It’s great to see this pattern of people realising that agents can specify the desired behavior then write code to conform to the specs.
TDD, verification, whatever your tool; verification suites of all sorts accrue over time into a very detailed repository of documentation of how things are supposed to work that, being executable, puts zero tokens in the context when the code is correct.
It’s more powerful than reams upon reams of markdown specs. That’s because it encodes details, not intent. Your intent is helpful at the leading edge of the process, but the codified result needs shoring up to prevent regression. That’s the area software engineering has always ignored because we have gotten by on letting teams hold context in their heads and docs.
As software gets more complex we need better solutions than “go ask Jim about that, bloke’s been in the code for years”.
Be careful here - make sure you encode the right details. I've seen many cases where the tests are encoding the details of how it was implemented and not what it is intended to do. This means that you can't refactor anything because your tests are enforcing a design. (refactor is changing code without deleting tests, the trick is how can you make design changes without deleting tests - which means you have to test as much as possible at a point where changing that part of the design isn't possible anyway)
While you are right that you need to be encoding the right details, I disagree on the tests enforcing a design point.
As part of the proper testing strategy, you will have tests that cover individual behavior of a small block/function (real "unit" tests), tests that cover integration points only up to the integration itself, and a small number of end-to-end or multi-component integration tests.
Only the last category should stay mostly idempotent under refactoring, depending on the type of refactor you are doing.
Integration tests will obviously be affected when you are refactoring the interfaces between components, and unit tests will be affected when you are refactoring the components themselves. Yes, you should apply the strategy that keeps it under incremental reverse TDD approach (do the refactor and keep the old interface, potentially by calling into new API from the old; then in second step replace use of old API as well, including in tests).
Tests generally define behavior and implementation in a TDD approach: it'd be weird if they do not need changing at all when you are changing the implementation.
Fine, but don't check in the tests that prove implementation since they will be deleted soon anyway. The only tests to check in are ones that - by failing - informed you that you broke something. We don't know which those tests are and because most tests run fast we tend to check in lots of tests that will never fail in a useful way.
The formal verification angle is def compelling, but I keep running into a harder problem upstream: if the agent's decision logic lives in a prompt, what exactly are you verifying exactly? You can check that the generated code satisfies a spec, but the reasoning that led to that code is opaque by design. You can't write a Lean proof about "the model thought this was the right trade-off."
What I've found in practice is that trustworthiness in agentic systems requires a separation of concerns that most architectures simply don't enforce: keeping
deterministic decision logic externalized from the model so it's actually inspectable. Once you've got that, tools like this become a lot more powerful because you've got something concrete to verify against. Without it, you're proving properties of outputs while the decision process remains a black box.
Curious how Leanstral handles cases where the agent's architectural choices (not just the implementation) need to be auditable.
I feel like the difference is minimal, if not entirely dismissable. Code in this sense is just a representation of the same information as someone would write in an .md file. The resolution changes, and that's where both detail and context are lost.
I'm not against TDD or verification-first development, but I don't think writing that as code is the end-goal. I'll concede that there's millions of lines of tests that already exist, so we should be using those as a foundation while everything else catches up.
Tests (and type-checkers, linters, formal specs, etc.) ground the model in reality: they show it that it got something wrong (without needing a human in the loop). It's empiricism, "nullius in verba"; the scientific approach, which lead to remarkable advances in a few hundred years; that over a thousand years of ungrounded philosophy couldn't achieve.
The scientific approach is not only or primarily empiricism. We didn't test our way to understanding. The scientific approach starts with a theory that does it's best to explain some phenomenon. Then the theory is criticized by experts. Finally, if it seems to be a promising theory tests are constructed. The tests can help verify the theory but it is the theory that provides the explanation which is the important part. Once we have explanation then we have understanding which allows us to play around with the model to come up with new things, diagnose problems etc.
The scientific approach is theory driven, not test driven. Understanding (and the power that gives us) is the goal.
> The scientific approach starts with a theory that does it's best to explain some phenomenon
At the risk of stretching the analogy, the LLM's internal representation is that theory: gradient-descent has tried to "explain" its input corpus (+ RL fine-tuning), which will likely contain relevant source code, documentation, papers, etc. to our problem.
I'd also say that a piece of software is a theory too (quite literally, if we follow Curry-Howard). A piece of software generated by an LLM is a more-specific, more-explicit subset of its internal NN model.
Tests, and other real CLI interactions, allow the model to find out that it's wrong (~empiricism); compared to going round and round in chain-of-thought (~philosophy).
Of course, test failures don't tell us how to make it actually pass; the same way that unexpected experimental/observational results don't tell us what an appropriate explanation/theory should be (see: Dark matter, dark energy, etc.!)
The ai is just pattern matching. Vibing is not understanding, whether done by humans or machines. Vibe programmers (of which there are many) make a mess of the codebase piling on patch after patch. But they get the tests to pass!
Vibing gives you something like the geocentric model of the solar system. It kind of works but but it's much more complicated and hard to work with.
No, the theory comes from the authors knowledge, culture and inclinations, not from the fact.
Obviously the author has to do much work in selecting the correct bits from this baggage to get a structure that makes useful predictions, that is to say predictions that reproduces observable facts. But ultimately the theory comes from the author, not from the facts, it would be hard to imagine how one can come up with a theory that doesn't fit all the facts known to an author if the theory truly "emanated" from the facts in any sense strict enough to matter.
It most certainly is not. All your tests are doing is seeding the context with tokens that increase the probability of tokens related to solving the problem being selected next. One small problem: if the dataset doesn't have sufficiently well-represented answers to the specific problem, no amount of finessing the probability of token selection is going to lead to LLMs solving the problem. The scientific method is grounded in the ability to reason, not probabilistically retrieve random words that are statistically highly correlated with appearing near other words.
This only holds if you understand what's in the tests, and the tests are realistic. The moment you let the LLM write the tests without understanding them, you may as well just let it write the code directly.
> The moment you let the LLM write the tests without understanding them, you may as well just let it write the code directly.
I disagree. Having tests (even if the LLM wrote them itself!) gives the model some grounding, and exposes some of its inconsistencies. LLMs are not logically-omniscient; they can "change their minds" (next-token probabilities) when confronted with evidence (e.g. test failure messages). Chain-of-thought allows more computation to happen; but it doesn't give the model any extra evidence (i.e. Shannon information; outcomes that are surprising, given its prior probabilities).
I disagree to some degree. Tests have value even beyond whether they test the right thing. At the very least they show something worked and now doesnt work or vice versa. That has value in itself.
Say you describe your kitchen as “I want a kitchen” - where are the knives? Where’s the stove? Answer: you abdicated control over those details, so it’s wherever the stochastic parrot decided to put them, which may or may not be where they ended up last time you pulled your LLM generate-me-a-kitchen lever. And it may not be where you want.
Don’t like the layout? Let’s reroll! Back to the generative kitchen agent for a new one! ($$$)
The big labs will gladly let you reroll until you’re happy. But software - and kitchens - should not be generated in a casino.
A finished software product - like a working kitchen - is a fractal collection of tiny details. Keeping your finished software from falling apart under its own weight means upholding as many of those details as possible.
Like a good kitchen a few differences are all that stands between software that works and software that’s hell. In software the probability that an agent will get 100% of the details right is very very small.
If it is fast enough, and cheap enough, people would very happily reroll specific subsets of decisions until happy, and then lock that down. And specify in more details the corner cases that it doesn't get just how you want it.
People metaphorically do that all the time when designing rooms, in the form of endless browsing of magazines or Tik Tok or similar to find something they like instead of starting from first principles and designing exactly what they want, because usually they don't know exactly what they want.
A lot of the time we'd be happier with a spec at the end of the process than at the beginning. A spec that ensures the current understanding of what is intentional vs. what is an accident we haven't addressed yet is nailed down would be valuable. Locking it all down at the start, on the other hand, is often impossible and/or inadvisable.
Agreed; often you don’t know quite what you want until you’ve seen it.
Spec is an overloaded term in software :) because there are design specs (the plan, alternatives considered etc) and engineering style specs (imagine creating a document with enough detail that someone overseas could write your documentation from it while you’re building it)
Those need distinct names or we are all at risk of talking past each other :)
That matches what I’ve seen as well — generation is the easy part, validation is the bottleneck.
I’ve been experimenting with a small sparse-regression system that infers governing equations from raw data, and it can produce a lot of plausible candidates quickly. The hard part is filtering out the ones that look right but violate underlying constraints.
For example, it recovered the Sun’s rotation (~25.1 days vs 27 actual) from solar wind data, but most candidate equations were subtly wrong until you enforced consistency checks.
Feels like systems that treat verification as the source of truth (not just an afterthought) are the ones that will actually scale.
Not just TDD. Amazon, for instance, is heading towards something between TDD and lightweight formal methods.
They are embracing property-based specifications and testing à la Haskell's QuickCheck: https://kiro.dev
Then, already in formal methods territory, refinement types (e.g. Dafny, Liquid Haskell) are great and less complex than dependent types (e.g. Lean, Agda).
Setting aside that model means something different now … MDD never really worked because the tooling never really dealt with intent. You would get so far with your specifications (models) but the semantic rigidity of the tooling mean that at some point your solution would have to part way. LLM is the missing piece that finally makes this approach viable where the intent can be inferred dynamically and this guides the
implementation specifics. Arguably the purpose of TDD/BDD was to shore up the gaps in communicating intent, and people came to understand that was its purpose, whereas the key intent in the original XP setting was to capture and preserve “known good” operation and guard against regression (in XP mindset, perhaps fatefully clear intent was assumed)
I've seen this sentiment and am a big fan of it, but I was confused by the blog post, and based on your comment you might be able to help: how does Lean help me? FWIW, context is: code Dart/Flutter day to day.
I can think of some strawmen: for example, prove a state machine in Lean, then port the proven version to Dart? But I'm not familiar enough with Lean to know if that's like saying "prove moon made of cheese with JavaScript, then deploy to the US mainframe"
yesterday I had to tell a frontier model to translate my code to tla+ to find a tricky cache invalidation bug which nothing could find - gpt 5.4, gemini 3.1, opus 4.6 all failed. translation took maybe 5 mins, the bug was found in seconds, total time to fix from idea to commit - about 15 mins.
if you can get a model to quickly translate a relevant subset of your code to lean to find tricky bugs and map lean fixes back to your codebase space, you've got yourself a huge unlock. (spoiler alert: you basically can, today)
Thanks for following up on this: I was really surprised by how much air this paeon to, idk, TDD, took out of the comments by getting off-topic.
Before you commented, I started poking at what you described for 15 minutes, then forget about it and fell asleep. Now I remembered, and I know it's viable and IIUC it's almost certainly going to make a big difference in my work practice moving forward. Cheers.
I don't think he's referring to Lean specifically, but any sort of executable testing methodology. It removes the human in the loop in the confidence assurance story, or at least greatly reduces their labor. You cannot ever get such assurance just by saying, "Well this model seems really smart to me!" At best, you would wind up with AI-Jim.
(One way Lean or Rocq could help you directly, though, would be if you coded your program in it and then compiled it to C via their built-in support for it. Such is very difficult at the moment, however, and in the industry is mostly reserved for low-level, high-consequence systems.)
What do you mean? It's a nice and simple language. Way easier to get started than OCaml or Haskell for example. And LLMs write programs in Lean4 with ease as well. Only issue is that there are not as many libraries (for software, for math proofs there is plenty).
But for example I worked with Claude Code and implemented a shell + most of unix coreutils in like a couple of hours. Claude did some simple proofs as well, but that part is obvs harder. But when the program is already in Lean4, you can start moving up the verification ladder up piece by piece.
Well, if you do not need to care about performance everything can be extremely simple indeed. Let me show you some data structure in coq/rocq while switching off notations and diplaying low level content.
You know you could just define the verified specs in lean and if performance is a problem, use the lean spec to extract an interface and tests for a more performant language like rust. You could at least in theory use Lean as an orchestrator of verified interfaces.
I don't think so? Lean is formal methods, so it makes sense to discuss the boons of formal and semiformal methods more generally.
I used to think that the only way we would be able to trust AI output would be by leaning heavily into proof-carrying code, but I've come to appreciate the other approaches as well.
But that's exactly my point. "It's natural to discuss the broader category" is doing a lot of heavy lifting here. The blog post is making a very specific claim: that formal proof, checked by Lean's kernel, is qualitatively different from testing, it lets you skip the human review loop entirely. cadamsdotcom's comment rounds that down to "executable specs good, markdown specs bad," which... sure, but that's been the TDD elevator pitch for 20 years.
If someone posted a breakthrough in cryptographic verification and the top comment was "yeah, unit tests are great," we'd all recognize that as missing the point. I don't think it's unrelated, I think it's almost related, which is worse, because it pattern-matches onto agreement while losing the actual insight.
The verification angle makes sense, especially for high-stakes domains.
But I wonder how this scales in practice outside of formal environments.
In most ML/LLM systems, the bottleneck isn’t just correctness of individual steps, but the interaction between components (data → tokenizer → model → inference). A lot of failures come from subtle mismatches across the pipeline rather than strictly invalid logic.
Formal specs are great when the system is well-defined, but many real-world pipelines are still exploratory and data-dependent.
It feels like there’s a gap between:
• formally verified components
• and emergent behavior in end-to-end systems
Curious how you see this approach handling those system-level uncertainties.
There are also software model checkers that can model distributed processes. You have to simplify the state a bit, otherwise you get a state space explosion.
I tried it out myself, I let AI add action transitions through the code, like: // A -> B: some description. Then I validate via a test that every action transition defined in my model is also defined somewhere commented in code, and other way around that every comment exists in the model.
Finally, I let AI write model check queries on particular properties. If I notice a particular bug, then I ask AI to analyze the model and the model check queries on why it could happen, and ask to strengthen it.
It sounds like a lot of effort, but I got it working in a half hour.
I just feel like Mistral is heading for bad financial times when they are focusing on fringe academic areas and not on building a business out of their research. Initial Mistral was largely based on LLaMA, then they added innovative MoE and since then disappeared, doing AI consulting for big EU companies instead.
> Instead of taking a stab in the dark, Leanstral rolled up its sleeves. It successfully built test code to recreate the failing environment and diagnosed the underlying issue with definitional equality. The model correctly identified that because def creates a rigid definition requiring explicit unfolding, it was actively blocking the rw tactic from seeing the underlying structure it needed to match.
In my experience the agent regularly breaks some current features while adding a new one - much more often than a human would. Agents too often forget about the last feature when adding the next and so will break things. Thus I find Agent generated tests important as they stop the agent from making a lot of future mistakes.
It is definitely not foolproof but IMHO, to some extent, it is easier to describe what you expect to see than to implement it so I don't find it unreasonable to think it might provide some advantages in terms of correctness.
In my experience, this tends to be more related to instrumentation / architecture than a lack of ability to describe correct results. TDD is often suggested as a solution.
Given the issues with AWS with Kiro and Github, We already have just a few high-profile examples of what happens when AI is used at scale and even when you let it generate tests which is something you should absolutely not do.
Don't "let it" generate tests. Be intentional. Define them in a way that's slightly oblique to how the production code approaches the problem, so the seams don't match. Heck, that's why it's good to write them before even thinking about the prod side.
That article is literally a definition of TDD that has been around for years and years. There's nothing novel there at all. It's literally test driven development.
Wild it’s taken people this long to realize this. Also lean tickets / tasks with all needed context to complete the task, including needed references / docs, places to look in source, acceptance criteria, other stuff.
The problem with the European independence story is, that it seems Mistral runs its own stuff also on US cloud act affected infrastructure. This makes them a very weird value proposition: If I accept a level of "independence" whereby I run on AWS or Azure, I could as well pay for Anthropic or GPT to have SOTA performance.
If I do not accept that level of independence but want more, I need to buy what's on OVH, Scaleway, Ionos etc. or host my own, but that usually means even smaller, worse models or a lot of investment.
Nevertheless, the "band" that Mistral occupies for economic success is very narrow. Basically just people who need independence "on paper" but not really. Because if I'm searching for actual independence, there's no way I could give them money at the moment for one of their products and it making sense, cause none of their plans are an actual independence-improvement over, let's say, Amazon Bedrock.
I really really want to support them, but it must make economic sense for my company, too, and it doesn't.
Then why does their list of subprocessors list Google and Microsoft "for cloud infrastructure", specifically for "Le Chat, La Plateforme, Mistral Code"? Sounds to me as if they're mainly running on Azure.
Also, they're listing CoreWeave as inference provider in "EEA" area, but CoreWeave is of course also an US company. Even if they have their data center physically in the EU, it must be considered open access for the USA due to the CLOUD act.
If what you say is true, they have a communications problem and they need to fix that urgently. Right now, this is why they don't get my business. Others will have made the same decision based on their own subprocessor list.
Or did you mean, they're like, right now building it and plan to move there, but it's not up yet?
I really hope you're right. Sadly, though, I don't see any evidence of UK companies disinvesting from big US tech. There aren't good alternatives and what there is is too complex. As long as 'everyone else is still using MS', it seems like it's a brave CTO that switches to European providers. Unless that happens, the network effect of having AI+data is likely to mean US tech still has a big advantage in corp settings. But, HN - please tell me I'm wrong!
they will, but the jagged frontier is fractal and each one will have different capabilities; you'll want to mix models and to get best results consistently you'll need to.
There have been a lot of conversations recently about how model alignment is relative and diversity of alignment is important - see the recent podcast episode between Jack Clark (co-founder of Anthropic) and Ezra Klein.
Many comments here point out that Mistral's models are not keeping up with other frontier models - this has been my personal experience as well. However, we need more diversity of model alignment techniques and companies training them - so any company taking this seriously is valuable.
Agreed. The idea is nice and honorable. At the same time, if AI has been proving one thing, it's that quality usually reigns over control and trust (except for some sensitive sectors and applications). Of course it's less capital-intense, so makes sense for a comparably little EU startup to focus on that niche. Likely won't spin the top line needle much, though, for the reasons stated.
Most Copilot customers use Copilot because Microsoft has been able to pinky promise some level of control for their sensitive data. That's why many don't get to use Claude or Codex or Mistral directly at work and instead are forced through their lobotomised Copilot flavours.
Remember, as of yet, companies haven't been able to actually measure the value of LLMs ... so it's all in the hands of Legal to choose which models you can use based on marketing and big words.
Ha, keep putting your prompts and workflows into cloud models. They are not okay with being a platform, they intend to cannibalize all businesses. Quality doesn't always reign over control and trust. Your data and original ideas are your edge and moat.
Treating "quality" as something you can reliably measure in AI proof tools sounds nice until you try auditing model drift after the 14th update and realize the "trust" angle stops being a niche preference and starts looking like the whole product. Brand is not a proof. Plenty of orgs will trade peak output for auditability, even if the market is bigger for YOLO feature churn.
EU could help them very much if they would start enforcing the Laws, so that no US Company can process European data, due to the Americans not willing to budge on Cloud Act.
That would also help to reduce our dependency on American Hyperscalers, which is much needed given how untrustworthy the US is right now. (And also hostile towards Europe as their new security strategy lays out)
How about not making these unenforced laws in the first place so that European companies could actually have a chance at competing? We're going to suffer the externalities of AI either way, but at least there would be a chance that a European company could be relevant.
The AI Act absolutely befuddled me. How could you release relatively strict regulation for a technology that isn't really being used yet and is in the early stages of development? How did they not foresee this kneecapping AI investment and development in Europe? If I were a tinfoil hat wearer I'd probably say that this was intentional sabotage, because this was such an obvious consequence.
Mistral is great, but they haven't kept up with Qwen (at least with Mistral Small 4). Leanstral seems interesting, so we'll have to see how it does.
Because the AI act was mostly written to address issues with ML products and services. It was mostly done before ChatGPT happened, so all the foundation model stuff got shoehorned in.
Speaking as someone who's been doing stats and ML for a while now, the AI act is pretty good. The compliance burden falls mostly on the companies big enough to handle it.
>Because the AI act was mostly written to address issues with ML products and services. It was mostly done before ChatGPT happened, so all the foundation model stuff got shoehorned in.
It's not an excuse. Anybody with half a working brain should've been able to tell that this was going to happen. You can't regulate a field in its infancy and expect it to ever function.
>The compliance burden falls mostly on the companies big enough to handle it.
You mean it falls on anyone that tries to compete with a model. There's a random 10^25 FLOPS compute rule in there. The B300 does 2500-3750 TFLOPS at fp16. 200 of these can hit that compute number in 6 months, which means that in a few years time pretty much every model is going to hit that.
And if somebody figures out fp8 training then it would only take 10 of these GPUs to hit it in 6 months.
The copyright rule and having to disclose what was trained on also means that it will be impossible to have enough training data for an EU model. And this even applies to people that make the model free and open weights.
I don't see how it is possible for any European AI model to compete. Even if these restrictions were lifted it would still push away investors because of the increased risk of stupid regulation.
Sorry, but what are you talking about? This is a 120B-A6B model, which isn't runnable on any laptop except the most beefed up Macbooks, and then will certainly drain its battery and cook your legs.
A single DGX Spark can service a whole department of mathematicians (or programmers), and you can cluster up to 4 of them them to fit very large models like GLM-5 and quants of Kimi K2.5. This is nearing frontier-level model size.
I understand the value proposition of the frontier cloud models, but we're not as far off from self-hosting as you think, and it's becoming more viable for domain-specific models.
Formal verification and code synthesis feel like natural companions for automated scientific discovery. I’ve been working on a small (~800‑line) Python agent that uses sparse regression to uncover governing equations directly from data; it’s managed to validate twelve physical laws, including deriving the Sun’s rotation rate from NASA plasma measurements and correcting Gemini’s plasma conservation. Having an agent like Leanstral that can reason about proofs and specifications would be a powerful complement to data‑driven model discovery — it closes the loop between experimentation and provable correctness.
Can someone please explain... If I don't know any Lean (and I suspect most people don't), is it of any direct value? Trying to understand if there's something it can help me with (e.g. automatically write proofs for my Go programs somehow... I'm not sure) or should I just cheer solely for more open models out there, but this one isn't for me?
Presumably the idea is that an agent generates a Lean4 specification against which the software is measured.
But then the Lean4 specification effectively becomes the software artifact.
And we're sort of back to square 1. How do you verify a Lean4 spec is correct (and that it describes what needs to be built in the first place) without human review?
Specifications are smaller than the full code, just as high level code is smaller than the functionally equivalent assembly. As we ascend the abstraction ladder the amount of reading a human needs to do decreases. I don't think this should really count as "back to square 1".
That has always been the perceived promise of higher-abstraction software specs: automated code generation from something higher-level, thus making programmers increasingly obsolete.
Among the RADs is Microsoft Visual Basic, which along with WinForms and SQL was supposed to make business programmers nearly obsolete, but instead became a new onramp into programming.
In particular, I'd like to highlight UML, which was supposed to mostly obsolete programming through auto-generated code from object-oriented class diagrams.[1] The promise was that "business domain experts" could model their domain via visual UML tooling, and the codegen would handle it from there. In practice, UML-built applications became maintenance nightmares.
In every one of these examples, the artifact that people made "instead of programming" became the de-facto programming language, needing to be maintained over time, abstracted, updated, consumed behind APIs, etc. -- and programmers had to be called in to manage the mess.
It's interesting that Spec4 can be auto-generated, then used to generate code. My question is - what do you do when you have (a) consumers depending on a stable API, and (b) requests for new features? Maybe hand the job to Claude Code or a human developer with a suite of unit tests to guarantee API compatibility, but at that point we're back to an agent (LLM or human) doing the work of programming, with the Spec4 code as the programming language being updated and maintained.
You're touching on the fundamental "who watches the watchmen" problem in formal verification. But I think the framing slightly misses the key asymmetry: reviewing a Lean4 spec is dramatically easier than reviewing the implementation it constrains.
A formal spec in Lean is typically 10-50x shorter than the code it proves correct. More importantly, Lean's type checker is itself a small, trusted kernel (~10k lines) that has been scrutinized by the PL community for years. So you're not trusting the agent — you're trusting the kernel.
The practical workflow isn't "agent writes spec + code." It's: human writes spec (the hard creative part), agent generates proof that code satisfies spec, Lean kernel mechanically checks the proof. The agent can hallucinate all it wants in step 2 — if the proof doesn't typecheck, it gets rejected deterministically.
The real bottleneck is step 1: writing good specs requires domain expertise. But that's exactly where humans should stay in the loop. It's a much better division of labor than reviewing thousands of lines of generated code.
Also a very good question btw, people do both. For some projects Lean is expressive and performant enough to use on its own (or call into using the reverse FFI), other projects use a model of a real programming language like Rust. The disadvantage of the latter is that the Lean model of Rust has to be trusted.
Trustworthy vibe coding. Much better than the other kind!
Not sure I really understand the comparisons though. They emphasize the cost savings relative to Haiku, but Haiku kinda sucks at this task, and Leanstral is worse? If you're optimizing for correctness, why would "yeah it sucks but it's 10 times cheaper" be relevant? Or am I misunderstanding something?
On the promising side, Opus doesn't look great at this benchmark either — maybe we can get better than Opus results by scaling this up. I guess that's the takeaway here.
I also don't understand the focus on vibe coding in the marketing. Vibe coding kind of has the image of being for non-devs, right?
I do like agents (like Claude Code), but I don't consider myself to be vibe coding when I use them. Either I'm using a language/framework I know and check every step. OR I'm learning, checking every step and asking for explanations.
I tried vibe coding, and really dislike the feeling I have when doing it. It feels like building a house, but without caring about it, and just using whatever tech. Sure I may have moisture problems later, but it's a throwaway house anyway. That's how I feel about it. Maybe I have a wrong definition.
Maybe it's good to not use "vibe coding" as a synonym for programming with agent assistance. Just to protect our profession. Like: "Ah you're vibing" (because you have Claude Code open), "No, I'm using CC to essentially type faster and prevent syntax errors and get better test coverage, maybe to get some smart solutions without deep research. But I understand and vouch for every loc here. 'We are not the same.'"
> I tried vibe coding, and really dislike the feeling I have when doing it. It feels like building a house, but without caring about it, and just using whatever tech. Sure I may have moisture problems later, but it's a throwaway house anyway. That's how I feel about it. Maybe I have a wrong definition.
No, I feel the same. I vibe-coded a few projects and after a few weeks I just threw them away, ultimately I felt I just wasted my time and wished I coudl get it back to do something useful.
Yeah, the original meaning of Vibe Coding was "not looking at the code, just going on vibes", but a lot of people now use it to mean "AI was involved in some way".
I see a whole spectrum between those two. I typically alternate between "writing code manually and asking AI for code examples" (ChatGPT coding), and "giving AI specific instructions like, write a function blarg that does foo".
The latter I call Power Coding, in the sense of power armor, because you're still in control and mostly moving manually, but you're much stronger and faster.
I like this better than "tell agent to make a bunch of changes and come back later" because first of all it doesn't break flow (you can use a smaller model for such fine-grained changes so it goes very fast -- it's "realtime"), and second, you don't ever desync from the codebase and need to spend extra time figuring out what the AI did. Each change is sanity-checked as it comes in.
So you stay active, and the code stays slop-free.
I don't hear a lot of people doing this though? Maybe we just don't have good language for it.
"I don't hear a lot of people doing this though? Maybe we just don't have good language for it."
Interesting thought. I guess we don't really, vibe coding is to powerful a term. But perhaps just call it LLM assisted programming? Where we used to do Stack Overflow assisted programming. LLM assisted programming is more focused, goes faster. But since you're wandering around less I guess you learn less, you're exposed to less new information, some of it was helpful in unexpected ways. Now you have to make learning a specific part of your flow, and that takes discipline/time. But is well worth it imho. Actually, for me it's the only way to enjoy it.
I myself am now and expert at insulation and all the vapor-permeable and vapor-blocking membranes/foils/foams that come with it.
It came at great cost though, I hated the process of learning and the execution. I was less than happy for some years. But I feel even more uncomfortable vibe-home-improving than I do vibe-coding. The place is starting to look nice now though.
They haven't made the chart very clear, but it seems it has configurable passes and at 2 passes it's better than Haiku and Sonnet and at 16 passes starts closing in on Opus although it's not quite there, while consistently being less expensive than Sonnet.
pass@k means that you run the model k times and give it a pass if any of the answers is correct. I guess Lean is one of the few use cases where pass@k actually makes sense, since you can automatically validate correctness.
Oh my bad. I'm not sure how that works in practice. Do you just keep running it until the tests pass? I guess with formal verification you can run it as many times as you need, right?
Assuming that what you're dealing with is assertable. I guess what I mean to say is that in some situations is difficult to articulate what is correct and what isn't depending in some situations is difficult to articulate what is correct and what isn't depending upon the situation in which the software executes.
I absolutely called this a couple of weeks ago, nice to be vindicated!
> I'm interested to see what it is in the age of LLMs or similar future tools. I suspect a future phase change might be towards disregarding how easy it is for humans to work with the code and instead focus on provability, testing, perhaps combined with token efficiency.
> Maybe Lean combined with Rust shrunk down to something that is very compiler friendly. Imagine if you could specify what you need in high level language and instead of getting back "vibe code", you get back proven correct code, because that's the only kind of code that will successfully compile.
It's important to keep in mind that no proof system ensures your proof is the correct proof, only that it's a valid proof. Completely understanding what a proof proves is often nearly as difficult as understanding the program it's proving. Normally you benefit because the process of building a proof forces you to develop your understanding more fully.
Uhm, no?
Even with "simple" examples like Dijkstra's shortest path, the spec is easier than the implementation. Maybe not for you, but try it out on an arbitrary 5-yr old.
On the extreme end, you have results in maths, like Fermat's Last Theorem. Every teenager can understand the statement (certainly after 10 mins of explanation) but the proof is thousands of pages of super-specialized maths.
It is a spectrum. For cryptography, compression, error-correction, databases, etc, the spec is often much simpler than the implementation.
I don't know why you created a new account for this, but take the textbook example of a nontrivial formally verified system: SeL4. That implementation was 8.7k of C code, which correspondend to 15k lines of Isabelle that ultimately needed 100k+ lines of proof to satisfy. And that was with the formal model excluding lots of important properties like hardware failure that actual systems deal with.
You are confusing the proof with the spec/theorem. A correct proof and a valid proof are the same thing. It doesn't really matter how long the proof is, and you don't even need to understand it for it to be correct, the machine can check that.
But indeed, if the spec includes 8.7k of C code, that is problematic. If you cannot look at the theorem and see that it is what you mean, that is a problem. That is why abstraction is so important; your ultimate spec should not include C-code, that is just too low-level.
I'm not confusing them. That's why I gave each of the numbers for SeL4 separately.
Knowing whether those theorems are the right theorems for the problem can be as difficult as understanding the implementation itself. Hence the example of SeL4 where the number of theorems exceeds lines of code in the original implementation and the formal model is large.
It's my experience that most people doing formal methods have seen cases where they actually proved something slightly different than what they intended to. This usually involves an unintentional assumption that isn't generally true.
I think you have been confusing them. Two theorems are the same if they have the same statement (spec). A proof is not a theorem, nobody cares about when two proofs are the same or not.
> I don't know why you created a new account for this
What value does this add to the conversation? I’m not seeing it: am I missing something? It comes across as a kind of insult.
They made a good point in my opinion! (The “Uhm no” part got it off on the wrong foot, I will admit.) But even if you felt annoyed or didn’t agree with the point, it was substantive and moved the conversation forward. I’m here for the (genuine) questions and (constructive) debate and (civil) pushback.
I like to welcome new users before they take too much of a beating. That can come later when they are too invested to leave and/or when morale needs improving.
So welcome! Bring a helmet, and don’t stop disagreeing.
Very cool but I haven’t been able to convince software developers in industry to write property based tests. I sometimes joke that we will start writing formal proofs until the tests improve. Just so that they will appreciate the difference a little more.
I can’t even convince most developers to use model checkers. Far more informal than a full proof in Lean. Still highly useful in many engineering tasks. People prefer boxes and arrows and waving their hands.
Anyway, I don’t know that I’d want to have a system vibe code a proof. These types of proofs, I suspect, aren’t going to be generated to be readable, elegant, and be well understood by people. Like programs they generate it will look plausible.
And besides, you will still need a human to review the proof and make sure it’s specifying the right things. This doesn’t solve that requirement.
Although I have thought that it would be useful to have a system that could prove trivial lemmas in the proof. That would be very neat.
Maybe a naive question: given that they see better performance with more passes but the effect hits a limit after a few passes, would performance increase if they used different models per pass, i.e leanstral, kimi, qwen and leanstral again instead of 4x leanstral?
That sounds quite interesting. Makes me wonder if sooner or later they will have to train multiple independent models that cover those different niches. But maybe we will see that sooner or later. Thanks for the link.
One would think that LoRAs being so successful in StableDiffusion, that more people would be focused on constructing framework based LoRas; but the economics of all this probably preclude trying to go niche in any direction and just keep building the do-all models.
The SD ecosystem in large part was grassroots and focused on nsfw. I think current LLM companies would have a hard time getting that to happen due to their safety stuff.
Fine-tuning does exist on the major model providers, and presumably already uses LoRA. (Not sure though.)
We saw last year that it's remarkably easy to bypass safety filters by fine-tuning GPT, even when the fine-tuning seems innocuous. e.g. the paper about security research finetuning (getting the model to add vulnerabilities) producing misaligned outputs in other areas. It seems like it flipped some kind of global evil neuron. (Maybe they can freeze that one during finetuning? haha)
Yes “open weights” conveys the reality more clearly: merely having the parameters is very different than able to run a process that creates them. Without openness of the full process start to finish, much is hidden.*
Remember, language is what we make it. Dictionaries are useful catalogs of usage but we make the judgment calls.
* Even with the process, much is not well understood! / The ethics of releasing an open weights model at some capability level is a separate discussion.
Is anyone using this approach with lean to ship production code? Writing lean spec as human, implementation and proof by agent? And then shipping lean or exporting to C? Would be great to understand how you are actually using this.
Naturally the Microsoft-owned language is getting the AI hype instead of the more mature options that could do this sort of work… Agda, ATS, Coq/Rocq, Dafny, Fstar, Idris, Isabelle, Why3 just to name a few.
You should check out the recent PR's to the Agda repo... the community is currently very divided about AI. For better or worse, the people driving the Lean project have been interested in AI for quite some time.
A bit uncharitable. I'm a diehard fan of Rocq, but it's nothing unusual to see the young new hotness that is Lean continue to get the spotlight. It's not a sign of Microsoft putting its thumb on the scales, and the hype for Lean has long predated LLMs.
It's certainly less mature when it comes to verified programming, but its appeal to mathematicians (rather than formal methods experts) has earned it much respect.
I use mistral-medium-3.1 for a lot of random daily tasks, along with the vibe cli. I'd state from my personal opinion that mistral is my preferred 'model vendor' by far at this point. They're extremely consistent between releases while each of them just feels better. I also have a strong personal preference to the output.
I actively use gemini-3.1-pro-preview, claude-4.6-opus-high, and gpt-5.3-codex as well. I prefer them all for different reasons, however I usually _start_ with mistral if it's an option.
MOE but 120B range. Man I wish it was an 80B. I have 2 GPUs with 62Gib of usable VRAM. A 4bit 80B gives me some context window, but 120B puts me into system RAM
I'm using this model for my first python project, coding using opencode along with devstral and Mistral Large 3. I know it's not as capable as other, more expensive models, but working with it this way is teaching me python. More directly to your point though, the speech to text model is really good.
It's funny because I just took a break from it to read some hn and found this post.
Pretty much all of my LLM usage has been using Mistral's open source models running on my PC. I do not do full agentic coding as when i tried it with Devstral Small 2 it was a bit too slow (though if i could get 2-3 times the speed of my PC from a second computer it'd be be a different story and AFAIK that is doable if i was willing to spend $2-3k on it). However i've used Mistral's models for spelling and grammar checks[0], translations[1][2], summaries[3] and trying to figure out if common email SPAM avoidance tricks are pointless in the LLM age :-P [4]. FWIW that tool you can see in the shots is a Tcl/Tk script calling a llama.cpp-based command-line utility i threw together some time ago when experimenting with llama.cpp.
I've also used Devstral Small to make a simple raytracer[5][6] (it was made using the "classic" chat by copy/pasting code, not any agentic approach and i did fix bits of it in the process) and a quick-and-dirty "games database" in Python+Flask+Sqlite for my own use (mainly a game backlog DB :-P).
I also use it to make various small snippets, have it generate some boilerplate stuff (e.g. i have an enum in C and want to write a function that prints names for each enum value or have it match a string i read from a json file with the appropriate enum value), "translate" between languages (i had it recently convert some matrix code that i had written in Pascal into C), etc.
That's likely because they're chasing enterprise - see deals with HSBC, ASML, AXA, BNP Paribas etc... Given swelling anti-US sentiment and their status as a French 'national champion', Mistral are probably in a strong position for now regardless of model performance, research quality or consumer uptake.
Me neither, they're not ready for prime imo. I have a yearly sub and the product is just orders of magnitude behind Anthropic's offering. I use Code for real world stuff and I am happy with the result, Mistral is just not something I can trust right now.
it clearly and demonstrably does not. in fact, from eyeballing their chart Qwen, Kimi, and GLM scale linearly whereas Leanstral does not. But this is not surprising because the Alibaba, Moonshot, and Zhipu have hundreds of employees each and hundreds of millions of dollars of investment each.
Mistral seems to focus on a different market than the others. Their best model is meh, their best ASR model locally is either rather slow compared to Parakeet on similar languages, or not as good for others (like qwen ASR).
Side note: Lean seems quite unreadable with tons of single letter variable names. Part of it is me being unaccustomed with it, but still.
Mistral seems to focus on some niche LLM model tooling that are somehow very needed in certain cases. Can't forget their OCR multimodal embedding model!
Public service announcement to
hopefully reduce unnecessary knife fights*:
There are two compatible and important (but different) questions in play:
1. Is a program correct relative to a formal specification?
2. Is the formal specification what we mean/want?
*: Worth asking: “What that other person necessarily wrong? Or perhaps they are discussing a different aspect or framing?” AKA: “be curious and charitable” I’m not going to link to the specific threads, but they are happened / are happening. Le Sigh.
It’s great to see this pattern of people realising that agents can specify the desired behavior then write code to conform to the specs.
TDD, verification, whatever your tool; verification suites of all sorts accrue over time into a very detailed repository of documentation of how things are supposed to work that, being executable, puts zero tokens in the context when the code is correct.
It’s more powerful than reams upon reams of markdown specs. That’s because it encodes details, not intent. Your intent is helpful at the leading edge of the process, but the codified result needs shoring up to prevent regression. That’s the area software engineering has always ignored because we have gotten by on letting teams hold context in their heads and docs.
As software gets more complex we need better solutions than “go ask Jim about that, bloke’s been in the code for years”.
> That’s because it encodes details, not intent.
Be careful here - make sure you encode the right details. I've seen many cases where the tests are encoding the details of how it was implemented and not what it is intended to do. This means that you can't refactor anything because your tests are enforcing a design. (refactor is changing code without deleting tests, the trick is how can you make design changes without deleting tests - which means you have to test as much as possible at a point where changing that part of the design isn't possible anyway)
While you are right that you need to be encoding the right details, I disagree on the tests enforcing a design point.
As part of the proper testing strategy, you will have tests that cover individual behavior of a small block/function (real "unit" tests), tests that cover integration points only up to the integration itself, and a small number of end-to-end or multi-component integration tests.
Only the last category should stay mostly idempotent under refactoring, depending on the type of refactor you are doing.
Integration tests will obviously be affected when you are refactoring the interfaces between components, and unit tests will be affected when you are refactoring the components themselves. Yes, you should apply the strategy that keeps it under incremental reverse TDD approach (do the refactor and keep the old interface, potentially by calling into new API from the old; then in second step replace use of old API as well, including in tests).
Tests generally define behavior and implementation in a TDD approach: it'd be weird if they do not need changing at all when you are changing the implementation.
Fine, but don't check in the tests that prove implementation since they will be deleted soon anyway. The only tests to check in are ones that - by failing - informed you that you broke something. We don't know which those tests are and because most tests run fast we tend to check in lots of tests that will never fail in a useful way.
The formal verification angle is def compelling, but I keep running into a harder problem upstream: if the agent's decision logic lives in a prompt, what exactly are you verifying exactly? You can check that the generated code satisfies a spec, but the reasoning that led to that code is opaque by design. You can't write a Lean proof about "the model thought this was the right trade-off."
What I've found in practice is that trustworthiness in agentic systems requires a separation of concerns that most architectures simply don't enforce: keeping deterministic decision logic externalized from the model so it's actually inspectable. Once you've got that, tools like this become a lot more powerful because you've got something concrete to verify against. Without it, you're proving properties of outputs while the decision process remains a black box.
Curious how Leanstral handles cases where the agent's architectural choices (not just the implementation) need to be auditable.
I feel like the difference is minimal, if not entirely dismissable. Code in this sense is just a representation of the same information as someone would write in an .md file. The resolution changes, and that's where both detail and context are lost.
I'm not against TDD or verification-first development, but I don't think writing that as code is the end-goal. I'll concede that there's millions of lines of tests that already exist, so we should be using those as a foundation while everything else catches up.
Tests (and type-checkers, linters, formal specs, etc.) ground the model in reality: they show it that it got something wrong (without needing a human in the loop). It's empiricism, "nullius in verba"; the scientific approach, which lead to remarkable advances in a few hundred years; that over a thousand years of ungrounded philosophy couldn't achieve.
The scientific approach is not only or primarily empiricism. We didn't test our way to understanding. The scientific approach starts with a theory that does it's best to explain some phenomenon. Then the theory is criticized by experts. Finally, if it seems to be a promising theory tests are constructed. The tests can help verify the theory but it is the theory that provides the explanation which is the important part. Once we have explanation then we have understanding which allows us to play around with the model to come up with new things, diagnose problems etc.
The scientific approach is theory driven, not test driven. Understanding (and the power that gives us) is the goal.
> The scientific approach starts with a theory that does it's best to explain some phenomenon
At the risk of stretching the analogy, the LLM's internal representation is that theory: gradient-descent has tried to "explain" its input corpus (+ RL fine-tuning), which will likely contain relevant source code, documentation, papers, etc. to our problem.
I'd also say that a piece of software is a theory too (quite literally, if we follow Curry-Howard). A piece of software generated by an LLM is a more-specific, more-explicit subset of its internal NN model.
Tests, and other real CLI interactions, allow the model to find out that it's wrong (~empiricism); compared to going round and round in chain-of-thought (~philosophy).
Of course, test failures don't tell us how to make it actually pass; the same way that unexpected experimental/observational results don't tell us what an appropriate explanation/theory should be (see: Dark matter, dark energy, etc.!)
The ai is just pattern matching. Vibing is not understanding, whether done by humans or machines. Vibe programmers (of which there are many) make a mess of the codebase piling on patch after patch. But they get the tests to pass!
Vibing gives you something like the geocentric model of the solar system. It kind of works but but it's much more complicated and hard to work with.
The theory still emanated from actual observations, didn't it ?
It did but they were meaningless without a human intellect trying to make sense of them.
No, the theory comes from the authors knowledge, culture and inclinations, not from the fact.
Obviously the author has to do much work in selecting the correct bits from this baggage to get a structure that makes useful predictions, that is to say predictions that reproduces observable facts. But ultimately the theory comes from the author, not from the facts, it would be hard to imagine how one can come up with a theory that doesn't fit all the facts known to an author if the theory truly "emanated" from the facts in any sense strict enough to matter.
It most certainly is not. All your tests are doing is seeding the context with tokens that increase the probability of tokens related to solving the problem being selected next. One small problem: if the dataset doesn't have sufficiently well-represented answers to the specific problem, no amount of finessing the probability of token selection is going to lead to LLMs solving the problem. The scientific method is grounded in the ability to reason, not probabilistically retrieve random words that are statistically highly correlated with appearing near other words.
This only holds if you understand what's in the tests, and the tests are realistic. The moment you let the LLM write the tests without understanding them, you may as well just let it write the code directly.
> The moment you let the LLM write the tests without understanding them, you may as well just let it write the code directly.
I disagree. Having tests (even if the LLM wrote them itself!) gives the model some grounding, and exposes some of its inconsistencies. LLMs are not logically-omniscient; they can "change their minds" (next-token probabilities) when confronted with evidence (e.g. test failure messages). Chain-of-thought allows more computation to happen; but it doesn't give the model any extra evidence (i.e. Shannon information; outcomes that are surprising, given its prior probabilities).
I disagree to some degree. Tests have value even beyond whether they test the right thing. At the very least they show something worked and now doesnt work or vice versa. That has value in itself.
This assumes that tests are realistic, which for the most part they are not.
Say you describe your kitchen as “I want a kitchen” - where are the knives? Where’s the stove? Answer: you abdicated control over those details, so it’s wherever the stochastic parrot decided to put them, which may or may not be where they ended up last time you pulled your LLM generate-me-a-kitchen lever. And it may not be where you want.
Don’t like the layout? Let’s reroll! Back to the generative kitchen agent for a new one! ($$$)
The big labs will gladly let you reroll until you’re happy. But software - and kitchens - should not be generated in a casino.
A finished software product - like a working kitchen - is a fractal collection of tiny details. Keeping your finished software from falling apart under its own weight means upholding as many of those details as possible.
Like a good kitchen a few differences are all that stands between software that works and software that’s hell. In software the probability that an agent will get 100% of the details right is very very small.
Details matter.
If it is fast enough, and cheap enough, people would very happily reroll specific subsets of decisions until happy, and then lock that down. And specify in more details the corner cases that it doesn't get just how you want it.
People metaphorically do that all the time when designing rooms, in the form of endless browsing of magazines or Tik Tok or similar to find something they like instead of starting from first principles and designing exactly what they want, because usually they don't know exactly what they want.
A lot of the time we'd be happier with a spec at the end of the process than at the beginning. A spec that ensures the current understanding of what is intentional vs. what is an accident we haven't addressed yet is nailed down would be valuable. Locking it all down at the start, on the other hand, is often impossible and/or inadvisable.
Agreed; often you don’t know quite what you want until you’ve seen it.
Spec is an overloaded term in software :) because there are design specs (the plan, alternatives considered etc) and engineering style specs (imagine creating a document with enough detail that someone overseas could write your documentation from it while you’re building it)
Those need distinct names or we are all at risk of talking past each other :)
That matches what I’ve seen as well — generation is the easy part, validation is the bottleneck.
I’ve been experimenting with a small sparse-regression system that infers governing equations from raw data, and it can produce a lot of plausible candidates quickly. The hard part is filtering out the ones that look right but violate underlying constraints.
For example, it recovered the Sun’s rotation (~25.1 days vs 27 actual) from solar wind data, but most candidate equations were subtly wrong until you enforced consistency checks.
Feels like systems that treat verification as the source of truth (not just an afterthought) are the ones that will actually scale.
AI is the reality that TDD never before had the opportunity to live up to
Not just TDD. Amazon, for instance, is heading towards something between TDD and lightweight formal methods.
They are embracing property-based specifications and testing à la Haskell's QuickCheck: https://kiro.dev
Then, already in formal methods territory, refinement types (e.g. Dafny, Liquid Haskell) are great and less complex than dependent types (e.g. Lean, Agda).
What about model-driven development? Spec to code was the name of the game for UML.
Setting aside that model means something different now … MDD never really worked because the tooling never really dealt with intent. You would get so far with your specifications (models) but the semantic rigidity of the tooling mean that at some point your solution would have to part way. LLM is the missing piece that finally makes this approach viable where the intent can be inferred dynamically and this guides the implementation specifics. Arguably the purpose of TDD/BDD was to shore up the gaps in communicating intent, and people came to understand that was its purpose, whereas the key intent in the original XP setting was to capture and preserve “known good” operation and guard against regression (in XP mindset, perhaps fatefully clear intent was assumed)
It makes sense to me as long as you're not vibe coding the PBTs.
The deluge of amazon bugs ive been seeing recently makes me hesitant to follow in amazon's lead.
Kiro is such garbage though
If you add why you think so we might learn something.
The same prompt in the same project gives different results/slightly worse results compared to Claude Code, both using Opus model.
I've seen this sentiment and am a big fan of it, but I was confused by the blog post, and based on your comment you might be able to help: how does Lean help me? FWIW, context is: code Dart/Flutter day to day.
I can think of some strawmen: for example, prove a state machine in Lean, then port the proven version to Dart? But I'm not familiar enough with Lean to know if that's like saying "prove moon made of cheese with JavaScript, then deploy to the US mainframe"
yesterday I had to tell a frontier model to translate my code to tla+ to find a tricky cache invalidation bug which nothing could find - gpt 5.4, gemini 3.1, opus 4.6 all failed. translation took maybe 5 mins, the bug was found in seconds, total time to fix from idea to commit - about 15 mins.
if you can get a model to quickly translate a relevant subset of your code to lean to find tricky bugs and map lean fixes back to your codebase space, you've got yourself a huge unlock. (spoiler alert: you basically can, today)
Thanks for following up on this: I was really surprised by how much air this paeon to, idk, TDD, took out of the comments by getting off-topic.
Before you commented, I started poking at what you described for 15 minutes, then forget about it and fell asleep. Now I remembered, and I know it's viable and IIUC it's almost certainly going to make a big difference in my work practice moving forward. Cheers.
I don't think he's referring to Lean specifically, but any sort of executable testing methodology. It removes the human in the loop in the confidence assurance story, or at least greatly reduces their labor. You cannot ever get such assurance just by saying, "Well this model seems really smart to me!" At best, you would wind up with AI-Jim.
(One way Lean or Rocq could help you directly, though, would be if you coded your program in it and then compiled it to C via their built-in support for it. Such is very difficult at the moment, however, and in the industry is mostly reserved for low-level, high-consequence systems.)
>Such is very difficult at the moment
What do you mean? It's a nice and simple language. Way easier to get started than OCaml or Haskell for example. And LLMs write programs in Lean4 with ease as well. Only issue is that there are not as many libraries (for software, for math proofs there is plenty).
But for example I worked with Claude Code and implemented a shell + most of unix coreutils in like a couple of hours. Claude did some simple proofs as well, but that part is obvs harder. But when the program is already in Lean4, you can start moving up the verification ladder up piece by piece.
Well, if you do not need to care about performance everything can be extremely simple indeed. Let me show you some data structure in coq/rocq while switching off notations and diplaying low level content.
Require Import String.
Definition hello: string := "Hello world!".
Print hello.
hello = String (Ascii.Ascii false false false true false false true false) (String (Ascii.Ascii true false true false false true true false) (String (Ascii.Ascii false false true true false true true false) (String (Ascii.Ascii false false true true false true true false) (String (Ascii.Ascii true true true true false true true false) (String (Ascii.Ascii false false false false false true false false) (String (Ascii.Ascii true true true false true true true false) (String (Ascii.Ascii true true true true false true true false) (String (Ascii.Ascii false true false false true true true false) (String (Ascii.Ascii false false true true false true true false) (String (Ascii.Ascii false false true false false true true false) (String (Ascii.Ascii true false false false false true false false) EmptyString))))))))))) : string
You know you could just define the verified specs in lean and if performance is a problem, use the lean spec to extract an interface and tests for a more performant language like rust. You could at least in theory use Lean as an orchestrator of verified interfaces.
But isn't that tantamount with "his comment is a complete non-sequitor"?
I don't think so? Lean is formal methods, so it makes sense to discuss the boons of formal and semiformal methods more generally.
I used to think that the only way we would be able to trust AI output would be by leaning heavily into proof-carrying code, but I've come to appreciate the other approaches as well.
But that's exactly my point. "It's natural to discuss the broader category" is doing a lot of heavy lifting here. The blog post is making a very specific claim: that formal proof, checked by Lean's kernel, is qualitatively different from testing, it lets you skip the human review loop entirely. cadamsdotcom's comment rounds that down to "executable specs good, markdown specs bad," which... sure, but that's been the TDD elevator pitch for 20 years.
If someone posted a breakthrough in cryptographic verification and the top comment was "yeah, unit tests are great," we'd all recognize that as missing the point. I don't think it's unrelated, I think it's almost related, which is worse, because it pattern-matches onto agreement while losing the actual insight.
The verification angle makes sense, especially for high-stakes domains.
But I wonder how this scales in practice outside of formal environments.
In most ML/LLM systems, the bottleneck isn’t just correctness of individual steps, but the interaction between components (data → tokenizer → model → inference). A lot of failures come from subtle mismatches across the pipeline rather than strictly invalid logic.
Formal specs are great when the system is well-defined, but many real-world pipelines are still exploratory and data-dependent.
It feels like there’s a gap between: • formally verified components • and emergent behavior in end-to-end systems
Curious how you see this approach handling those system-level uncertainties.
There are also software model checkers that can model distributed processes. You have to simplify the state a bit, otherwise you get a state space explosion.
I tried it out myself, I let AI add action transitions through the code, like: // A -> B: some description. Then I validate via a test that every action transition defined in my model is also defined somewhere commented in code, and other way around that every comment exists in the model.
Finally, I let AI write model check queries on particular properties. If I notice a particular bug, then I ask AI to analyze the model and the model check queries on why it could happen, and ask to strengthen it.
It sounds like a lot of effort, but I got it working in a half hour.
I just feel like Mistral is heading for bad financial times when they are focusing on fringe academic areas and not on building a business out of their research. Initial Mistral was largely based on LLaMA, then they added innovative MoE and since then disappeared, doing AI consulting for big EU companies instead.
The real world success they report reminds me of Simon Willison’s Red Green TDD: https://simonwillison.net/guides/agentic-engineering-pattern...
> Instead of taking a stab in the dark, Leanstral rolled up its sleeves. It successfully built test code to recreate the failing environment and diagnosed the underlying issue with definitional equality. The model correctly identified that because def creates a rigid definition requiring explicit unfolding, it was actively blocking the rw tactic from seeing the underlying structure it needed to match.
If Agent is writing the tests itself, does it offer better correctness guarantees than letting it write code and tests?
In my experience the agent regularly breaks some current features while adding a new one - much more often than a human would. Agents too often forget about the last feature when adding the next and so will break things. Thus I find Agent generated tests important as they stop the agent from making a lot of future mistakes.
It is definitely not foolproof but IMHO, to some extent, it is easier to describe what you expect to see than to implement it so I don't find it unreasonable to think it might provide some advantages in terms of correctness.
That definitely depends upon the situation. More often than not, properly testing a component takes me more time than writing it.
In my experience, this tends to be more related to instrumentation / architecture than a lack of ability to describe correct results. TDD is often suggested as a solution.
Given the issues with AWS with Kiro and Github, We already have just a few high-profile examples of what happens when AI is used at scale and even when you let it generate tests which is something you should absolutely not do.
Otherwise in some cases, you get this issue [0].
[0] https://sketch.dev/blog/our-first-outage-from-llm-written-co...
Don't "let it" generate tests. Be intentional. Define them in a way that's slightly oblique to how the production code approaches the problem, so the seams don't match. Heck, that's why it's good to write them before even thinking about the prod side.
The linked article does not speak of tests, it speaks of a team that failed to properly review an LLM refactor then proceeds to blame the tooling.
LLMs are good at writing tests in my experience.
That article is literally a definition of TDD that has been around for years and years. There's nothing novel there at all. It's literally test driven development.
TDD == Prompt Engineering, for Agentic coding tasks.
Wild it’s taken people this long to realize this. Also lean tickets / tasks with all needed context to complete the task, including needed references / docs, places to look in source, acceptance criteria, other stuff.
AI agents will become a comodity.
Europeans not wanting to be dependent, and they are giving for free what US investors planed to charge with 90% margin.
Amazing! What a blast. Thank you for your service (this first 100M$ burned to POC GPT1 and from here, we are so good to go)
The problem with the European independence story is, that it seems Mistral runs its own stuff also on US cloud act affected infrastructure. This makes them a very weird value proposition: If I accept a level of "independence" whereby I run on AWS or Azure, I could as well pay for Anthropic or GPT to have SOTA performance.
If I do not accept that level of independence but want more, I need to buy what's on OVH, Scaleway, Ionos etc. or host my own, but that usually means even smaller, worse models or a lot of investment.
Nevertheless, the "band" that Mistral occupies for economic success is very narrow. Basically just people who need independence "on paper" but not really. Because if I'm searching for actual independence, there's no way I could give them money at the moment for one of their products and it making sense, cause none of their plans are an actual independence-improvement over, let's say, Amazon Bedrock.
I really really want to support them, but it must make economic sense for my company, too, and it doesn't.
I don’t care about the servers, they are a comodity already.
The key is to avoid chantage, remember Oracle with DBs, people learned not to build on top of unreplaceable stuff
They are building their own infra - south of Paris and another one was announced in Sweden recently.
Then why does their list of subprocessors list Google and Microsoft "for cloud infrastructure", specifically for "Le Chat, La Plateforme, Mistral Code"? Sounds to me as if they're mainly running on Azure.
Also, they're listing CoreWeave as inference provider in "EEA" area, but CoreWeave is of course also an US company. Even if they have their data center physically in the EU, it must be considered open access for the USA due to the CLOUD act.
https://trust.mistral.ai/subprocessors
If what you say is true, they have a communications problem and they need to fix that urgently. Right now, this is why they don't get my business. Others will have made the same decision based on their own subprocessor list.
Or did you mean, they're like, right now building it and plan to move there, but it's not up yet?
I really hope you're right. Sadly, though, I don't see any evidence of UK companies disinvesting from big US tech. There aren't good alternatives and what there is is too complex. As long as 'everyone else is still using MS', it seems like it's a brave CTO that switches to European providers. Unless that happens, the network effect of having AI+data is likely to mean US tech still has a big advantage in corp settings. But, HN - please tell me I'm wrong!
> There aren't good alternatives and what there is is too complex.
Sounds like a worth challenge for this community, mind giving actual examples and see what others can suggest?
Vertical integration and breadth and depth of offerings on the cloud and customer lock-in from dominating it for 20 years
I wonder what the biggest (non-AI) moats are for US tech against the alternatives?
they will, but the jagged frontier is fractal and each one will have different capabilities; you'll want to mix models and to get best results consistently you'll need to.
There have been a lot of conversations recently about how model alignment is relative and diversity of alignment is important - see the recent podcast episode between Jack Clark (co-founder of Anthropic) and Ezra Klein.
Many comments here point out that Mistral's models are not keeping up with other frontier models - this has been my personal experience as well. However, we need more diversity of model alignment techniques and companies training them - so any company taking this seriously is valuable.
they ll get there
Curious if anyone else had the same reaction as me
This model is specifically trained on this task and significantly[1] underperforms opus.
Opus costs about 6x more.
Which seems... totally worth it based on the task at hand.
[1]: based on the total spread of tested models
Agreed. The idea is nice and honorable. At the same time, if AI has been proving one thing, it's that quality usually reigns over control and trust (except for some sensitive sectors and applications). Of course it's less capital-intense, so makes sense for a comparably little EU startup to focus on that niche. Likely won't spin the top line needle much, though, for the reasons stated.
> quality usually reigns over control and trust
Most Copilot customers use Copilot because Microsoft has been able to pinky promise some level of control for their sensitive data. That's why many don't get to use Claude or Codex or Mistral directly at work and instead are forced through their lobotomised Copilot flavours.
Remember, as of yet, companies haven't been able to actually measure the value of LLMs ... so it's all in the hands of Legal to choose which models you can use based on marketing and big words.
Ha, keep putting your prompts and workflows into cloud models. They are not okay with being a platform, they intend to cannibalize all businesses. Quality doesn't always reign over control and trust. Your data and original ideas are your edge and moat.
Treating "quality" as something you can reliably measure in AI proof tools sounds nice until you try auditing model drift after the 14th update and realize the "trust" angle stops being a niche preference and starts looking like the whole product. Brand is not a proof. Plenty of orgs will trade peak output for auditability, even if the market is bigger for YOLO feature churn.
EU could help them very much if they would start enforcing the Laws, so that no US Company can process European data, due to the Americans not willing to budge on Cloud Act.
That would also help to reduce our dependency on American Hyperscalers, which is much needed given how untrustworthy the US is right now. (And also hostile towards Europe as their new security strategy lays out)
This would be unfortunately a rather nuclear option due to the continent’s insane reliance on technology that breaks its unenforced laws.
How about not making these unenforced laws in the first place so that European companies could actually have a chance at competing? We're going to suffer the externalities of AI either way, but at least there would be a chance that a European company could be relevant.
The AI Act absolutely befuddled me. How could you release relatively strict regulation for a technology that isn't really being used yet and is in the early stages of development? How did they not foresee this kneecapping AI investment and development in Europe? If I were a tinfoil hat wearer I'd probably say that this was intentional sabotage, because this was such an obvious consequence.
Mistral is great, but they haven't kept up with Qwen (at least with Mistral Small 4). Leanstral seems interesting, so we'll have to see how it does.
Because the AI act was mostly written to address issues with ML products and services. It was mostly done before ChatGPT happened, so all the foundation model stuff got shoehorned in.
Speaking as someone who's been doing stats and ML for a while now, the AI act is pretty good. The compliance burden falls mostly on the companies big enough to handle it.
The foundation model parts are stupid though.
>Because the AI act was mostly written to address issues with ML products and services. It was mostly done before ChatGPT happened, so all the foundation model stuff got shoehorned in.
It's not an excuse. Anybody with half a working brain should've been able to tell that this was going to happen. You can't regulate a field in its infancy and expect it to ever function.
>The compliance burden falls mostly on the companies big enough to handle it.
You mean it falls on anyone that tries to compete with a model. There's a random 10^25 FLOPS compute rule in there. The B300 does 2500-3750 TFLOPS at fp16. 200 of these can hit that compute number in 6 months, which means that in a few years time pretty much every model is going to hit that.
And if somebody figures out fp8 training then it would only take 10 of these GPUs to hit it in 6 months.
The copyright rule and having to disclose what was trained on also means that it will be impossible to have enough training data for an EU model. And this even applies to people that make the model free and open weights.
I don't see how it is possible for any European AI model to compete. Even if these restrictions were lifted it would still push away investors because of the increased risk of stupid regulation.
Alignment tax directly eats to model quality, double digit percents.
I'm never sure how much faith one can put into such benchmarks but in any case the optics seem to shift once you have pass@2 and pass@3.
Still, the more interesting comparison would be against something such as Codex.
But you can run this model for free on a common battery powered laptop sitting on your laps without cooking your legs.
Sorry, but what are you talking about? This is a 120B-A6B model, which isn't runnable on any laptop except the most beefed up Macbooks, and then will certainly drain its battery and cook your legs.
Yeah my bad, it requires an expensive MacBook.
I think it would still be fine for the legs and on battery for relatively short loads: https://www.notebookcheck.net/Apple-MacBook-Pro-M5-2025-revi...
But 40 degrees and 30W of heat is a bit more than comfortable if you run the agent continuously.
You can easily run a quant of this on a DGX Spark though. Seems like a small investment if it meaningful improves Lean productivity.
Is it though?
Most people I know that use agents for building software and tried to switch to local development, every single time they switch back to Claude/codex.
It's just not worth it. The models are that much better and continue to get released / improve.
And it's much cheaper unless you're doing like 24/7 stuff.
Even on the $200/m plan, that's cheaper than buying a $3k dgx or $5k m4 max with enough ram.
Not to mention you can no longer use your laptop as a laptop as the power draw drains it - you'd need to host separately and connect
A single DGX Spark can service a whole department of mathematicians (or programmers), and you can cluster up to 4 of them them to fit very large models like GLM-5 and quants of Kimi K2.5. This is nearing frontier-level model size.
I understand the value proposition of the frontier cloud models, but we're not as far off from self-hosting as you think, and it's becoming more viable for domain-specific models.
That's great news- I wonder if that will help drive cloud costs down too
the model is open source, you can run it locally. You don't think thats significant ?
Formal verification and code synthesis feel like natural companions for automated scientific discovery. I’ve been working on a small (~800‑line) Python agent that uses sparse regression to uncover governing equations directly from data; it’s managed to validate twelve physical laws, including deriving the Sun’s rotation rate from NASA plasma measurements and correcting Gemini’s plasma conservation. Having an agent like Leanstral that can reason about proofs and specifications would be a powerful complement to data‑driven model discovery — it closes the loop between experimentation and provable correctness.
Can someone please explain... If I don't know any Lean (and I suspect most people don't), is it of any direct value? Trying to understand if there's something it can help me with (e.g. automatically write proofs for my Go programs somehow... I'm not sure) or should I just cheer solely for more open models out there, but this one isn't for me?
Presumably the idea is that an agent generates a Lean4 specification against which the software is measured.
But then the Lean4 specification effectively becomes the software artifact.
And we're sort of back to square 1. How do you verify a Lean4 spec is correct (and that it describes what needs to be built in the first place) without human review?
> And we're sort of back to square 1.
Specifications are smaller than the full code, just as high level code is smaller than the functionally equivalent assembly. As we ascend the abstraction ladder the amount of reading a human needs to do decreases. I don't think this should really count as "back to square 1".
That has always been the perceived promise of higher-abstraction software specs: automated code generation from something higher-level, thus making programmers increasingly obsolete.
Among the RADs is Microsoft Visual Basic, which along with WinForms and SQL was supposed to make business programmers nearly obsolete, but instead became a new onramp into programming.In particular, I'd like to highlight UML, which was supposed to mostly obsolete programming through auto-generated code from object-oriented class diagrams.[1] The promise was that "business domain experts" could model their domain via visual UML tooling, and the codegen would handle it from there. In practice, UML-built applications became maintenance nightmares.
In every one of these examples, the artifact that people made "instead of programming" became the de-facto programming language, needing to be maintained over time, abstracted, updated, consumed behind APIs, etc. -- and programmers had to be called in to manage the mess.
It's interesting that Spec4 can be auto-generated, then used to generate code. My question is - what do you do when you have (a) consumers depending on a stable API, and (b) requests for new features? Maybe hand the job to Claude Code or a human developer with a suite of unit tests to guarantee API compatibility, but at that point we're back to an agent (LLM or human) doing the work of programming, with the Spec4 code as the programming language being updated and maintained.
[0] https://en.wikipedia.org/wiki/Fourth-generation_programming_...
[1] https://news.ycombinator.com/item?id=26934795
You're touching on the fundamental "who watches the watchmen" problem in formal verification. But I think the framing slightly misses the key asymmetry: reviewing a Lean4 spec is dramatically easier than reviewing the implementation it constrains.
A formal spec in Lean is typically 10-50x shorter than the code it proves correct. More importantly, Lean's type checker is itself a small, trusted kernel (~10k lines) that has been scrutinized by the PL community for years. So you're not trusting the agent — you're trusting the kernel.
The practical workflow isn't "agent writes spec + code." It's: human writes spec (the hard creative part), agent generates proof that code satisfies spec, Lean kernel mechanically checks the proof. The agent can hallucinate all it wants in step 2 — if the proof doesn't typecheck, it gets rejected deterministically.
The real bottleneck is step 1: writing good specs requires domain expertise. But that's exactly where humans should stay in the loop. It's a much better division of labor than reviewing thousands of lines of generated code.
Does that mean your production code is lean? Or do you translate some other language code to lean to verify it?
Also a very good question btw, people do both. For some projects Lean is expressive and performant enough to use on its own (or call into using the reverse FFI), other projects use a model of a real programming language like Rust. The disadvantage of the latter is that the Lean model of Rust has to be trusted.
Trustworthy vibe coding. Much better than the other kind!
Not sure I really understand the comparisons though. They emphasize the cost savings relative to Haiku, but Haiku kinda sucks at this task, and Leanstral is worse? If you're optimizing for correctness, why would "yeah it sucks but it's 10 times cheaper" be relevant? Or am I misunderstanding something?
On the promising side, Opus doesn't look great at this benchmark either — maybe we can get better than Opus results by scaling this up. I guess that's the takeaway here.
I also don't understand the focus on vibe coding in the marketing. Vibe coding kind of has the image of being for non-devs, right?
I do like agents (like Claude Code), but I don't consider myself to be vibe coding when I use them. Either I'm using a language/framework I know and check every step. OR I'm learning, checking every step and asking for explanations.
I tried vibe coding, and really dislike the feeling I have when doing it. It feels like building a house, but without caring about it, and just using whatever tech. Sure I may have moisture problems later, but it's a throwaway house anyway. That's how I feel about it. Maybe I have a wrong definition.
Maybe it's good to not use "vibe coding" as a synonym for programming with agent assistance. Just to protect our profession. Like: "Ah you're vibing" (because you have Claude Code open), "No, I'm using CC to essentially type faster and prevent syntax errors and get better test coverage, maybe to get some smart solutions without deep research. But I understand and vouch for every loc here. 'We are not the same.'"
> I tried vibe coding, and really dislike the feeling I have when doing it. It feels like building a house, but without caring about it, and just using whatever tech. Sure I may have moisture problems later, but it's a throwaway house anyway. That's how I feel about it. Maybe I have a wrong definition.
No, I feel the same. I vibe-coded a few projects and after a few weeks I just threw them away, ultimately I felt I just wasted my time and wished I coudl get it back to do something useful.
Yeah, the original meaning of Vibe Coding was "not looking at the code, just going on vibes", but a lot of people now use it to mean "AI was involved in some way".
I see a whole spectrum between those two. I typically alternate between "writing code manually and asking AI for code examples" (ChatGPT coding), and "giving AI specific instructions like, write a function blarg that does foo".
The latter I call Power Coding, in the sense of power armor, because you're still in control and mostly moving manually, but you're much stronger and faster.
I like this better than "tell agent to make a bunch of changes and come back later" because first of all it doesn't break flow (you can use a smaller model for such fine-grained changes so it goes very fast -- it's "realtime"), and second, you don't ever desync from the codebase and need to spend extra time figuring out what the AI did. Each change is sanity-checked as it comes in.
So you stay active, and the code stays slop-free.
I don't hear a lot of people doing this though? Maybe we just don't have good language for it.
"I don't hear a lot of people doing this though? Maybe we just don't have good language for it."
Interesting thought. I guess we don't really, vibe coding is to powerful a term. But perhaps just call it LLM assisted programming? Where we used to do Stack Overflow assisted programming. LLM assisted programming is more focused, goes faster. But since you're wandering around less I guess you learn less, you're exposed to less new information, some of it was helpful in unexpected ways. Now you have to make learning a specific part of your flow, and that takes discipline/time. But is well worth it imho. Actually, for me it's the only way to enjoy it.
> It feels like building a house, but without caring about it, and just using whatever tech.
So, most homebuilders (in the US) unfortunately.
I myself am now and expert at insulation and all the vapor-permeable and vapor-blocking membranes/foils/foams that come with it.
It came at great cost though, I hated the process of learning and the execution. I was less than happy for some years. But I feel even more uncomfortable vibe-home-improving than I do vibe-coding. The place is starting to look nice now though.
They haven't made the chart very clear, but it seems it has configurable passes and at 2 passes it's better than Haiku and Sonnet and at 16 passes starts closing in on Opus although it's not quite there, while consistently being less expensive than Sonnet.
pass@k means that you run the model k times and give it a pass if any of the answers is correct. I guess Lean is one of the few use cases where pass@k actually makes sense, since you can automatically validate correctness.
Oh my bad. I'm not sure how that works in practice. Do you just keep running it until the tests pass? I guess with formal verification you can run it as many times as you need, right?
It’s really not hard — just explicitly ask for trustworthy outputs only in your prompt, and Bob’s your uncle.
Assuming that what you're dealing with is assertable. I guess what I mean to say is that in some situations is difficult to articulate what is correct and what isn't depending in some situations is difficult to articulate what is correct and what isn't depending upon the situation in which the software executes.
And Bob’s your uncle.
I absolutely called this a couple of weeks ago, nice to be vindicated!
> I'm interested to see what it is in the age of LLMs or similar future tools. I suspect a future phase change might be towards disregarding how easy it is for humans to work with the code and instead focus on provability, testing, perhaps combined with token efficiency.
> Maybe Lean combined with Rust shrunk down to something that is very compiler friendly. Imagine if you could specify what you need in high level language and instead of getting back "vibe code", you get back proven correct code, because that's the only kind of code that will successfully compile.
https://news.ycombinator.com/item?id=47192116
It's important to keep in mind that no proof system ensures your proof is the correct proof, only that it's a valid proof. Completely understanding what a proof proves is often nearly as difficult as understanding the program it's proving. Normally you benefit because the process of building a proof forces you to develop your understanding more fully.
Uhm, no? Even with "simple" examples like Dijkstra's shortest path, the spec is easier than the implementation. Maybe not for you, but try it out on an arbitrary 5-yr old. On the extreme end, you have results in maths, like Fermat's Last Theorem. Every teenager can understand the statement (certainly after 10 mins of explanation) but the proof is thousands of pages of super-specialized maths. It is a spectrum. For cryptography, compression, error-correction, databases, etc, the spec is often much simpler than the implementation.
I don't know why you created a new account for this, but take the textbook example of a nontrivial formally verified system: SeL4. That implementation was 8.7k of C code, which correspondend to 15k lines of Isabelle that ultimately needed 100k+ lines of proof to satisfy. And that was with the formal model excluding lots of important properties like hardware failure that actual systems deal with.
You are confusing the proof with the spec/theorem. A correct proof and a valid proof are the same thing. It doesn't really matter how long the proof is, and you don't even need to understand it for it to be correct, the machine can check that.
But indeed, if the spec includes 8.7k of C code, that is problematic. If you cannot look at the theorem and see that it is what you mean, that is a problem. That is why abstraction is so important; your ultimate spec should not include C-code, that is just too low-level.
I'm not confusing them. That's why I gave each of the numbers for SeL4 separately.
Knowing whether those theorems are the right theorems for the problem can be as difficult as understanding the implementation itself. Hence the example of SeL4 where the number of theorems exceeds lines of code in the original implementation and the formal model is large.
It's my experience that most people doing formal methods have seen cases where they actually proved something slightly different than what they intended to. This usually involves an unintentional assumption that isn't generally true.
I think you have been confusing them. Two theorems are the same if they have the same statement (spec). A proof is not a theorem, nobody cares about when two proofs are the same or not.
> I don't know why you created a new account for this
What value does this add to the conversation? I’m not seeing it: am I missing something? It comes across as a kind of insult.
They made a good point in my opinion! (The “Uhm no” part got it off on the wrong foot, I will admit.) But even if you felt annoyed or didn’t agree with the point, it was substantive and moved the conversation forward. I’m here for the (genuine) questions and (constructive) debate and (civil) pushback.
I like to welcome new users before they take too much of a beating. That can come later when they are too invested to leave and/or when morale needs improving.
So welcome! Bring a helmet, and don’t stop disagreeing.
Very cool but I haven’t been able to convince software developers in industry to write property based tests. I sometimes joke that we will start writing formal proofs until the tests improve. Just so that they will appreciate the difference a little more.
I can’t even convince most developers to use model checkers. Far more informal than a full proof in Lean. Still highly useful in many engineering tasks. People prefer boxes and arrows and waving their hands.
Anyway, I don’t know that I’d want to have a system vibe code a proof. These types of proofs, I suspect, aren’t going to be generated to be readable, elegant, and be well understood by people. Like programs they generate it will look plausible.
And besides, you will still need a human to review the proof and make sure it’s specifying the right things. This doesn’t solve that requirement.
Although I have thought that it would be useful to have a system that could prove trivial lemmas in the proof. That would be very neat.
The point is you just need to scrutinize the theorem. Not easy either, but still significantly less work than writing the proof.
Maybe a naive question: given that they see better performance with more passes but the effect hits a limit after a few passes, would performance increase if they used different models per pass, i.e leanstral, kimi, qwen and leanstral again instead of 4x leanstral?
This is called a "LLM alloy", you can even do it in agentic, where you simply swap the model on each llm invocation.
It does actually significantly boost performance. There was an article on here about it recently, I'll see if I can find it.
Edit: https://news.ycombinator.com/item?id=44630724
They found the more different the models were (the less overlap in correctly solved problems), the more it boosted the score.
That sounds quite interesting. Makes me wonder if sooner or later they will have to train multiple independent models that cover those different niches. But maybe we will see that sooner or later. Thanks for the link.
Mixture of Mixtures of Experts ;)
One would think that LoRAs being so successful in StableDiffusion, that more people would be focused on constructing framework based LoRas; but the economics of all this probably preclude trying to go niche in any direction and just keep building the do-all models.
The SD ecosystem in large part was grassroots and focused on nsfw. I think current LLM companies would have a hard time getting that to happen due to their safety stuff.
Fine-tuning does exist on the major model providers, and presumably already uses LoRA. (Not sure though.)
We saw last year that it's remarkably easy to bypass safety filters by fine-tuning GPT, even when the fine-tuning seems innocuous. e.g. the paper about security research finetuning (getting the model to add vulnerabilities) producing misaligned outputs in other areas. It seems like it flipped some kind of global evil neuron. (Maybe they can freeze that one during finetuning? haha)
Found it: Emergent Misalignment
https://news.ycombinator.com/item?id=43176553
https://news.ycombinator.com/item?id=44554865
Pleasant surprise: someone saying "open source" and actually meaning Open Source. It looks like the weights are Apache-2.0 licensed.
Based on community definitions I've seen, this is considered "open weights". If you can't reproduce the model, it's not "open source"
Yes “open weights” conveys the reality more clearly: merely having the parameters is very different than able to run a process that creates them. Without openness of the full process start to finish, much is hidden.*
Remember, language is what we make it. Dictionaries are useful catalogs of usage but we make the judgment calls.
* Even with the process, much is not well understood! / The ethics of releasing an open weights model at some capability level is a separate discussion.
FYI The Lean 4 paper: https://dl.acm.org/doi/10.1007/978-3-030-79876-5_37
Is anyone using this approach with lean to ship production code? Writing lean spec as human, implementation and proof by agent? And then shipping lean or exporting to C? Would be great to understand how you are actually using this.
The tricky part is that prompts can look “correct” but still behave unpredictably depending on phrasing.
Naturally the Microsoft-owned language is getting the AI hype instead of the more mature options that could do this sort of work… Agda, ATS, Coq/Rocq, Dafny, Fstar, Idris, Isabelle, Why3 just to name a few.
You should check out the recent PR's to the Agda repo... the community is currently very divided about AI. For better or worse, the people driving the Lean project have been interested in AI for quite some time.
A bit uncharitable. I'm a diehard fan of Rocq, but it's nothing unusual to see the young new hotness that is Lean continue to get the spotlight. It's not a sign of Microsoft putting its thumb on the scales, and the hype for Lean has long predated LLMs.
It's certainly less mature when it comes to verified programming, but its appeal to mathematicians (rather than formal methods experts) has earned it much respect.
Am I missing something? Isn’t that the language most are using currently when looking at research at openai, google, deepseek etc?
Automated theorem provers running on a $5k piece of hardware is a cool version of the future
I don't understand how this can impact my JS (+yaml, css, etc) code writing in a complex app.
What are these "passes" they reference here? Haven't seen that before in LLM evals
Could definitely be interesting for having another model run over the codebase when looking for improvements
It's the number of attempts at answering the question.
Does Mistral come close to Opus 4.6 with any of their models?
I use mistral-medium-3.1 for a lot of random daily tasks, along with the vibe cli. I'd state from my personal opinion that mistral is my preferred 'model vendor' by far at this point. They're extremely consistent between releases while each of them just feels better. I also have a strong personal preference to the output.
I actively use gemini-3.1-pro-preview, claude-4.6-opus-high, and gpt-5.3-codex as well. I prefer them all for different reasons, however I usually _start_ with mistral if it's an option.
Why not Large 3? It's larger and cheaper
Mistral hasn't been in the running for SOTA for quite awhile now
Not at the moment, but a release of Mistral 4 seems close which likely bridges the gap.
Mistral Small 4 is already announced.
MOE but 120B range. Man I wish it was an 80B. I have 2 GPUs with 62Gib of usable VRAM. A 4bit 80B gives me some context window, but 120B puts me into system RAM
Either some q3 or since it's a MoE, maybe a REAP version of q4 might work (or could be terrible, I'm not sure about REAP'd models).
I don’t know a single person using Mistral models.
Isn't their latest speech to text model SOTA? When I tested it on jargon, it was amazing.
https://news.ycombinator.com/item?id=46886735
I'm using this model for my first python project, coding using opencode along with devstral and Mistral Large 3. I know it's not as capable as other, more expensive models, but working with it this way is teaching me python. More directly to your point though, the speech to text model is really good.
It's funny because I just took a break from it to read some hn and found this post.
I used Ministral for data cleaning.
I was surprised: even tho it was the cheapest option (against other small models from Anthropic) it performed the best in my benchmarks.
Mistral is super smart in smaller context and asking questions about it
Pretty much all of my LLM usage has been using Mistral's open source models running on my PC. I do not do full agentic coding as when i tried it with Devstral Small 2 it was a bit too slow (though if i could get 2-3 times the speed of my PC from a second computer it'd be be a different story and AFAIK that is doable if i was willing to spend $2-3k on it). However i've used Mistral's models for spelling and grammar checks[0], translations[1][2], summaries[3] and trying to figure out if common email SPAM avoidance tricks are pointless in the LLM age :-P [4]. FWIW that tool you can see in the shots is a Tcl/Tk script calling a llama.cpp-based command-line utility i threw together some time ago when experimenting with llama.cpp.
I've also used Devstral Small to make a simple raytracer[5][6] (it was made using the "classic" chat by copy/pasting code, not any agentic approach and i did fix bits of it in the process) and a quick-and-dirty "games database" in Python+Flask+Sqlite for my own use (mainly a game backlog DB :-P).
I also use it to make various small snippets, have it generate some boilerplate stuff (e.g. i have an enum in C and want to write a function that prints names for each enum value or have it match a string i read from a json file with the appropriate enum value), "translate" between languages (i had it recently convert some matrix code that i had written in Pascal into C), etc.
[0] https://i.imgur.com/f4OrNI5.png
[1] https://i.imgur.com/Zac3P4t.png
[2] https://i.imgur.com/jPYYKCd.png
[3] https://i.imgur.com/WZGfCdq.png
[4] https://i.imgur.com/ytYkyQW.png
[5] https://i.imgur.com/FevOm0o.png (screenshot)
[6] https://app.filen.io/#/d/e05ae468-6741-453c-a18d-e83dcc3de92... (C code)
[7] https://i.imgur.com/BzK8JtT.png
That's likely because they're chasing enterprise - see deals with HSBC, ASML, AXA, BNP Paribas etc... Given swelling anti-US sentiment and their status as a French 'national champion', Mistral are probably in a strong position for now regardless of model performance, research quality or consumer uptake.
I'm building a knowledge graph on personal data (emails, files) with Ministral 3:3b. I try with Qwen 3.5:4b as well but mostly Ministral.
Works really well. Extracts companies you have dealt with, people, topics, events, locations, financial transactions, bills, etc.
Me neither, they're not ready for prime imo. I have a yearly sub and the product is just orders of magnitude behind Anthropic's offering. I use Code for real world stuff and I am happy with the result, Mistral is just not something I can trust right now.
I use them solely.
I read it as Lanestra, and thought of that story :D
Curious if pass@2 was tested for haiku and sonnet?
The TDD foundation! We might need one of those. :)
"and continues to scale linearly"
it clearly and demonstrably does not. in fact, from eyeballing their chart Qwen, Kimi, and GLM scale linearly whereas Leanstral does not. But this is not surprising because the Alibaba, Moonshot, and Zhipu have hundreds of employees each and hundreds of millions of dollars of investment each.
love the opensource push for agents, the fleet grows!
From https://mistral.ai/news/leanstral :
This is great, congratulations to the Mistral team! I'm looking forward to the code arena benchmark results. Thanks for sharing.
Congratulations on the launch!
Mistral seems to focus on a different market than the others. Their best model is meh, their best ASR model locally is either rather slow compared to Parakeet on similar languages, or not as good for others (like qwen ASR).
Side note: Lean seems quite unreadable with tons of single letter variable names. Part of it is me being unaccustomed with it, but still.
Mistral seems to focus on some niche LLM model tooling that are somehow very needed in certain cases. Can't forget their OCR multimodal embedding model!
Public service announcement to hopefully reduce unnecessary knife fights*:
There are two compatible and important (but different) questions in play:
1. Is a program correct relative to a formal specification?
2. Is the formal specification what we mean/want?
*: Worth asking: “What that other person necessarily wrong? Or perhaps they are discussing a different aspect or framing?” AKA: “be curious and charitable” I’m not going to link to the specific threads, but they are happened / are happening. Le Sigh.
Truly exciting
Here we go.
is the haiku comparison because they've distilled from the model?
lol, why does the paper abstract assume I know what Lean is and it goes on to talk about lean 4 improvements?
Why do you expect to understand an article you randomly read off the interwebs?