Semver was an attempt to make versioning true-or-false: either a release breaks compatibility or it doesn’t. In practice it became good-or-bad anyway. Different ecosystems interpret it differently. Some communities treat it as sacred law, others as rough guidance. The spec’s existence didn’t settle the question. Hyrum’s Law makes it worse: a bug fix for one user is a breaking change for someone who depended on that bug. There’s no objectively correct version bump, only outcomes that help or hurt specific people.

This and other good points, in Package Management is a Wicked Problem.

The stories of the early days of Slack have reached the icky stage.

Get your hands dirty and use the tools. Build something real. Build a prototype. Build a thousand little experiments in an afternoon. Challenge yourself to try to do something you previously would have assumed is impossible, or infeasible, or unaffordable. Find one of the ways that you’re worried that the new tools are going to lead to trouble, and actively fix it. Then examine the things you’re learning. Update your constants.

This feels very true to me.

In software—and only in software—ops has become a dirty word. Nobody wants to claim it. Operations teams got renamed to DevOps teams, SRE, infrastructure, production engineering, or more recently, platform engineering teams. Anything but ops.

All of the coding agents are fluent in using Git’s features, both basic and advanced. This fluency means we can be more ambitious about how we use Git ourselves. We don’t need to memorize how to do things with Git, but staying aware of what’s possible means we can take advantage of the full suite of Git’s abilities.

This is specifically about Git, a notoriously tricky tool, but is very true for many others too.

Solid advice for writing software to last 10+ years. I’ve done this once; I hope to manage to do it again before retiring.

I also find it annoying when Claude Code picks an old action version, but dependabot immediately opens a PR to fix it, so that seems as convenient as remembering to point it at something like this. It was more annoying when it used to do web searches to try to get the hash to pin to (which was always wrong), but these days I think there’s a baked in skill that knows how to use Git to get the right one.

I like “numbers you should know”, like this Python one or this latency one, but I feel you should have a sense of the difference here, and maybe some idea of the algorithmic complexity and CPU/memory trade-offs for some, but don’t need to actually know them. You can always check them, or look up pages like this.

(The Python one is a very nicely put together page, though.)

Interesting insights into managing Go dependency updates particularly in light of the recent Trivy issues.

First and most obvious, when we read to hit a goal rather than simply for pleasure, everybody reads as fast as possible to hike up their numbers.

I don’t find this to be true. If I wanted to bump my numbers, I would read short books. The goal encourages me to read rather than scroll TikTok or other less healthy ways to consume time.

More worrisome, when we read fast, we experience nothing. The book does not have a chance to burrow into our heart.

I don’t think this is true, either. I have read fast my entire life, and absolutely there have been books that burrowed into my heart.

If we’re gamifying our reading, we stop reading widely: we pick different versions of a story that we are guaranteed to like, and with that we lose a sense of well-roundedness, a sense of discovery and surprise.

Surprise, I disagree with this too. I read more widely now than I did in years past, and I track my reading now and didn’t in the past. I don’t believe there is any connection at all between these two things.

From What We Lose When We Gamify Reading - I’m not sure I would classify tracking reading and an annual goal as gamifying either.

None of them are what I would call evangelists; they are people who have felt reticent to adopt these tools, and still have some serious feelings about being complicit in the environmental cost and the politics around who’s building these tools and why. But now they’re using AI—Claude, mostly—to do the vast majority of their programming work.

👋 🙋

I don’t love Spotify, and I have issues with Wrapped, but this is an interesting insight into producing more than a billion AI generated reports.

I keep coming back to the trees. I’ve been maintaining Open Source projects for close to two decades now. The last startup I worked on, I spent 10 years at. That’s not because I’m particularly disciplined or virtuous. It’s because I or someone else, planted something, and then I kept showing up, and eventually the thing had roots that went deeper than my enthusiasm on any given day. That’s what time does! It turns some idea or plan into a commitment and a commitment into something that can shelter and grow other people.

A bit like a Mainland ad but for AI (and more thoughtful).

Clearly a bit biased, but good advice on feature flags that goes beyond Posthog.

I used a feature flags system like described here for something like a decade before moving to LaunchDarkly and it worked amazingly well. I’m not sure the move to LaunchDarkly actually paid off.

An interesting summary of expression end detection across a bunch of programming languages. Including Odin, which I have never heard of previously.

All of these lessons learned are ones I’ve seen before, but it’s a nice collection.

I particularly resonate with:

Admitting what you don’t know creates more safety than pretending you do.

and

Writing forces clarity. The fastest way to learn something better is to try teaching it.

An interesting use of nullcontext to have a parametrized test with valid and raising values.

An interesting categorisation of forks (towards the end of the post) from Monty of MySQL and MariaDB fame.

“Defense in Depth: A Practical Guide to Python Supply Chain Security”: an incredibly high quality post by someone with respectable credentials. Read the whole thing.

I like (a lot) webs of trust, but this human.json doesn’t seem like it will scale or give great information or really own the trust. I do like seeing example.com/~user style URLs, though, very nostalgic.

We need to expect to earn our keep. In the ZIRP era I think a lot of software devs lived in some world where they expected to make a lot of money in a way that was divorced from the realities of the business.

I feel there’s truth here, as much as I sympathise with those hit by change (and as much as it was painful when I was).

A depressing read on how ads have screwed up websites. I read almost everything either via RSS (NetNewsWire) or a read-later tool (Instapaper) so see almost none of this. The bit I hit most is the “CLS Disaster” on Goodreads, where I know now I need to wait when entering data because all the links I want will jump down the page after a giant ad loads at the top.

how come so many coders don’t just hate LLMs for stealing their work the way that most writers and photographers and musicians do? The answer boils down to three things:

Coders have long had a history of openly sharing code with each other, as part of an open source, collaborative culture that goes back for more than half a century. Tools for writing and creating code have almost always offered a certain degree of automation and reuse of work, so generating code doesn’t feel like as radical a departure from past practices. Software development is one of the fields with the least-advanced cultures around labor, as workers have almost no history of organizing, and many coders tend to side much more with management as they’ve been conditioned to think of themselves as “future founders” rather than being in solidarity with other workers.

Insightful comments on the likely impact of LLMs on dev work.

Handy suggestions for serving docs so that AI can get them more effectively. Unfortunately, with a ReadTheDocs site behind Cloudflare, I don’t think I can do any of this. Maybe some instructions to get the source files for specific sites, but they can’t be custom then.