movpasd

Stirling's approximation is also used a lot in statistical mechanics, because you often have to calculate logs of state space sizes, which means lots of combinatorics and thus lots of factorials. Plus it's continuous so you can do calculus.

This is significantly more thorough than any testing I've done, and in a totally different domain, but my anecdotal experience getting agents to use Hypothesis was quite poor.

The agent really, really struggled to bridge the gap between the code and the actual business rules it was meant to be modelling. It also struggled to work out which functions at which layer were appropriate to write tests for. So, its tests tended to be very brittle to changes to domain logic.

Essentially, agents have always seemed to struggle with modularity and problem decomposition. Good testing is about finding the right things to test, which means working out how to subdivide the input state into an appropriate product state, and checking each behaviour independently. IMO, this is the most difficult and complicated thing about programming, so I won't say it struggled _more_ than a human would --- but humans have the advantage of being able to sleep on it?

One minor thing I observed was that it tended to get really hung up on floating point edge cases (NaNs, infinities). Maybe floating point edge cases are over-represented in the property-testing training data, but it's essentially irrelevant for my usecase, at least as far as the business rules go.

I don't know. I have a fascination with code structure which is definitely primarily aesthetic, not practical --- which is a compulsion I have to suppress when doing code professionally for practical reasons. (Sometimes you have to deliver.)

I find it incredible when the concepts in a system fall into place and start lining up and you get these unexpected relationships that "just work". It's beautiful in the same way mathematics is beautiful. It's lovely when something is made of lots of pieces at first and then suddenly collapse and simplify into a single structure.

I also enjoy other bits of programming. I like the flow state, I like the act of creation, I like feeling like I have an impact, I like being paid. So it's not like people fall into one category or another, and it's not like programming is one single thing. There's room within any discipline for creativity and for art.

I think many professional artists probably spend much of their time problem-solving, too --- authors trying to fit plot points together, sculptors working out which materials will work with which ones.

I would absolutely love to see the transcript of that, if it's available to publish!

How would one start learning about it?

It's just economics. VCs dump large amounts of money into early-stage platforms with the express purpose of exploiting it later. This is an inevitable part of the lifecycle of high capital, low margin industries with strong network effects.

This is not VCs being evil and corrupting a pristine engineer- or hacker-defined concept of true value. The VCs are all following the rules and are trying to make money off of risky investments (it's "venture" capital, after all). But the incentive structure just forces the market into either an oligopoly of largely extractive services (or into everything being free: that's why open source is also a stable point for software).

My hope is that in time, basic software services, like for communication, socialising, community hosting, and so on, will eventually become seen as core social infrastructure. I don't really think this can happen via existing institutions, even open source, because the fixed costs of making software are really high. You really need _tax_ to support this. But it's very difficult to do because the internet cuts across borders.

It's LLM-like in the details, but there are certain patterns of repetition and idiosyncrasies, also certain analogies I don't think an LLM would make (like the aliens thing), that make me believe that it's written by a human.

Forgive me if a dumb question, but is there no compaction? Do you ever run out of context?

Me as well. One of my formative experiences was reading Refactoring (Fowler). At the time I felt skeptical that it could make a difference --- I had mostly written scientific/research code at this point. I decided as an experiment to apply it to a dissatisfaction I had with the structure of a codebase and it was a revelation. It really made it really concrete what people meant by "a codebase is a system", made me see code from a higher level, almost like a continuous tissue or web you could pull and prod.

It's quite sad to think this experience might be on its way out, and it really highlights that "junior problem" from AI. There is just no way around diving in to get an intuition for things. Naur warned us of this 40 years ago but the lesson never seems to get learned.

I feel like the development of AI has really shown what a gigantic spectrum intelligence actually is.

There's an "expert trap" in here I think, at least this is something I experience. When you are good at something, it's a lot less effort to draw on that stuff, and you usually get instant easy positive feedback from non-experts. So, for an engineer, that's code and tools and the like, but also productivity systems and other abstract thinking modes that are useful in programming. On the flipside, actually thinking about your product and domain is hard. So, it becomes psychologically easy to get caught up in technical things that are comparatively easy mental and social reward per unit effort, and you feel productive. But it's not always objectively the best use of time.

I don't expect technology companies to do the right thing by the consumer. There is a niche for companies or NGOs that manage to stick to their core mission, but the incentive structure for software (very high capital cost, near zero marginal cost, huge opportunities for rent-seeking) is just always going to push them to these behaviours. It's not a conspiracy theory, either. VCs deliberately bank on this --- they have to if they want to get return on investment.

By all means, put pressure on companies who are doing this. Public opinion does matter. But the fundamentals are just not going to change without regulation. It is a tough nut to crack.

You're talking about the material of the Twitter discussions where I'm more discussing the article itself, which is about the community and discourse surrounding it.

If you want my opinion on that, Odin seems pretty notable to me, and I would expect there to be a Wikipedia article for it, and I'd be all for engaging with the editors on that. I wouldn't expect Wikipedia moderators to be plugged into the world of programming languages the way I am, though. Like the article says, this seems like subject-specific notability guidelines could help. It's definitely true that the programming world is missing the kind of institutional material that works well for, say, science or news.

I despise the "what's true is true" attitude exhibited by Casey and Bill in these tweets. I find it to be an instant flag of unreliability. The smartest human alive is still a human. We have a stake in every conversation, we bring our baggage and our allegiances.

So often, this attitude is couched as a desire for truth and objectivity. Do you know what somebody does when they _actually_ love the truth? They work hard to find it, they examine their own assumptions, they try to build systems that extract truth from an unbelievably complex world of unreliable narrators. And most importantly, they are curious: for example, curious about how other communities operate and what they can learn from them.

It is the hypocrisy that I find unbearable. The language of truth and objectivity wielded to win arguments which are fundamentally emotional in nature. There is nothing wrong with being frustrated that a community dear and significant to you, clearly notable, seems to be overlooked by a figure of (informational) authority. Let's take that frustration and work together to improve our truth-seeking institutions.