The matrix multiplication is only deterministic for sparse-dense products under these settings:
> torch.bmm() when called on sparse-dense CUDA tensors
And it's not listed under the operations that raise an exception otherwise, so I'm not sure the docs promise that dense-dense matrix-matrix products are deterministic.
It may be an implementation detail, but in practice, if the only way to get a deterministic output is to run on the CPU, then it's not going to be usable.
Chaitin's constant does not count? Depends on your definition of constructed, but contrary to "easy" normal numbers such as Champernowne's constant, it's not defined by its sequence of digits.
I thought fair use was decided on a case by case basis, and could not be guaranteed? If true, wouldn't that mean that in other cases it could be ruled differently?
> For example, in a variant of environment TR87, Opus 4.6 scores 0.0% with no harness and 97.1% with the Duke harness (12), yet in environment BP35, Opus 4.6 scores 0.0% under both configuration
This is with a harness that has been designed to tackle "a small set of public environments: ls20, ft09, and vc33" (of the arc-agi-3 challenge), yet it looks like it does not solve the full arc-agi-3 benchmark, just some of them.
No, APIs fall under copyright, but the Supreme Court found that Google's reimplementation of Java's API was falling under fair use. Fair use is decided case by case, one cannot use that decision as a precedent.
Not quite in my opinion. The output of an LLM from a simple prompt falls into the public domain, but if you also give a copyrighted work as input, the mechanistic transformation performed will not alter the original license (same as encoding a video does not change its license).
There's a difference between "I've read a LGPL code once, maybe I could do something similar" and "I've been reading this LGPL code for 12 years and now I'm going to do exactly the same thing".
Everyone writes as if he just fed the spec and tests to Claude Code. Ignoring for now that the tests are under LGPL as well, the commit history shows that this has been done with two weeks of steering Claude Code towards the desired output. At every one of these interactions, the maintainer used his deep knowledge of the chardet codebase to steer Claude.
Google vs Oracle ruled that APIs fall under copyright (the contrary was thought before). However, it was ruled that, in that specific case, fair use applied, because of interoperability concerns. That's the important part of this case: fair use is never automatic, it is assessed case by case.
Regarding chardet, I'm not sure "I wanted to circumvent the license" is a good way to argue fair use.
The test suite was also licensed under the LGPL. The reimplementation can be seen as a derivative work of the test suite, and thus should fall under the LGPL. This does not even mention the fact that the coding agent, AND the user steering it, both had ample exposure to chardet's source code, making it hard to argue that the reimplementation is a new ship.
Even the most permissive open source licenses such as MIT require attribution. Releasing as open source would therefore benefit the author through publicity. Bein able to say that you're the author of library X, used by megacorp Y with great success, is a good selling point in a job interview.
I'm in strong agreement with this. Even though I'd prefer winter time all year round, I would rather enjoy permanent summer time over switching twice a year. And I'm living in France, so my "winter time" is actually already a DST compared to the standard time (France's timezone should be UTC like the UK, but WW2 changed that to UTC+1 and we never switched back), so the "summer time" is actually a "double DST".
> torch.bmm() when called on sparse-dense CUDA tensors
And it's not listed under the operations that raise an exception otherwise, so I'm not sure the docs promise that dense-dense matrix-matrix products are deterministic.