We tasked Opus 4.6 using agent teams to build a C Compiler

Yeah, didn't mention gas or ld, for similar reasons. I agree that a compiler doesn't necessarily "need" those.

I don't agree that all the claims are backed up by their own comments, which means that there's probably other places where it falls down.

Its... Misrepresentation.

Like Chicken is a Scheme compiler. But they're very up front that it depends on a C compiler.

Here, they wrote a C compiler that is at least sometimes reliant on having a different C compiler around. So is the project at 50%? 75%?

Even if its 99%, thats not the same story as they tried to write. And if they wrote that tale instead, it would be more impressive, rather than "There's some holes. How many?"

I am surprised by the number of comments that say the assembler is trivial - it is admittedly perhaps simpler than some other parts of the compiler chain, but it’s not trivial.

What you are doing is kinda serialising a self-referential graph structure of machine code entries that reference each others addresses, but you don’t know the addresses because the (x86) instructions are variable-length, so you can’t know them until you generate the machine code, chicken-and-egg problem.

Personally I find writing parsers much much simpler than writing assemblers.

Huh. A second person mentioning the assembler. Don't think I ever referred to one...?

i'm sure claude has been trained on every open source compiler

> You won't know until you've finished spending the money whether it will fail or not.

How do you conclude that? You start off with a bunch of tests and build these things incrementally, why would you spend 20k before realizing there’s a problem?

> It cost $20,000

I'm curious - do you have ANY idea what it costs to have humans write 100,000 lines of code???

You should look it up. :)

> On top of that, Anthropic is losing money on it.

It seems they are *not* losing money on inference: https://bsky.app/profile/steveklabnik.com/post/3mdirf7tj5s2e

That's a good point! Here claude opus wrote a C compiler. Outrageously cool.

Earlier today, I couldn't get opus to replace useEffect-triggered-redux-dispatch nonsense with react-query calls. I already had a very nice react-query wrapper with tons of examples. But it just couldn't make sense of the useEffect rube goldberg machine.

To be fair, it was a pretty horrible mess of useEffects. But just another data point.

Also I was hoping opus would finally be able to handle complex typescript generics, but alas...

it's 20,000 in 2026, with the price of tokens halving every year (at a given perf level), this will be around 1,000 dollars in 2030

Progress can be reviewed over time, and I'd think that'd take a lot of the risk out.

Also, heaven knows if the result in maintainable or easy to change.

> On top of that, Anthropic is losing money on it

This has got to be my favorite one of them all that keeps coming up in too many comments… You know who also was losing money in the beginning?! every successful company that ever existed! some like Uber were losing billions for a decade. and when was the last time you rode in a taxi? (I still do, my kid never will). not sure how old you are and if you remember “facebook will never be able to monetize on mobile…” - they all lose money, until they do not

> This is an "in distribution" test. There are a lot of C compilers out there, including ones with git history, implemented from scratch. "In distribution" tests do not test generalization.

It's still really, really impressive though.

Like, economics aside this is amazing progress. I remember GPT3 not being able to hold context for more than a paragraph, we've come a long way since then.

Hell, I remember bag of words being state of the art when I started my career. We have come a really, really, really long way since then.

There are two compilers that can handle the Linux kernel. GCC and LLVM. Both are written in C, not Rust. It's "in distribution" only if you really stretch the meaning of the term. A generic C compiler isn't going to be anywhere near the level of rigour of this one.

That distinction doesn't change my point. I am not surprised that a 40 year old project generates better code than this brand new one.

well, if in this period it is a matter of cost, tomorrow won't be anymore. 4GB of RAM in the 80s would have cost tens of millions of dollars, now even your car runs 4 gb memory only for the infotainment systems, and runs dozens GBs of RAM for the most complex assistants. So i would see this achievement more as a warning, the final result is not what's concerning, it is the premonition behind it

Application-specific AI models can be much smaller and faster than the general purpose, do-everything LLM models. This allows them to run locally.

They can also be made to be deterministic. Some extra care is required to avoid computation paths that lead to numerical differences on different machines, but this can be accomplished reliably with small models that use integer math and use kernels that follow a specific order of operations. You get a lot more freedom to do these things on the small, application-specific models than you do when you're trying to run a big LLM across different GPU implementations in floating point.

Some people care more about compile times than the performance of generated code. Perhaps even the correctness of generated code. Perhaps more so than determinism of the generated code. Different people in different contexts can have different priorities. Trying to make everyone happy can sometimes lead to making no one happy. Thus dichotomies like `-O2` vs `-Os`.

EDIT (since HN is preventing me from responding):

> Some people care more about compiler speed than the correctness?

Yeah, I think plenty of people writing code in languages that have concepts like Undefined Behavior technically don't really care as much about correctness as they may claim otherwise, as it's pretty hard to write large volumes of code without indirectly relying on UB somewhere. What is correct in such case was left up to interpretation of the implementer by ISO WG14.

Did you do diffs to confirm the code as stolen or are you just speculating.

Even that is underselling it; jobs are a necessary evil that should be minimised. If we can have more stuff with fewer people needing to spend their lives providing it, why would we NOT want that?

Jobs are the only way that you survive in this society (food, shelter). Look how we treat unhoused people without jobs. AI is taking jobs away and that is putting people's survival at risk.

git switch v6.9

The riscv build succeeded. For the x86-64 build I ran into

    % make O=/tmp/linux/x86 ARCH=x86_64 CC=/tmp/p/claudes-c-compiler/target/release/ccc-x86 HOSTCC=/tmp/p/claudes-c-compiler/target/release/ccc-x86 LDFLAGS=-fuse-ld=bfd LD=ld.bfd -j30 vmlinux -k
    make[1]: Entering directory '/tmp/linux/x86'
    ...
      CC      arch/x86/platform/intel/iosf_mbi.o
    ccc: error: lgdtl requires memory operand
      AR      arch/x86/platform/intel-mid/built-in.a
    make[6]: *** [/home/ray/Dev/linux/scripts/Makefile.build:362: arch/x86/realmode/rm/wakeup_asm.o] Error 1
    ld.bfd: arch/x86/entry/vdso/vdso32/sigreturn.o: warning: relocation in read-only section `.eh_frame'
    ld.bfd: error in arch/x86/entry/vdso/vdso32/sigreturn.o(.eh_frame); no .eh_frame_hdr table will be created
    ld.bfd: warning: creating DT_TEXTREL in a shared object
    ccc: error: unsupported pushw operand

tinyconfig and allnoconfig have fewer errors.

    RELOCS  arch/x86/realmode/rm/realmode.relocs
    Invalid absolute R_386_32 relocation: real_mode_seg

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The classical definition of a clean room implementation is something that's made by looking at the output of a prior implementation but not at the source.

I agree that having a reference compiler available is a huge caveat though. Even if we completely put training data leakage aside, they're developing against a programmatic checker for a spec that's already had millions of man hours put into it. This is an optimal scenario for agentic coding, but the vast majority of problems that people will want to tackle with agentic coding are not going to look like that.

If you read the entire GCC source code and then create a compatible compiler, it's not clean room. Which Opus basically did since, I'm assuming, its training set contained the entire source of GCC. So even if they were actively referencing GCC I think that counts.

With just a few thousand dollars of API credits you too can inefficiently download a lossy copy of a C compiler!

I disagree. A year ago the models would not come close to doing this, no matter what tools you gave them or how many tokens you generated. Even three months ago. Effectively using tools to complete long tasks required huge improvements in the models themselves. These improvements were driven not by pretraining like before, but by RL with verifiable rewards. This can continue to scale with training compute for the foreseeable future, eliminating the "data wall" we were supposed to be running into.

We've been hearing this for 3 years now. And especially 25 was full of "they've hit a wall, no more data, running out of data, plateau this, saturated that". And yet, here we are. Models keep on getting better, at more broad tasks, and more useful by the month.

This quote would be more impactful if people haven't been repeating it since gpt-4 time.

What if it plateaus smarter than us? You wouldn't be able to discern where it stopped. I'm not convinced it won't be able to create its own training data to keep improving. I see no ceiling on the horizon, other than energy.

Cool I guess. Kind of a meaningless statement yeah? Let's hit the bend, then we'll talk. Until then repeating, 'It's an S Curve guys and what's more, we're near the bend! trust me" ad infinitum is pointless. It's not some wise revelation lol.

"clean room" usually means "without looking at the source code" of other similar projects. But presumably the AIs training data would have included GCC, Clang, and probably a dozen other C compilers.

The result is a fuzzy reproduction of the training input, specifically of the compilers contained within. The reproduction in a different, yet still similar enough programming language does not refute that. The implementation was strongly guided by a compiler and a suite of tests as an explicit filter on those outputs and limiting the acceptable solution space, which excluded unwanted interpolations of the training set that also result from the lossy input compression.

The fact that the implementation language for the compiler is rust doesn't factor into this. ML based natural language translation has proven that model training produces an abstract space of concepts internally that maps from and to different languages on the input and output side. All this points to is that there are different implicitly formed decoders for the same compressed data embedded in the LLM and the keyword rust in the input activates one specific to that programming language.

Well, "Reimplement the c4 compiler - C in four functions" is absolutely something older models can do. Because most are trained, on that quite small product - its 20kb.

But reimplementing that isn't impressive, because its not a clean room implementation if you trained on that data, to make the model that regurgitates the effort.

This comparison is only meaningful with comparable numbers of parameters and context window tokens. And then it would mainly test the efficiency and accuracy of the information encoding. I would argue that this is the main improvement over all model generations.

Perhaps 4.5 could also do it? We don’t know really until we try. I don’t trust the marketing material as much. The fact that the previous version (smaller versions) couldn’t or could do it does not really disprove that claim.

Are you really asking for "all the previous versions were implemented so poorly they couldn't even do this simple, basic LLM task"?

This sounds very wrong to me.

Take the C4 training dataset for example. The uncompressed, uncleaned, size of the dataset is ~6TB, and contains an exhaustive English language scrape of the public internet from 2019. The cleaned (still uncompressed) dataset is significantly less than 1TB.

I could go on, but, I think it's already pretty obvious that 1TB is more than enough storage to represent a significant portion of the internet.

A lot of the internet is duplicate data, low quality content, SEO spam etc. I wouldn't be surprised if 1 TB is a significant portion of the high-quality, information-dense part of the internet.

This is obviously wrong. There is a bunch of knowledge embedded in those weights, and some of it can be recalled verbatim. So, by virtue of this recall alone, training is a form of lossy data compression.

The point is - for a NEW project, no one has an extensive test suite. And if an extensive test suite exists, it's probably because the product that uses it also exists, already.

If it could translate the C++ standard INTO an extensive test suite that actually captures most corner cases, and doesn't generate false positives - again, without internet access and without using gcc as an oracle, etc?

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I am all-daily user (multiple claude max accounts). this fits my mental model mostly but not model I had before but developed with daily use. my job revolves around two core things:

1. data analysis / visualization / …

2. “is this possible? can this even be done?”

for #1 - I don’t do much anymore, for #2 I mostly do it still all “by hand” not for the lack of serious trying. so “it can do #1 1000x better than me cause it is generally solved problem(s) it is trained on while it can’t effectively do #2 otherwise” fits perfectly

I apologize for making that assumption.

It's more like "We were promised, over and over again, that the squirrel would be autonomous grand master level. We spent insane amounts of money, labour, and opportunity costs of human progress on this. Now, here's a very expensive squirrel, that still needs guidance from a human grandmaster, and most of it's moves are just replications of existing games. Oh, it also can't move the pieces by itself, so it depends on Piece Mover library."

I'm not trying to get coached in chess by the squirrel for 200 per month though.

"The squirrel can do my job and more? It can do five years of my work in a month? For only $20k? Pssh, but I bet it copied someone's homework."

Developer salaries are about to tank.

This is the end of the line. People are just in denial.

Soon companies will hire the squirrel instead of you. And the squirrel will transform into enormous infrastructure we can't afford ourselves.

"One mega squirrel to implement your own operating system overnight. Just $100k."

It's going to be out of the reach of humans / ICs soon. Purely industrial. And all innovation will accrue to the capital holders.

Open weights models are our only hope of keeping a foot in the door.

But the Squirrel is only playing chess because someone stuffed the pieces with food and it has learned that the only way to release it is by moving them around in some weird patterns.

But people have been telling us for years that the squirrel was going to improve at chess at an exponential rate and take over the world through sheer chess-mastery.

Because it leads to poor and nonconstructive discourse that doesn't educate anyone about the implications of the tech, which is expected on social media but has annoyingly leaked to Hacker News.

There's been more than enough drive-by comments from new accounts/green names even in this HN submission alone.

https://www.open-std.org/jtc1/sc22/wg14/www/docs/n3685.pdf

Read section J.1.

Unspecified behaviour is defined in the glossary at the start of the spec and the term "unspecified" appears over a hundred times...

You're welcome to try writing a C compiler and standard library doing no research other than reading the spec.

So let’s say you fund a single engineer for an open‑source project with $20k. The outcome will be a prototype with some interesting ideas. And yes, with a few hundred bucks' worth of AI assistance that single engineer might get much further than without (but not using any of the techniques presented in this blog). People can coalesce around the project as contributors. A seed was planted and watered a bit.

In this case, the $20k has been burned and produced zero value. Just look at the repo issues: looks like someone trying to get attention by spamming the issue tracker and opening hundreds of PRs. As an open source project, it’s a dead end.

So it doesn’t matter that this is “likely decrease in price over the next few years”? The value is zero, so even if superintelligence can produce this in an instant at zero cost in six months, the outcome is still worth zero.

You’re assuming a kind of inverse relationship between production cost and value.

In terms of quality, to anyone using those coding agents, it should be clear by now that letting them run autonomously and in parallel is a bad idea. That’s not going to change unless you believe LLMs will turn into something entirely different over time.

Note that what works with humans—social interaction creating some emergent properties like innovation—doesn’t translate to LLM agents for a simple reason: they don’t have agency, shared goals, or accountability, so the social dynamics that generate innovation can’t form.

Can confirm (on aarch64 host)

    $ ./target/release/ccc-arm -I /usr/include/ -I /usr/local/include/ -I /usr/lib/gcc/aarch64-redhat-linux/15/include/ -o hello hello.c 

    $ ./hello
    Hello from CCC!

Hmm, I didn't have to do that. https://i.imgur.com/OAEtgvr.png

But yeah, either way it just needs to know where to find the stdlib.

The impressiveness of converting C to Rust by any means is kind of contingent on how much unnecessary unsafe there is in the end result though.

I think the careful response to this is:

(1) There are compilers written in C in the training set

(2) LLMs demonstrably can near-perfectly memorize training-set inputs (see other comments here)

(3) LLMs are very good at translation tasks (natural language or code, e.g.: C to Rust)

I don't think this necessarily completely deflates the impressiveness of this accomplishment, but it does qualify it to some degree.