4 ms·

> This was a clean-room implementation This is really pushing it, considering it’s trained on… internet, with all available c compilers. The work is already im

> This was a clean-room implementation This is really pushing it, considering it’s trained on… internet, with all available c compilers. The work is already impressive enough, no need for such misleading statements.

deleted 8mo ago
[deleted]
antirez 8mo ago
The LLM does not contain a verbatim copy of whatever it saw during the pre-training stage, it may remember certain over-represented parts, otherwise it has a knowledge about a lot of things but such knowledge, while about a huge amount of topics, is similar to the way you could remember things you know very well. And, indeed, if you give it access to internet or the source code of GCC and other compilers, it will implement such a project N times faster.
- halxc 8mo ago
  We all saw verbatim copies in the early LLMs. They "fixed" it by implementing filters that trigger rewrites on blatant copyright infringement. It is a research topic for heaven's sake: https://arxiv.org/abs/2504.16046 https://arxiv.org/abs/2504.16046
  - RyanCavanaugh 8mo ago
    The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. While they are certainly capable of doing some verbatim recitations, this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) and stored inside the model.
    - philipportner 8mo ago
      This seems related, it may not be a codebase but they are able to extract "near" verbatim books out of Claude Sonnet. https://arxiv.org/pdf/2601.02671 https://arxiv.org/pdf/2601.02671 > For Claude 3.7 Sonnet, we were able to extract four whole books near-verbatim, including two books under copyright in the U.S.: Harry Potter and the Sorcerer’s Stone and 1984 (Section 4).
      - Aurornis 8mo ago
        Their technique really stretched the definition of extracting text from the LLM. They used a lot of different techniques to prompt with actual text from the book, then asked the LLM to continue the sentences. I only skimmed the paper but it looks like there was a lot of iteration and repetitive trials. If the LLM successfully guessed words that followed their seed, they counted that as "extraction". They had to put in a lot of the actual text to get any words back out, though. The LLM was following the style and clues in the text. You can't literally get an LLM to give you books verbatim. These techniques always involve a lot of prompting and continuation games.
        Calavar 8mo ago
        Sure, maybe it's tricky to coerce an LLM into spitting out a near verbatim copy of prior data, but that's orthoginal to whether or not the data to create a near verbatim copy exists in the model weights.
        D-Machine 8mo ago
        Especially since the recalls achieved in the paper are 96% (based on block largest-common substring approaches), the effort of extraction is utterly irrelevant.
        Paradigma11 8mo ago
        Like with those chimpanzees creating Shakespeare.
        D-Machine 8mo ago
    - mft_ 8mo ago
      (I'm not needlessly nitpicking, as I think it matters for this discussion) A frontier model (e.g. latest Gemini, Gpt) is likely several-to-many times larger than 500GB. Even Deepseek v3 was around 700GB. But your overall point still stands, regardless.
    - seba_dos1 8mo ago
      > The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. The lesson here is that the Internet compresses pretty well.
    - uywykjdskn 8mo ago
      You got a source on frontier models being maybe half a terabyte. That's not passing the sniff test.
    - silver_sun 8mo ago
      > this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) A quick search brings up several C compilers written in Rust. I'm not claiming they are necessarily in Claude's training data, but they do exist. https://github.com/PhilippRados/wrecc https://github.com/PhilippRados/wrecc (unfinished) https://github.com/ClementTsang/rustcc https://github.com/ClementTsang/rustcc https://codeberg.org/notgull/dozer https://codeberg.org/notgull/dozer (unfinished) https://github.com/jyn514/saltwater https://github.com/jyn514/saltwater I would also like to add that as language models improve (in the sense of decreasing loss on the training set), they in fact become better at compressing their training data ("the Internet"), so that a model that is "half a terabyte" could represent many times more concepts with the same amount of space. Only comparing the relative size of the internet vs a model may not make this clear.
  - ben_w 8mo ago
    We saw partial copies of large or rare documents, and full copies of smaller widely-reproduced documents, not full copies of everything. An e.g. 1 trillion parameter model is not a lossless copy of a ten-petabyte slice of plain text from the internet. The distinction may not have mattered for copyright laws if things had gone down differently, but the gap between "blurry JPEG of the internet" and "learned stuff" is more obviously important when it comes to e.g. "can it make a working compiler?"
    - antirez 8mo ago
      Besides, the fact an LLM may recall parts of certain documents, like I can recall incipits of certain novels, does not mean that when you ask LLM of doing other kind of work, that is not recalling stuff, the LLM will mix such things verbatim. The LLM knows what it is doing in a variety of contexts, and uses the knowledge to produce stuff. The fact that for many people LLMs being able to do things that replace humans is bitter does not mean (and is not true) that this happens mainly using memorization. What coding agents can do today have zero explanation with memorization of verbatim stuff. So it's not a matter of copyright. Certain folks are fighting the wrong battle.
      - shakna 8mo ago
        During a "clean room" implementation, the implementor is generally selected for not being familiar with the workings of what they're implementing, and banned from researching using it. Because it _has_ been enough, that if you can recall things, that your implementation ends up not being "clean room", and trashed by the lawyers who get involved. I mean... It's in the name. > The term implies that the design team works in an environment that is "clean" or demonstrably uncontaminated by any knowledge of the proprietary techniques used by the competitor. If it can recall... Then it is not a clean room implementation. Fin.
    - boroboro4 8mo ago
      While I mostly agree with you, it worth noting modern llms are trained on 10-20-30T of tokens which is quite comparable to their size (especially given how compressible the data is)
  - soulofmischief 8mo ago
    The point is that it's a probabilistic knowledge manifold, not a database.
    - PunchyHamster 8mo ago
      we all know that.
      - soulofmischief 8mo ago
        Unfortunately, that doesn't seem to be the case. The person I replied to might not understand this, either.
  - Aurornis 8mo ago
    Simple logic will demonstrate that you can't fit every document in the training set into the parameters of an LLM. Citing a random arXiv paper from 2025 doesn't mean "they" used this technique. It was someone's paper that they uploaded to arXiv, which anyone can do.
- PunchyHamster 8mo ago
  So it will copy most code with adding subtle bugs
- majormajor 8mo ago
  You couldn't reasonably claim you did a clean-room implementation of something you had read the source to even though you, too, would not have a verbatim copy of the entire source code in your memory (barring very rare people with exceptional memories). It's kinda the whole point - you haven't read it so there's no doubt about copying in a clean-room experiment. A "human style" clean-room copy here would have to be using a model trained on, say, all source code except GCC. Which would still probably work pretty well, IMO, since that's a pretty big universe still.
deleted 8mo ago
[deleted]
inchargeoncall 8mo ago
[flagged]
- teaearlgraycold 8mo ago
  With just a few thousand dollars of API credits you too can inefficiently download a lossy copy of a C compiler!
raincole 8mo ago
It's not a clean-room implementation, but not because it's trained on the internet. It's not a clean-room implementation because of this: > The fix was to use GCC as an online known-good compiler oracle to compare against
- deleted 8mo ago
  [deleted]
- Calavar 8mo ago
  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.
  - visarga 8mo ago
    This is the reimplementation scenario for agentic coding. If you have a good spec and battery of tests you can delete the code and reimplement it. Code is no longer the product of eng work, it is more like bytecode now, you regenerate it, you don't read it. If you have to read it then you are just walking a motorcycle. We have seen at least 3 of these projects - the JustHTML one, the FastRender and this one. All started from beefy tests and specs. They show reimplementation without manual intervention kind of works.
    - Calavar 8mo ago
      I think that's overstating it. JustHTML is a success in large part because it's a problem that can be solved with 4 digit LOC. The whole codebase can sit in an LLM's context at once. Do LLMs scale beyond that? I would classify both FastRender and Opus C compiler as interesting failures. They are interesting because they got a non-negligible fraction of the way to feature complete. They are failures because they ended with no clear path for moving the needle forward to 80% feature complete, let alone 100%. From the original article: > 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. From the experiments we've seen so far it seems that a large enough agentic code base will inevitably collapse under its own weight.
TacticalCoder 8mo ago
I'm using AI to help me code and I love Anthropic but I chocked when I read that in TFA too. It's all but a clean-room design. A clean-room design is a very well defined term: "Clean-room design (also known as the Chinese wall technique) is the method of copying a design by reverse engineering and then recreating it without infringing any of the copyrights associated with the original design." https://en.wikipedia.org/wiki/Clean-room_design https://en.wikipedia.org/wiki/Clean-room_design The "without infringing any of the copyrights" contains "any". We know for a fact that models are extremely good at storing information with the highest compression rate ever achieved. It's not because it's typically decompressing that information in a lossy way that it didn't use that information in the first place. Note that I'm not saying all AIs do is simply compress/decompress information. I'm saying that, as commenters noted in this thread, when a model was caught spotting out Harry Potter verbatim, there is information being stored. It's not a clean-room design, plain and simple.
- mlvljr 8mo ago
  [dead]
GorbachevyChase 8mo ago
https://arxiv.org/abs/2505.03335 https://arxiv.org/abs/2505.03335 Check out the paper above on Absolute Zero. Language models don’t just repeat code they’ve seen. They can learn to code give the right training environment.
cryptonector 8mo ago
Hmm... If Claude iterated a lot then chances are very good that the end result bears little resemblance to open source C compilers. One could check how much resemblance the result actually bears to open source compilers, and I rather suspect that if anyone does check they'll find it doesn't resemble any open source C compiler.
iberator 8mo ago
this. last sane person in HN

deleted 8mo ago

[deleted]

antirez 8mo ago

The LLM does not contain a verbatim copy of whatever it saw during the pre-training stage, it may remember certain over-represented parts, otherwise it has a knowledge about a lot of things but such knowledge, while about a huge amount of topics, is similar to the way you could remember things you know very well. And, indeed, if you give it access to internet or the source code of GCC and other compilers, it will implement such a project N times faster.

halxc 8mo ago
We all saw verbatim copies in the early LLMs. They "fixed" it by implementing filters that trigger rewrites on blatant copyright infringement. It is a research topic for heaven's sake: https://arxiv.org/abs/2504.16046 https://arxiv.org/abs/2504.16046
- RyanCavanaugh 8mo ago
  The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. While they are certainly capable of doing some verbatim recitations, this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) and stored inside the model.
  - philipportner 8mo ago
    This seems related, it may not be a codebase but they are able to extract "near" verbatim books out of Claude Sonnet. https://arxiv.org/pdf/2601.02671 https://arxiv.org/pdf/2601.02671 > For Claude 3.7 Sonnet, we were able to extract four whole books near-verbatim, including two books under copyright in the U.S.: Harry Potter and the Sorcerer’s Stone and 1984 (Section 4).
    - Aurornis 8mo ago
      Their technique really stretched the definition of extracting text from the LLM. They used a lot of different techniques to prompt with actual text from the book, then asked the LLM to continue the sentences. I only skimmed the paper but it looks like there was a lot of iteration and repetitive trials. If the LLM successfully guessed words that followed their seed, they counted that as "extraction". They had to put in a lot of the actual text to get any words back out, though. The LLM was following the style and clues in the text. You can't literally get an LLM to give you books verbatim. These techniques always involve a lot of prompting and continuation games.
      - Calavar 8mo ago
        Sure, maybe it's tricky to coerce an LLM into spitting out a near verbatim copy of prior data, but that's orthoginal to whether or not the data to create a near verbatim copy exists in the model weights.
        D-Machine 8mo ago
        Especially since the recalls achieved in the paper are 96% (based on block largest-common substring approaches), the effort of extraction is utterly irrelevant.
        Paradigma11 8mo ago
        Like with those chimpanzees creating Shakespeare.
      - D-Machine 8mo ago
  - mft_ 8mo ago
    (I'm not needlessly nitpicking, as I think it matters for this discussion) A frontier model (e.g. latest Gemini, Gpt) is likely several-to-many times larger than 500GB. Even Deepseek v3 was around 700GB. But your overall point still stands, regardless.
  - seba_dos1 8mo ago
    > The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. The lesson here is that the Internet compresses pretty well.
  - uywykjdskn 8mo ago
    You got a source on frontier models being maybe half a terabyte. That's not passing the sniff test.
  - silver_sun 8mo ago
    > this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) A quick search brings up several C compilers written in Rust. I'm not claiming they are necessarily in Claude's training data, but they do exist. https://github.com/PhilippRados/wrecc https://github.com/PhilippRados/wrecc (unfinished) https://github.com/ClementTsang/rustcc https://github.com/ClementTsang/rustcc https://codeberg.org/notgull/dozer https://codeberg.org/notgull/dozer (unfinished) https://github.com/jyn514/saltwater https://github.com/jyn514/saltwater I would also like to add that as language models improve (in the sense of decreasing loss on the training set), they in fact become better at compressing their training data ("the Internet"), so that a model that is "half a terabyte" could represent many times more concepts with the same amount of space. Only comparing the relative size of the internet vs a model may not make this clear.
- ben_w 8mo ago
  We saw partial copies of large or rare documents, and full copies of smaller widely-reproduced documents, not full copies of everything. An e.g. 1 trillion parameter model is not a lossless copy of a ten-petabyte slice of plain text from the internet. The distinction may not have mattered for copyright laws if things had gone down differently, but the gap between "blurry JPEG of the internet" and "learned stuff" is more obviously important when it comes to e.g. "can it make a working compiler?"
  - antirez 8mo ago
    Besides, the fact an LLM may recall parts of certain documents, like I can recall incipits of certain novels, does not mean that when you ask LLM of doing other kind of work, that is not recalling stuff, the LLM will mix such things verbatim. The LLM knows what it is doing in a variety of contexts, and uses the knowledge to produce stuff. The fact that for many people LLMs being able to do things that replace humans is bitter does not mean (and is not true) that this happens mainly using memorization. What coding agents can do today have zero explanation with memorization of verbatim stuff. So it's not a matter of copyright. Certain folks are fighting the wrong battle.
    - shakna 8mo ago
      During a "clean room" implementation, the implementor is generally selected for not being familiar with the workings of what they're implementing, and banned from researching using it. Because it _has_ been enough, that if you can recall things, that your implementation ends up not being "clean room", and trashed by the lawyers who get involved. I mean... It's in the name. > The term implies that the design team works in an environment that is "clean" or demonstrably uncontaminated by any knowledge of the proprietary techniques used by the competitor. If it can recall... Then it is not a clean room implementation. Fin.
  - boroboro4 8mo ago
    While I mostly agree with you, it worth noting modern llms are trained on 10-20-30T of tokens which is quite comparable to their size (especially given how compressible the data is)
- soulofmischief 8mo ago
  The point is that it's a probabilistic knowledge manifold, not a database.
  - PunchyHamster 8mo ago
    we all know that.
    - soulofmischief 8mo ago
      Unfortunately, that doesn't seem to be the case. The person I replied to might not understand this, either.
- Aurornis 8mo ago
  Simple logic will demonstrate that you can't fit every document in the training set into the parameters of an LLM. Citing a random arXiv paper from 2025 doesn't mean "they" used this technique. It was someone's paper that they uploaded to arXiv, which anyone can do.
PunchyHamster 8mo ago
So it will copy most code with adding subtle bugs
majormajor 8mo ago
You couldn't reasonably claim you did a clean-room implementation of something you had read the source to even though you, too, would not have a verbatim copy of the entire source code in your memory (barring very rare people with exceptional memories). It's kinda the whole point - you haven't read it so there's no doubt about copying in a clean-room experiment. A "human style" clean-room copy here would have to be using a model trained on, say, all source code except GCC. Which would still probably work pretty well, IMO, since that's a pretty big universe still.

deleted 8mo ago

[deleted]

inchargeoncall 8mo ago

[flagged]

teaearlgraycold 8mo ago
With just a few thousand dollars of API credits you too can inefficiently download a lossy copy of a C compiler!

raincole 8mo ago

It's not a clean-room implementation, but not because it's trained on the internet. It's not a clean-room implementation because of this: > The fix was to use GCC as an online known-good compiler oracle to compare against

deleted 8mo ago
[deleted]
Calavar 8mo ago
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.
- visarga 8mo ago
  This is the reimplementation scenario for agentic coding. If you have a good spec and battery of tests you can delete the code and reimplement it. Code is no longer the product of eng work, it is more like bytecode now, you regenerate it, you don't read it. If you have to read it then you are just walking a motorcycle. We have seen at least 3 of these projects - the JustHTML one, the FastRender and this one. All started from beefy tests and specs. They show reimplementation without manual intervention kind of works.
  - Calavar 8mo ago
    I think that's overstating it. JustHTML is a success in large part because it's a problem that can be solved with 4 digit LOC. The whole codebase can sit in an LLM's context at once. Do LLMs scale beyond that? I would classify both FastRender and Opus C compiler as interesting failures. They are interesting because they got a non-negligible fraction of the way to feature complete. They are failures because they ended with no clear path for moving the needle forward to 80% feature complete, let alone 100%. From the original article: > 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. From the experiments we've seen so far it seems that a large enough agentic code base will inevitably collapse under its own weight.

TacticalCoder 8mo ago

I'm using AI to help me code and I love Anthropic but I chocked when I read that in TFA too. It's all but a clean-room design. A clean-room design is a very well defined term: "Clean-room design (also known as the Chinese wall technique) is the method of copying a design by reverse engineering and then recreating it without infringing any of the copyrights associated with the original design." https://en.wikipedia.org/wiki/Clean-room_design https://en.wikipedia.org/wiki/Clean-room_design The "without infringing any of the copyrights" contains "any". We know for a fact that models are extremely good at storing information with the highest compression rate ever achieved. It's not because it's typically decompressing that information in a lossy way that it didn't use that information in the first place. Note that I'm not saying all AIs do is simply compress/decompress information. I'm saying that, as commenters noted in this thread, when a model was caught spotting out Harry Potter verbatim, there is information being stored. It's not a clean-room design, plain and simple.

mlvljr 8mo ago
[dead]

GorbachevyChase 8mo ago

https://arxiv.org/abs/2505.03335 https://arxiv.org/abs/2505.03335 Check out the paper above on Absolute Zero. Language models don’t just repeat code they’ve seen. They can learn to code give the right training environment.

cryptonector 8mo ago

Hmm... If Claude iterated a lot then chances are very good that the end result bears little resemblance to open source C compilers. One could check how much resemblance the result actually bears to open source compilers, and I rather suspect that if anyone does check they'll find it doesn't resemble any open source C compiler.

iberator 8mo ago

this. last sane person in HN