worse, your audience) is unlikely to make much of a change, and certainly not likely to be convincing to an audience. Instead, you could ask yourself some questions, and use them for a jumping-off point for some research:

1. Why do I think this — is the problem in my perception, or in the world?
2. If there is a problem in my perception, is this a common misperception? If it’s common, what is leading to it being common? If it’s unique to me, what sort of work do I need to do to correct it?
3. If the problem is real, what are its causes? Is there anything that I, or my audience, could do to address those causes?

The answers to these questions also inform step 6 of the process I outlined above: the doing stuff part of the process.

At some level, all communication is persuasive communication. Everything you say that another person might hear, everything you say that a person might hear, is part of a sprachspiel where you are attempting to achieve something. There is always an implied call to action; even “do nothing, accept the status quo” is itself an action. My call to action right now is to ask you to never make your call to action “you should feel bad, and you should feel bad about feeling bad”. When you communicate in public, your words have power.

Use that power for good.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! Special thanks also to Cassandra Granade, who provided some editorial feedback on this post; any errors, of course, remain my own.

The Cost of Coffee

One of the ideas that James Hoffmann — probably the most influential… influencer in the coffee industry — works hard to popularize is that “coffee needs to be more expensive”.

The coffee industry is famously exploitative. Despite relatively thin margins for independent café owners¹, there are no shortage of horrific stories about labor exploitation and even slavery in the coffee supply chain.

To summarize a point that Mr. Hoffman has made over a quite long series of videos and interviews², some of this can be fixed by regulatory efforts. Enforcement of supply chain policies both by manufacturers and governments can help spot and avoid this type of exploitation. Some of it can be fixed by discernment on the part of consumers. You can try to buy fair-trade coffee, avoid brands that you know have problematic supply-chain histories.

Ultimately, though, even if there is perfect, universal, zero-cost enforcement of supply chain integrity… consumers still have to be willing to, you know, pay more for the coffee. It costs more to pay wages than to have slaves.

The Price of Software

The problem with the coffee supply chain deserves your attention in its own right. I don’t mean to claim that the problems of open source maintainers are as severe as those of literal child slaves. But the principle is the same.

Every tech company uses huge amounts of open source software, which they get for free.

I do not want to argue that this is straightforwardly exploitation. There is a complex bargain here for the open source maintainers: if you create open source software, you can get a job more easily. If you create open source infrastructure, you can make choices about the architecture of your projects which are more long-term sustainable from a technology perspective, but would be harder to justify on a shorter-term commercial development schedule. You can collaborate with a wider group across the industry. You can build your personal brand.

But, in light of the recent xz Utils / SSH backdoor scandal, it is clear that while the bargain may not be entirely one-sided, it is not symmetrical, and significant bad consequences may result, both for the maintainers themselves and for society.

To fix this problem, open source software needs to get more expensive.

A big part of the appeal of open source is its implicit permission structure, which derives both from its zero up-front cost and its zero marginal cost.

The zero up-front cost means that you can just get it to try it out. In many companies, individual software developers do not have the authority to write a purchase order, or even a corporate credit card for small expenses.

If you are a software engineer and you need a new development tool or a new library that you want to purchase for work, it can be a maze of bureaucratic confusion in order to get that approved. It might be possible, but you are likely to get strange looks, and someone, probably your manager, is quite likely to say “isn’t there a free option for this?” At worst, it might just be impossible.

This makes sense. Dealing with purchase orders and reimbursement requests is annoying, and it only feels worth the overhead if you’re dealing with a large enough block of functionality that it is worth it for an entire team, or better yet an org, to adopt. This means that most of the purchasing is done by management types or “architects”, who are empowered to make decisions for larger groups.

When individual engineers need to solve a problem, they look at open source libraries and tools specifically because it’s quick and easy to incorporate them in a pull request, where a proprietary solution might be tedious and expensive.

That’s assuming that a proprietary solution to your problem even exists. In the infrastructure sector of the software economy, free options from your operating system provider (Apple, Microsoft, maybe Amazon if you’re in the cloud) and open source developers, small commercial options have been marginalized or outright destroyed by zero-cost options, for this reason.

If the zero up-front cost is a paperwork-reduction benefit, then the zero marginal cost is almost a requirement. One of the perennial complaints of open source maintainers is that companies take our stuff, build it into a product, and then make a zillion dollars and give us nothing. It seems fair that they’d give us some kind of royalty, right? Some tiny fraction of that windfall? But once you realize that individual developers don’t have the authority to put $50 on a corporate card to buy a tool, they super don’t have the authority to make a technical decision that encumbers the intellectual property of their entire core product to give some fraction of the company’s revenue away to a third party. Structurally, there’s no way that this will ever happen.

Despite these impediments, keeping those dependencies maintained does cost money.

Some Solutions Already Exist

There are various official channels developing to help support the maintenance of critical infrastructure. If you work at a big company, you should probably have a corporate Tidelift subscription. Maybe ask your employer about that.

But, as they will readily admit there are a LOT of projects that even Tidelift cannot cover, with no official commercial support, and no practical way to offer it in the short term. Individual maintainers, like yours truly, trying to figure out how to maintain their projects, either by making a living from them or incorporating them into our jobs somehow. People with a Ko-Fi or a Patreon, or maybe just an Amazon wish-list to let you say “thanks” for occasional maintenance work.

Most importantly, there’s no path for them to transition to actually making a living from their maintenance work. For most maintainers, Tidelift pays a sub-hobbyist amount of money, and even setting it up (and GitHub Sponsors, etc) is a huge hassle. So even making the transition from “no income” to “a little bit of side-hustle income” may be prohibitively bureaucratic.

Let’s take myself as an example. If you’re a developer who is nominally profiting from my infrastructure work in your own career, there is a very strong chance that you are also a contributor to the open source commons, and perhaps you’ve even contributed more to that commons than I have, contributed more to my own career success than I have to yours. I can ask you to pay me³, but really you shouldn’t be paying me, your employer should.

What To Do Now: Make It Easy To Just Pay Money

So if we just need to give open source maintainers more money, and it’s really the employers who ought to be giving it, then what can we do?

Let’s not make it complicated. Employers should just give maintainers money. Let’s call it the “JGMM” benefit.

Specifically, every employer of software engineers should immediately institute the following benefits program: each software engineer should have a monthly discretionary budget of $50 to distribute to whatever open source dependency developers they want, in whatever way they see fit. Venmo, Patreon, PayPal, Kickstarter, GitHub Sponsors, whatever, it doesn’t matter. Put it on a corp card, put the project name on the line item, and forget about it. It’s only for open source maintenance, but it’s a small enough amount that you don’t need intense levels of approval-gating process. You can do it on the honor system.

This preserves zero up-front cost. To start using a dependency, you still just use it⁴. It also preserves zero marginal cost: your developers choose which dependencies to support based on perceived need and popularity. It’s a fixed overhead which doesn’t scale with revenue or with profit, just headcount.

Because the whole point here is to match the easy, implicit, no-process, no-controls way in which dependencies can be added in most companies. It should be easier to pay these small tips than it is to use the software in the first place.

This sub-1% overhead to your staffing costs will massively de-risk the open source projects you use. By leaving the discretion up to your engineers, you will end up supporting those projects which are really struggling and which your executives won’t even hear about until they end up on the news. Some of it will go to projects that you don’t use, things that your engineers find fascinating and want to use one day but don’t yet depend upon, but that’s fine too. Consider it an extremely cheap, efficient R&D expense.

A lot of the options for developers to support open source infrastructure are already tax-deductible, so if they contribute to something like one of the PSF’s fiscal sponsorees, it’s probably even more tax-advantaged than a regular business expense.

I also strongly suspect that if you’re one of the first employers to do this, you can get a round of really positive PR out of the tech press, and attract engineers, so, the race is on. I don’t really count as the “tech press” but nevertheless drop me a line to let me know if your company ends up doing this so I can shout you out.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! I am also available for consulting work if you think your organization could benefit from expertise on topics such as “How do I figure out which open source projects to give money to?”.

This year I will be going to two conferences: PyCon 2024, and North Bay Python 2024.

At PyCon, I will be promoting my open source work and my writing on this blog. As I’m not giving a talk this year, I am looking forward to organizing and participating in some open spaces about topics like software architecture, open source sustainability, framework interoperability, and event-driven programming.

At North Bay Python, though, I will either be:

1. doing a lot more of that, or
2. looking for new full-time employment, pausing the patreon, and winding down this experiment.

If you’d like to see me doing the former, now would be a great time to sign up to my Patreon to support the continuation of my independent open source work and writing.

The Bad News

It has been nearly a year since I first mentioned that I have a Patreon on this blog. That year has been a busy one, with consulting work and personal stuff consuming more than enough time that I have never been full time on independent coding & blogging. As such, I’ve focused more on small infrastructure projects and less on user-facing apps than I’d like, but I have spent the plurality of my time on it.

For that to continue, let alone increase, this work needs to—at the very least—pay for health insurance. At my current consulting rate, a conservative estimate based on some time tracking is that I am currently doing this work at something like a 98.5% discount. I do love doing it! I would be happy to continue doing it at a significant discount! But “significant” and “catastrophic” are different thresholds.

As I have said previously, this is an appeal to support my independent work; not to support me. I will be fine; what you will be voting for with your wallet is not my well-being but a choice about where I spend my time.

Hiding The Hat

When people ask me what I do these days, I sometimes struggle to explain. It is confusing. I might say I have a Patreon, I do a combination of independent work and consulting, or if I’m feeling particularly sarcastic I might say I’m an ✨influencer✨. But recently I saw the very inspiring Matt Ricardo describing the way he thinks about his own Patreon, and I realized what I am actually trying to do, which is software development busking.

Previously, when I’ve been thinking about making this “okay, it’s been a year of Patreon, let’s get serious now” post, I’ve been thinking about adding more reward products to my Patreon, trying to give people better value for their money before asking for anything more, trying to finish more projects to make a better sales pitch, maybe making merch available for sale, and so on. So aside from irregular weekly posts on Friday and acknowledgments sections at the bottom of blog posts, I’ve avoided mentioning this while I think about adding more private rewards.

But busking is a public performance, and if you want to support my work then it is that public aspect that you presumably want to support. And thus, an important part of the busking process is to actually pass the hat at the end. The people who don’t chip in still get to see the performance, but everyone else need to know that they can contribute if they liked it.¹

I’m going to try to stop hiding the metaphorical hat. I still don’t want to overdo it, but I will trust that you’ll tell me if these reminders get annoying. For my part today, in addition to this post, I’m opening up a new $10 tier on Patreon for people who want to provide a higher level of support, and officially acknowledging the rewards that I already provide.

What’s The Deal?

So, what would you be supporting?

What You Give (The Public Part)

1. I have tended to focus on my software, and there has been a lot of it! You’d be supporting me writing libraries and applications and build infrastructure to help others do the same with Python, as well as maintaining existing libraries (like the Twisted ecosystem libraries) sometimes. If I can get enough support together to more than bare support for myself, I’d really like to be able to do things like pay people to others to help with aspects of applications that I would struggle to complete myself, like accessibility or security audits.
2. I also do quite a bit of writing though, about software and about other things. To make it easier to see what sort of writing I’m talking about, I’ve collected just the stuff that I’ve written during the period where I have had some patrons, under the supported tag.
3. Per my earlier sarcastic comment about being an “influencer”, I also do quite a bit of posting on Mastodon about software and the tech industry.

What You Get (Just For Patrons)

As I said above, I will be keeping member benefits somewhat minimal.

1. I will add you to SponCom so that your name will be embedded in commit messages like this one on a cadence appropriate to your support level.
2. You will get access to my private Patreon posts where I discuss what I’ve been working on. As one of my existing patrons put it:

   I figure I’m getting pretty good return on it, getting not only targeted project tracking, but also a peek inside your head when it comes to Sores Business Development. Maybe some of that stuff will rub off on me :)

3. This is a somewhat vague and noncommittal benefit, but it might be the best one: if you are interested in the various different projects that I am doing, you can help me prioritize! I have a lot of things going on. What would you prefer that I focus on? You can make suggestions in the comments of Patreon posts, which I pay a lot more attention to than other feedback channels.
4. In the near future² I am also planning to start doing some “office hours” type live-streaming, where I will take audience questions and discuss software design topics, or maybe do some live development to showcase my process and the tools I use. When I figure out the mechanics of this, I plan to add some rewards to the existing tiers to select topics or problems for me to work on there.

If that sounds like a good deal to you, please sign up now. If you’re already supporting me, sharing this and giving a brief testimonial of something good I’ve done would be really helpful. Github is not an algorithmic platform like YouTube, despite my occasional jokey “remember to like and subscribe”, nobody is getting recommended DBXS, or Fritter, or Twisted, or Automat, or this blog unless someone goes out and shares it.

New Release

Yesterday I published a new release of DBXS for you all. It’s still ZeroVer, but it has graduated from double-ZeroVer as this is the first nonzero minor version.

More to the point though, the meaning of that version increment this version introduces some critical features that I think most people would need to give it a spin on a hobby project.

What’s New

• It has support for MySQL and PostgreSQL using native asyncio drivers, which means you don’t need to take a Twisted dependency in production.

• While Twisted is still used for some of the testing internals, Deferred is no longer exposed anywhere in the public API, either; your tests can happily pretend that they’re doing asyncio, as long as they can run against SQLite.

• There is a new repository convenience function that automatically wires together multiple accessors and transaction discipline. Have a look at the docstring for a sense of how to use it.

• Several papercuts, like confusing error messages when messing up query result handling, and lack of proper handling of default arguments in access protocols, are now addressed.

It’s A Good Time To Contribute!

If you’ve been looking for an open source project to try your hand at contributing to, DBXS might be a great opportunity, for a few reasons:

1. The team is quite small (just me, right now!), so it’s easy to get involved.

2. It’s quite generally useful, so there’s a potential for an audience, but right now it doesn’t really have any production users; there’s still time to change things without a lot of ceremony.

3. Unlike many other small starter projects, it’s got a test suite with 100% coverage, so you can contribute with confidence that you’re not breaking anything.

4. There’s not that much code (a bit over 2 thousand SLOC), so it’s not hard to get your head around.

5. There are a few obvious next steps for improvement, which I’ve filed as issues if you want to pick one up.

Share and enjoy, and please let me know if you do something fun with it.

Acknowledgments

Thank you to my patrons who are supporting my writing on this blog. If you like what you’ve read here and you’d like to read more of it, or you’d like to support my various open-source endeavors, you can support my work as a sponsor! I am also available for consulting work if you think your organization could benefit from expertise on topics such as “How do I shot SQL?”.

Contrary to popular belief, it’s possible to ship portable executables of Python applications without sending your users to Python packaging hell.

I do consulting¹ on software architecture, network protocol development, python software infrastructure, streamlined cloud deployment, and open source strategy, among other nerdy things. I enjoy solving challenging, complex technical problems or contributing to the open source commons. On the best jobs, I get to do both.

Today I would like to share with you a secret of the software technology consulting trade.

I should note that this secret is not specific to me. I have several colleagues who have also done software consulting and have reflected versions of this experience back to me.²

We’ll get to the secret itself in a moment, but first, some background.

Companies do not go looking for consulting when things are going great. This is particularly true when looking for high-level consulting on things like system architecture or strategy. Almost by definition, there’s a problem that I have been brought in to solve. Ideally, that problem is a technical challenge.

In the software industry, your team probably already has some software professionals with a variety of technical skills, and thus they know what to do with technical challenges. Which means that, as often as not, the problem is to do with people rather than technology, even it appears otherwise.

When you hire a staff-level professional like myself to address your software team’s general problems, that consultant will need to gather some information. If I am that consultant and I start to suspect that the purported technology problem that you’ve got is in fact a people problem, here is the secret technique that I am going to use:

I am going to go get a pen and a pad of paper, then schedule a 90-minute meeting with the most senior IC³ engineer that you have on your team. I will bring that pen and paper to the meeting. I will then ask one question:

What is fucked up about this place?

I will then write down their response in as much detail as I can manage. If I have begun to suspect that this meeting is necessary, 90 minutes is typically not enough time, and I will struggle to keep up. Even so, I will usually manage to capture the highlights.

One week later, I will schedule a meeting with executive leadership, and during that meeting, I will read back a very lightly edited⁴ version of the transcript of the previous meeting. This is then routinely praised as a keen strategic insight.

I should pause here to explicitly note that — obviously, I hope — this is not an oblique reference to any current or even recent client; if I’d had this meeting recently it would be pretty awkward to answer that “so, I read your blog…” email.⁵ But talking about clients in this way, no matter how obfuscated and vague the description, is always a bit professionally risky. So why risk it?

The thing is, I’m not a people manager. While I can do this kind of work, and I do not begrudge doing it if it is the thing that needs doing, I find it stressful and unfulfilling. I am a technology guy, not a people person. This is generally true of people who elect to go into technology consulting; we know where the management track is, and we didn’t pick it.

If you are going to hire me for my highly specialized technical expertise, I want you to get the maximum value out of it. I know my value; my rates are not low, and I do not want clients to come away with the sense that I only had a couple of “obvious” meetings.

So the intended audience for this piece is potential clients, leaders of teams (or organizations, or companies) who have a general technology problem and are wondering if they need a consultant with my skill-set to help them fix it. Before you decide that your issue is the need to implement a complex distributed system consensus algorithm, check if that is really what’s at issue. Talk to your ICs, and — taking care to make sure they understand that you want honest feedback and that they are safe to offer it — ask them what problems your organization has.

During this meeting it is important to only listen. Especially if you’re at a small company and you are regularly involved in the day-to-day operations, you might feel immediately defensive. Sit with that feeling, and process it later. Don’t unload your emotional state on an employee you have power over.⁶

“Only listening” doesn’t exclusively mean “don’t push back”. You also shouldn’t be committing to fixing anything. While the information you are gathering in these meetings is extremely valuable, and you should probably act on more of it than you will initially want to, your ICs won’t have the full picture. They really may not understand why certain priorities are set the way they are. You’ll need to take that as feedback for improving internal comms rather than “fixing” the perceived problem, and you certainly don’t want to make empty promises.

If you have these conversations directly, you can get something from it that no consultant can offer you: credibility. If you can actively listen, the conversation alone can improve morale. People like having their concerns heard. If, better still, you manage to make meaningful changes to address the concerns you’ve heard about, you can inspire true respect.

As a consultant, I’m going to be seen as some random guy wasting their time with a meeting. Even if you make the changes I recommend, it won’t resonate the same way as someone remembering that they personally told you what was wrong, and you took it seriously and fixed it.

Once you know what the problems are with your organization, and you’ve got solid technical understanding that you really do need that event-driven distributed systems consensus algorithm implemented using Twisted, I’m absolutely your guy. Feel free to get in touch.

Today is the 40th anniversary of the announcement of the Macintosh. Others have articulated compelling emotional narratives that easily eclipse my own similar childhood memories of the Macintosh family of computers. So instead, I will ask a question:

What is the Macintosh?

As this is the anniversary of the beginning, that is where I will begin. The original Macintosh, the classic MacOS, the original “System Software” are a shining example of “fake it till you make it”. The original mac operating system was fake.

Don’t get me wrong, it was an impressive technical achievement to fake something like this, but what Steve Jobs did was to see a demo of a Smalltalk-76 system, an object-oriented programming environment with 1-to-1 correspondences between graphical objects on screen and runtime-introspectable data structures, a self-hosting high level programming language, memory safety, message passing, garbage collection, and many other advanced facilities that would not be popularized for decades, and make a fake version of it which ran on hardware that consumers could actually afford, by throwing out most of what made the programming environment interesting and replacing it with a much more memory-efficient illusion implemented in 68000 assembler and Pascal.

The machine’s RAM didn’t have room for a kernel. Whatever application was running was in control of the whole system. No protected memory, no preemptive multitasking. It was a house of cards that was destined to collapse. And collapse it did, both in the short term and the long. In the short term, the system was buggy and unstable, and application crashes resulted in system halts and reboots.

In the longer term, the company based on the Macintosh effectively went out of business and was reverse-acquired by NeXT, but they kept the better-known branding of the older company. The old operating system was gradually disposed of, quickly replaced at its core with a significantly more mature generation of operating system technology based on BSD UNIX and Mach. With the removal of Carbon compatibility 4 years ago, the last vestigial traces of it were removed. But even as early as 2004 the Mac was no longer really the Macintosh.

What NeXT had built was much closer to the Smalltalk system that Jobs was originally attempting to emulate. Its programming language, “Objective C” explicitly called back to Smalltalk’s message-passing, right down to the syntax. Objects on the screen now did correspond to “objects” you could send messages to. The development environment understood this too; that was a major selling point.

The NeXSTEP operating system and Objective C runtime did not have garbage collection, but it provided a similar developer experience by providing reference-counting throughout its object model. The original vision was finally achieved, for real, and that’s what we have on our desks and in our backpacks today (and in our pockets, in the form of the iPhone, which is in some sense a tiny next-generation NeXT computer itself).

The one detail I will relate from my own childhood is this: my first computer was not a Mac. My first computer, as a child, was an Amiga. When I was 5, I had a computer with 4096 colors, real multitasking, 3D graphics, and a paint program that could draw hard real-time animations with palette tricks. Then the writing was on the wall for Commodore and I got a computer which had 256 colors, a bunch of old software that was still black and white, an operating system that would freeze if you held down the mouse button on the menu bar and couldn’t even play animations smoothly. Many will relay their first encounter with the Mac as a kind of magic, but mine was a feeling of loss and disappointment. Unlike almost everyone at the time, I knew what a computer really could be, and despite many pleasant and formative experiences with the Macintosh in the meanwhile, it would be a decade before I saw a real one again.

But this is not to deride the faking. The faking was necessary. Xerox was not going to put an Alto running Smalltalk on anyone’s desk. People have always grumbled that Apple products are expensive, but in 2024 dollars, one of these Xerox computers cost roughly $55,000.

The Amiga was, in its own way, a similar sort of fake. It managed its own miracles by putting performance-critical functions into dedicated hardware which rapidly became obsolete as software technology evolved much more rapidly.

Jobs is celebrated as a genius of product design, and he certainly wasn’t bad at it, but I had the rare privilege of seeing the homework he was cribbing from in that subject, and in my estimation he was a B student at best. Where he got an A was bringing a vision to life by creating an organization, both inside and outside of his companies.

If you want a culture-defining technological artifact, everybody in the culture has to be able to get their hands on one. This doesn’t just mean that the builder has to be able to build it. The buyer also has to be able to afford it, obviously. Developers have to be able to develop for it. The buyer has to actually want it; the much-derided “marketing” is a necessary part of the process of making a product what it is. Everyone needs to be able to move together in the direction of the same technological future.

This is why it was so fitting that Tim Cook was made Jobs's successor. The supply chain was the hard part.

The crowning, final achievement of Jobs’s career was the fact that not only did he fake it — the fakes were flying fast and thick at that time in history, even if they mostly weren’t as good — it was that he faked it and then he built the real version and then he bridged the transitions to get to the real thing.

I began here by saying that the Mac isn’t really the Mac, and speaking in terms of a point in time analysis that is true. Its technology today has practically nothing in common with its technology in 1984. This is not merely an artifact of the length of time here: the technology at the core of various UNIXes in 1984 bears a lot of resemblance of UNIX-like operating systems today¹. But looking across its whole history from 1984 to 2024, there is undeniably a continuity to the conceptual “Macintosh”.

Not just as a user, but as a developer moving through time rather than looking at just a few points: the “Macintosh”, such as it is, has transitioned from the Motorola 68000 to the PowerPC to Intel 32-bit to Intel 64-bit to ARM. From obscurely proprietary to enthusiastically embracing open source and then, sadly, much of the way back again. It moved from black and white to color, from desktop to laptop, from Carbon to Cocoa, from Display PostScript to Display PDF, all the while preserving instantly recognizable iconic features like the apple menu and the cursor pointer, while providing developers documentation and SDKs and training sessions that helped them transition their apps through multiple near-complete rewrites as a result of all of these changes.

To paraphrase Abigail Thorne’s first video about Identity, identity is what survives. The Macintosh is an interesting case study in the survival of the idea of a platform, as distinct from the platform itself. It is the Computer of Theseus, a thought experiment successfully brought to life and sustained over time.

If there is a personal lesson to be learned here, I’d say it’s that one’s own efforts need not be perfect. In fact, a significantly flawed vision that you can achieve right now is often much, much better than a perfect version that might take just a little bit longer, if you don’t have the resources to actually sustain going that much longer². You have to be bad at things before you can be good at them. Real artists, as Jobs famously put it, ship.

So my contribution to the 40th anniversary reflections is to say: the Macintosh is dead. Long live the Mac.

Acknowledgments

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