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Learnings from 100K lines of Rust with AI (2025)

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Re: Learnings from 100K lines of Rust with AI (2025)

#121

Post by claytongulick »

CrazyStat wrote: post #69
ai_fry_ur_brain wrote: post #62

Earlier quoted context omitted.

I hate how seriously people take the output of an LLMs or how reliable they think it is. Have Claude produce that spec 10 times, use the same prompt and same context. Identical requests, but you'll get 10 unique answers that wil contradict each other with each response seeming extermely confident. Its scary how confident you people are in these outputs.

If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers that will contradict each other and (depending on who you asked) may be just as confident. There are real decisions to be made when going from a vague prompt to a spec. It's not surprising that an LLM would produce different specs for the same work on different runs. If the prompt…

> If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers

But they didn't ask humans, they asked a machine. We expect our machines to behave in predictable ways.

> If the prompt already contained answers to all the decision points that come up when writing the spec then the prompt would already be the spec itself.

This is one of the best arguments against using LLMs I've seen.

It reduces to the classic argument- at the point where you've described a problem and solution in sufficient detail to be confident in the results, you've invented a programming language.

Re: Learnings from 100K lines of Rust with AI (2025)

#122

Post by slopinthebag »

chadd wrote: post #54

We're working on a large Rust codebase, heavily assisted development with Claude and Codex, and one critical workflow is after you have written a spec, have the other LLM critique it thoroughly. This back and forth will take quite a while, but the resulting implementation plan will be 10x better than the original. You can automate this by giving Codex a goal, and a skill to call Claude to review the implementation sp…

It's incredible how much developers will do to avoid having to look at or think about code.

Re: Learnings from 100K lines of Rust with AI (2025)

#123

Post by claytongulick »

dnautics wrote: post #84
b40d-48b2-979e wrote: post #72

Earlier quoted context omitted.

LLMs aren't people. They don't reason. They're token generators, a black box. Your analogy falls on its face with any scrutiny.

LLMs do reason (they just sometimes don't reason well ). I assure you I've met many devs and "engineers" that reason less than LLMs, and are black boxes, especially in terms of the code they write.

> LLMs do reason

No, they don't.

They are token predictors that use statistical techniques to emit the randomly weighted next most likely token given the previous token list.

The result is a strange mimic of human reasoning, because the tokens it predicts are trained on strings that were produced by humans that were reasoning, but that's not the same thing.

Human cognition is complex and poorly understood, and the nature of the mind is an area of study almost as old as consciousness itself. We don't know exactly how it works, or what its exact relationship to the brain is, but we do know that it is not a simple token predictor.

LLMs, by their very nature are constrained to the concept of language and the relationship between existing words in a corpus. This is a box they can not escape.

Modern neuroscience suggests that the human brain is much more vast than that, and in many ways looks like it is constrained by language, but certainly not limited to it.

Re: Learnings from 100K lines of Rust with AI (2025)

#124

Post by dxxvi »

The thing that impresses me most is that the author knows everything (from the high level architecture to the small details) of "multi-Paxos consensus engine" (I have no idea what it is, but it must be very complicated) and can write everything out for AI to read (or did he/she use an app to convert speech to text)?

Re: Learnings from 100K lines of Rust with AI (2025)

#125

Post by dxxvi »

CrazyStat wrote: post #69
ai_fry_ur_brain wrote: post #62

Earlier quoted context omitted.

I hate how seriously people take the output of an LLMs or how reliable they think it is. Have Claude produce that spec 10 times, use the same prompt and same context. Identical requests, but you'll get 10 unique answers that wil contradict each other with each response seeming extermely confident. Its scary how confident you people are in these outputs.

If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers that will contradict each other and (depending on who you asked) may be just as confident. There are real decisions to be made when going from a vague prompt to a spec. It's not surprising that an LLM would produce different specs for the same work on different runs. If the prompt…

> It's not surprising that an LLM would produce different specs for the same work on different runs This is what I don't understand: AI is a computer program with its own data. If we give the same input to that computer program every time, why does it produce different outputs every time? Or does the input include LLM data + our prompt + some random data that computer program picks from its Internet search?

Re: Learnings from 100K lines of Rust with AI (2025)

#126

Post by CrazyStat »

dxxvi wrote: post #125
CrazyStat wrote: post #69

Earlier quoted context omitted.

If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers that will contradict each other and (depending on who you asked) may be just as confident. There are real decisions to be made when going from a vague prompt to a spec. It's not surprising that an LLM would produce different specs for the same work on different runs. If the prompt…

> It's not surprising that an LLM would produce different specs for the same work on different runs This is what I don't understand: AI is a computer program with its own data. If we give the same input to that computer program every time, why does it produce different outputs every time? Or does the input include LLM data + our prompt + some random data that computer program picks from its Internet search?

LLMs have a temperature parameter. At zero temperature they are deterministic: they always choose the most likely next token at each step based on what came before and the model weights, and they will always generate the same output given the same input.

As you raise the temperature they will start (pseudo)randomly choosing tokens other than the single most likely token (though that one will still be the most likely to be chosen). It turns out this is almost always better than zero temperature, which has a tendency to get caught in repetitive loops. I imagine all the frontier labs have spent thousands (millions?) of CPU hours tuning the temperature parameters on their models for optimal performance.

Re: Learnings from 100K lines of Rust with AI (2025)

#127

Post by CrazyStat »

claytongulick wrote: post #121
CrazyStat wrote: post #69

Earlier quoted context omitted.

If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers that will contradict each other and (depending on who you asked) may be just as confident. There are real decisions to be made when going from a vague prompt to a spec. It's not surprising that an LLM would produce different specs for the same work on different runs. If the prompt…

> If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers But they didn't ask humans, they asked a machine. We expect our machines to behave in predictable ways. > If the prompt already contained answers to all the decision points that come up when writing the spec then the prompt would already be the spec itself. This is one of the be…

> We expect our machines to behave in predictable ways.

I expect LLMs to produce randomly varying output. Maybe it's the thousands of hours I spent doing monte carlo simulations for my PhD.

> This is one of the best arguments against using LLMs I've seen.

> It reduces to the classic argument- at the point where you've described a problem and solution in sufficient detail to be confident in the results, you've invented a programming language.

I'm not an LLM true believer, but I use codex for various small tasks and it often (not always) does a thoroughly decent job. Yesterday I gave it a pretty vague request to set up a new Home Assistant dashboard and it handled it just fine--I told it what I wanted to see but it figured out itself which helper variables it would need to set up to realize that vision and wrote all the config for it.

I probably could have done it in 15 minutes if I was familiar with Home Assistant's yaml configuration schema and all, but I'm not so it probably would have taken me closer to an hour. Asking codex took me 30 seconds and it did just fine.

I am skeptical that LLM's are going to kill all white collar jobs or whatever anytime soon. Not being able to truly learn things is an issue. Reality has a surprising amount of detail[1], and while codex does well at things like writing Home Assistant configs and setting up a Minecraft server, where there are thousands of examples online of how to do it, when I've asked it to do some more esoteric things it has sometimes failed spectacularly. I don't think having the LLM keep notes and then read them back (filling up the context window) is a real solution here.

[1] http://johnsalvatier.org/blog/2017/reality-has-a-surprising-...

Re: Learnings from 100K lines of Rust with AI (2025)

#128

Post by johnnyanmac »

embedding-shape wrote: post #118
knivets wrote: post #103

Earlier quoted context omitted.

This is astrology for devs.

Unless you can somehow provide some arguments against it, I feel like you're the one who is trying to cargo-cult stuff here. Say what you will with proper reasoning or arguments if you feel compelled, tired reddit-commentary like that helps no one.

> Unless you can somehow provide some arguments against it,

We're year 4 into this discussion and camps have only gotten more bifrucated. There's no 1-1 discussion to have about this as of now, at least not before the crash.

Your only hope in such discourse is not trying to convince the other party how wrong they are, but appealing to an as of yet undecided party. Be it with reason, or simply pointing out how absurd some comments sound to the average person.

Re: Learnings from 100K lines of Rust with AI (2025)

#129

Post by embedding-shape »

johnnyanmac wrote: post #128
embedding-shape wrote: post #118

Earlier quoted context omitted.

Unless you can somehow provide some arguments against it, I feel like you're the one who is trying to cargo-cult stuff here. Say what you will with proper reasoning or arguments if you feel compelled, tired reddit-commentary like that helps no one.

> Unless you can somehow provide some arguments against it, We're year 4 into this discussion and camps have only gotten more bifrucated. There's no 1-1 discussion to have about this as of now, at least not before the crash. Your only hope in such discourse is not trying to convince the other party how wrong they are, but appealing to an as of yet undecided party. Be it with reason, or simply pointing out how absurd…

> Your only hope in such discourse is not trying to convince the other party how wrong they are

I don't care about convincing anyone, the ones I reply to or others, but if you take the time to leave a comment, at least make it something to read and think about instead of soundbites like "This is astrology for devs", it's plain boring to read and makes HN worse.

Re: Learnings from 100K lines of Rust with AI (2025)

#130

Post by johnnyanmac »

CrazyStat wrote: post #69
ai_fry_ur_brain wrote: post #62

Earlier quoted context omitted.

I hate how seriously people take the output of an LLMs or how reliable they think it is. Have Claude produce that spec 10 times, use the same prompt and same context. Identical requests, but you'll get 10 unique answers that wil contradict each other with each response seeming extermely confident. Its scary how confident you people are in these outputs.

If you ask 10 different humans to produce the spec with the same information (prompt and context) they will also produce 10 unique answers that will contradict each other and (depending on who you asked) may be just as confident. There are real decisions to be made when going from a vague prompt to a spec. It's not surprising that an LLM would produce different specs for the same work on different runs. If the prompt…

The issue is Lllms don't learn, despite the name. A human re-implementing a spec would strive to iterate towards what they feel is a better spec. They can take in their own input and self-correct. The work of implementing the spec gives insight into pain points and strengths, even if they never actually test the spec (they 100% should, but this is to emphasize that struggle for humans is in itself iteration, even before external feedback comes in).

An LLM is isn't deterministic but also isn't iterative without an existing human. You give it the same spec 10 times and it produces 10 results that aren't far off itself but vastly different when you go into the weeds. And not different in a way of improvement. |

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