An agent inside Signal using decentralized compute paid for with bitcoin @moneyball demos a fork of Signal built by @matbalez using @Mesh_LLM and @lexeapp ↓ Timestamps: 0:00 - Demo: agent inside Signal 10:51 - The economics of decentralized compute 14:39 - Running inference at home

Some personal news: I'm joining @PrimeIntellect as Head of Scientific AI! A billion copies of the same very smart, agreeable mind will not be a scientifically fruitful world. Scientific superintelligence should be weird and definitionally nonconsensus. It should be highly plural, consisting of many minds, human and artificial, that see the world differently and hold each other accountable, with human judgement still playing the central role in setting the trajectory. Science is about asking the right questions and reducing uncertainty. Professionalizing science made discovery a viable career, but it also introduced incentives to work on what is fundable and publishable. Neither is the same thing as being true or impactful (reward hacking exists in the real world too!). We are very good at accumulating incremental knowledge and rather bad at stewarding unorthodox ideas through infancy, even though these are often the ideas that open entirely new fields of inquiry. If we aren't careful, scientific AI could reproduce these same incentives at a much larger scale. If everyone relies on the same few large models, we risk inheriting the same priors and blind spots. Open tools let us shape our own intelligence and take it in directions a frontier lab might never prioritize. This matters for scientific freedom and is central to Prime’s vision. If you are a scientist, staying at the frontier will mean training your own models on your own research and data. I'll work closely with scientific teams across domains to help them build scientific agents by turning their research problems into environments where agents can learn. This means closing the loop from hypothesis to experiment to validated result, with agents learning from each cycle. Teams will be able to own their intelligence, using tools previously available only to frontier labs, and point it at the questions they care about most, especially those that others might not be asking. I've had the good fortune of seeing how science operates across academia, industry, government, startups, and venture. I worked on experimental therapeutics for cancer in academia before moving to the National Institutes of Health to work on DNA damage and repair. I cofounded Molecule, where we funded dozens of research projects to speed the translation of early-stage therapeutics to market, focusing on areas traditionally ignored by pharma. Most recently, I started Triplicate, a small fund backing teams and tools accelerating scientific research. Many of the problems I care about most are increasingly soluble with advances in AI, and I hope to bring what I've learned about the bottlenecks in translating research to radically accelerate science in all fields. I share Alexander Grothendieck's ethos of discovery and believe Prime does too: "Discovery is a child's privilege. I mean the small child, the child who is not afraid to be wrong, to look silly, to not be serious, and to act differently from everyone else." The goal of progress is not more intelligence for its own sake. In my view, it is to better understand reality, and, in doing so, give more people, and more kinds of minds, the chance to flourish. If you have a research problem where today's tools fall short, my DMs are open. If our approach resonates and you want to build this with us, we're hiring!