Meta released TRIBE v2 last week - a foundation model that predicts fMRI brain activation from video, audio, and text. The question I kept coming back to was:
How do we actually compare AI models to the brain in a rigorous, statistical way?
So I built CortexLab - an open-source toolkit that adds the missing analysis layer on top of TRIBE v2.
Most wishlist tools follow the same playbook:
download an app, create an account, allow notifications, and then – maybe – you can share a list.
But when we looked at what users actually struggle with, a few patterns stood out:
I's been a while since the last post, hey! Let's waste no time and dive straight in.
This is the one everything has been building toward.
We have a producer that sends messages to the broker. We have a broker that stores them in topic queues. All that's missing is the consumer — the thing that actually reads those messages.
Let's build it.
I've been building two small Android utility apps as a solo developer, and I've hit the Google Play wall: you need 12 testers who stay opted in for 14 days before you can publish to production.
Currently at 1 tester (me). Need 11 more.
A clean focus timer for deep work sessions. Pomodoro-style with session tracking. No ...
AI in research and tech circles is evolving fast, with new tools shaping how we build and access data.
Researchers are uncovering biological parallels that may improve AI efficiency.
This insight highlights potential cross-disciplinary advances for developers.
How I used Open Wallet Standard, Tavily, Bright Data, Featherless, Allium, Uniblock, Zerion, CoinGecko, and x402 framing to build a real operator console for autonomous agent teams.
Most agent demos stop at orchestration. They show multiple bots talking to each other, but they avoid the hard production questions: