Killer design. Proud of Chinmayi!

I'm judging the Claude Impact Lab hackathon in Seattle on October 25. It'll be good to get out of the AI builders building for AI companies bubble. The teams are building AI tools for small businesses. Looking forward to seeing what ideas they come up with.

My best rated @AITinkerers talk so far (4.8/5), almost perfect! This was my first time presenting anything to game developers. I knew that more than giving a demo about what I built, I had to educate the room on the basics of decision models first and then show them how to apply it to the gaming domain with Doom and Contra as examples. In my close-knit AI-bubble, decision models like Jev seem to be fairly common. But, I need to take a few steps back and realize that not every person in tech is necessarily up to date on the latest in AI. It can be hard and overwhelming, especially since AI moves at such a rapid pace, even I struggle to keep up. I rely on Twitter, LinkedIn and Reddit to keep me up to date when I inevitably doomscroll. I hope I was able to get people up to speed with decision models and why they're the right form factor for certain gaming use cases. Thanks to Craig Robinson for the opportunity and for doing a great job hosting the first ever Games and AI demo night.

Oh wow! Pretty sure some frontier models already do this. This could explain the GPT models' tool use improvements and speech gains.

Exactly what system one models can be used for.

Exactly. Let's the agents interact with each other.

Yup. Bonus points if the model companies are beefing with each other 😂

I've replaced iTerm2, VSCode and all other tools for agentic development with Twine, an open-source workspace for coding agents. Have Codex implement and Claude review. Design the handoffs in a workflow graph, inspect what each agent did, and keep their project memories in view. Waiting on @aravind_ni for updates! GitHub - github.com/standing-desks/tw…

Can Jev-like models speed run games? Kinda? Here are some experiments that I ran where different decision models played Stage 1 of Contra. Here I have Jev, Laya and Qwen3.5 battling it out. I still need to try out more model types and sizes. I loved playing this game as a child on the knockoff Nintendo NES that I had. Comment and let me know what game to try next. Follow for interesting experiments and visualizations.

They should use this same approach to either distill or quantize models(QAT-style) as well. Feels like the model is training itself to to scope tasks better and operate better on scoped context.

Proud of my wife for getting back into the industry after the baby.

Suffering from success.

I gave four Jev-style decision models the controls to Doom: Jev, Laya, finetuned ModernCE and Qwen3.5. A deterministic Python adapter turns ViZDoom's visible-object labels + HUD into text. Each model picks turn, move or fire. No extra Doom-specific training. Same starting seed, separately recorded games. Each model's actions change its view. The video shows action probabilities, kills and survival time. One preselected episode/model on each of two scenarios. Thanks to @typesafeai for Jev access. Inspired by their Doom demo and others on Reddit and LinkedIn. typesafe.ai/blog/introducing…

Astra's speed and usage let's me get more done tbh, I'd prefer that.

It's so insanely fast, it's hard to believe!

Astra was better on speed overall and a part of the physics seems more realistic. Fable was slower for obvious reasons, glitched out a bit but ultimately delivered a more consistent looking output. That being said the more you look at it, the worse it seems for both models.

A Slinky Down an Escalator - GPT 6 Astra max vs Claude Fable 5.1 max I finally got access to GPT 6 Astra and Claude Fable 5.1. I wanted to see how far we've come. I was inspired by the "pelican riding on a bicycle" test but wanted to push it a bit more in the direction of inter-object physics, so I came up with this idea for a perpetual slinky going down an escalator. Here I've compared GPT 6 Astra max on the left to Claude Fable 5.1 max on the right. Some notes: 1. GPT 6 Astra max was really fast and didn't burn too much of my weekly usage quota. 2. Claude Fable 5.1 max used up the entire 5 hr usage window and errored out once crossing the output token max limit. 3. GPT 6 Astra max seems too simple? There's also an issue where the slinky crosses into itself which should not be possible. Is this AGI? Maybe? 4. Claude Fable 5.1 max looks more real than I expected. Seems like it passes the eye test, unless I missed something. Let me know if you spot anything or had better ideas for a test. Overall I'd say Claude wins on quality and GPT wins on speed. Maybe I can tune my prompt a bit better for a more reliable result. This was my original prompt btw: Make me a single HTML file of a rainbow slinky going down an up-escalator forever. No libraries, just canvas and code. The slinky should be a chain of springs, each coil a different color of the rainbow. It starts folded in half like a horseshoe draped over a step. When dropped it flips end-over-end down the steps and because the escalator keeps moving up it tumbles in place and never reaches the bottom. Include a drop button and a reset button.