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We're not observers writing about AI from the sidelines. We're practitioners who got frustrated with the fragmentation and decided to build the infrastructure we wished existed.
AI building is fragmented. Knowledge is scattered across Discord servers, buried in Twitter threads, locked in private repos.We're creating the public research layer.
Designing the frameworks that turn scattered experiments into coherent research programs. Former systems architect turned AI researcher.
Crafting experiments that produce actionable insights, not just interesting data. Believes in shipping MVPs over writing specs.
Building the shared infrastructure that makes AI development less lonely. Every tool documented, every pattern shared.
Ensuring research reaches everyone who can use it. Turning academic insights into practical guides.
The lab is always looking for curious builders. Contribute to join.
We've developed a methodology that balances rigor with speed, openness with focus. Here's how we operate:
We don't theorize endlessly. We build, test, document, iterate. Real learning comes from real attempts.
Unless there's a compelling reason otherwise, everything is public. Code, data, failures, learnings.
If it's not documented, it didn't happen. We write for our future selves and for the community.
Small, frequent releases over big reveals. Feedback loops matter more than polish.
“The best way to predict the future of AI is to build it—together, in the open, learning from each iteration.”
Contributions welcome. The lab is open.