Hey HN! Wanted to share a fun project I've been hacking on. Given Jev can make decisions really fast (but not fast enough to play Doom yet sadly), I wanted to try and push it to play a more complex game than Tetris. So I went with Pokémon.
I've spent endless hours playing this game as a child so building this was a ton of fun.
Yeah I would have expected it to only decide which button to press, not something abstract like the choice of "go east to lavender town" for the goal of "in lavender town, climb the pokemon tower"
Looking at the diagram in the gh repo, it looks like this is entirely jev. Are there any examples of people having a big model like Fable handle high level goals?
Hm wondering what a first pass optimal setup might be - jev for overworld navigation, escalate to sonnet for easy battles, opus for medium difficulty battles, and fable for gym bosses could probably have jev also manage all the escalation / de-escalation to different models.
This is kinda chill to have in the background. I wish there were livestreams showing live reasoning of top models which are currently trying to solve cancer or whatever. Imagine the pogs in chat when it does.
This is so interesting to watch. For a couple minutes I was in awe of how quick and cheap it was. Then I saw just how bad the decision are and how it would get stuck in strange loops of going in and out of the same door to no end.
This seems like a technology heading in the right direction but not quiet there yet. Excited for what they are cooking up but probably won't start building around it yet.
Like others have mentioned in this post, I think a mix of models like Jev for simple stuff + a smarter reasoning model for more strategic thinking is the optimal solution. This experiment however is purely Jev. Which sometimes can be kinda dumb.
This entire conversation around Jev seems weird to me. Like... we started from neural nets that could do basic decision making and classifications pretty well, then trained larger and larger language models to get to where we are now. Now suddenly everyone is going crazy because someone trained a smaller model that is adequate at making decisions? We already went through the "look this AI can play pokemon terribly" phase like a decade ago.
I've spent endless hours playing this game as a child so building this was a ton of fun.
I open sourced everything in case you want to hack on it yourself here: https://github.com/christianmat/jev-pokemon
The game is being streamed live including the tokens and cost - hopefully we get all the badges and don't get stuck in a cave :)
That's actually fun to watch. Did you experiment with nicknaming before you turned it off? I'd be a little curious to see how it behaves.
Jev can't come up with original text, but I did consider giving it a list of hilarious names.
Sure it can, just ask it for next char or "done" in a loop
True!
I just took your advice and added it to the list of Jev decisions. Watch it name its next Pokemon!
Maybe letter by letter spelling?
with such a fat harness, this is more like watching a walk thru play the game.
Yeah I would have expected it to only decide which button to press, not something abstract like the choice of "go east to lavender town" for the goal of "in lavender town, climb the pokemon tower"
Looking at the diagram in the gh repo, it looks like this is entirely jev. Are there any examples of people having a big model like Fable handle high level goals?
Hm wondering what a first pass optimal setup might be - jev for overworld navigation, escalate to sonnet for easy battles, opus for medium difficulty battles, and fable for gym bosses could probably have jev also manage all the escalation / de-escalation to different models.
There frontier reasoning models do pretty well in Pokemon: https://github.com/benchflow-ai/pokemon-gym
The interesting thing here imo is the cost and latency. So far we're at 4 badges for less than $0.5
I'm not sure if you're asking about whether using Fable makes playing the game possible or if you're just curious about Jev + LLM interactions.
However, https://x.com/TynanSylvester/status/2096965749369720970 Astra was able to beat RimWorld. So LLMs are definitely able to drive these sorts of games to completion with their current abilities.
This is kinda chill to have in the background. I wish there were livestreams showing live reasoning of top models which are currently trying to solve cancer or whatever. Imagine the pogs in chat when it does.
People need to be live streaming their AI more!
Actually, yes, that would be at least interesting
watched it stuck at rocket hideout for 10 mins.... let me check 1hr later to see if it can find a way out
It’s currently stuck at an elevator and deciding to teach Pokemon various TMs and HMs instead of progressing… pretty hilarious!
it made it through!
Did I miss something? I thought one of the demo videos was it doing pretty decent at the first level of Doom?
In my experience it was too slow to do an FPS with 30 ticks per second reliably. It gets killed too fast.
Is Frigade going to use Jev to do object detection?
Working on it :)
Nice. When I saw your connection to Frigade, I knew there was a connection haha
Considering it just made Charizard forget its only fire-type move "Ember" to learn "Counter", I note no signs of intelligence.
Rookie mistake clearly..
This is so interesting to watch. For a couple minutes I was in awe of how quick and cheap it was. Then I saw just how bad the decision are and how it would get stuck in strange loops of going in and out of the same door to no end.
This seems like a technology heading in the right direction but not quiet there yet. Excited for what they are cooking up but probably won't start building around it yet.
Like others have mentioned in this post, I think a mix of models like Jev for simple stuff + a smarter reasoning model for more strategic thinking is the optimal solution. This experiment however is purely Jev. Which sometimes can be kinda dumb.
This entire conversation around Jev seems weird to me. Like... we started from neural nets that could do basic decision making and classifications pretty well, then trained larger and larger language models to get to where we are now. Now suddenly everyone is going crazy because someone trained a smaller model that is adequate at making decisions? We already went through the "look this AI can play pokemon terribly" phase like a decade ago.