Yeah you can: https://thinkingmachines.ai/blog/defeating-nondeterminism-in....

Why do they need to run benchmarks to confirm performance? Can't they run an example prompt and verify they get the exact same output token probabilities for all prompts? The fact that they are not doing this makes me suspicious that they are in fact not doing the exact same thing as vLLM.

It is also a bit weird that they are not incorporating speculative decoding, that seems like a critical performance optimization, especially for decode heavy workloads.

IMO the core of the issue is the awful Github Actions Cache design. Look at the recommendations to avoid an attack by this extremely pernicious malware proof of concept: https://github.com/AdnaneKhan/Cacheract?tab=readme-ov-file#g.... How easy is it to mess this up when designing an action?

The LLM is a cute way to carry out this vulnerability, but in fact it's very easy to get code execution and poison a cache without LLMs, for example when executing code in the context of a unit test.

Wow that is wild, that is exactly along the lines of my fantasy language. It'd be so easy to go into the deep end building tooling and improving a language like this.

That argument was dead _at least_ 2 years ago, when we gave LLMs tools.