Roslyn also has an extra constraint of integrating with live editing on the fly; I think you can get simpler and/or have different constraints if your requirement is only incremental compilation.

Really fun and fascinating problem to work on.

But it seems like we're finally starting to accept that "accidentals" like network effects, ownership, accountability, etc. are important. Of course, that's why many of us fled to technical corners in the first place - because the "accidentals" become tied up with things like nepotism, unfair and arbitrary judges from random humans who don't understand your merit, the need for bullshitting more than real technical value. Supposedly, anyways.

Efficiency and finding the right abstractions and tradeoffs is literally identical with making new capacities and capabilities. If all of your systems were truly "run by AI", we wouldn't have systems. Systems are systems because the AI uses well developed tools and protocols that humans have put together. It's a stable capacity.

Given that AI is already so good at swe at this point, it feels harder to get a callback because they're probably super AI pilled and I have no idea how good AI is at writing the things that these guys need for performance (I mean, judging from my local work, they seem pretty good at what I'm trying to learn, but I am a beginner in the field). I've received no callbacks either though so I don't know.

Not talking about prompting Claude "please write me a philosophical dialogue". It has no skin in the game. If it writes 20 paragraphs of slop and is factually wrong and costs other people labor, is there any correction mechanism?

We can correct it explicitly via training I guess, but it's debatable to what extent accountability applies to AI.

We like to pretend that we have our nice epistemology, separated into a priori and a posteroi and all that, but at the end of the day, accountability is what underpins social relations and keeps things going (even if you're libertarian, you have to accept this - even if you don't agree some state should centralize it, it's still just true in terms of basic human social relations).

Of course, another possibility is utter domination - I am so much better than you (for some definition of better) that none of your puny responses matter. OK. Then don't claim both that, and that you're weird for disliking AI and that AI has no problems when integrating with society - too many people have these basic cognitive dissonances.

Do I reflect on the objectively correct structure of the story? Do I "agree" with everything Dostoevsky is saying? Not really.

I don't know if this offends literature people or not. But this sure as hell is incompatible with the AI bro paradigm in which all thought can be objectively reduced to some shared standard to benchmark on.

My position will be read into as deep irrationality from many opposing positions, but it's fine.

Now, none of this doesn't "preclude* the ability for AI to have the same kinds of network effects. Of course it can - that's what I've been saying for the longest time. But don't confuse, becoming integrated with society, with this ideal of superhuman X.

Now of course, you could argue, "there is no such thing as anything else besides becoming integrated with society. There is no other standard that works." Very good! Darwinian-adjacnet positions are interesting. This is a position - but then don't claim it's equivalent, or god forbid, the exact same, as the "super AGI" ontology. These require fundamentally different assumptions about what justified what, and an attempt to try and pretend otherwise is sophistry.

If you're saying that math has a consolidating network effect, sure. There are a lot of people who think similar problems are important. I guarantee you that when math was small, that is not the case, and when coming up with new math, that's not the case, and when coming up with sub problems to tackle large problems, that is not the case.

It's actually a very goated term but not everything is causal, it also has precise technical meanings (although those get blurred too given that causal can mean anything from intervention proper, to mere depdnence on something prior)

It's probably the determinism and reproducibility that's the key constraint in HFT settings. In other industries it's just generic textbook numerical analysis (analyze batched eps error), and then in engineering land it's "ehh good enough".

That's important because in plenty of other software - databases, OSes, compilers and binary formats, networks, etc. name your favorite CS topic, there is an open standard for it.

You can either achieve interoperability by... achieving interoperability, or by dominating market share and edging the user's action space to solely stay in your ecosystem (change the action distribution of your user) that for all intents and purposes that it "interoperates with everything" means that, "since I bought entirely into this ecosystem, I interoperate with everything".

So ultimately I agree with you, but to present it as a failure of objective engineering prowess on open source is weird. As you said yourself, it's clearly the network effects that prop the business model. And sure, gaining market share isn't a trivial task, but it's not engineering.

I think framing it in the interoperability way is healthier - because it actually provides a potential new out to work towards, rather than this weird beration double-bind that frames Microsoft's approach as The Objective Way to do things. It's not and doesn't have to be.

Anyone trusting AI as the single authoritative source of information is stupid - but this follows from the fact that trusting anyone as a "singular point" as a source of information is stupid. You corroborate, you intervene on the world to test your mental model, you discuss with other people. That's what learning is. I've never learned from start to back to a textbook before as the single source of information (besides one philosophy of science textbook; in which I spent a month digging around adjacent fields, and then it just so happened that that one textbook synthesized every piece of information I looked up, and it was mostly a consolidating review).

If your study pre-supposes certain courses of action and artificially constrains the action space for the sake of "reproducibility", you may get a result, and a "scientifically rigorous one". But it's not going to say anything about reality in any meaningful way. While anecdotes and the complexity of real life isn't "science" (in that it's a controlled, repeatable, interventional experiment that's subject to a community of critics who want to hold you up to standards of rigor), there's far more truth in how people actually proceed and engage with these tools.

I will say, generally, it depends on what you mean. There's like, numerical drift, and then there's converting to the discrete part. Generally, I'd say that "getting into the discrete part" of an algorithm, unless you're trying to match behavior of another piece of software, is fine. Like, if you're playing a game, people are not going to care if the shot just grazed them and missed, but the "Actual True Value" is like 10^-9 off or something. They will just chalk it up to "close, unlucky". If you have one canonical place for your floating point errors from which your "discrete" parts derive, it's no biggie.

But at work, I recently debugged an algorithm in which the author, effectively wanted to compute the integer and fractional parts of a double type. Their reasoning was that, for the fractional part, there can be imprecision, so you should do some fancy math to recover the fractional part.

This is giga wrong for the application we were building. You never want to try computing the "same double" in two different ways and compose the results like this. Intuitively, if one path "thinks" your double is 15.00001, and the other path "thinks" its 14.99999, you're now saying that the double is 15 (integer) + .99999 (fractional) = 15.99999: this is way off now!

You should just use one double, and extract out the integer and fractional parts. It doesn't matter if the double is imprecise - it's authoritatively imprecise. Much easier to reason about.

This is the first time I've debugged an edge case like this since I wouldn't implement something like this, but it's an example of how subtle floating point errors can be.

(Although in general, there's no true difference between "I answered the question correctly, but the question was mapped to this thing we call 'reality' wrong", and "I answered the question incorrectly", because you can (try) adding the constraints that you really wanted targeted in case A, to case B, and boom, suddenly a question/answer pair that was "Answered correctly, but question doesn't map to reality" now becomes, "You answered this question wrong". However, individuals generally tend to have some breakpoint to differentiate between the two).

There are two ways to solve a problem. Either solve the problem, or deem it irrelevant.

The implication here is that, you, the human operator, clearly are just confused. The LLM knows best. You're just a stupid human. The LLM knows objective truth, you do not. You have concerns, questions, the LLM didn't understand your question "properly"? Do not worry, the LLM objectively knows the optimal course of action. It thought through the implications of what you said, took into account all possible data, and came to the objectively correct design for your software, your society, your life.

In some sense, this problem would have been a societal problem within the next several decades anyways, but it's been hyper-accelerated by AI.

It's not a new problem in some sense. If you've dealt with really smart but really arrogant friends, they might jump ahead 10 steps and assume your rebuttal, posit theirs, assume your rebuttal to their posit, etc. etc. without... actually taking the time to listen carefully. On the national scale, this looks like forced trust in government authorities about what is "objectively best".

People need to get it through their skulls that, even if an AI, or any intelligence, could even solve the damn Riemann Hypothesis: if it's wrong, it's wrong. Of course, I think all of us know the objection - we see it on hackernews all the time. "You guys are just stupid contrarians who can't understand AI's deep reasoning". OK. The second inference? "therefore you are unable to govern yourselves properly - your concerns are all fallacies, misunderstandings, bad for you, etc.".

You might think that the second inference is extreme and nobody actually believes that, but as always, it's a gradient. Before AI, you might've had an extremely strong sense of self. Now? You look at OpenAI solving open math problems left and right on a public foundation model, and you think, "Maybe I should just trust it more. If I spend cycles thinking, it's probably going to outdo me anyways." The AI model silently makes 5 different assumptions and transformations? "Well, maybe it was rational in the space of tradeoffs to do that. The AI knows best, after all". You might be thinking of an architecture with 5 different key constraints based on lived experience, in which the AI keeps misunderstanding. "Oh, well, this genius-level mathematician/programmer AI isn't understanding my words - surely I must be mistaken, right? It's only humble to think that way".

I can't convince people otherwise though. After all, I can't "prove" that you should have a backbone when talking to AI. it could just as easily be "you're arrogant, this machine is in the top of all academic fields and is coming for all white collar jobs, who's to say you're right about anything?" All I can say is, there's a reason why Dostoevsky is one of my favorite authors.