Planet Twisted

(The following talks about zope.interface interfaces, but applies equally well to Java interfaces, Go interfaces, and probably other similar constructs.)

When we write a function, we can sometimes change it in backwards-compatible ways. For example, we can loosen the type of a variable. We can restrict the type of the return value. We can add an optional argument.

We can even have a backwards compatible path to make an argument required. We add an optional argument, and encourage people to change it. Then, in the next version, we make the default value be one that causes a warning. In a version after that, we make the value required. At each point, someone could write a library that worked with at least two consecutive versions.

In a similar way, we can have a path to remove an argument. First make it optional. Then warn when it is passed in. Finally, remove it and make it an error to pass it in.

As long as we do not intend to support inheritance, making backwards compatible changes to classes also works. For example, to remove a method we first have a version that warns when you call it, and then remove it in a succeeding version.

However, what changes can we make to an interface?

Assume we have an interface like:

from zope.interface import Interface, implements

class IFancyFormat(Interface):

    def fancify_int(value: int) -> str:
        pass

It is a perfectly reasonable, if thin, interface. Implementing it seems like fun:

@implements(IFancyFormat)
@attr.s(auto_attribs=True)
class FancySuffixer:
    suffix: str

    def fancify_int(self, value: int) -> str:
        return str(value) + self.suffix

Using it also seems like fun:

def dashify_fancy_five(fancifier: IFancyFormat) -> str:
    return f"---{fancifier.fancify_int(5)}---"

These are very different kinds of fun, though! Probably the kind of fun that appeals to different people. The first implementation is in the superfancy open-source library. The second one is in the dash_five open-source library. Such is the beauty of open source: it takes all kinds of people.

We cannot add a method to IFancyFormat: the superfancy library has a unit test that uses verifyImplements, which will fail if we add a method. We cannot remove the method fancify_int, since this will break dash_five: the mypy check will fail, since IFancifySuffixer will not have that method.

Similarly, we cannot make the parameter optional without breaking superfancy, or loosen the return type without breaking dash_five. Once we have published IFancyFormat as an API, it cannot change.

The only way to recover from a bad interface is to create a new interface, IAwesomeFancyFormat. Then write conversion functions from and to IFancyFormat and IAwesomeFancyFormat. Then deprecate using the IFancyFormat interface. Finally, we can remove the interface. Then we can alias IFancyFormat == IAwesomeFancyFormat, and eventually, maybe even deprecate the name IAwesomeFancyFormat.

When publishing interfaces, one must be careful: to a first approximation, they are forever.

(Thanks to Glyph Lefkowitz for his helpful suggestions. Any mistakes or issues that are left are my responsibility.)

Prompted by Amber Brown’s presentation at the Python Language Summit last month, Christian Heimes has followed up on his own earlier work on slimming down the Python standard library, and created a proper Python Enhancement Proposal PEP 594 for removing obviously obsolete and unmaintained detritus from the standard library.

PEP 594 is great news for Python, and in particular for the maintainers of its standard library, who can now address a reduced surface area. A brief trip through the PEP’s rogues gallery of modules to deprecate or remove¹ is illuminating. The python standard library contains plenty of useful modules, but it also hides a veritable necropolis of code, a towering monument to obsolescence, threatening to topple over on its maintainers at any point.

However, I believe the PEP may be approaching the problem from the wrong direction. Currently, the standard library is maintained in tandem with, and by the maintainers of, the CPython python runtime. Large portions of it are simply included in the hope that it might be useful to somebody. In the aforementioned PEP, you can see this logic at work in defense of the colorsys module: why not remove it? “The module is useful to convert CSS colors between coordinate systems. [It] does not impose maintenance overhead on core development.”

There was a time when Internet access was scarce, and maybe it was helpful to pre-load Python with lots of stuff so it could be pre-packaged with the Python binaries on the CD-ROM when you first started learning.

Today, however, the modules you need to convert colors between coordinate systems are only a pip install away. The bigger core interpreter is just more to download before you can get started.

Why Didn’t You Review My PR?

So let’s examine that claim: does a tiny module like colorsys “impose maintenance overhead on core development”?

The core maintainers have enough going on just trying to maintain the huge and ancient C codebase that is CPython itself. As Mariatta put it in her North Bay Python keynote, the most common question that core developers get is “Why haven’t you looked at my PR?” And the answer? It’s easier to not look at PRs when you don’t care about them. This from a talk about what it means to be a core developer!

One might ask, whether Twisted has the same problem. Twisted is a big collection of loosely-connected modules too; a sort of standard library for networking. Are clients and servers for SSH, IMAP, HTTP, TLS, et. al. all a bit much to try to cram into one package?

I’m compelled to reply: yes. Twisted is monolithic because it dates back to a similar historical period as CPython, where installing stuff was really complicated. So I am both sympathetic and empathetic towards CPython’s plight.

At some point, each sub-project within Twisted should ideally become a separate project with its own repository, CI, website, and of course its own more focused maintainers. We’ve been slowly splitting out projects already, where we can find a natural boundary. Some things that started in Twisted like constantly and incremental have been split out; deferred and filepath are in the process of getting that treatment as well. Other projects absorbed into the org continue to live separately, like klein and treq. As we figure out how to reduce the overhead of setting up and maintaining the CI and release infrastructure for each of them, we’ll do more of this.

But is our monolithic nature the most pressing problem, or even a serious problem, for the project? Let’s quantify it.

As of this writing, Twisted has 5 outstanding un-reviewed pull requests in our review queue. The median time a ticket spends in review is roughly four and a half days.² The oldest ticket in our queue dates from April 22, which means it’s been less than 2 months since our oldest un-reviewed PR was submitted.

It’s always a struggle to find enough maintainers and enough time to respond to pull requests. Subjectively, it does sometimes feel like “Why won’t you review my pull request?” is a question we do still get all too often. We aren’t always doing this well, but all in all, we’re managing; the queue hovers between 0 at its lowest and 25 or so during a bad month.

By comparison to those numbers, how is core CPython doing?

Looking at CPython’s keyword-based review queue queue, we can see that there are 429 tickets currently awaiting review. The oldest PR awaiting review hasn’t been touched since February 2, 2018, which is almost 500 days old.

How many are interpreter issues and how many are stdlib issues? Clearly review latency is a problem, but would removing the stdlib even help?

For a quick and highly unscientific estimate, I scanned the first (oldest) page of PRs in the query above. By my subjective assessment, on this page of 25 PRs, 14 were about the standard library, 10 were about the core language or interpreter code; one was a minor documentation issue that didn’t really apply to either. If I can hazard a very rough estimate based on this proportion, somewhere around half of the unreviewed PRs might be in standard library code.

So the first reason the CPython core team needs to stop maintaining the standard library because they literally don’t have the capacity to maintain the standard library. Or to put it differently: they aren’t maintaining it, and what remains is to admit that and start splitting it out.

It’s true that none of the open PRs on CPython are in colorsys³. It does not, in fact, impose maintenance overhead on core development. Core development imposes maintenance overhead on it. If I wanted to update the colorsys module to be more modern - perhaps to have a Color object rather than a collection of free functions, perhaps to support integer color models - I’d likely have to wait 500 days, or more, for a review.

As a result, code in the standard library is harder to change, which means its users are less motivated to contribute to it. CPython’s unusually infrequent releases also slow down the development of library code and decrease the usefulness of feedback from users. It’s no accident that almost all of the modules in the standard library have actively maintained alternatives outside of it: it’s not a failure on the part of the stdlib’s maintainers. The whole process is set up to produce stagnation in all but the most frequently used parts of the stdlib, and that’s exactly what it does.

New Environments, New Requirements

Perhaps even more importantly is that bundling together CPython with the definition of the standard library privileges CPython itself, and the use-cases that it supports, above every other implementation of the language.

Podcast after podcast after podcast after keynote tells us that in order to keep succeeding and expanding, Python needs to grow into new areas: particularly web frontends, but also mobile clients, embedded systems, and console games.

These environments require one or both of:

• a completely different runtime, such as Brython, or MicroPython
• a modified, stripped down version of the standard library, which elides most of it.

In all of these cases, determining which modules have been removed from the standard library is a sticking point. They have to be discovered by a process of trial and error; notably, a process completely different from the standard process for determining dependencies within a Python application. There’s no install_requires declaration you can put in your setup.py that indicates that your library uses a stdlib module that your target Python runtime might leave out due to space constraints.

You can even have this problem even if all you ever use is the standard python on your Linux installation. Even server- and desktop-class Linux distributions have the same need for a more minimal core Python package, and so they already chop up the standard library somewhat arbitrarily. This can break the expectations of many python codebases, and result in bugs where even pip install won’t work.

Take It All Out

How about the suggestion that we should do only a little a day? Although it sounds convincing, don’t be fooled. The reason you never seem to finish is precisely because you tidy a little at a time. [...] The ultimate secret of success is this: If you tidy up in one shot, rather than little by little, you can dramatically change your mind-set.

While incremental slimming of the standard library is a step in the right direction, incremental change can only get us so far. As Marie Kondō says, when you really want to tidy up, the first step is to take everything out so that you can really see everything, and put back only what you need.

It’s time to thank those modules which do not spark joy and send them on their way.

We need a “kernel” version of Python that contains only the most absolutely minimal library, so that all implementations can agree on a core baseline that gives you a “python”, and applications, even those that want to run on web browsers or microcontrollers, can simply state their additional requirements in terms of requirements.txt.

Now, there are some business environments where adding things to your requirements.txt is a fraught, bureaucratic process, and in those places, a large standard library might seem appealing. But “standard library” is a purely arbitrary boundary that the procurement processes in such places have drawn, and an equally arbitrary line may be easily drawn around a binary distribution.

So it may indeed be useful for some CPython binary distributions — perhaps even the official ones — to still ship with a broader selection of modules from PyPI. Even for the average user, in order to use it for development, at the very least, you’d need enough stdlib stuff that pip can bootstrap itself, to install the other modules you need!

It’s already the case, today, that pip is distributed with Python, but isn’t maintained in the CPython repository. What the default Python binary installer ships with is already a separate question from what is developed in the CPython repo, or what ships in the individual source tarball for the interpreter.

In order to use Linux, you need bootable media with a huge array of additional programs. That doesn’t mean the Linux kernel itself is in one giant repository, where the hundreds of applications you need for a functioning Linux server are all maintained by one team. The Linux kernel project is immensely valuable, but functioning operating systems which use it are built from the combination of the Linux kernel and a wide variety of separately maintained libraries and programs.

Conclusion

The “batteries included” philosophy was a great fit for the time when it was created: a booster rocket to sneak Python into the imagination of the programming public. As the open source and Python packaging ecosystems have matured, however, this strategy has not aged well, and like any booster, we must let it fall back to earth, lest it drag us back down with it.

New Python runtimes, new deployment targets, and new developer audiences all present tremendous opportunities for the Python community to soar ever higher.

But to do it, we need a newer, leaner, unburdened “kernel” Python. We need to dump the whole standard library out on the floor, adding back only the smallest bits that we need, so that we can tell what is truly necessary and what’s just nice to have.

I hope I’ve convinced at least a few of you that we need a kernel Python.

Now: who wants to write the PEP?

Acknowledgments

Thanks to Jean-Paul Calderone, Donald Stufft, Alex Gaynor, Amber Brown, Ian Cordasco, Jonathan Lange, Augie Fackler, Hynek Schlawack, Pete Fein, Mark Williams, Tom Most, Jeremy Thurgood, and Aaron Gallagher for feedback and corrections on earlier drafts of this post. Any errors of course remain my own.

Since the acquisition of Travis CI, the future of their free offering is unclear. Azure Pipelines has a generous free tier, but the examples I found are discouragingly complex and take advantage of features like templating that most projects don’t need. To close that gap, this article shows you how to move a Python project with simple CI needs from Travis CI to Azure Pipelines.

The Stack Overflow Survey Results for 2019 are in! There is some official analysis, that mentioned some things that mattered to me, and some that did not. I decided to dig into the data and see if I can find some things that would potentially interest my readership.

import csv, collections, itertools

with open("survey_results_public.csv") as fpin:
    reader = csv.DictReader(fpin)
    responses = list(reader)

len(responses)

88883

Wow, almost 90K respondents! This is the sweet spots of "enough to make meaningful generalizations" while being able to analyze with rudimentary tools, not big-data-ware.

pythonistas = [x for x in responses if 'Python' in x['LanguageWorkedWith']]

len(pythonistas)/len(responses)

0.41001091322300104

About 40% of the respondents use Python in some capacity. That is pretty cool! This is one of the things where I wonder if there is bias in the source data. Are people who use Stack Overflow, or respond to surveys for SO, more likely to be the kind of person who uses Python? Or less?

In any case, I am excited! This means my favorite language, for all its issues, is doing well. This is also a good reminder that we need to think about the consequences of our decisions on a big swath of developers we will never ever meet.

opensource = collections.Counter(x['OpenSourcer'] for x in pythonistas)

sorted(opensource.items(), key=lambda x:x[1], reverse=True)

[('Never', 11310),
 ('Less than once per year', 10374),
 ('Less than once a month but more than once per year', 9572),
 ('Once a month or more often', 5187)]

opensource['Once a month or more often']/len(pythonistas)

0.1423318607139917

Python is open source. Almost all important libraries (Django, Pandas, PyTorch, requests) are open source. Many important tools (Jupyter) are open source. The number of people who contribute to them with any kind of regular cadence is less than 15%.

general_opensource = collections.Counter(x['OpenSourcer'] for x in responses)
sorted(general_opensource.items(), key=lambda x:x[1], reverse=True)

[('Never', 32295),
 ('Less than once per year', 24972),
 ('Less than once a month but more than once per year', 20561),
 ('Once a month or more often', 11055)]

The Python community does compare well to the general populace, though!

devtype = collections.Counter(itertools.chain.from_iterable(x["DevType"].split(";") for x in pythonistas))

devtype['DevOps specialist']/len(responses)

0.052282213696657406

About 5% of total respondents are my peers: using Python for DevOps. That is pretty exciting! My interest in that is not merely theoretical, my upcoming book targets that crowd.

general_devtype = collections.Counter(itertools.chain.from_iterable(x["DevType"].split(";") for x in responses))
general_devtype['DevOps specialist']/len(responses), devtype['DevOps specialist']/len(pythonistas)

(0.09970410539698255, 0.12751420025793705)

In general, DevOps specialists are 10% of respondents.

devtype['DevOps specialist']/general_devtype['DevOps specialist']

0.524373730534868

Over 50% of DevOps specialists use Python!

def safe_int(x):
    try:
        return int(x)
    except ValueError:
        return -1

intermediate = sum(1 for x in pythonistas if 1<=safe_int(x['YearsCode'])<=5)

My next hush-hush (for now!) project is going to be targeting intermediate Python developers. I wish I could slice by "number of years writing in Python, but this is the best I could do. (I treat "NA" responses as "not intermediate". This is OK, since I prefer to underestimate rather than overestimate.)

intermediate/len(responses)

0.11346376697456206

11%! Not bad.

general_intermediate = sum(1 for x in responses if 1<=safe_int(x['YearsCode'])<=5)
intermediate/len(pythonistas), general_intermediate/len(responses)

(0.27673352907279863, 0.2671264471271222)

Seems like using Python does not change much the chances of someone being intermediate.

I am the parent of two young kids. It is easy to sink into random stuff, and not follow up on goals. Strict time management and prioritization means I get to work on open source projects, write programming books and update my blog with a decent cadence. Since a lot of people were asking me how to do it, I wanted to share my methodology. The following is descriptive, not prescriptive.

One thing I am proud of is that the initial draft for the post was written a year ago. I have done my edits for clarity, but found that my description of the process, for the most part, has remained the same. This made me confident that it is time to publish: this process has existed in its current form for at least a year, and I believe almost two years. This is not some fad diet for me: this process has proved its worth.

Glyph has already written at length about how a full Inbox is a sign of misprioritized tasks. Saying "no" is one example (in other words, prioritizing away). But when saying "yes", it is a good idea to know when it can be done, when should you give up, and potentially apologize, and when should you give a heads-up that it is being delayed.

His description, being more high-level, is prescriptive. The follow-up is the process I use, shaped by those general ideas.

The tool I use is TODOist. The first time I tried it, I decided it lacked some necessary features. I still feel this way -- about the free version. The free version is completely unusable. The premium version is perfectly usable.

The salient features of TODOist, that the rest of the explanation depends on, are:

• Android integration. I use Android on my phone, and depend on good phone support. TODOist has a widget which lets me add a task without waiting for an app-launch. It integrates with Google Assistant -- it is possible to configure all "Note to self" to be new task creations. Finally, it integrates with the "Share" menu, so sharing things can create tasks.
• E-mail integration: a customized e-mail address which opens a task for each e-mail
• Browser plugin: add a task without opening the site, as well as "Add website as task" for current page.
• A task can have arbitrary attachments.

There are many good reasons to not go to every talk possible when attending conferences. However increasingly it became hip to boast with not going to talks at all – encouraging others to follow suit. As a speaker, that rubs me the wrong way and I’ll try to explain why.

Looking for a job as a software developer can be scary, exhausting, or overwhelming. Where you apply and how you interview impacts whether you’ll get a job offer, and how good it will be, so in some sense the whole job search is a form of negotiation.

So how do you learn to make a good impression, to convince people of your worth, to get picked by the job you want? There are many skills to learn, and in this article I’d like to cover one particular subset.

Let us travel to England, some 200 years in the past, and see what we can learn.

Jane Austen, Game Theorist

What does a novelist writing in the early 19th century have to do with getting a programming job?

In his book Jane Austen, Game Theorist, Michael Suk-Young Chwe argues quite convincingly that Austen’s goal in writing her books is to teach strategic thinking: understanding what and why people do what they do, and how to interact with them accordingly, in order to achieve the outcomes you want.

Strategic thinking is a core skill in negotiation: you’re trying to understand what the other side wants (even if they don’t explicitly say it), and to find a way to use that to get what you want. The hiring manager might want someone who both understands their particular technical domain and can help a team grow, whereas you might want a higher salary, or a shorter workweek. Strategic thinking can help you use the one to achieve the other.

Strategic thinking is of course a useful skill for anyone, but why would Jane Austen in particular care about strategic thinking? To answer that we need a little historical context.

The worst job search ever

Imagine you could only get one job your whole life, that leaving your job was impossible, and that you’d be married to your boss. This is the “job search” that Austen faced in her own life, and is one the main topics covered in her books.

Austen’s own family, and the people she writes about, were part of a very small and elite minority. Even the poorest of the families Austen writes about have at least one servant, for example.

While the men of the English upper classes, if they were not sufficiently wealthy, could and did work—as lawyers, doctors, officers—their wives and daughters for the most part could not. So if they weren’t married and didn’t have sufficient wealth of their own, upper-class women had very few choices—they could live off money from relations, or take on the social status loss of becoming a governess.

Marriage was therefore the presumed path to social status, economic security, and of course it determined who they would live with for the rest of their lives (divorce was basically impossible).

Finding the right husband was very important. And getting that husband—who had all the legal and social authority—to respect their wishes after marriage was just as important. And of course the women who didn’t marry lived at the mercy of the family members who supported them.

And that’s where strategic thinking comes in: it was a critical skill for women in Austen’s class and circumstances.

Learning from Austen

If, as Michael Chwe argues, Austin’s goal with her books is to teach strategic thinking, how can you use them to improve your negotiation skills?

All of Austen’s books are worth reading—excepting the unfortunate Mansfield Park—but for educational purposes Northanger Abbey is a good starting point. Northanger Abbey is the story of Catherine, a naive young woman, and how she becomes less naive and more strategic.

Instead of just reading it as an entertaining novel, you can use it to actively practice your own strategic understanding:

1. In every social interaction, Catherine has a theory about other people’s motivations, why they’re doing or saying certain things.
2. Notice the assumptions underlying her theory, and then come up with your alternative theory or explanation for other characters’ actions.
3. Then, compare both theories as the plot unfolds and you learn more.

Other characters also offer a variety of opportunities to see strategic thinking—or lack of it—in action. Once you’ve gone through the book and experienced the growth of Catherine’s strategic thinking, start practicing those skills in your life.

Why are your coworkers, family, and friends doing what they’re doing? Do they have the same motivations, goals, and expectations that you do? The more you pay attention and compare your assumptions to reality, the more you’ll learn—and the better you’ll do at your next job interview.

Ready to get started? You can get a paper copy from the library, or download a free ebook from Project Gutenberg.

Struggling with a 40-hour workweek? Too tired by the end of the day to do anything but collapse on the sofa and watch TV?

Learn how you can get a 3-day weekend, every single week.

If you’re tired of working long hours, a part-time—or even just 4 days a week—programming jobs seems appealing. You’ll still get paid, you’ll still hopefully enjoy your job—but you’ll also have more time for other things in your life.

Hypothetically you could negotiate for more free time, but obviously no company would ever agree to a shorter workweek, right?

And indeed there are plenty of people—on Hacker News especially—who will explain to you in great detail why this can’t be done, that no manager would ever agree to this, that it’s a logical impossibility, a mirage, a delusion, not even worth considering.

But—

The fact is there are quite a few software developers who work less than full-time. And to help convince you, I figured I would share just a few of the examples I know of.

I’ve done it

Personally I’ve worked at three different software jobs at between 28 and 35 hours a week. And before that, when I left my last full-time job, my manager offered to help me find a part-time job there so that I would stay.

People who have read my book have done it

Since I appreciated having a shorter workweek so much, I ended up writing a book about negotiating a 3-day weekend, and a number of people who read my book have successfully done so.

I could share quotes from people who did it, and the sales page above includes just some of them, but you might feel that lacks a little credibility. So let’s move on—

People I’ve interviewed have done it

I also interviewed a number of people for the book, including a guy by the name of Mike who has been working 4 days a week for 15 years now. You can read the full interview with Mike if you want to get his perspective.

But he’s just one person, so let’s move on to the final category: random people on the Internet.

Random people on the Internet have done it

Here’s just a sample:

pushcx on lobste.rs: “I’ve worked part-time for about six years of my career.”

Seitsebb on lobste.rs: “I work four days a week and can recommend it.”

stsp on lobste.rs: “I was fortunate enough to be able to negotiate [Fridays off] while employed and it had a very positive impact on both my work and quality of life in general.”

acflint on dev.to: “I negotiated a 4 day weekend so I could spend time on my side project … and enjoy life more.”

autarch on Hacker News: “As part of my negotiations for my current job, I negotiated a 4-day (32 hour) work week. I take Fridays off and do my own projects and volunteer work.”

Boycy on Hacker News: “I asked my then employer if I could drop to 4 days a week, pro-rata, and was surprised when the answer was yes!”

notacoward on Hacker News: “When I reduced my hours, I was amused to notice that everyone from the VP who approved it down to the person in HR who handled the paperwork said they wished they could do the same. I told them all that they could.”

lubonay on Hacker News: “I worked on a 4-day week for about a year between 2017 and 2018 for a small consultancy company.”

duckworthd on Hacker News: “I’ve been working a 4 day/week schedule for 1.5 years now.”

I could go on, but no doubt this is getting repetitive.

You can do it too

Want to join us and get more time for yourself?

For most programmers, the easiest place to negotiate a 3-day weekend is at your current job.

Struggling with a 40-hour workweek? Too tired by the end of the day to do anything but collapse on the sofa and watch TV?

Learn how you can get a 3-day weekend, every single week.

Working as a programmer will keep you busy; parenting a baby is a massive amount of work. Doing both at once isn’t easy!

While it does get better with time, there are ways you can make it calmer, simpler, and easier in the short term. Based on my experience as a new parent and programmer, in the rest of this article I’ll cover:

1. Mitigating sleep deprivation.
2. Dealing with limited work hours.
3. Other random tips.

Sleep deprivation is terrible

Sleep deprivation is awful. It makes you less focused, more irritable and cranky, and in many ways it’s similar to being drunk. When done deliberately sleep deprivation is literally a form of torture.

If you’re lucky your child will start sleeping through the night after a few months, but (from personal experience) not everyone is so lucky. So here are some ways to deal with lack of sleep.

Be kind

The irritability that results from sleep deprivation is going to impact all of your relationships—with your spouse/partner, your friends, and your coworkers. Keep in mind that you are going to get annoyed more easily, and try to compensate.

Remind yourself that the reason you’re so annoyed by the code you’re reviewing is probably nothing to do with your coworker’s skill, and everything to do with being woken up at 1AM, 3AM, and finally at 5AM.

Compensate for cognitive impairment

Besides being irritable, you are also cognitively impaired—you’re less smart than you usually are. You can compensate for this in a variety of ways:

• Spend more planning up front than you usually would; you’re more likely to forget about important details otherwise.
• Avoid writing complex code, since you’ll have an even harder time than usual keeping it in your head. Figuring out a simpler solution may take longer, but it’s worth it.
• Keep a “lab notebook”: write down what you’re planning on doing next, what you’ve already done, and status notes. This will help mitigate the memory problems from lack of sleep. It will also help you deal better with interruptions, and to get going at the start of the work day when you’ve already been “awake” for 7 hours and you can’t remember what or why you’re at the office.

Your time is limited

Even if you used to work longer hours (and you really shouldn’t have), you really shouldn’t be working long hours as a new parent. That means:

• Learning should be done not at home but on the job, which in any case is the best place to learn new skills.
• You need to learn how to say no to your boss, and how to set boundaries in general.
• Learn to prioritize. Only the truly most important things should be done first. Everything else will be done next—and if you don’t reach it, that’s OK, it was less important. “But this is almost as important!” Nope, not happening. “It would be really nice…” No.

Other advice

Pumping milk at the office: Pumping milk multiple times a day at the office can be time consuming. If your baby is healthy, I’m told you can pump once in the morning, stick the equipment in the fridge without cleaning it, and then pump a second time later in day. This saves you one cleaning cycle at the office.

(I am not a medical professional, ask your pediatrician first before doing this.)

Working at home: Some babies, I’m told, will just lie there happily babbling to themselves while you work. If you have the other kind of baby, the kind that screams continuously if they’re not held, you might be able to get a little work done at home by putting them in a baby carrier and using a standing desk.

A shorter workweek: Even if you’re not working long hours, a full-time 40-hours-a-week job may still be too much as a new parent. You can often negotiate a shorter workweek at your existing job fairly easily.

What really matters to you?

However efficient you are, having a child is going to take up a whole lot of time. And that means you’re going to have to make some choices about priorities: what things really matter you? Where do you really want to spend your time?

It’s a personal choice—I am glad I got to work part-time and take care of my kid the rest of the time, but I would hate to take care of a baby full-time. Your preferences may well be different.

But whatever you decide, just remember you need to choose: you can’t do everything.

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The world is full of problems—from poverty to climate change—and it seems like software ought to be able to help. And yet your own programming job seems pointless, doing nothing to make things better. Far too many jobs are just about making some rich people just a little bit richer.

So how can you do something meaningful with your life?

There are no easy answers, but here’s a starting point, at least.

Don’t make things worse

Even beyond your moral obligations, working on something you actively find wrong is bad for you:

• Either you end up hating yourself for doing it.
• Or, in self-defense you become cynical and embittered, assuming the worst of everyone. This is not pleasant, nor is it an accurate view of the surprisingly varied threads of humanity.

If you find yourself in this situation, you have the opportunity to try to make things a little better, by pushing your organization to change. But you can also just go look for another job elsewhere.

Some jobs are actually good

Of course, most software jobs aren’t evil, but neither are they particularly meaningful. You can help an online store come up with a better recommendation engine, or optimize their marketing funnel, or build a web UI for support staff—but does it really matter that people buy at store A instead of store B?

So it’s worth thinking in detail about what exactly it is you would find meaningful, and seeing if there’s work that matches your criteria. There may not be a huge number of jobs that qualify, but chances are some exist.

If you care about climate change, for example, there are companies building alternative energy systems, working on public transportation planning, and more broadly just making computing more efficient.

Your job needn’t be the center of your life

You may not be able to find such a job, or get such a job. So there’s something to be said for not making your work the center of your life’s existence.

As a programmer you are likely get paid well, and you can even negotiate a shorter workweek. Given enough free time and no worries about making a living, you have the ability to find meaning outside your work.

• Make the world a better place, just a little: I’ve been volunteering with a local advocacy group, and the ability to see the direct impact of my work is extremely gratifying.
• Beauty and nature: Programming as a job can end up leaving you unbalanced as a person—it’s worth seeing the world in other ways as well.
• Religion: While it makes no sense to me (apparently even as a very young child), apparently many people find their religion deeply satisfying.
• Creation for creation’s sake: Many of us become programmers because we want to create things, but having a job means turning to instrumental creation, work that isn’t for its own sake. Try creating something not for its utility, but because you want to.
• Find people who understand you: Being part of a social group that fundamentally doesn’t match who you are and how you view the world is exhausting and demoralizing. I ended up moving to a whole new country because of this. But if you live in a large city, quite possibly the people who will understand you can be found just down the block.

No easy answers

Unless you want to join a group that will tell you exactly what to think and precisely what to do—and there are certainly no lack of those—meaning is something you need to figure out for yourself.

It’s unlikely that you’ll solve it in one fell swoop, nor is it likely to be a fast process. The best you can do is just get started: a meaningful life isn’t a destination, it’s a journey.

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• twisted.web.client.HostnameCachingHTTPSPolicy was added as a new contextFactory option. This reduces the performance overhead for making many TLS connections to the same host.
• twisted.conch.ssh.keys can now read private keys in the new "openssh-key-v1" format, introduced in OpenSSH 6.5 and made the default in OpenSSH 7.8.
• The sample code in the "Twisted Web In 60 Seconds" tutorial runs on Python 3.
• DeferredLock and DeferredSemaphore can be used as asynchronous context managers on Python 3.5+.
• twisted.internet.ssl.CertificateOptions now uses 32 random bytes instead of an MD5 hash for the ssl session identifier context.
• twisted.python.failure.Failure.getTracebackObject now returns traceback objects whose frames can be passed into traceback.print_stack for better debugging of where the exception came from.
• Much more! 20+ tickets closed overall.

A while ago, I decided I wanted to self-publish a book on improving your Python skills. It was supposed to be short, sweet, and fairly inexpensive.

The journey was a success, but had some interesting twists along the way.

From the beginning, I knew what technology I wanted to write the book with: Sphinx. This was because I knew that I can use Sphinx to create something reasonable: I have previously ported my "Calculus 101" book to Sphinx, and I have written other small things in it. Sphinx uses ReStructuredText, which I am most familiar with.

I decided I wanted to publish as PDF (for self-printers or others who find it convenient), as browser-ready HTML directory, and as an ePub.

The tox environments I created are: epub builds the ePub, html builds the browser-ready HTML, and pdf builds the PDF.

Initially, the epub environment created a "singlehtml", and I used Calibre command-line utility to transform it into an ePub. This made for a prettier ePub than the one sphinx creates: it had a much nicer cover, which is what most book reading applications use as an icon. However, that rendered poorly on Books.app (AKA iBooks).

One of the projects I still plan to tackle is how to improve the look of the rendered ePub, and add a custom cover image.

Finally, a script runs all the relevant tox environments, and then packs everything into a zip file. This is the zip file I upload to Gumroad, so that people can buy it.

I have tried to use other sellers, but Gumroad was the one with the easiest store creation. In order to test my store, even before the book was ready, I created a simple "Python cheat-sheet" poster, and put it on my store.

I then asked friends to buy it, as well as trying to do it myself. After it all worked, I refunded all the test-run purchases, of course!

Refunding on Gumroad is a pleasant process, which means that if people buy the book, and are unhappy with it, I am happy to refund their money.

(Thanks to Glyph Lefkowitz for his feedback on an earlier draft. All mistakes that remain are my responsibility.)

There’s always another bug, another feature, another deadline. So it’s easy to fall into the trap of taking on too much, saying “yes” one time too many, staying at the office a little later, answering a work email on the weekend…

If you’re not careful you can end up setting unreasonable expectations, and ending up tethered to your work email and Slack. Your manager will expect you to work weekends, and your teammates will expect you to reply to bug reports in the middle of your vacation.

What you want is the opposite: when you’re at home or on vacation, you should be spending your time however you want, merry and free.

You need to set boundaries, which is what I’ll be discussing in the rest of this article.

Prepping for a new job

Imagine you’re starting a new job in a week, you enjoy programming for fun, and you want to be productive as soon as possible. Personally, I wouldn’t do any advance preparation for a new job: ongoing learning is part of a programmer’s work, and employers ought to budget time for it. But you might choose differently.

If so, it’s tempting to ask for some learning material so you can spend a few days beforehand getting up to speed. But you’re failing to set boundaries if you do that, and they might give you company-specific material, in which case you’re just doing work for free.

Learning general technologies is less of a problem—knowing more technologies is useful in your career in general, and maybe you enjoy programming for fun. So instead of asking for learning material, you can go on your own and learn the technologies you know they use, without telling them you’re doing so.

Work email and Slack

Never set up work email or Slack on your phone or personal computer:

1. It will tempt you to engage with work in your free time.
2. When you do engage, you’ll be setting expectations that you’re available to answer questions 24/7.

While you’re at work you’ll always have your computer, so you don’t need access on your phone. If you do need to set up work email on your phone for travel, remove the account when you’re back home.

And if you want to have your work calendar on your phone, you can share it with your personal calendar account; that way you’re sharing only your calendar, nothing else.

Vacations

When you’re on vacation, you’re on vacation: no work allowed. That means you’re not taking your work laptop with you, or turning it on if you’re at home.

A week or so in advance of your vacation, explain to your team that you won’t be online, and that you won’t have access to work files. Figure out what information they might need—documentation, in-progress work you want to hand off, and the like—and write it all down where they can find it.

If you must, give your personal phone number for emergencies: given you lack access to your work credentials and email, the chances of your being called for something unimportant are quite low.

You’re paid for your normal work hours (and that’s it)

A standard workweek in the US is 40 hours a week; elsewhere it can be a little less. Whatever it is, outside those hours you shouldn’t be working, because you’re not being paid for that work. Your evenings, your weekends, your holidays, your vacations—all of these belong to you, not your employer.

If you don’t enforce that boundary between work and non-work, you are sending the message that your time doesn’t belong to you. And if you have a bad manager, they’re going to take advantage of that—or you might end up working long hours out of a misplaced sense of obligation.

So unless you’re dealing with an emergency, you should forget your job exists when your workday ends—and unless you’re on your on-call rotation, you should make sure you’re inaccessible by normal work channels.

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"It is a truth universally acknowledged, that a shared state in possession of mutability, must be in want of a bug." -- with apologies to Jane Austen

As Ms. Austen, and Henrik Eichenhardt, taught us, shared mutable state is the root of all evil.

Yet, the official documentation of functools tells us to write code like:

@lru_cache(maxsize=32)
def get_pep(num):
    'Retrieve text of a Python Enhancement Proposal'
    resource = 'http://www.python.org/dev/peps/pep-%04d/' % num
    try:
        with urllib.request.urlopen(resource) as s:
            return s.read()
    except urllib.error.HTTPError:
        return 'Not Found'

(This code is copied from the official documentation, verbatim.)

The decorator, @lru_cache(maxsize=32), is now... module-global mutable state. It doesn't get any more shared, in Python, than module-global: every import of the module will share the object!

We try and pretend like there is no "semantic" difference: the cache is "merely" an optimization. However, very quickly things start falling apart: after all, why would the documentation even tell us how to get back the original function (answer: .__wrapped__) if the cache is so benign?

No, decorating the function with lru_cache is anything but benign! For one, because it is shared-thread mutable state, we have introduced some thread locking, with all the resulting complexity, and occasional surprising performance issues.

Another example of non-benign-ness is that, in the get_pep example, sometimes a transient error, such as a 504, will linger on, making all subsequent requests "fail", until a cache eviction (because an unrelated code path went through several PEPs) causes a retry. These are exactly the kind of bugs which lead to warnings against shared mutable state!

If we want to cache, let us own it explicitly in the using code, and not have a global implementation dictate it. Fortunately, there is a way to properly use the LRU cache.

First, remove the decorator from the implementation:

def get_pep(num):
    'Retrieve text of a Python Enhancement Proposal'
    # Same code as an in official example

Then, in the using code, build a cache:

def analyze_peps():
    cached_get_pep = lru_cache(maxsize=32)(get_pep)
    all_peps, pep_by_type = analyze_index(cached_get_pep(0))
    words1 = get_words_in_peps(cached_get_pep, all_peps)
    words2 = get_words_in_informational(cached_get_pep,
                                        pep_by_type["Informational"])
    do_something(words1, words2)

Notice that in this example, the lifetime of the cache is relatively clear: we create it in the beginning of the function, passed it to called functions, and then it goes out of scope and is deleted. (Barring one of those functions sneakily keeping a reference, which would be a bad implementation, and visible when reviewing it.)

This means we do not have to worry about cached failures if the function is retried. If we retry analyze_peps, we know that it will retry retrieving any PEPs, even if those failed before.

If we wanted the cache to persist between invocations of the function, the right solution would be to move it one level up:

def analyze_peps(cached_get_peps):
    # ...

Then it is the caller's responsibility to maintain the cache: once again, we avoid shared mutable state by making the state management be explicit.

In this example, based on the official lru_cache documentation, we used a network-based function to show some of the issues with a global cache. Often, lru_cache is used for performance reasons. However, even there, it is easy to create issues: for example, one function using non-common inputs to the LRU-cached functions can cause massive cache evictions, with surprising performance impacts!

The lru_cache implementation is great: but using it as a decorator means making the cache global, with all the bad effects. Using it locally is a good use of a great implementation.

(Thanks to Adi Stav, Steve Holden, and James Abel for their feedback on early drafts. Any issues that remain are my responsibility.)

As a programmer you face an ever-growing stream of new technologies: new frameworks, new libraries, new tools, new languages, new paradigms. Keeping up is daunting.

• How can you find the time to learn how to use every relevant tool?
• How do you keep your skills up-to-date without using up all your free time?
• How can you even learn all the huge number of existing technologies?

The answer, of course, is that you can’t learn them all—in depth. What you can do is learn about the tools’ existence, and learn just enough about them to know when it might be worth learning more.

Quite often, spending 5 minutes learning about a new technology will give you 80% of the benefit you’d get from spending 5 days on it.

In the rest of this article I’ll cover:

1. The cost of unnecessary in-depth learning.
2. Why breadth of knowledge is so useful.
3. Some easy ways to gain breadth of knowledge.

The cost of in-depth learning

Having a broad range of tools and techniques to reach for is a valuable skill both at your job and when looking for a new job. But there are different levels of knowledge you can have: you can be an expert, or you can have some basic understanding of what the tool does and why you might use it.

The problem with becoming an expert is that it’s time consuming, and you don’t want to put that level of effort into every new tool you encounter.

1. Some new technologies will die just as quickly as they were born; there’s no point wasting time on a dead end.
2. Most technologies just aren’t relevant to your current situation. GitHub keeps recommending I look at a library for analyzing pulsar data, and not being an astrophysicist I’m going to assume I can safely ignore it.
3. Software changes over time: even if you end up using a new library in a year or two, by that point the API may have changed. Time spent learning the current API would be wasted.

If you try to spend a few days—or even hours—on every intriguing new technology you hear about, you’re going to waste a lot of time.

The alternative: shallow breadth of knowledge

Most of the time you don’t actually need to use new tools and techniques. As long as you know a tool exists you’ll be able to learn more about it when you need to.

For example, there is a tool named Logstash that takes your logs and sends them to a central location. That’s pretty much all you have to remember about it, and it took you just a few seconds to read that previous sentence.

Maybe you’ll never use that information… or maybe one day you’ll need to get logs from a cluster of machines to a centralized location. At that point you’ll remember the name “Logstash”, look it up, and have the motivation to actually go read the documentation and play around with it.

This is also true when it comes to finding a new job. I was once asked in a job interview about the difference between NoSQL and traditional databases. At the time I’d never used MongoDB or any other NoSQL database, but I knew enough to answer satisfactorily. Being able to answer that question told the interviewer I’d be able to use that tool, if necessary, even if I hadn’t done it before.

Gaining breadth of knowledge

Learning about the existence of tools can be a fairly fast process. And since this knowledge will benefit your employer and you don’t need to spend significant time on it, you can acquire it during working hours.

You’re never actually working every single minute of your day, you always have some time when you’re slacking off on the Internet. Perhaps you’re doing so right now! You can use that time to expand your knowledge.

Here are a couple ways you can get pointers to new tools and techniques:

Newsletters

A great way to learn new tools and techniques are weekly email newsletters. There are newsletters on many languages and topics, from DevOps to PostgreSQL: here’s one fairly detailed list of potential newsletters you can sign up for.

Conferences and Meetups (you don’t have to go!)

Another good source of new tools and techniques are conferences and Meetups. Good conferences and Meetups will aim for a broad range of talks, on topics both new and classic.

But you don’t have to go to the conference or Meetup to benefit, or even watch a recording of the talks to learn something. Just skimming talk topics will give you a sense of what the community is talking and thinking about—and if something sounds particularly relevant to your interests you can spend the extra time to learn more.

Of course, if you can convince your employer to send you to a conference that’s even better: you’ll learn more, and you’ll do it on the company’s dime and time.

Your time is valuable—use it accordingly

There are only so many hours in the day, so many days in a year. That means you need to work efficiently, spending your limited time in ways that have the most impact:

1. Spend an hour a week learning about new tools, just enough to know when they might be useful.
2. Keep a record of these tools so you can find them when you need to: star them on GitHub, or add them your bookmarks or note-taking system.
3. Only spend the extra time and effort needed to gain more in-depth understanding once you actually need to use the tool. And when you do learn a new tool, do it at your job if you can.

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Recently a reader wrote in with a question:

I’ll be going to [a coding boot camp]. [After I graduate], my current view is to try hard to negotiate for whatever I can and then get better for my second job, but both of those steps are based on the assumption that I understand what an acceptable range for pay, benefits, etc are, and I feel like it’s leaving money (or time) on the table.

I’m not even sure if entry level jobs should be negotiated since they seem to be such a commodity. Do you have any advice for someone standing on the edge of the industry, looking to jump in?

What I told him, and what I’d like to share with you as well, is this:

• Don’t think of yourself as a commodity—you’re just undermining yourself.
• Don’t present yourself as a commodity—it’s bad marketing.
• You are not a commodity—because no one is.

This is perhaps more obvious if you have lots of experience, but it’s just as true for someone looking for their first job.

We all have different strengths, different weaknesses, different experiences and background. So when it comes to finding a job, you should be highlighting your strengths, instead of all the ways you’re the same as everyone else.

In the rest of this article I’ll show just a few of the ways this can be applied by someone who is switching careers into the tech industry; elsewhere I talk more about the more theoretical side of marketing yourself.

Negotiating as a bootcamp graduate

Since employment is a negotiated relationship, negotiation starts not when you’re discussing your salary with a particular company, but long before that when you start looking for a job.

Here are just some of the ways you can improve your negotiating strength.

1. Highlight your previous experience

If you’re going to a coding bootcamp chances are you’ve had previous job experience. Many of those job skills will translate to your new career as a software developer: writing software as an employee isn’t just about writing code.

Whether you worked as a marketer or a carpenter, your resume and interviews should highlight the relevant skills you learned in your previous career. Those skills will make you far more competent than the average college graduate.

This might include people management, project management, writing experience, knowing when to cut corners and when not to, attention to detail, knowing how to manage your time, and so on.

And if you can apply to jobs where your business knowledge is relevant, even better: if you used to work in insurance, you’ll have an easier time getting a programming job at an insurance company.

2. Do your research

Research salaries in advance. There are a number of online salary surveys—e.g. StackOverlow has one—which should give you some sense of what you should be getting.

Keep in mind that top companies like Google or some of the big name startups use stock and bonuses as a large part of total compensation, and salary doesn’t reflect that. Glassdoor has per-company salary surveys but they often tend to be skewed and lower than actual salaries.

3. Get multiple job offers if you can

Imagine candidate A and candidate B: as far as the hiring manager is concerned they seem identical. However, if candidate B has another job offer already, that is evidence that someone elsewhere has decided they like them. So candidate B is no longer seen as a commodity.

Try to apply to multiple jobs at once, and not to say “yes” immediately to the first job offer you can get. If you can get two offers at the same time, chances you’ll be able to get better terms from one or the other.

In fact, even just saying “I’m in the middle of interviewing elsewhere” can be helpful.

You are not a commodity, so don’t act like one

Notice how all of the suggestions above aren’t about that final conversation about salary. There’s a reason: by the time you’ve reached that point, most of the decisions about your salary range have already been made. You still need to ask for more, but there’s only a limited upside at that point.

So it’s important to present your unique strengths and capabilities from the start: you’re not a commodity, and you shouldn’t market yourself like one.

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You’re looking for a new job, or feel like it’s time for a raise, or maybe you just want to set some boundaries with your boss. And that means negotiating, and you hate the whole idea: asking for things is hard, you don’t want to be treated specially, the idea of having the necessary conversation just makes you super-uncomfortable.

And that’s a problem, because you can’t avoid negotiating: employment is a negotiated relationship. From the minute you start looking for a job until you leave for a new one, you are negotiating.

And maybe you didn’t quite realize that, and maybe you didn’t ever ask for what you want, but in that case you’re still negotiating. You’re just negotiating badly.

But once you internalize this idea, negotiation can get easier.

That awkward, scary conversation where you ask for what you want is really just a small fraction of the negotiation. Which means if you do it right, that final conversation can be shorter, more comfortable, and much more likely to succeed.

To see why, let’s take the example of a job search, and see how the final conversation where you discuss your salary is just a small part of the real negotiation.

How your salary is determined

To simplify matters, we will specifically focus just on your salary as a programmer.

Companies tend to have different job titles based on experience, with corresponding ranges of salaries. Your salary is determined first by the prospective job title, and second by the specific salary within that title’s range.

The process goes something like this:

1. When you send in your resume the HR or recruiting person who reads it puts you into some sort of mental bucket: “this is a senior software engineer.”
2. The hiring manager reads your resume and refines that initial guess.
3. The interview process then hardens that job title, and gives the company some sense of how much they want you and therefore where in that title’s salary range to put you.
4. Finally, you get an offer, and you can push back and try to get more.

That final step, the awkward conversation we tend to think of as the negotiation, is only the end of a long process. By the time you’ve reached it much of your scope for negotiation has been restricted: you’ll have a harder time convincing the company you’re a Software Engineer IV if they’ve decided you’re a Software Engineer II.

Employment is a negotiated relationship

Negotiation isn’t a one-time event, it’s an ongoing part of how you interact with an employer You start negotiating for your salary, for example, from the day you start applying:

1. You can choose companies to apply to where your enthusiasm will come across, or where you have highly relevant technical skills.
2. You can get yourself introduced by a friend on the inside, instead of just sending in your resume.
3. You can ensure you’ve demonstrated your correct level of problem-solving skills in your resume. If you can identify problems, it’s very easy to give the impression you can only solve problems if you don’t phrase things right (“I switched us over from VMs to Kubernetes” vs. “I identified hand-built VMs as a problem, investigated, chose Kubernetes, etc.”).
4. You can interview for multiple jobs at once, so you can use a job offer from company A as independent proof of your value to company B.
5. You can do well on the technical interview, which correlates with higher salaries.
6. You can avoid whiteboard puzzles if you tend not to do well on those sorts of interviews.

All of these—and more—are part of the negotiation, long before you get the offer and discuss your salary.

You still need to ask (and negotiation doesn’t stop then)

Yes, you do need to ask for what you want at the end. And yes, that’s going to be scary and awkward and no fun at all. But asking for things is something you can practice in many different contexts, not just job interviews.

But if you treated the whole job interview process as a negotiation, that final conversation will be much easier because the company will really want to hire you—and because they’ll be worried you’ll take that other job offer you mentioned.

You’re not done negotiating when you’ve accepted the offer, though.

When your boss asks you to do something, you don’t have to say yes. In fact, as a good employee it’s your duty not to say yes, but to listen, dig a little deeper, and find the real problem.

Similarly, how many hours you work is not just up to your boss, is also about how you push back on unreasonable demands. And again, it’s your duty as a good employee to push back, because work/life balance makes you a better software engineer.

All of which is to say:

1. You can’t avoid negotiating.
2. Negotiation is far broader than just that awkward conversation where you make the ask.
3. Being a good negotiator will make you a far more effective software engineer.

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There is a lot of code that overloads the __call__ method. This is the method that "calling" an object activates: something(x, y, z) will call something.__call__(x, y, z) if something is a member of a Python-defined class.

At first, like every operator overload, this seems like a nifty idea. And then, like most operator overload cases, we need to ask: why? Why is this better than a named method?

The first use-case is easily done better with a named method, and more readably: accepting callbacks. Let's say that the function interesting_files will call the passed-in callback with names of interesting files.

We can, of course, use __call__:

class PrefixMatcher:

    def __init__(self, prefix):
        self.prefix = prefix
        self.matches = []

    def __call__(self, name):
        if name.startswith(self.prefix):
            self.matches.append(name)

    def random_match(self):
        return random.choice(self.matches)

matcher = PrefixMatcher("prefix")
interesting_files(matcher)
print(matcher.random_match())

But it is more readable, and obvious, if we...don't:

class PrefixMatcher:

    def __init__(self, prefix):
        self.prefix = prefix
        self.matches = []

    def get_name(self, name):
        if name.startswith(self.prefix):
            self.matches.append(name)

    def random_match(self):
        return random.choice(self.matches)

matcher = PrefixMatcher("prefix")
interesting_files(matcher.get_name)
print(matcher.random_match())

We can pass the matcher.get_name method, which is already callable directly to interesting_files: there is no need to make PrefixMatcher callable by overloading __call__.

If something really is nothing more than a function call with some extra arguments, then either a closure or a partial would be appropriate.

In the example above, the random_match method was added to make sure that the class PrefixMatcher is justified. If this was not there, either of these implementations would be more appropriate:

def prefix_matcher(prefix):
    matches = []
    def callback(name):
        if name.startswith(prefix):
            matches.append(name)
    return callback, matches

matcher, matches = prefix_matcher("prefix")
interesting_files(matcher)
print(random.choice(matches))

This uses the function closure to capture some variables and return a function.

def prefix_matcher(prefix, matches, name):
    if name.startswith(prefix):
        matches.append(name)

matches = []
matcher = functools.partial(prefix_matcher, "prefix", matches)
interesting_files(matcher)
print(random.choice(matches))

This uses the funcotools.partial functions to construct a function that has some of the arguments "prepared".

There is one important use case for __call__, but it is specialized: it is a powerful tool when constructing a Python-based DSL. Indeed, this is exactly the time when we want to trade away "doing exactly when the operator always does" in favor of "succint syntax dedicated to the task at hand."

A good example of such a DSL is stan, where the __call__ function is used to construct XML tags with attributes: div(style="color: blue").

In almost every other case, avoid the temptation to make your objects callable. They are not functions, and should not be pretending.

When you’re searching for a new programming job you want to avoid companies with long work hours. You can ask about work/life balance during the interview (and unless you’re desperate, you always should ask), but that means wasting time applying and interviewing at companies where you don’t want to work.

What you really want is to only apply to companies that actually allow—or even better, encourage—good work/life balance.

Close reading of the job posting and company hiring pages can sometimes help you do that, but some good companies don’t talk about it, and some bad companies will just lie.

So in this article I’ll explain how you can use GitHub to empirically filter out at least some companies with bad work-life balance.

Let’s look at GitHub profiles

If you go to the profile for a GitHub user (here’s mine) you will see a chart showing contributions over time. The columns are weeks, and each row is a day of the week.

Each square shows contribution on a particular day: the darker the color, the more contributions.

What’s most interesting here are the first and last rows, which are Sundays and Saturdays respectively. As you can see in the image above, this GitHub user doesn’t tend to code much on the weekends: the weekend boxes are mostly blank.

You can also use this UI to see if the company uses GitHub at all. Given this many boxes, the user’s employer probably does use GitHub, but you can also click on a particular box and see the specific contributions. In this case, you would see that on one particular weekday the user contributed to “private repositories”, presumably those for their employer:

On the other hand, if you were to click on the weekend boxes you would see that all the weekend contributions are to open source projects. In short, this user doesn’t code much on the weekend, and when they do it’s on personal open source projects, not work projects.

Generalizing this process will allow you to filter out companies that don’t have work/life balance for developers.

Investigating work/life balance using GitHub

There are two assumptions here:

• This is a relatively small company; large companies tend to vary more across groups, so you’ll need to adjust the process somewhat to focus on programmers in the group you’re applying to.
• The company uses GitHub for most of their source code.

Company size you can figure out from the company page or LinkedIn, usage of GitHub will be something we can figure out along the way.

This is not a perfect process, since users can disable showing private repository contributions, or it’s possible the developer has personal private repositories. This is why you want to check as many profiles as possible.

Here’s what you should do:

1. Find a number of programmers who work for the company. You can do this via the company’s website, and by the company’s page on LinkedIn, which will list employees who have LinkedIn profiles. You can also check for members of the company’s GitHub organization, if they have a public list, but don’t rely on this exclusively since it will likely omit many employees.
2. For each programmer, use your favorite search engine and search for “$NAME github” to find their GitHub profile. Try to do some sanity checks to make sure it’s the right person, especially if they have a common name: location, organization membership, technologies used, etc..
3. For each programmer, check if they contribute to private repositories during the week. You do can this by visually seeing if there are lots of green boxes, and by clicking on the Monday-Friday boxes in the timeline and reading the results below. If they mostly don’t, the company might not use GitHub.
4. If they do use GitHub at work, for each programmer, check if they code on the weekend. You can visually see if they have lots of green boxes on Sundays and Saturdays.
5. If they do code on the weekend, check if those are work contributions. You can do this by clicking on the weekend boxes and seeing if those are contributions to private repositories.

Additional tips:

• If the company mostly writes open source code, you can check if the programmers contribute to the relevant open source repositories during the weekend.
• Look for correlated weekend work dates across different people: if 5 people are working on private repos on the same 4 weekends, that’s probably a sign of crunch time.

By the end of the process you will often have a decent sense if most of the programmers are coding on the weekend. Note that this method can only tell you if they don’t have work/life balance—you still can’t know if they do have work/life balance.

So even if you decide to apply to a company that passed this filter, you will still need to ask questions about work/life balance and listen closely during the interview process.

Always do your research

Before you apply for a job you should always do your research: read their website, reads all their job postings, talk to a friend there if you have one… and as I explained in this article, their GitHub profiles as well. You’ll still need to look for warning signs during interviews, but research is still worth it.

The more you research, the more you’ll know whether you want to work there. And if you do want to work there, the more you know there better your interview will go.

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A couple of months ago, a successful attack against the Node ecosystem resulted in stealing an undisclosed amount of bitcoins from CoPay wallets.

The technical flow of the attack is well-summarized by the NPM blog post. Quick summary:

1. nodemon, a popular way to run Node applications, depends on event-stream.
2. The event-stream maintainer has not had time to maintain it.
3. right9control asked event-stream maintainer for commit privileges to help maintain it.
4. right9control added a dependency on a new library, flatmap-stream.
5. flatmap-stream contained malicious code to steal wallets.

You’re dreaming of a programming job with 30 hours a week, a job where you’ll have time for your own projects, your own hobbies. But this sort of job seems practically non-existent—almost no one advertises programming jobs with shorter workweeks.

How do you carve out a job like this, a job with a shorter workweek?

The ideal would be some company or employer where you just can ask for a shorter workweek, without having to apply or interview, and have a pretty good chance of getting a “yes”.

In this post I’ll talk about the easiest way to get what you want: negotiating at your current job.

The value of being an existing employee

as an existing employee you are much more valuable than an equally experienced programmer who doesn’t work there.

During your time at your employer you have acquired a variety of organization-specific knowledge and skills. It can take quite a while for a new employee to acquire these, and during the ramp-up period they will also take up their colleagues’ time.

Here are just a few of the things that you’ve likely learned during your time as an employee:

• The existing codebase.
• Local best practices, idioms, and coding conventions.
• The business processes at the company.
• The business domain.
• The informal knowledge network in the company, i.e. who is the expert in what.

Not only do you have hard to-replace skills and knowledge, you also have your work record to build on: your manager knows what you can do. Once you’ve been working for a manager for a while they’ll know your skills, and whether they can trust you.

A real-life example

In my own career, being an existing employee has benefited me on multiple occasions:

After a number of years working as a software engineer for one company, I got a bad case of RSI (repetitive strain injury). I could no longer type, which meant I could no longer work as a programmer. But I did stay on as an employee: one of the managers at the company, who I’d worked for in an earlier role, offered me a position as a product manager.

In part this was because the company was run by decent people, who for the most part took care of their employees. But it was also because I had a huge amount of hard-to-replace business and technical knowledge of the company’s product, in a highly complex domain.

I worked as a product manager for a few years, but I never really enjoyed it. And with time my hands recovered, at least partially, potentially allowing me to take up programming again. After my daughter was born, my wife and I decided that I’d become a part-time consultant, and take care of our child the rest of the time, while she continued working full-time.

My manager was upset when I told him I was leaving. I felt bad—but really, if your boss is unhappy when you’re leaving, that’s a good thing.

In fact, my boss offered to help me find a less-than-full-time programming position within the company so I wouldn’t have to leave. I’d already made up my mind to go, but under other circumstances I might have taken him up on the offer.

Notice how I was offered reduced hours, even though companies will never advertise such positions. That’s the value of being an existing employee.

Asking for what you want

Unless you work for a really bad manager—or a really badly managed company—a reasonable manager would much prefer to have your experience for 4 days a week than have to find a train a replacement. That doesn’t mean they’ll be happy if you ask for a shorter workweek: you are likely to get some pushback.

But if you have a strong negotiating position—financial savings, valuable work, the willingness to find another job if necessary—there’s a decent chance they will eventually say “yes”.

Does negotiating seem too daunting, or not something you can do? Plenty of other programmers have done it, even those with no previous negotiation experience.

Much of this article was an excerpt from my book, Negotiate a 3-Day Weekend. It covers everything from negotiation exercises you can do on the job, to a specific script for talking to your boss, to negotiating at new employers if you can’t do it at your current job.

With a little bit of practice, you can get the free time you need.

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You’re applying for jobs, you’re hoping to get an offer soon—and when you do you’ll have to face the scary issue of negotiation.

If you read a negotiation guide like Valerie Aurora’s HOWTO (and you should!) you’re told you need to negotiate: you need to ask for more. And that’s good advice.

The problem is that asking for more is scary: it feels confrontational, your body will kick in with an unhelpful adrenaline surge, it’s just not comfortable. And honestly, given you only negotiate your salary every few years, that adrenaline surge probably isn’t going to go away.

But I think you can get past that and negotiate anyway—by embracing your inner 6-year old. In particular, a 6-year old negotiating for snacks on a playdate.

Snack time!

Let’s set the scene.

A 6-year old named Emma is visiting her friend Jackson, and now it’s time for a snack. Jackson’s parent—Michael—now needs to feed Emma.

Michael would prefer the children not eat crackers, but he has a problem. Michael has some authority over Jackson since he’s his parent, and some knowledge of what Jackson is willing to eat. So he can say “you’re eating one of these mildly healthy snacks” and that’s the end of it.

But Emma is just visiting: Michael has less authority, less knowledge, and a hungry 6-year old is Bad News. So Michael comes up with an acceptable range of snacks, and tries his best to steer towards the ones he considers healthier.

The conversation goes something like this:

Michael: “Would you like some fruit?”

Emma: blank stare.

Michael: “How about same cheese?”

Emma: shakes her head.

Michael: “Yogurt?”

Emma: shakes her head.

Michael: “Crackers?”

Emma and Jackson: “Yes!”

Michael has committed to feeding Emma something, he doesn’t want her to go hungry—but he doesn’t have the normal leverage he has as a parent. As a result, Emma can just stall until she gets what she wants. Particularly enterprising children will ask for candy (even when they would never get candy at home!), but stalling seems well within the capacity of most 6-year olds.

Salary time!

The dynamic of hiring a new employee is surprisingly similar.

Whoever is making the offer—HR, an internal recruiter, or the hiring manager—has already committed to hiring you. They’ve decided: they had interviews and meetings and they want to get it over with and just get you on board.

So they come up with an acceptable salary range, and offer you the low end of the range. If you accept that, great. And if you say “can you do better?”

Well, they’ve already decided on their acceptable salary range: they’ll just move up within that range. They’re not insulted, they’re used to this. They’re not horrified at a hole in their budget, this is still within their acceptable range.

You went from fruit to crackers, and they can live with that. All you have to do is ask.

Embrace your inner 6-year old

Much of what determines your salary happens before you get the offer, when the decision is made about what salary range to offer you.

You can influence this by the language on your resume, by how you present yourself, how you interview, and by noting you have competing offers. It may not feel like negotiation, but it’s all part of the process—and while it’s a set of skills you can master as an adult, that part is far beyond what your 6-year old self could do.

But the actual conversation about salary? Pretend you’re 6, pretend it’s a snack, and ask for more—chances are you’ll get some delicious crackers.

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JSON is a useful format. It might not be ideal for hand-editing, but it does have the benefit that it can be hand-edited, and it is easy enough to manipulate programmatically.

For this reason, it is likely that at some point or another, checking in a JSON file into your repository will seem like a good idea. Perhaps it is even beyond your control: some existing technology uses JSON as a configuration file, and the easiest thing is to go with it.

It is useful to still keep the benefit of programmatic manipulation. For example, if the JSON file encodes a list of numbers, and we want to add 1 to every even number, we can do:

with open("myfile.json") as fp:
    content = json.load(fp)
content = [x + (2 % i) for i, x in enumerate(content)]
with open("myfile.json", "w") as fp:
    json.dumps(fp, content)

However, this does cause a problem: presumably, before, the list was formatted in a visually-pleasing way. Having dumped it, now the diff is unreadable -- and hard to audit visually.

One solution is to enforce consistent formatting.

For example, using pytest, we can write the following test:

def test_formatting():
    with open("myfile.json") as fp:
        raw = fp.read()
    content = json.loads(raw)
    redumped = json.dumps(content, indent=4) + "\n"
    assert raw == redumped

Assuming we gate merges to the main branches on passing tests, it is impossible to check in something that breaks the formatting. Automated programs merely need to remember to give the right options to json.dumps. However, what happens when humans make mistakes?

It turns out that Python already has a command-line tool to reformat:

$ python -m json.tool myfile.json > myfile.json.formatted
$ mv myfile.json.formatted myfile.json

A nice test failure will remind the programmer of this trick, so that it is easy to do and check in.

A reader of my newsletter wrote in, talking about the problem of finding a job with work/life balance in Silicon Valley:

It seems like us software engineers are in a tough spot: companies demand a lot of hard work and long hours, and due to the competitiveness here in Silicon Valley, you have to go along with it (or else there’s some bright young Stanford grad just dying to work all nighters on Red Bull to take your place).

But they throw you aside once the company has become established and they no longer need the “creative” types.

In short, how do you get a job with work/life balance when you’re competing against people willing to work long hours?

All nighters make for bad software

The starting point is realizing that working long hours makes you a much less productive employee, to the point that your total output will actually decrease (see Evan Robinson on crunch time). If you want to become an effective and productive worker, you’re actually much better off working normal hours and having a personal life than working longer hours.

Since working shorter hours makes you more productive, that gives you a starting point for why you should be hired.

Instead of focusing on demonstrative effort by working long hours, you can focus on identifying and solving valuable problems, especially the bigger and longer term problems that require thought and planning to solve.

Picking the right company

Just because you’re a valuable, productive programmer doesn’t mean you’re going to get hired, of course. So next you need to find the right company.

You can imagine three levels of managerial skill:

• Level 1: These managers have no idea how to recognize effective workers, so they only judge people by hours worked.
• Level 2: These managers can recognize effective workers, but don’t quite know how to create a productive culture. That means if you choose work long hours they won’t stop you, however pointless these long hours may be. But, they won’t force you work long hours so long as you’re doing a decent job.
• Level 3: These managers can recognize effective workers and will encourage a productive culture. Which is to say, they will explicitly discourage working long hours except in emergencies, they will take steps to prevent future emergencies, etc..

When you look for a job you will want to avoid Level 1 managers. However good your work, they will be happy to replace you with someone incompetent so long as they can get more hours out of them. So you’ll be forced to work long hours and work on broken code.

Level 3 managers are ideal, and they do exist. So if you can find a job working for them then you’re all set.

Level 2 managers are probably more common though, and you can get work/life balance working for them—if you set strong boundaries. Since they can recognize actual competence and skills, they won’t judge you during your interview based on how many hours you’re willing to work. You just need to convey your skill and value, and a reasonable amount of dedication to your job.

And once you’ve started work, if you can actually be productive (and if you work 40 hours/week you will be more productive!) they won’t care if you come in at 9 and leave at 5, because they’ll be happy with your work.

Unlike Level 3 managers, however, you need to be explicit about boundaries during the job interview, and even more so after you start. Elsewhere I wrote up some suggestions about how to convey your value, and how to say “no” to your boss.

Employment is a negotiated relationship

To put all this another way: employment is a negotiated relationship. Like it or not, you are negotiating from the moment you start interviewing, while you’re on the job, and until the day you leave.

You are trying to trade valuable work for money, learning opportunities, and whatever else your goals are (you can, for example, negotiate for a shorter workweek). In this case, we’re talking about negotiating for work/life balance:

1. Level 1 managers you can’t negotiate with, because what they want (long hours) directly conflicts with what you want.
2. Level 2 managers you can negotiate with, by giving them one of the things they want: valuable work.
3. Level 3 managers will give you what you want without your having to do anything, because they know it’s in the best interest of everyone.

Of course, even for Level 3 managers you will still need to negotiate other things, like a higher salary.

So how do you get a job with work/life balance? Focus on providing and demonstrating valuable long-term work, avoid bad companies, and make sure you set boundaries from the very start.

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Automated tests are immensely useful. Once you’ve started writing tests and seen their value, the idea of writing software without them becomes unimaginable.

But as with any technique, you need to understand its limitations. When it comes to automated testing—unit tests, BDD, end-to-end tests—it’s tempting to think that if your tests pass, your software is correct.

But tests don’t, tests can’t tell you that your software is correct. Let’s see why.

How to write correct software

To implement a feature or bugfix, you go through multiple stages; they might be compressed or elided, but they are always necessary:

1. Identification: Figure out what the problem is you’re trying to solve.
2. Solution: Come up with a solution.
3. Specification: Define a specification, the details of how the solution will be implemented.
4. Implementation: Implement the specification in code.

Your software might end up incorrect at any of these points:

1. You might identify the wrong problem.
2. You might choose the wrong solution.
3. You might create a specification that doesn’t match the solution.
4. You might write code that doesn’t match the specification.

Only human judgment can decide correctness

Automated tests are also a form of software, and are just as prone to error. The fact that your automated tests pass doesn’t tell you that your software is correct: you may still have identified the wrong problem, or chosen the wrong solution, and so on.

Even when it comes to ensuring your implementation matches your specification, tests can’t validate correctness on their own. Consider the following test:

def test_addition():
    assert add(2, 2) == 5

From the code’s perspective—the perspective of an automaton with no understanding—the correct answer of 4 is the one that will cause it to fail. But merely by reading that you can tell it’s wrong: you, the human, are key.

Correctness is something only a person can decide.

The value of testing: the process

While passing tests can’t prove correctness, the process of writing tests and making them pass can help make your software correct. That’s because writing the tests involves applying human judgment: What should this test assert? Does match the specification? Does this actually solve our problem?

When you go through the loop of writing tests, writing code, and checking if tests pass, you continuously apply your judgment: is the code wrong? is the test wrong? did I forget a requirement?

You write the test above, and then reread it, and then say “wait that’s wrong, 2 + 2 = 4”. You fix it, and then maybe you add to your one-off hardcoded tests some additional tests based on core arithmetic principles. Correctness comes from applying the process, not from the artifacts created by the process.

This may seem like pedantry: what does it matter whether the source of correctness is the tests themselves or the process of writing the tests? But it does matter. Understanding that human judgment is the key to correctness can keep you from thinking that passing tests are enough: you also need other forms of applied human judgment, like code review and manual testing.

(Formal methods augment human judgment with automated means… but that’s another discussion.)

The value of tests: stability

So if correctness comes from writing the tests, not the tests themselves, why do we keep the tests around?

Because tests ensure stability. once we judge the software is correct, the tests can keep the software from changing, and thus reduce the chances of its becoming incorrect. The tests are never enough, because the world can change even if the software isn’t, but stability has its value.

(Stability also has costs if you make the wrong abstraction layer stable…)

Tests are useful, but they’re not sufficient

To recap:

1. Write automated tests.
2. Run those tests.
3. Don’t mistake passing tests for correctness: you will likely need additional processes and techniques to achieve that.

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If you want to speak to me, 1-on-1, about anything, I want to be able to help. I am a busy person. I have commitments. But I will make the time to talk to you.

As a programmer looking for a job, you need to be on the lookout for badly managed companies. Whether it’s malicious exploitation or just plain incompetence, the less time you waste applying for these jobs, the better.

Some warning signs are subtle, but not all. One of the most blatant is a simple phrase: “must be willing to work under pressure.”

The distance between we and you

Let’s take a look at some quotes from real job postings. Can you spot the pattern?

• “Ability to work under pressure to meet sometimes aggressive deadlines.”
• “Thick skin, ability to overcome adversity, and keep a level head under pressure.”
• “Ability to work under pressure and meet tight deadlines.”
• “Willing to work under pressure” and “working extra hours if necessary.”

If you look at reviews for these companies, many of them mention long working hours, which is not surprising. But if you read carefully there’s more to it than that: it’s not just what they’re saying, it’s also how they’re saying it.

When it comes to talking about the company values, for example, it’s always in the first person: “we are risk-takers, we are thoughtful and careful, we turn lead into gold with a mere touch of our godlike fingers.” But when it comes to pressure it’s always in the second person or third person: it’s always something you need to deal with.

Who is responsible for the pressure? It’s a mysterious mystery of strange mystery.

But of course it’s not. Almost always it’s the employer who is creating the pressure. So let’s switch those job requirements to first person and see how it reads:

• “We set aggressive deadlines, and we will pressure you to meet them.”
• “We will say and do things you might find offensive, and we will pressure you.”
• “We set tight deadlines, and we will pressure you to meet them.”
• “We will pressure you, and we will make you work long hours.”

That sounds even worse, doesn’t it?

Dysfunctional organizations (that won’t admit it)

When phrased in the first person, all of these statements indicate a dysfunctional organization. They are doing things badly, and maybe also doing bad things.

But it’s not just that they’re dysfunctional: it’s also that they won’t admit it. Thus the use of the second or third person. It’s up to you to deal with this crap, cause they certainly aren’t going to try to fix things. Either:

1. Whoever wrote the job posting doesn’t realize they’re working for a dysfunctional organization.
2. Or, they don’t care.
3. Or, they can’t do anything about it.

None of these are good things. Any of them would be sufficient reason to avoid working for this organization.

Pressure is a choice

Now, I am not saying you shouldn’t take a job involving pressure. Consider the United States Digital Service, for example, which tries to fix and improve critical government software systems.

I’ve heard stories from former USDS employees, and yes, sometimes they do work under a lot of pressure: a critical system affecting thousands or tens of thousands of people goes down, and it has to come back up or else. But when the USDS tries to hire you, they’re upfront about what you’re getting in to, and why you should do it anyway.

They explain that if you join them your job will be “untangling, rewiring and redesigning critical government services.”. Notice how “untangling” admits that some things are a mess, but also indicates that your job will be to make things better, not just to passively endure a messed-up situation.

Truth in advertising

There’s no reason why companies couldn’t advertise in the some way. I fondly imagine that someone somewhere has written a job posting that goes like this:

“Our project planning is a mess. We need you, a lead developer/project manager who can make things ship on time. We know you’ll have to say ‘no’ sometimes, and we’re willing to live with that.”

Sadly, I’ve never actually encountered such an ad in the real world.

Instead you’ll be told “you must be able to work under pressure.” Which is just another way of saying that you should find some other, better jobs to apply to.

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We have more ways to manage dependencies in Python applications than ever. But how do they fare in production? Unfortunately this topic turned out to be quite polarizing and was at the center of a lot of heated debates. This is my attempt at an opinionated review through a DevOps lens.

(Thanks to Nelson Elhage and Saivickna Raveendran for their feedback on earlier drafts. All mistakes that remain are mine.)

When talking about garbage collection, the notion of "generational collection" comes up. The usual motivation given for generational garbage collection is that "most objects die young". Therefore, we put the objects that survive a collection cycle (and therefore have proven some resistance) in a separate generation that we scan less often.

This is an optimization if the probability of an object that has survived a cycle to be garbage by the time the next collection cycle has come around is lower than the probability of a newly allocated object to be garbage.

In a foundational paper Infant mortality and generational garbage collection, Dr. Baker laid out an argument deceptive in its simplicity.

Dr. Baker asks the question: "Can we model a process where most objects become garbage fast, but generational garbage collection would not improve things?". His answer is: of course. This is exactly the probability distribution of radioactive decay.

If we have a "fast decaying element", say with a half-life of one second, than 50% of the element's atoms decay in one second. However, keeping the atoms that "survived a generation" apart from newly created atoms is unhelpful: all remaining atoms decay with probability of 50%.

We can bring the probability for "young garbage" as high up as we want: a half-life of half a second, a quarter second, or a microsecond. However, that is not going to make generational garbage collection any better than a straightforward mark-and-sweep.

The Poisson distribution, which models radioactive decay, has the property that P(will die in one second) might be high, but P(will die in one second|survived an hour) is exactly the same: the past does not give us information about the future. This is called the "no memory property" of Poisson distribution.

When talking about generational garbage collection, and especially if we are making theoretical arguments about its helpfulness, we need to make arguments about the distribution, not about the averages. In other words, we need to make an argument that some kinds of objects hang around for a long time, while others tend to die quickly.

One way to model it is "objects are bimodal": if we model objects as belonging to a mix of two Gaussian distributions, one with a small average and one with a big average, then the motivation for generational collection is clear: if we tune it right, most objects that survive the first cycle belong to the other distribution, and will survive for a few more cycles.

To summarize: please choose your words carefully. "Young objects are more likely to die" is an accurate motivation, "Most objects die young" is not. This goes doubly if you do understand the subtlety: do not assume the people you are talking with have an accurate model of how garbage works.

As an aside, some languages decided that generational collection is more trouble than it is worth because the objects that "die young" go through a different allocation style. For example, Go has garbage collection, but it tries to allocate objects on the stack if it can guarantee at compile-time they do not "escape". Because of that, the "first generation" is collected at stack popping time.

CPython has generational garbage collection, but it also has a "zeroth generation" of sorts: when functions return, all local variables get a "decref": a decrease in reference count. Those for whom that results in a 0 reference counts, which is often quite a few, get collected immediately.

Most mornings I take Phoenix to school, as his school is two blocks away from work.

We take the subway to school, having about a half hour window to leave as the school has a half-hour play window before school really starts, which inevitably gets eaten up by collecting all the things, putting on all the clothes, picking the mode of transportation (no, not the stroller; please take the step so we can go fast), and getting out the door.

At the time we make it out, the subway is usually full of people, as are the cars, so we shuffle in and Phoenix searches for a seat, which is not available, but as long as he gets close enough to a pole and a person who looks like they’d be willing to give up a seat once they pay attention, he seems to get his way more often than not. And sometimes, the person next to them also offers up their seat to me. Which is when the fun begins.

Because, like any parent knows these days, as soon as you sit down next to each other, that one question will come:

“Papa, papa, papa… mag ik jouw telefoon?” (Can I have your phone? – Phoenix and I speak Dutch exclusively to each other. Well, I do to him.)

At which point, as a tired parent in the morning, you have a choice – let them have that Instrument of Brain Decay which even Silicon Valley parents don’t let their toddlers use, or push yourself to make every single subway ride an engaging and entertaining fun-filled program for the rest of eternity.

Or maybe… there is a middle way. Which is how, every morning, Phoenix and I engage in the same routine. I answer: “Natuurlijk mag jij mijn telefoon… als je éérst een verhaaltje vertelt.” (Of course you can have my phone – if you first tell me a story.)

Phoenix furrows his brows, and asks the only logical follow-up question there is – “Welk verhaaltje?” (Which story?)

And I say “Ik wil het verhaaltje horen van het jongetje en zijn vader die met de metro naar school gaan” (I want to hear the story of the little boy and his dad who take the subway to school.)

And he looks at me with big eyes and says, “Dat verhaaltje ken ik niet.” (I don’t know that story)

And I begin to tell the story:

“Er was eens… een jongetje en zijn vader.” (Once upon a time, there was a little boy and his father. Phoenix already knows the first three words of any story.)
“En op een dag… gingen dat jongetje en zijn vader met de metro naar school.” (And one day… the little boy and his father took the subway to school. The way he says “op een dag” whenever he pretends to read a story from a book is so endearing it is now part of our family tradition.)

“Maar toen de jongen en zijn vader op de metro stapten zat de metro vol met mensen. En het jongetje wou zitten, maar er was geen plaats. Tot er een vriendelijke mevrouw opstond en haar plaats gaf aan het jongetje, en het jongetje ging zitten. En toen stond de meneer naast de mevrouw ook recht en de papa ging naast het jongetje zitten.” (But when the little boy and his father got on the subway, it was full of people. And the little boy wanted to sit but there was no room. Until a friendly woman stood up and gave up her seat to the little boy, so the little boy sat down. And then the man next to the woman also stood up and his father sat down next to him.)

“En toen de jongen op de stoel zat, zei het jongetje, Papa papa papa papa papa papa papa…”(And when the boy sat down on the chair, he said Papa papa papa papa papa papa)

“Ja?, zei papa.” (Yes?, said papa.)

“Papa, mag ik jouw telefoon”? (Papa, can I have your phone?)

“Natuurlijk jongen….. als je éérst een verhaaltje vertelt.” (Of course son… if you first tell me a story.)

At which point, the story folds in on itself and recurses, and Phoenix’s eyes light up as he mouths parts of the sentences he already remembers, and joins me in telling the next level of recursion of the story.

I apologize in advance to all the closing parentheses left dangling like the terrible lisp programmer I’ve never given myself the chance to be, but making that train ride be phoneless every single time so far is worth it.

Do you love programming for its own sake, or do you do for the outcomes it allows? Depending on which describes you best you will face different problems in your career as a software developer.

Enthusiasts code out of love. If you’re an enthusiast you’d write software just for fun, but one day you discovered your hobby could also be your career, and now you get paid to do what you love.

Pragmatists may enjoy coding, but they do it for the outcomes. If you’re a pragmatist, you write software because it’s a good career, or for what it enables you to do and build.

There’s nothing inherently good or bad about either, and this is just a simplification. But understanding your own starting point can help you understand and avoid some of the problems you might encounter in your career.

In this post I will cover:

1. Why many companies prefer to hire enthusiasts.
2. The career problems facing enthusiasts, and how they can solve them.
3. The career problems facing pragmatists, and how they can solve them.

Why companies prefer hiring enthusiasts

Before we move on to specific career problems you might face, it’s worth looking at the bigger picture: the hiring and work environment.

Many companies prefer to hire enthusiast programmers: from the way they screen candidates to the way they advertise jobs, they try to hire people who care about the technology for its own sake. From an employer’s point of view, enthusiasts have a number of advantages:

1. In a rapidly changing environment, they’re more likely to keep up with the latest technologies. Even better, they’re more likely to do so in their free time, which means the company can spend less on training.
2. Since they’d write software for free, it’s easier to pay enthusiasts less money.
3. It’s also easier to get enthusiasts to work long hours.
4. Finally, since enthusiasts care more about the technical challenge than the goals of the product, they’re less likely to choose their work based on ethical or moral judgments.

But while many companies prefer enthusiasts, this isn’t always in the best interest of either side, as we’ll see next.

The career problems facing enthusiasts

So let’s say you’re an enthusiast. Here are some of the career problems you might face; not everyone will have all these problems, but it’s worth paying attention to see if you’re suffering from one or more of them.

1. Exploitation

As I alluded to above, companies like enthusiasts because they’re worse negotiators.

If you love what you do you’ll accept less money, you’ll work long hours, and you’ll ask less questions. This can cause you problems in the long run:

• Long hours can lead to burnout, in which case you might not enjoy coding at all, even in your spare time.
• Less money reduces your options outside of work.
• Finally, you really shouldn’t do work that’s immoral or unethical: there are plenty of fun problems that won’t make you feel guilty.

So even if you code for fun, you should still learn how to negotiate, if only out of self-defense.

2. Being less effective as an employee

Matt Dupree has an excellent writeup about why being an enthusiast can make you a worse worker; I don’t want to repeat his well-stated points here. Here are some additional ways in which enthusiasm can make you worse at your job:

• Shiny Object Syndrome: As an enthusiast it’s easy to choose a trendy technology or technique for your work because you want to play with it, not because it’s actually necessary in your situation. The most egregious example I’ve seen in recent years is microservices, where an organizational pattern designed for products with hundreds of programmers is being applied by teams with just a handful of developers.
• Writing code instead of solving problems: If you enjoy writing code for its own sake, it’s tempting to write more code just because it’s fun. Productivity as a programmer, however, comes from solving problems with as little work as needed.

3. Work vs. art

Finally, as an enthusiast you might face a constant sense of frustration. As an enthusiast, you want to write software for fun: solve interesting problems, write quality code, fine-tune your work until it’s beautiful.

But a work environment is all about outcomes, not about craft. And that means a constant pressure to compromise your artistic standards, a constant need to work on things that aren’t fun, and a constant need to finish things on time, rather than when you’re ready.

So unless you want to become a pragmatist, you might want to get back more time for yourself, time where you can write code however you like. You could, for example, negotiate a 3-day weekend.

The career problems facing pragmatists

Pragmatists face the opposite set of problems; again, not all pragmatists will have all of these problems, but you should keep your eye out to see if they’re affecting you.

1. It’s harder to find a job

Since many companies actively seek out enthusiasts, finding a job as a pragmatist can be somewhat harder. Here are some things you can do to work around this:

• Actively seek out companies that talk about work/life balance.
• When interviewing, amplify your enthusiasm for technology beyond what it actually is. After all, you will learn what you need to to get the results you want, right?
• Demonstrate the ways in which pragmatism actually makes you a more valuable employee.

2. You need to actively keep your skills up

Since you don’t care about technology for technology’s sake, it can be easy to let your skills get out of date, especially if you work for a company that doesn’t invest in training. To avoid this:

• Actively seek out tasks, projects, or new jobs that will force you to learn new technologies on the job, in depth.
• Spend an hour every week gathering a broad but superficial knowledge of new technologies.

3. Pressure to work long hours

Finally, you will often encounter pressure both from management and—indirectly—from enthusiast peers to work long hours. Just remember that working long hours is bad for you and your boss (even if they don’t realize it).

Programmer, know thyself

So are you an enthusiast or a pragmatist?

These are not exclusive categories, nor will they stay frozen with time—these days I’m more of a pragmatist, but I used to be more of an enthusiast—but there is a difference in attitudes. And that difference will lead to different choices, and different problems.

Once you know who you are, you can figure out what you want—and avoid the inevitable obstacles along the way.

By the time you get home from work and finish your chores you’re too tired to do anything but watch TV and then collapse into bed. And weekends are just as bad.

But you could have a 3-day weekend not just on holidays, but every single week: learn how you can do it.

Chicken incubators are hot: eggs need heat to thrive. This was a different sort of incubator, a glass enclosure within a VC office. The VCs had the best views, but even we could look down ten stories and see the sun sparkling on the Charles River and reflecting off the buildings of downtown Boston.

It was summer when I joined the startup, but even so the incubator was freezing. The thermostat was skewed by a hot light sensor right below it, and the controls reset every night, so mornings were frigid. I took to wearing sweaters and fingerless gloves; the team at the other side of the incubator had figured out a way cover the AC vents with cardboard in a way that wasn’t visible to passersby.

But I didn’t have to suffer from the cold for very long. Soon after I joined the startup I unwittingly helped trigger our eviction from the rent-free Eden of the incubator to the harsher world outside.

The fall from grace

Most of the people who worked out of the incubator just used a laptop, or perhaps a monitor. But I like working with a standing desk, with a large Kinesis keyboard, and an external monitor on top.

My desk towered over everyone else: it was big and imposing and not very neat. Which is to say, the incubator started looking like a real office, not a room full of identical tables with a few Macbook Airs scattered hither and yon. And standing under the freezing air conditioner vent made my arms hurt, so I had to setup my desk in a way that was visible from the outside of the incubator.

And that view was too much for one of the partners in the VC firm. There were too many of us, we had too many cardboard boxes, my standing desk was just too big: it was time for us to leave the incubator.

The dream of workerless wealth

VCs take money, and (if all goes well) turn it into more money. But the actual reality of the work involved was too unpleasantly messy, too uncouth to be exposed to the sensibilities of wealthy clients.

We had to be banished out of sight, the ever-so-slightly grubby realities of office work hidden away, leaving only the clean cold dream of capital compounding through the genius of canny investors.

This dream—a dream of profit without workers—is the driving force behind many an investment fad:

• The dream in its purest form, Bitcoin and cryptocurrencies promise wealth pulled from thin air, spinning hay into gold without the involvement of any brutish Rumpelstiltskins.
• Driverless cars promise fleets of assets without those dirty, messy, expensive drivers; but until they come round, Uber and Lyft’s horde of drivers are safely kept at a distance as independent contractors with five star reviews.
• Artificial intelligence promises decision making without human involvement, an objective encoding of subjective prejudice that will never feel moral qualms.

And if you work for a VC-funded startup, this dream takes on a nightmarish tinge when it turns to consider you.

Unbanished—for now

The point here is not that VCs want to reduce effort: who wouldn’t want a more efficient world? The dream is not driven by visions of efficiency, it’s about status and aesthetics: doing actual work is ugly, and paying for work is offensive.

Of course, some level of work is always necessary and unavoidable. And so VC firms understand that the startups they fund must hire workers like me and you.

But the cold dream is always there, whispering in the background: these workers take equity, they take cash, they’re grubby. So when times are good hiring has to be done as quickly as possible, but when times are bad the layoffs come just as fast.

And when you are working, you need to work as many hours as humanly possible, not because it’s efficient—it isn't—but because paying for your time is offensive, and so you better damn well work hard. Your work may be necessary, but to the cold dream it’s a necessary—and ugly—evil.

By the time you get home from work and finish your chores you’re too tired to do anything but watch TV and then collapse into bed. And weekends are just as bad.

But you could have a 3-day weekend not just on holidays, but every single week: learn how you can do it.

As my friend Thursday said in her excellent talk (sadly, not up as of this time) naming things is important. Avoiding in-jokes is, in general, a good idea.

It is with mixed feelings, therefore, that my pun-loving heart reacted to Chris's disclosure that the most common suggestion was to call the conference "Pythaluma". However, he decided to go with the straightforward legible name, "North Bay Python".

North of the city by the bay, lies the quiet yet chic city of Petaluma, where North Bay Python takes place. In a gold-rush-city turned sleepy wine country, a historical cinema turned live show venu hosted Python enthusiasts in a single-track conference.

Mariatta opened the conference with her gut-wrenching talk about being a core Python developer. "Open source sustainability" might be abstract words, but it is easy to forget that for a language that's somewhere between the first and fifth most important (depending on a metric) there are less than a hundred people supporting its core -- and if they stop, the world breaks.

R0ml opened the second day of the conference talking about how:

• Servers are unethical.
• Python is the new COBOL.
• I put a lot of pressure on him before his talk.

Talks are still being uploaded to the YouTube channel, and I have already had our engineering team at work watch Hayley's post-mortem of Jurassic Park.

If you missed all of it, I have two pieces of advice:

• Watch the videos. Maybe even mine.
• Sign up to the mailing list so you will not miss next year's.

If you went there, I hope you told me hi. Either way, please say hi next year!

You’ve got to meet your deadlines, you’ve got to fix the bug, you’ve got to ship the product.

But you’ve also got to think about the future: every bug you introduce now will have to be fixed later, using up even more time. And all those deprecated APIs, out-of-date dependencies, and old ways of doing things really shouldn’t be there.

So when do you clean up your code?

Do you do it now?

Later?

Never?

In this article I’ll go over a set of heuristics that will help you decide when to apply three kinds of fixes:

1. Updating dependencies that are out-of-date and usages of deprecated APIs.
2. Refactoring to fix bad abstractions.
3. Miscellaneous Cleanups of anything else, from coding standard violations to awkward idioms.

Heuristics by situation

Prototyping

Before you start building something in earnest, you might start with a prototype (or what Extreme Programming calls a “spike”). You’re not going to keep this code, you’re just exploring the problem and solution space to see what you can learn.

Given you’re going to throw away this code, there’s not much point in Updating or Miscellaneous Cleanups. And if you’re just trying to understand an existing API or technical issue, you won’t be doing much Refactoring wither.

On the other hand, if your goal with prototyping is to find the right abstraction, you will be doing lots of Refactoring.

1. Updating: never.
2. Refactoring: now if you’re trying to prototype an API or abstraction, otherwise never.
3. Miscellaneous Cleanups: never.

A new project

When you’re starting a completely new project, the decisions you make will have a strong impact on the maintenance code going forward.

This is a great opportunity to start with the latest (working) dependencies, situation-specific best practices and maintainable code, and the best abstractions you can come up with. You probably won’t get them completely right, but it’s usually worth spending the time to try to get it as close as possible.

1. Updating: now.
2. Refactoring: now.
3. Miscellaneous Cleanups: now.

An emergency bugfix

You need to get a bug fix to users ASAP. While you might see problems along the way, but unless they’re relevant to this particular bug fix, it’s best to put them off until later.

Sometimes that might mean fixing the bug twice: once in a quick hacky way, and a second time after you’ve done the necessary cleanups.

1. Updating: later.
2. Refactoring: later.
3. Miscellaneous Cleanups: later.

New feature or non-urgent bugfix

When you have a project in active development and you’re doing ongoing work, whether features or bug fixes, you have a great opportunity to incrementally clean up your code.

You don’t need to fix everything every time you touch the code. Instead, an ongoing cleanup of code you’re already touching will cumulatively keep your codebase in good shape. See Ron Jefferies’ excellent article for details.

1. Updating: now, for code you’re touching.
2. Refactoring: now, for code you’re touching.
3. Miscellaneous Cleanups: now, for code you’re touching.

A project in maintenance mode

Eventually your project will be finished: not much new development is done, and mostly it just gets slightly tweaked every few months when something breaks or a drop-down menu needs an extra option.

Your goal at this point is to do the minimum work necessary to keep the project going. Refactoring and Miscellaneous Cleanups aren’t necessary, but Updates might be—dependencies can stop working, or need security updates. And jumping your dependencies 5 years ahead is often much harder than incrementally doing 5 dependency updates at yearly intervals.

So whenever you have to do fix a bug, you should update the dependencies—ideally to Long Term Support releases to reduce the need for API usage updates.

1. Updating: now, ideally to Long Term Support releases.
2. Refactoring: never.
3. Miscellaneous Cleanups: never.

Balancing present and future

Software projects tend to ongoing processes, not one-off efforts. A cleanup now might save you time later—but if you have a deadline to meet now, it might be better to put it off even at the cost of slightly more work later on.

So takes this article only a starting point: as with any heuristic, there will be exceptions to the rule, and you need to be guided by your situation and your goals.

By the time you get home from work and finish your chores you’re too tired to do anything but watch TV and then collapse into bed. And weekends are just as bad.

But you could have a 3-day weekend not just on holidays, but every single week: learn how you can do it.

How to ensure that your tests run code that you think they are running, and how to measure your coverage over multiple tox runs (in parallel!).

Deadlines are hard to navigate.

On the one hand you risk crashing into the rocks of late delivery, and on the other you risk drowning in a whirlpool of broken software and technical debt.

And if you end up working long hours—if your only solution is crunch time—you risk both burnout and technical debt at the same time. Metaphorically, you risk drowning in a whirlpool full of fire-breathing rocks.

Given too much work and not enough time, you need to say “no” to your boss and negotiate a better set of deliverables—but what exactly should you be aiming for?

• You could argue for maintainable, well-tested code, and deadlines be damned. But what if the deadline is important?
• Or you could argue for meeting the deadline no matter what, even if that means shipping untested or buggy code. But doesn’t the code need to work?

This dilemma is a false dichotomy. Quality is situation-specific and feature-specific, and rote answers aren’t enough to decide on an implementation strategy.

The real answer is to prioritize the work that really matters in your particular situation, and jettison the rest until after the deadline. And that means saying “no”: saying no to your boss, saying no to your customer, and saying no to yourself.

No, all the code can’t be perfect (but this part needs to be rock solid).

No, that feature isn’t important for addressing our goals.

No, we can’t just add one small thing. But yes, we will do what really matters.

Let’s consider two examples: the same task, but very different goals and implementations.

Deadline #1: Raise money or lose your job

I once worked at a startup that was starting to run out of money: our existing business was not working. Luckily, the co-founders had come up with a plan. We would pivot to a trendy new area—Docker was just gaining traction—where we could build something no one else could do at the time.

Now, this meant were going to be building a distributed system, a notoriously difficult and complex task. And we had a deadline we had to meet: we were going to do a press campaign for our initial release, and that requires a few weeks of lead time. And of course on a broader level we needed to make enough of an impression that we could get funding before our current money ran out.

How do you build a complex, sophisticated piece of software with a short deadline? You start with your goals.

Start with your goals

Our goal was to demonstrate the key feature that only our software could do. We decided to do that by having users walk through a tutorial that demonstrated our value proposition. If the user was impressed with our tutorial then we’d succeeded; we explicitly told users that the software was not yet usable in the real world.

Based on this operational goal we were able to make a whole slew of simplifications:

1. A production system would need to support parallel operation, but our tutorial only needed to be used by a single user. So we implemented the distributed system by having a CLI that SSHed into a machine and ran another CLI.
2. A production system would need to handle errors, but our tutorial could run in a controlled environment. We created a Vagrant config that started two virtual machines, and didn’t bother writing error handling for lost connections, inaccessible machines, and so on.
3. A production system would need to be upgradeable and maintainable, but our tutorial would only be run once. So we didn’t bother writing unit tests for most of it, and focused instead on manually running through the tutorial.

Now, you might be thinking “but you’re choosing to abandon quality”. But again, none of the things we dropped were relevant to our goal at that time. Spending time on “best practices” that aren’t relevant to your particular situation is a waste of time.

We were very clear in all our documentation and marketing efforts that this was a demonstration only, and that we would be following up later with a production-ready release.

Prioritize features by goals

Even with these simplifications, we ended up dropping features to meet the deadline. How did we decide which futures to drop?

We started by implementing the minimal features needed to demonstrate our core value, and so when we ran out of time we dropped the remaining, less critical features. We stopped coding before the drop date (a week or a few days, I believe), and focused just on testing and polishing our documentation.

Dropping features was quite alright: the idea was good enough, the tutorial was compelling enough, and our VP of Marketing was skilled enough, that we were able to raise a $12 million Series A based off that unscalable, unmaintainable piece of software. And after the initial release and publicity push we had time to implement those later features to keep up our momentum.

Deadline #2: Production-ready software

Once we had VC funding we rebuilt the product with the same set of features, but a very different goal: a production-ready product. That meant we needed a good architecture, an installer, error handling, good test coverage, and so on. It took much longer, and required a much bigger team, but that was OK: we had a different goal, and enough funding to allow for a longer deadline (again, based on publicity needs).

Even so, we made sure to choose and prioritize our work based on our goal: getting users and customers for our product. Our initial prototype had involved a peer-to-peer storage transfer mechanism, and making that production ready would have been a large R&D exercise. So in the short term we focused on cloud storage, a much simpler problem.

And we made sure to drop less important features as deadlines approached. We certainly didn’t do a perfect job, e.g. we dropped one feature that was half-implemented. We would have done better not starting it at all, since it was less important. But, we succeeded: the code was maintainable, people started using it, and we didn’t have to rely on crunch time to deliver.

Beyond universal solutions

There is no universal solution that will work everywhere, no easy answers. All you can do is ask “why”: why are we doing this, and why does this matter?

Once you know your goals, you can try and prioritize—and negotiate for—a solution that achieves your goals, meets the deadline, and doesn’t involve long hours:

1. Drop features that don’t further your goals, and start with the most important features, in case you run out of time for the rest.
2. Write high-quality code in the places where it matters, and drop “quality” where it doesn’t (what Havoc Pennington calls “professional corner-cutting”, emphasis on professional.)
3. And if you still have too much work, it’s time to have a discussion with your boss or customer about what tradeoffs they actually want.

There’s always too much work to do: too many features to implement, too many bugs to fix—and working evenings and weekends won’t help.

The real solution is working fewer hours. Learn how you can get a 3-day weekend.

These are a few quick notes to self, rather than a cogent thesis. I want to get this out while it’s still fresh, and I want to lower my own mental barrier to publishing here.

I’ve been thinking about test coverage recently, inspired by conversations that followed DRMacIver’s recent post.

Here’s my current working hypothesis:

• every new project should have 100% test coverage
• every existing project should have a ratchet that enforces increasing coverage
• “100% coverage” means that every line is either:
  • covered by the test suite
  • or has some markup in code saying that it is explicitly not covered, and why that’s the case
• these should be enforced in CI

The justification is that “the test of all knowledge is experiment” [0]. While we should absolutely make our code easy to reason about, and prove as much as we can about it, we need to check what it does against actual reality.

Simple testing really can prevent most critical failures. It’s OK to not test some part of your code, but that should be a conscious, local, recorded decision. You have to explicitly opt out of test coverage. The tooling should create a moment where you either write a test, or you turn around and say “hold my beer”.

Switching to this for an existing project can be prohibitively expensive, though, so a ratchet is a good idea. The ratchet should be “lines of uncovered code”, and that should only be allowed to go down. Don’t ratchet on percentages, as that will let people add new lines of uncovered code.

Naturally, all of this has to be enforced in CI. No one is going to remember to run the coverage tool, and no one is going to remember to check for it during code review. Also, it’s almost always easier to get negative feedback from a robot than a human.

I tagged this post with Haskell, because I think all of this is theoretically possible to achieve on a Haskell project, but requires way too much tooling to set up.

• hpc is great, but it is not particularly user friendly.
• Existing code coverage SaaS services don’t support expression-level coverage.
• hpc has mechanisms for excluding code from coverage, but it’s not by marking up your code
• hpc has some theoretically correct but pragmatically unfortunate defaults, e.g. it’ll report partial coverage for an otherwise guard, because it’s never run through when otherwise is False
• There are no good ratchet tools

As a bit of an experiment, I set up a test coverage ratchet with graphql-api. I wanted both to test out my new enthusiasm for aiming for 100% coverage, and I wanted to make it easier to review PRs.

The ratchet script is some ad hoc Python, but it’s working. External contributors are actually writing tests, because the computer tells them to do so. I need to think less hard about PRs, because I can look at the tests to see what they actually do. And we are slowly improving our test coverage.

I want to build on this tooling to provide something genuinely good, but I honestly don’t have the budget for it at present. I hope to at least write a good README or user guide that illustrates what I’m aiming for. Don’t hold your breath.

You’ve been working as a programmer for a few years, you’ve been promoted once or twice, and now you’re wondering what’s next. The path until this point was straightforward: you learned how to work on your own, and then you get promoted to Senior Software Engineer or some equivalent job title.

But now there’s no clear path ahead.

Do you become a manager and stop coding?

Do you just learn new technologies, or is that not enough?

What should you be aiming for?

In this post I’d like to present an alternative career progression, an alternative that will give you more autonomy, and more bargaining power. And unlike becoming a manager, it will still allow you to write code.

From coding to solving problems

In the end, your job as a programmer is solving problems, not writing code. Solving problems requires:

1. Finding and identifying the problem.
2. Coming up with a solution.
3. Implementing the solution.

Each of these can be thought of a skill-tree: a set of related skills that can be developed separately and in parallel. In practice, however, you’ll often start in reverse order with the third skill tree, and add the others on one by one as you become more experienced.

Randall Koutnik describes these as job titles of a sort, a career progression: Implementers, Solvers, and Finders.

As an Implementer, you’re an inexperienced programmer, and your tasks are defined by someone else: you just implement small, well-specified chunks of code.

Let’s imagine you work for a company building a website for animal owners. You go to work and get handed a task: “Add a drop-down menu over here listing all iguana diseases, which you can get from the IGUANA_DISEASE table. Selecting a menu item should redirect you the appropriate page.”

You don’t know why a user is going to be listing iguana diseases, and you don’t have to spend too much time figuring out how to implement it. You just do what you’re told.

As you become more experienced, you become a Solver: are able to come up with solutions to less well-defined problems.

You get handed a problem: “We need to add a section to the website where pet owners can figure out if their pet is sick.” You figure out what data you have and which APIs you can use, you come up with a UI together with the designer, and then you create an implementation plan. Then you start coding.

Eventually you become a Finder: you begin identifying problems on your own and figuring out their underlying causes.

You go talk to your manager about the iguanas: almost no one owns iguanas, why are they being given equal space on the screen as cats and dogs? Not to mention that writing iguana-specific code seems like a waste of time, shouldn’t you be writing generic code that will work for all animals?

After some discussion you figure out that the website architecture, business logic, and design are going to have to be redone so that you don’t have to write new code every time a new animal is added. If you come up with the right architecture, adding a new animal will take just an hour’s work, so the company can serve many niche animal markets at low cost. Designing and implementing the solution will likely be enough work that you’re going to have to work with the whole team to do it.

The benefits of being a Finder

Many programmers end up as Solvers and don’t quite know what to do next. If management isn’t your thing, becoming a Finder is a great next step, for two reasons: autonomy and productivity.

Koutnik’s main point is that each of these three stages gives you more autonomy. As an Implementer you have very little autonomy, as a Solver you have more, and as a Finder you have lots: you’re given a pile of vague goals and constraints and it’s up to you to figure out what to do. And this can be a lot of fun.

But there’s another benefit: as you move from Implementer to Solver to Finder you become more productive, because you’re doing less unnecessary work.

• If you’re just implementing a solution someone else specified, then you might be stuck with an inefficient solution.
• If you’re just coming up with a solution and taking the problem statement at face value, then you might end up solving the wrong problem, when there’s another more fundamental problem that’s causing all the trouble.

The better you are at diagnosing and identifying underlying problems, coming up with solutions, and working with others, the less unnecessary work you’ll do, and the more productive you’ll be.

Leveraging your productivity

If you’re a Finder you’re vastly more productive, which makes you a far more valuable employee. You’re the person who finds the expensive problems, who identifies the roadblocks no one knew where there, who discovers what your customers really wanted.

And that means you have far more negotiating leverage:

• You can more easily negotiate a raise.
• You can find better jobs, even with a technology you don’t know.
• You can even negotiate a 3-day weekend.

So if you want to keep coding, and you still want to progress in your career, start looking for problems. If you pay attention, you’ll find them everywhere.

There’s always too much work to do: too many features to implement, too many bugs to fix—and working evenings and weekends won’t help.

The real solution is working fewer hours. Learn how you can get a 3-day weekend.

(Thanks to Brian for his feedback. All mistakes and omissions that remain are mine.)

Some commands have a --dry-run option, which simulates running the command but without taking effect. Sometimes the option exists for speed reasons: just pretending to do something is faster than doing it. However, more often this is because doing it can cause big, possibly detrimental, effects, and it is nice to be able to see what would happen before running the script.

For example, ansible-playbook has the --check option, which will not actually have any effect: it will just report what ansible would have done. This is useful when editing a playbook or changing the configuration.

However, this is the worst possible default. If we have already decided that our command can cause much harm, and one way to mitigate the harm is to run it in a "dry run" mode and have a human check that this makes sense, why is "cause damage" the default?

As someone in SRE/DevOps jobs, many of the utilities I run can cause great harm without care. They are built to destroy whole environments in one go, or to upgrade several services, or to clean out unneeded data. Running it against the wrong database, or against the wrong environment, can wreak all kinds of havoc: from disabling a whole team for a day to actual financial harm to the company.

For this reason, the default of every tool I write is to run in dry run mode, and when wanting to actually have effect, explicitly specify --no-dry-run. This means that my finger accidentally slipping on the enter key just causes something to appear on my screen. After I am satisfied with the command, I up-arrow and add --no-dry-run to the end.

I now do it as a matter of course, even for cases where the stakes are lower. For example, the utility that publishes this blog has a --no-dry-run that publishes the blog. When run without arguments, it renders the blog locally so I can check it for errors.

So I really have no excuses... When I write a tool for serious production system, I always implement a --no-dry-run option, and have dry runs by default. What about you?