Learnings from 100K lines of Rust with AI (2025)
zfhuang99.github.io
zfhuang99.github.io
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 spec until they both agree it's done.
Then, for critical code, have them both implement the spec in a worktree, then BOTH critique each other's implementation.
More often than not, Claude will say to take 2 or 3 pieces from it's design over to Codex, but ship the Codex implementation.
I mean that if you ask codex on gpt 5.5 to submit to a plan reviewer subagent that uses gpt5.5, this is enough to have a very good reviewing and reassessment of the plan.
My hypothesis is that it’s even better than opus.
The reason why submitting the product of one LLM to another to review is that you need a fresh trajectory. The previous context might have “guided” the planer into some bias. Removing the context is enough to break free from that trajectory and start fresh.
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.
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 already contained answers to all the decision points that come up when writing the spec then the prompt would already be the spec itself.
With moral agency and the ability to learn (even if we presume you are correct, which I don't think you are).
If the behavior of the llm is the same as the behavior of reasonable people then the behavior of the llm is reasonable, regardless of how black of a box they generate tokens out of.
Reasonable people will generate divergent specs for the same prompt. Thus it is reasonable for an LLM to generate divergent specs out of the same prompt.
Edit: I use “reasonable” here in the legal sense of the “reasonable person” standard, not to imply any reasoning process.
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.
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.
Reasoning is making analogies between logical patterns found in conceptual space, with a direction of time (statements precede conclusions). For example. A => B and B => C. You may now deduce A => C. For something fuzzier, A~D and B~E, you may now deduce that D~=>E. This is the sort of thing that higher layer attention mechanism is capable of doing.
> This is a box they can not escape.
Would you say that Helen Keller was less capable of abstract reasoning because she had more constrained access to sensory input?
Technically if it has that, it'd be singularity no? So basically the premise is they are doing nothing of the sort. Prove any LLM enough and it really does show it has no quarrels contradicting itself or being bossed around. Has no belief / no orientation etc. It's truly mindless but tricks our mind and soul (or whatever) probably.
reasoning is not black and white. It is possible to reason poorly. Most people cannot do basic math proofs, even math majors struggle with the hardest math proofs. Reasoning in humans is also context/token dependent. I just spent one HOUR trying to show my mom (who has mild dementia) how to use amazon fire (push DOWN until your channel shows up, push RIGHT until the channel becomes big) and she could not figure it out. Rewrote the instructions in japanese and she followed the logic relatively smoothly. Ironically, i'm pretty sure her english is better than her japanese, vocabulary wise.
> it's simply nothing like the wetware reasoning to get to the same answer.
but you don't know how wetware reasoning works, so you are incapable of making that proclamation. I'm pretty sure when I do math proofs (I'm not an amazing mathematician) sometimes I have to literally tick my way through each step of the proof, sometimes breaking it down to super-basic substeps, which to me feels awful lot like what an LLM could be doing. For that matter we don't know how LLM reasoning works but my claim is that these LLMs are in principle capable of reasoning due to architecture.
If this doesn't make sense I suggest you look over the architecture of LLMs carefully and try to understand my point.
(BTW I'm not talking about "reasoning models" with "thinking turns", that's just marketing speak, I'm talking about ANY transformer-based model, even the "dumbest UX architecture" completion models)
Your posts are generally insightful. Thanks for the contribution. Even if it’s a bit cranky and gruff :)
Decision making can be done by trained machines following rules, but that's different that reasoning. A thermostat isn't reasoning when it decides to turn on the air conditioner, to argue otherwise expands the definition of "reason" to be so broad that it becomes useless.
LLMs are trained on human knowledge and reasoning that results from human cognition, and they are excellent at stochastic mimicry - if the argument is that they are actually reasoning, then some sort of equivalent to human cognition must be present for that to be true. Lacking that, they are nothing more than "token extrusion machines" with some potentially useful characteristics.
Aren’t humans just “action potential” extrusion machines? What is unique about our neural pattern recognition to make our cognition different in nature rather than merely degree?
It seems clear at this point that the greatest insight that unlocked our current AI acceleration was scaling alone would unlock emergent properties and abilities.
Agreed but I would frame it in the negative, "don't worry about overfitting, the lucky ticket hypothesis just works "
In the meantime, these [1] are pretty funny.
Sounds like an implementation detail. Now describe how human reasoning works and explain why that process of chemical and electrical signals results in "reasoning" whereas what LLMs do isn't.
The problem with being this reductive is you can do it to anything, including humans. You can’t be reductive about LLMs and refuse to be reductive about humans - that's poor reasoning, and an LLM would out-reason you on this point, further negating your case.
For an example, look at some of Julia Mossbridge's work.
If even a small part of her work is true and valid, it points to something far outside our current framework.
You don't need to go as far afield as Mossbridge, though - that's an extreme example. Pretty much any modern neuroscience will make you question a lot of assumptions, at least it did for me.
Never heard of her but I just spent about 5 minutes looking.
Her PhD is in communication sciences and disorders [1], but apparently she’s a quantum physicist now:
> AMELIA is built on the Causally Ambiguous Duration-Sorting (CADS) effect — a breakthrough discovery by Dr. Julia Mossbridge showing that light, under classical boundary conditions, behaves differently based on future temporal boundaries. [2]
Filed under crank, not going to bother investigating further.
[1] https://books.google.com/books/about/Have_a_Nice_Disclosure....
It really can be useful. It's very different from old world programming.
The people who want to believe they actually reason just ignore all obvious evidence of contrary and cherry pick the times reasoning was faked well enough.
The people who don't want to believe will just take a second to understand how they work and then come up with ways to reveal they were faking all along. Like asking how many letters there are in a word lol.
It's only the people who don't want to believe that count because reality is what happens despite of what you believe.
Further, why does that mean “it doesn’t reason”. Logic can be encoded in language, symbols or code. If I say “all apples are red” -> “all fruit in the bowl are apples” -> “therefor all the fruit are red”. It doesn’t really matter if I understand the logic or what red is or fruit/apples are, the logic is contained in the structure of the syntax. If an LLM can output the conclusion reliably from predictive operations it is able to have the effect of reason and we don’t need to know or care about whether it “understands” the reasoning.
A prompt like "write these two files on disk" will very likely make the LLM do some sort of an atomic write/swap operation, unlike the average developer which will just write the two files and maybe later encounter a race condition bug. You can argue the LLM output is overkill, but it will also be more robust on average.
What has always mattered is how you decide the specs, not the specs in themselves.
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.
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-...
I don't think they include areas where correctness, determinism or human reasoning are important.
At least, not in isolation.
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.
> 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.
"A value proportional to the reciprocal of β is sometimes referred to as the temperature: β = 1/kT, where k is typically 1 or the Boltzmann constant and T is the temperature. A higher temperature results in a more uniform output distribution (i.e. with higher entropy; it is "more random"), while a lower temperature results in a sharper output distribution, with one value dominating."
"Temperature" in the context of softmax does not change a "winning" token, it changes how much probable (in the sense of softmax distribution) winning token will be. If the winning token is "New York", it will be a winner with temperature close to 0 and with temperature of 1e9.
The actual selection of the random token is done separately by using inputs outside of the softmax distribution, for example, by using random number generator. I believe most of LLM configs have a seed for the random number generator.
More than that, generation of code in most programming languages is done with the more guardrails such as beam search guided by schema, syntax and semantics.
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. |
That being said I agree people trust AI too much. Especially people with less experience. It’s easy to forget the models are mirrors of we are as the drivers of the input context not mentors that will guide us to best practices reliably.
Jokes aside, I agree about having LLMs iterate. Bouncing between GPT and Opus is good in my experience, but even having the same LLM review its own output in a new session started fresh without context will surface a lot of problems.
This process takes a lot of tokens and a lot of time, which is find because I’m reviewing and editing everything myself during that time.
By the end you have piecemeal "tickets" for your coding agent, if you have multiple developers you can sync them all up into github, and someone could take some locally, or you can just have Claude work on all of them with subagents. The key feature there is because its all piecemeal the context stays per task.
Then I run a /loop 15m If you're currently working ignore this. Start on the next task in gur if you have not. If you finished all work and cannot pass one gate, work on the next available task.
(Note: gur is my shorthand for GuardRails)
I also added a concept called "gates" so a task cannot complete without an attached gate, gates are arbitrary, they can be reused but when assigned to a task those specific assignments are unique per task. A task is basically anything you want it to be: unit test, try building it, or even seek human confirmation. At least when I was using Beads it did not have "gates" but I'm not sure if it has added anything like it since I stopped using Beads.
Claude will ignore the loop if it's currently working, and when its "out of work" it will review all available tasks.
If anyone's curious its MIT Licensed and on GitHub:
Say what you will with proper reasoning or arguments if you feel compelled, tired reddit-commentary like that helps no one.
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.
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.
That's fine. Others will care for you.
>it's plain boring to read and makes HN worse.
I chuckled at the joke. Surprising amount of layers to it.
Though I never strove to be a comic nor writer, that kind of terse, compact punch makes me envy those of such literary talent.
What joke?
Hopefully it's understandable now, and hopefully you don't disagree :)
> Please don't post comments saying that HN is turning into Reddit. It's a semi-noob illusion, as old as the hills
And please, do better next time!
What is this place for? Dang tells us, curious discussion. The guidelines explicitly state that certain comments are not in the spirit.
But the community seems to have decided otherwise, which is a shame.
I don't mind the downvotes, the points aren't really the reason I'm here anyways, I just want fun and interesting discussions with people and read other's perspectives, the points don't hinder that :)
and in both cases i both “know” that i can tell the difference and “know that i cannot tell the difference”. what anyone takes from that in terms of what it says about me, personally, is a bit of a Rorschack test, but Astrology is about as apt a description as there is… xD
But it's arguably less accurate to the original recording.
(I don't think that's the full picture but, there's definitely something fishy going on there.)
the, como se dice, "misalignment" between the content of reasoning tokens and the actual output following the end of the reasoning is a separate problem, extensively studied by e.g. Anthropic
I do this with other languages, too, not just Rust. Thing is, you have to put a hard stop at some point because the models will always find something to nitpick.
I know LOC is a silly metric, but ~1300 tests for 130k lines averages out to a test per 100 lines - isn't this awfully low for a highly complex piece of code, even discounting the fact that it's vibecoded? 100 LOC can carry a lot of logic for a single test, even for just happy paths.
No joke: it works for me. I have a 45kLOC prod code (just code, no comments, no blanks), tested by a 30kLOC test code containing 1600 tests (that run in 30secs).
I helped with the test infrastructure/architecture. Sometimes I had to write the first few tests of a particular kind, but now Claude TDDs for me.
A fair share of my CLAUDE.md instructs in how I like my tests, when to write them (first), different types of tests (unit, faked-services, db, e2e, etc.)
Asking Claude to find weak tests has helped a lot in getting here. I also do review AI-gen'd code, pretty much line-by-line, before accepting it.
If you're building a distributed system and you don't have more tests and testing code than actual code, by an order of magnitude most likely, then you're missing test coverage.
Honestly, despite all the hype around Rust in the community, the fact that AI can't handle lifetimes reliably makes me reluctant to use it. The AI constantly defaults to spamming .clone() or wrapping things in Rc, completely butchering idiomatic Rust and making the output a pain to work with.
On the other hand, it writes higher-level languages better than I do. For those succeeding with it, how exactly are you configuring or prompting the AI to actually write good, idiomatic Rust
format: glob: ".rs" run: cargo fmt -- --check
lint: glob: ".rs" run: cargo clippy -- -D warnings
tests: run: cargo test
audit: run: cargo audit
+ hooks that shove the lefthook automatically in the ai's face
---
rustfmt.toml:
edition = "2021" newline_style = "Unix" use_small_heuristics = "Max" max_width = 100
What harness and model you've been using? For the last few months, essentially since I did the whole "One Human + One Agent = One Browser From Scratch" experiment, I've almost exclusively been doing cross-platform native desktop development with Rust, currently with my own homegrown toolkit basically written from scratch, all with LLMs, mostly with codex.
But I can't remember a single time the agent got stuck on lifetime errors, that's probably the least common issue in regards with agents + Rust I come across. Much bigger issue is the ever-expanding design and LLMs being unable to build proper abstractions that are actually used practically and reduces the amount of code instead of just adding to the hairball.
The issue I'm trying to overcome now is that each change takes longer and longer to make, unless you're really hardcore about pulling back the design/architecture when the LLM goes overboard. I've only succeeded in having ~10 minute edits in +100K LOC codebases in two of the projects I've done so far, probably because I spent most of the time actually defining and thinking of the design myself instead of outsourcing it to the LLM. But this is the biggest issue I'm hitting over and over with agents right now.
The complexities LLMs end up putting themselves in is more about the bigger architecture/design of the program, rather than concrete lines, where things end up so tangled that every change requires 10s of changes across the repository, you know, typical "avoid the hairball" stuff you come across in larger applications...
this. create pre-commit hooks that enforce project conventions, code quality checks, and regression testing. it saves you so much headache
It sets up your repo to ensure agents use a workflow which breaks your user requests down into separate beads, works on them serially, runs a judge agent after every bead is complete to apply code quality rules, and also strict static checks of your code. It's really helpful in extracting long, high-quality turns from the agent. It's what we used to build Offload[1].
0: https://github.com/imbue-ai/rust-bucket : A rusty bucket to carry your slop ;)
Fixed.
My issue is specifically with how the AI uses it. In AI code, .clone() is almost always used as a brute-force escape hatch
Maybe it's harder to reason about the lifetime semantics while also writing code, and works better as a second phase (the de-cloning).
> So .clone() significantly reduces the mental overhead of using rust with a small performance impact? I'm intrigued :)
> Maybe it's harder to reason about the lifetime semantics while also writing code, and works better as a second phase (the de-cloning).
This is a problem when language designers are mathematicians and don’t understand typographical nuance and visual weights.
The whole "with AI" kind of reduces my hate for Rust though, and increases the appreciation for how strict the language is, especially when the agents themselves does the whole "do change > see error/warning > adjust code > re-check > repeat" loop themselves, which seems to work better the more strict the language is, as far as I can tell.
The "helpful" error messages from Rust can be a bit deceiving though, as the agents first instinct seems to be to always try what the error message recommends, but sometimes the error is just a symptom of a deeper issue, not the actual root issue.
I mean God help us should a crustacean try to understand the merits of my claim.
“Oh he’s saying something negative about rust…” Downvote!
I think with AI the language should still be readable. Humans need to be able to understand what’s going on!
However, if I link to gestalt theory of psychology; The Elements of Typographical Style by Robert Bringhurst; and The Primer of Visual literacy by Donis Dondis, folks will undoubtedly NOT read it and still downvote because they have been in Rust code and so have naturally become accustomed to its monstrous appearance. :)
Perhaps I should design a language that is typographically sound—something like brainf*ck haha
Kotlin is basically a Ruby (OO first with lots of FP goodness) with a serious type system. And where Ruby uses C-written libs in some places, with Kotlin one uses Java written libs from time to time.
See http4k for a nice implementation of Rack + a lot of goodness from Rails, without becoming a framework (it's just a lib).
Yes Kotlin is nice too. Type systems are important and helpful. Performance is a must too.. that's why we all in some point left ruby... but ruby makes you happy.. Maybe because my experience with Kotlin is restricted to Android, i didnt love that that much. Same with Crystal or even JRuby.. it's almost ruby, but not really.
(Yes, I know the 'a lifetimes are a bit weird, and that's not something that exist in typescript, but that's also not something you use everyday in Rust either.
If you want to give it a fair shot, it does take some time to get used to, coming from something like Python or Ruby. I won't deny that. I've found that using LSP-assissted semantic syntax highlighting helps, for me, on the typographic front.
I don't think typographic design is a key consideration in most languages' designs, though, and I don't think it should be. The main thing I look for is consistent, relatively predictable rules around the syntax, as far as that layer of language choice goes.
In tsz I have hard gates that disallow doing work in the wrong crate etc.
Maybe I'm using agents wrong, but I'm not sure how you'd end up in that situation in the first place? When I start codex, codex literally only has access to the directory I'm launching it, with no way to navigate, read or edit stuff elsewhere on my disk, as it's wrapped in isolation with copied files into it, with no sync between the host.
Hearing that others seemingly let agents have access to their full computer, I feel like I'm vastly out of date about how development happens nowadays, especially when malware and virus lurks around all the package registries.
I've not done any particular/ special prompting.
What model are you using, and what frameworks are you using?
This is not a hard problem for LLMs to solve.
Rust is nearly the perfect language for LLMs.
It's exceptionally expressive, and it forbids entirely the most common globally complex bugs that LLMs simply do not (and won't for some time) have the context window size to properly reason about.
Dynamically typed languages are a disaster for LLMs because they allow global complexity WRT to implicit type contracts (that they do not and cannot be relied on to withhold).
If you're going to add types, as someone pointed out earlier, why are you even telling an LLM to write Python anyways?
Rust is barely harder to read than Python with types. It's highly expressive.
You have the `&mut` which seems alien, verbose (safe) concurrency, and lifetimes - which - if you're vibe coding... you don't really need to understand that thoroughly.
You want an LLM to write code in a language where "if it complies, it works" - because... let me tell you, if you vibe code in a language where errors are caught at runtime instead of compile time... It will definitely NOT work.
- Garbage collected so no reasoning tokens or dev cycles are wasted on manual memory management. You say if you're vibe coding you can ignore lifetimes, but in response to a post that says AI can't do a good job and constantly uses escape hatches that lose the benefits of Rust (and can easily make it worse, copying data all over the place is terrible for performance).
- Very fast iteration speed due to JIT, a fast compiler and ability to use precompiled libraries. Rust is slow to compile.
- High level code that reads nearly like English.
- Semantically compatible with Java and Java libs, so lots of code in the training set.
- Unit tests are in separate files from sources. Rust intermixes them, bloating the context window with tests that may not be relevant to the current task.
Sure if you want to vibe code a TODO app where it's literally just copying and pasting one it's already seen 10,000 times before, it can do it in Python.
Sounds like your work doesn’t need Rust and that’s ok.
But don’t generalize.
Everything that people find great on Rust with exception of the borrow checker, can be found in any compiled language from ML linage. And even that is fading away as they introduce a mix of linear types, dependent types, effects and formal logic.
The situation is a little more complicated than what I just wrote because two programs written in different ML-style languages could communicate via inter-process communication. But I don't see that. (Maybe my experience is not broad enough?) What I see is, e.g., Python libraries written in C and C++ (and Fortran, which is also not memory-safe) for performance reasons where the only memory-safe language that could have been used instead is Rust.
Likewise those Python program should have use a dynamic managed compiled language, like Common Lisp or Julia, which was originally designed exactly to avoid that.
Maybe one day replaced by Mojo, if they get lucky with it.
Too many devs see a specific language as their solution for everything, and when it doesn't fit we end up in such sandwiches.
I agree. I also find that a lot of software should have this mindset anyways so Rust is a great fit for that domain. Anything that others depend on should be high integrity and memory efficient.
Not to mention the ecosystem is very wide in Rust. Other ML languages may not have what one needs.
But python or typescript are full of errors all the time. I rather fallback to perl than python. Perl has been excellent all along.
Yes you need to help it with the types of tests: you still need to know what you want from it. But once you have all types of tests (unit, db, fake-services, e2e, etc.) in place and documented; it can basically write tests until you cov-tool says it's 85%. Then you can ask it to find the weakest tests: you review those and make sure they are not weak, or Claude understands why they are not weak. Then let it find the next batch of weakest tests. Etc.
TDD finally makes sense economically for me on the types of projects I usually work on.
This hasn't been true since around gpt-4.5 on the OpenAI side of things. The 5.x models have been pretty much solid on Rust for a while now.
This is from 2025 - I would like to see an update now how that system turned out to be after the vibe hype
If it is, and it works well, then to me this is far more meaningful than the fact that AI wrote 130K lines of code.
The interesting thing is that it was manageable solo (in many ways it's _more_ manageable solo+AIs than with coworkers+(their)AIs), and in such a short amount of time.
In the end it is just a lot of unmaintainable code quickly generated by AI.
Rust makes no promise of being terser than C++, and RSL does less than this considering the optimization.
Also it's only 45/50k LOC so not so very from the 36k LOC.
The blog post mentioned the project is 130k LoC multiple times. Where 45/50k LoC comes from?
>Rust makes no promise of being terser than C++
True, but Rust has no header files, this alone is a great LoC saver.
But it's not apples to apples because they seem to have done much more performance work though, this is far from code golfing.
Having 90k LoC of tests for 50k LoC codebase also a problem. At least in my experience LLM generate too many tests. It does not evolve test suite but throws more code into it as development happens. Unless I aggressively refactor tests I quickly end up with a test suite that I don’t understand. Then LLM modifies tests to “make code work” and I have no idea if this is a legit edit or LLM cheats. I wonder if the same thing is happening or about to happen with this codebase.
That's great, non-test code is only ~47k lines of code.
I also had it implement a wasm geodesic calculator in Rust and it's amazing and in my use case is better than geodesiclib using the same updated algorithm.
I'm a "C-nile" Rust folks love to hate and did my first hacking in C Deep Blue C on Atari 8-bits. But I'm very impressed with these products and with the ability to leverage some features of Rust with them. (e.g. audit every unsafe instance and define its invariants, etc.)
I also agree with the commenter who said these LLMs are today, at the present moment, good at Go. The only language I notice it seems to be really good above and beyond others at is javascript, I assume because there's so much of it.
If you're fine with the generalized form "learned a lesson", then surely "learnings" is fine too. There's no point in trying to police a completely normal and sensible use of language.
Anyway, I accept this usage of the word "lesson", so I also accept "learnings". My point was one of hypocrisy, not policing people in how they can use the word "lesson".
Change the skills, ask the agent to do exactly the same in something else.
I am slowly focusing on agent orchestration tools, which make the actual programming language as relevant as doing SOA with BPEL.
Also it is kind of interesting that there is so much enthusiasm to use Claude and Claw all over the place, yet lack of vision on how much the whole infrastructure will improve.
Even when it finally bursts and we get into another AI Winter, what was already achieved isn't going away.
Original RSL library has 36 KLoC across C++ source and headers files. Rust supposed to be more expressive and concise. Yet, AI generated 130k LoCs. I guess nobody understands how this code works and nobody can tell if it actually works.
It works for humans because when we get a borrow-check failure, we take a step back and think about the global shape of our code and ownership. LLMs path straight to the goal. Problem: code doesn't compile. Solution: more clone()
You can’t have contracts defined in comments in code because there’s no guarantee they won’t be deleted or changed.
Even better, we need the ability to embed directives to LLMs which are NOT comments, but a type of programming construct specifically for this purpose.
Depending on your backend you either ignore them, check them all of the time, some of the time, or have SMT-solvers prove that if you uphold the first one all else must follow.
If you're interested in the last one, have a look at Dafny[0]
Go is much better target, i've observed rails/ruby code is also much easier for AI to spit out.
And Haskell flies with AI
Rust doesn't add anything over Go for LLM coding.