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

I spent a good part of my career (nearly a decade) at Google working on getting Clang to build the linux kernel. https://clangbuiltlinux.github.io/

This LLM did it in (checks notes):

> Over nearly 2,000 Claude Code sessions and $20,000 in API costs

It may build, but does it boot (was also a significant and distinct next milestone)? (Also, will it blend?). Looks like yes!

> The 100,000-line compiler can build a bootable Linux 6.9 on x86, ARM, and RISC-V.

The next milestone is:

Is the generated code correct? The jury is still out on that one for production compilers. And then you have performance of generated code.

> The generated code is not very efficient. Even with all optimizations enabled, it outputs less efficient code than GCC with all optimizations disabled.

Still a really cool project!

This is a much more reasonable take than the cursor-browser thing. A few things that make it pretty impressive:

> This was a clean-room implementation (Claude did not have internet access at any point during its development); it depends only on the Rust standard library. The 100,000-line compiler can build Linux 6.9 on x86, ARM, and RISC-V. It can also compile QEMU, FFmpeg, SQlite, postgres, redis

> I started by drafting what I wanted: a from-scratch optimizing compiler with no dependencies, GCC-compatible, able to compile the Linux kernel, and designed to support multiple backends. While I specified some aspects of the design (e.g., that it should have an SSA IR to enable multiple optimization passes) I did not go into any detail on how to do so.

> Previous Opus 4 models were barely capable of producing a functional compiler. Opus 4.5 was the first to cross a threshold that allowed it to produce a functional compiler which could pass large test suites, but it was still incapable of compiling any real large projects.

And the very open points about limitations (and hacks, as cc loves hacks):

> It lacks the 16-bit x86 compiler that is necessary to boot [...] Opus was unable to implement a 16-bit x86 code generator needed to boot into 16-bit real mode. While the compiler can output correct 16-bit x86 via the 66/67 opcode prefixes, the resulting compiled output is over 60kb, far exceeding the 32k code limit enforced by Linux. Instead, Claude simply cheats here and calls out to GCC for this phase

> It does not have its own assembler and linker;

> Even with all optimizations enabled, it outputs less efficient code than GCC with all optimizations disabled.

Ending with a very down to earth take:

> The resulting compiler has nearly reached the limits of Opus’s abilities. I tried (hard!) to fix several of the above limitations but wasn’t fully successful. New features and bugfixes frequently broke existing functionality.

All in all, I'd say it's a cool little experiment, impressive even with the limitations, and a good test-case as the author says "The resulting compiler has nearly reached the limits of Opus’s abilities". Yeah, that's fair, but still highly imrpessive IMO.

My first reaction: wow, incredible.

My second reaction: still incredible, but noting that a C compiler is one of the most rigorously specified pieces of software out there. The spec is precise, the expected behavior is well-defined, and test cases are unambiguous.

I'm curious how well this translates to the kind of work most of us do day-to-day where requirements are fuzzy, many edge cases are discovered on the go, and what we want to build is a moving target.

> This was a clean-room implementation (Claude did not have internet access at any point during its development); it depends only on the Rust standard library. The 100,000-line compiler can build Linux 6.9 on x86, ARM, and RISC-V. It can also compile QEMU, FFmpeg, SQlite, postgres, redis, and has a 99% pass rate on most compiler test suites including the GCC torture test suite. It also passes the developer's ultimate litmus test: it can compile and run Doom.

This is incredible!

But it also speaks to the limitations of these systems: while these agentic systems can do amazing things when automatically-evaluable, robust test suites exist... you hit diminishing returns when you, as a human orchestrator of agentic systems, are making business decisions as fast as the AI can bring them to your attention. And that assumes the AI isn't just making business assumptions with the same lack of context, compounded with motivation to seem self-reliant, that a non-goal-aligned human contractor would have.

I'm not particularly impressed that it can turn C into an SSA IR or assembly etc. The optimizations, however sophisticated is where anything impressive would be. Then again, we have lots of examples in the training set I would expect. C compilers are probably the most popular of all compilers. What would be more impressive is for it to have made a compiler for a well defined language that isn't very close to a popular language.

What I am impressed by is that the task it completed had many steps and the agent didn't get lost or caught in a loop in the many sessions and time it spent doing it.

The interesting thing here is what's this code worth (in money terms)? I would say it's worth only the cost of recreation, apparently $20,000, and not very much more. Perhaps you can add a bit for the time taken to prompt it. Anyone who can afford that can use the same prompt to generate another C compiler, and another one and another one.

GCC and Clang are worth much much more because they are battle-tested compilers that we understand and know work, even in a multitude of corner cases, over decades.

In future there's going to be lots and lots of basically worthless code, generated and regenerated over and over again. What will distinguish code that provides value? It's going to be code - however it was created, could be AI or human - that has actually been used and maintained in production for a long time, with a community or company behind it, bugs being triaged and fixed and so on.

Cool project, but they really could have skipped the mention of clean room. Something trained on every copyrighted thing known to mankind is the opposite of clean room

It's weird to see the expectation that the result should be perfect.

All said and done, that its even possible is remarkable. Maybe these all go into training the next Opus or Sonnet and we start getting models that can create efficient compilers from scratch. That would be something!

I would like to see the following published:

- All prompts used

- The structure of the agent team (which agents / which roles)

- Any other material that went into the process

This would be a good source for learning, even though I'm not ready to spend 20k$ just for replicating the experiment.

Now this is fairly "easy" as there are multitude of implementations/specs all over the Internet. How about trying to design a new language that is unquestionably better/safer/faster for low-level system programming than C/Rust/Zig? ML is great in aping existing stuff but how about pushing it to invent something valuable instead?

It's cool that you can look at the git history to see what it did. Unfortunately, I do not see any of the human written prompts (?).

First 10 commits, "git log --all --pretty=format:%s --reverse | head",

  Initial commit: empty repo structure
  Lock: initial compiler scaffold task
  Initial compiler scaffold: full pipeline for x86-64, AArch64, RISC-V
  Lock: implement array subscript and lvalue assignments
  Implement array subscript, lvalue assignments, and short-circuit evaluation
  Add idea: type-aware codegen for correct sized operations
  Lock: type-aware codegen for correct sized operations
  Implement type-aware codegen for correct sized operations
  Lock: implement global variable support
  Implement global variable support across all three backends

This is like a working version of the Cursor blog. The evidence - it compiling the Linux kernel - is much more impressive than a browser that didn't even compile (until manually intervened)

This is my favorite article this year. Just very insightful and honest. The learnings are worth thousands for me.

> when agents started to compile the Linux kernel, they got stuck. [...] Every agent would hit the same bug, fix that bug, and then overwrite each other's changes.

> [...] The fix was to use GCC as an online known-good compiler oracle to compare against. I wrote a new test harness that randomly compiled most of the kernel using GCC, and only the remaining files with Claude's C Compiler. If the kernel worked, then the problem wasn’t in Claude’s subset of the files. If it broke, then it could further refine by re-compiling some of these files with GCC. This let each agent work in parallel

This is a remarkably creative solution! Nicely done.

Ha yes classic showcase of:

1) obvious green field project 2) well defined spec which will definitely be in the training data 3) an end result which lands you 90% from the finish

Now comes the hard part, the last 10%. Still not impressed here. Since fixing issues in the end was impossible without introducing bugs I have doubts about quality

I'm glad they do call it out in the end. That's fair

How much of this result is effectively plagiarized open source compiler code? I don't understand how this is compelling at all: obviously it can regurgitate things that are nearly identical in capability to already existing code it was explicitly trained on...

It's very telling how all these examples are all "look, we made it recreate a shitter version of a thing that already exists in the training set".

> The compiler is an interesting artifact on its own [...]

its funny bacause by (most) definitions, it is not an artifact:

> a usually simple object (such as a tool or ornament) showing human workmanship or modification as distinguished from a natural object

> Over nearly 2,000 Claude Code sessions and $20,000 in API costs

Well there goes my weekend project plans

However it was achieved, building a such a complex project like a C compiler on a 20k $ budget in full autonomy is quite impressive.

Imho some commenters focus way too much on the (many, and honestly also shared by the blog post too) cons, that they forget to be genuinely impressed by the steps forward.

If I, a human, read the source code of $THING and then later implement my own version, that's not a "clean-room" re-implementation. The whole point of "clean-room" is that no single person has access to both the original code and the new code. That way, you can legally prove that no copyright infringement took place.

But when an AI does it, now it counts? Opus is trained on the source code of Clang, GCC, TCC, etc. So this is absolutely not "clean-room".

Does it make a conforming preprocessor?

At this point, I genuinely don't know what to learn next to not become obsolete when another Opus version gets released

So did the Linux compiled with this compiler worked? Does it work the same as GCC-compiled Linux (but slower due to generating non optimized code?)

> To stress test it, I tasked 16 agents with writing a Rust-based C compiler, from scratch, capable of compiling the Linux kernel. Over nearly 2,000 Claude Code sessions and $20,000 in API costs, the agent team produced a 100,000-line compiler that can build Linux 6.9 on x86, ARM, and RISC-V.

If you don't care about code quality, maintainability, readability, conformance to the specification, and performance of the compiler and of the compiled code, please, give me your $20,000, I'll give you your C compiler written from scratch :)

Next time can you build a Rust compiler in C? It doesn't even have to check things or have a borrow checker, as long as it reduces the compile times so it's like a fast debug iteration compiler.

Cool article, interesting to read about their challenges. I've tasked Claude with building an Ada83 compiler targeting LLVM IR - which has gotten pretty far.

I am not using teams though and there is quite a bit of knowledge needed to direct it (even with the test suite).

Maybe I'm naive, but I find these re-engineering complex product posts underwhelming. C Compilers exist and realistically Claudes training corpus contains a ton of C Compiler code. The task is already perfectly defined. There exists a benchmark of well-adopted codebases that can be used to prove if this is a working solution. Half the difficulty in making something is proving it works and is complete.

IMO a simpler novel product that humans enjoy is 10x more impressive than rehashing a solved problem, regardless of difficulty.

How about we get the LLM's to collaborate and design a perfect programming language for LLM coding, it would be terse (less tokens) easy for pattern searches etc and very fast to build, iterate over.

They should add this to the benchmark suite, and create a custom eval for how good the resulting compiler is, as well as how maintainable the source code.

I think it's funny how me and I assume many others tried to do the same thing and they probably saw it being a popular query or had the same idea.

It can compile the linux kernel, but does it boot?

It means that if you already have or a willing to build very robust test suite and the task is a complicated but already solved problem, you can get a sub-par implementation for a semi-reasonable amount of money.

This is not entirely ridiculous.

So it copied one of the C compilers? This was always possible but now you need to pay $1000 in API costs to Anthropic

> I tried (hard!) to fix several of the above limitations but wasn’t fully successful. New features and bugfixes frequently broke existing functionality.

This has been my experience of vibe coding too. Good for getting started, but you quickly reach the point where fixing one thing breaks another and you have to finish the project yourself.

I'm sure this is impressive, but it's probably not the best test case given how many C compilers there are out there and how they presumably have been featured in the training data.

This is almost like asking me to invent a path finding algorithm when I've been thought Dijkstra's and A*.

Nothing in the post about whether the compiled kernel boots.

There's a terrible bug where once it compacts then it sometimes pulls in .o or binary files and immediately fills your entire context. Then it compacts again...10m and your token budget is gone for the 5 hour period. edit: hooks that prevent it from reading binary files can't prevent this.

Please fix.. :)

> This was a clean-room implementation (Claude did not have internet access at any point during its development);

This is absolutely false and I wish the people doing these demonstrations were more honest.

It had access to GCC! Not only that, using GCC as an oracle was critical and had to be built in by hand.

Like the web browser project this shows how far you can get when you have a reference implementation, good benchmarks, and clear metrics. But that's not the real world for 99% of people, this is the easiest scenario for any ML setting.

> The generated code is not very efficient. Even with all optimizations enabled, it outputs less efficient code than GCC with all optimizations disabled.

Worse than "-O0" takes skill...

So then, it produced something much worse than tcc (which is better than gcc -O0), an equivalent of which one man can produce in under two weeks. So even all those tokens and dollars did not equal one man's week of work.

Except the one man might explain such arbitrary and shitty code as this:

https://github.com/anthropics/claudes-c-compiler/blob/main/s...

why x9? who knows?!

Oh god the more i look at this code the happier I get. I can already feel the contracts coming to fix LLM slop like this when any company who takes this seriously needs it maintained and cannot...

[flagged]

> So, while this experiment excites me, it also leaves me feeling uneasy. Building this compiler has been some of the most fun I’ve had recently, but I did not expect this to be anywhere near possible so early in 2026

What? Didn’t cursed lang do something similar like 6 or 7 months ago? These bombastic marketing tactics are getting tired.

Can it create employment? How is this making life better. I understand the achievement but come on, wouldn´t it be something to show if you created employment for 10000 people using your 20000 USD!

Microsoft, OpenAI, Anthropic, XAI, all solving the wrong problems, your problems not the collective ones.

100.000 lines of code for something that is literally a text book task?

I guess if it only created 1.000 lines it would be easy to see where those lines came from.

You could hire a reasonably skilled dev in India for a week for $1k —- or you could pay $20k in LLM tokens, spend 2 hours writing essays to explain what you want, and then get a buggy mess.