The model can't reason comprehensively (e.g., like Sol XHigh would to solve a complicated problem), but it's designed to be able to answer anything a human reasonably could quickly and intuitively, i.e., system one thinking: <a href="https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow" rel="nofollow">https://en.wikipedia.org/wiki/Thinking,_Fast_and_Slow
I was wondering the same! So I asked Astra to build me <a href="https://jev-chess-master.vercel.app/" rel="nofollow">https://jev-chess-master.vercel.app/ where you play against Jev AI as chess player.
You play White, and Jev plays Black. Rather than asking an LLM to generate move strings or JSON, the backend feeds all server-validated legal candidate moves into Vercel AI SDK's experimental_evaluate(). Jev picks Black's move and outputs its probability distribution across all legal candidates in a single forward pass (~300ms, ~$0.00004/move).
dinobones · · focus · HN ↗
Typesafe.AI sounds like some typescript/structured output type of tool…
What even is “system one” ?
IMO the product/tech is really there, just needs better communication.
zenlikethat · · focus · HN ↗
vintermann · · focus · HN ↗
leo4242 · · focus · HN ↗
You play White, and Jev plays Black. Rather than asking an LLM to generate move strings or JSON, the backend feeds all server-validated legal candidate moves into Vercel AI SDK's experimental_evaluate(). Jev picks Black's move and outputs its probability distribution across all legal candidates in a single forward pass (~300ms, ~$0.00004/move).
Github link: <a href="https://github.com/qibinlou/jev-chess" rel="nofollow">https://github.com/qibinlou/jev-chess
Give a try and let me know your thoughts! I am having lots of fun coming up with different chess strategies for Jev to try out.
haute_cuisine · · focus · HN ↗