spagettnet

If something is more efficient 95% of the time, and as efficient as normal for the rest of the time, its still a good solution.

I think the researchers agree with your premise. The “evidence” is not that chicks have more language understanding than previously understood, but rather that the source of the universality of bouba/kiki is due to something more primitive than built in human language hardware.

Modern LLM are certainly fine tuned on data that includes examples of tool use, mostly the tools built into their respective harnesses, but also external/mock tools so they dont overfit on only using the toolset they expect to see in their harnesses.

axolotl is great on consumer hardware.

depends ln your goals of course. but worth mentioning there are plenty of narrowish tasks (think text-to-sql, and other less general language tasks) where llama8b or phi-4 (14b) or even up to 30b with quantization can be trained on 8xa100 with great results. plus these smaller models benefit from being able to be served on a single a100 or even L4 with post training quantization, with wicked fast generation thanks to the lighter model.

on a related note, at what point are people going to get tired of waiting 20s for an llm to answer their questions? i wish it were more common for smaller models to be used when sufficient.

Of all the great things people say about UV, this is the one that sold me on it when I found this option in the docs. Such a nice feature.

Subtext is that the solution is always the best possible move sequence. OP’s comment is clarifying that sometimes after executing the best move sequence, the puzzle ends with a capture, and sometimes ends with a checkmate (“winning”).

Do you have a writeup or video of the aim bot you made? Would love to see it!

I love this. Although if I sat down and started handwriting this article—as the author did—I would make it to the second sentence where the word “percolating” is, and I’d have to pull out my phone to double check the spelling.