Links/#ai×/14 entries

The intersection of AI tooling and open source projects is interesting to me. I’ve also come around to the reality that LLM-powered coding agents like Claude code or codex are pretty clearly game changing when it comes to how software gets produced.

AI is a tool, just like other tools we use. And it's clearly a useful one.

It may not have been that "clearly" even just a year ago, but it's no longer in question today.

I also agree this wasn’t true even a year ago, but the last couple of generations of models have seen some sort of threshold crossed that we didn’t know was there and they are legitimately very useful in my day to day work now.

They also fundamentally change the nature of open source software development. Now that writing code by hand is virtually an obsolete skill, where does that leave maintainers? And what is the point of open source?

Sure, the social angle of working on open source is important and often a very motivating part of the project, but in the end that's a side benefit, not the point of the project.

This was part of it, and a big part of what I love about it. To me it’s really enjoyable working on interesting projects with a globally distributed team of people who also enjoy the work. Part of it was also performative, a way to demonstrate publicly that you could write decent software. I think that is mostly over.

In the kernel community we do open source because it results in better technology, not because of religious reasons.

Maybe that just leaves us with this — writing open source because it produces better results. This doesn’t strike me as universally true though. I guess it depends what you mean by “better”, but I can think of many examples of software where the proprietary version is much better than the OSS one.

I always appreciate these kinds of candid analyses. I’ve become convinced that all the various things people refer to as “AI” are indeed very useful, but they don’t obviate the need for actual engineering. At this point it’s been many years since LLMs became broadly available and the benefits remain largely hypothetical and in the future but the harms are very much real and present today.

This really hit home for me. Lately I've been feeling very overwhelmed and bored at work, feeling like I'm just drowning in AI slop and cannot possibly keep up. AI-generated PRs are flying through our previously robust process, production problems are way up, most of what I read day to day is AI-generated, and it's frankly exhausting.

It feels like something has shifted just in the last few months. I'm not sure if it actually has, or if my organization has just taken up AI to a new extent recently, but somehow the day to day enjoyment I used to get out of programming has mostly disappeared. I've been reflecting on why that is and what I might need to change going forward, but I'm not sure what that is yet. For now I enjoy lamenting the loss of the old way of doing things with other old fogeys around the internet.

This was a great read, an interesting takeaway is that we should be thinking about interacting with APIs differently when writing "agents" (I like Simon Willison's definition of "agent" as an LLM that runs "tools in a loop to achieve a goal").

Normally a human user of an API knows exactly what they want and makes a specific request for it. LLMs have no idea what they're looking for (they should have some means of validating or benchmarking the responses they get, but that's another too-often-ignored problem for another day), so they need to be able to iterate faster. Instead of sending a single (AI-generated) request, getting some (AI-generated) response, checking it, then repeating, we should be sending multiple requests and evaluating multiple responses in parallel before converging on the "best" answer. This means adding much faster and more robust support for branching queries and rollbacks, since in the world of agents these are extremely common compared to the rare cases they're needed during human interaction.