211–220 of 787 posts
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The result is hardly a clean room implementation. It was rather a brute force attempt to decompress fuzzily stored knowledge contained within the network and it required close steering (using a big suite of tests) to get a reasonable approximation to the desired output. The compression and storage happened during the LLM training. Prove this statement wrong.
> Prove this statement wrong. If all it takes is "trained on the Internet" and "decompress stored knowledge", then surely gpt3, 3.5, 4, 4.1, 4o, o1, o3, o4, 5, 5.1, 5.x should have been able to do it, right? Claude 2, 3, 4, 4.1, 4.5? Surely.
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.
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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.
> We've been hearing this for 3 years now Not from me you haven't! > "they've hit a wall, no more data, running out of data, plateau this, saturated that" Everyone thought Moore's Law was infallible too, right until they hit that bend. What hubris to think these AI models are different! But you've probably been hearing that for 3 years too (though not from me). > Models keep on getting better, at more broad tasks, an…
Since then, all improvements came at a tradeoff, and there was a definite flattening of progress.
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Every S-curve looks like an exponential until you hit the bend.
This quote would be more impactful if people haven't been repeating it since gpt-4 time.
So where is that?
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 bootab…
> 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 Does it really boot...?
They don't need 16b x86 support for the RISCV or ARM ports, so yes, but depends on what 'it' we're talking about here.
Also, FWIW, GCC doesn't directly assemble to machine code either; it shells out to GAS (GNU Assembler). This blog post calls it "GCC assembler and linker" but to be more precise the author should edit this to "GNU binutils assembler and linker." Even then GNU binutils contains two linkers (BFD and GOLD), or did they excise GOLD already (IIRC, there was some discussion a few years ago about it)?
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For employment I mean "WHATEVER LEADS TO REWARD COLLECTIVE HUMANS TO SURVIVE". Call it as you wish, but I am certainly not talking about coding values.
I'm struggling to even parse the syntax of "WHATEVER LEADS TO REWARD COLLECTIVE HUMANS TO SURVIVE", but assuming that you're talking about resource allocation, my answer is UBI or something similar to it. We only need to "reward" for action when the resources are scarce, but when resources are plentiful, there's no particular reason not to just give them out. I know it's "easier to imagine an end to the world than an…
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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 m…
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…
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.
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In this case it's not reproducing training data verbatim but it probably is using algorithms and data structures that were learned from existing C compilers. On one hand it's good to reuse existing knowledge but such knowledge won't be available if you ask Claude to develop novel software.
How often do you need to invent novel algorithms or data structures? Most human written code is just rehashing existing ideas as well.
The part I find concerning is that I wouldn't be in the place I am today without spending a fair amount of time in that monotony and really delving in to understand it and slowly push outside it's boundary. If I was starting programming today I can confidently say I would've given up.
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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
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.
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.