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).
I am horrible at chess and easily won. It does play very very badly.
But it is essentially playing bullet chess right? No time to reason and is basically forced to go with it's knee jerk reaction. So maybe it plays reasonably against another average bullet chess player?
This is an instructive demo and makes me pause a bit when thinking about where I would trust using a classifier like this. Also there is no way to fine tune it right? So you are just left to its interpretation of the classification schema.
Building classifiers is hard and forces you into thinking about your problem space and your comfort level with type1 or type2 errors. I fear this will encourage sloppy work because all those decisions are black boxed.
Not like we were in a utopia careful ML applications before this.
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.
ulcer · · focus · HN ↗
But it is essentially playing bullet chess right? No time to reason and is basically forced to go with it's knee jerk reaction. So maybe it plays reasonably against another average bullet chess player?
This is an instructive demo and makes me pause a bit when thinking about where I would trust using a classifier like this. Also there is no way to fine tune it right? So you are just left to its interpretation of the classification schema.
Building classifiers is hard and forces you into thinking about your problem space and your comfort level with type1 or type2 errors. I fear this will encourage sloppy work because all those decisions are black boxed.
Not like we were in a utopia careful ML applications before this.