hkalbasi

So maybe everything is now low hanging fruit, with your definition.

> OpenAI's own Decisions API [1] beats it

Jev is 42$/B but OpenAI is 100$/B token.

So we can let the ai generate some math problems based on the solutions found? Other fields (computer science, physics, ...) can generate math problems too.

Is it possible to fine tune this model and unlock / extend its cybersecurity capabilities? I'm scared that maybe we are not ready for an open-weight model with high cybersecurity skills.

I guess this data is not intended to be used all the time. But occasionally you may find it useful in blame or bisect or similar things.

This makes me think about using a game engine and a coding agent instead of current video generation AIs. It will probably cost much more, but it will have almost zero consistency problems. Is this line explored?

> A LLM (...) merely produces syntactically likely combinations of the training data

While doesn't matter much in the rest of the policy, this is a common misconception among AI skeptics. It is not the case for a long time (since RL is used heavily in the training) and a LLM may go beyond its training data.

Someone should fire up an AI and create Fil-Rust by connecting the Fil-C llvm backend to rustc, ending this flamewar. I guess it is well within the capabilities of a Fable class model, even if the driver doesn't have experience in compiler development.

Is there a gallery of all pelicans generated by simon over time?