I made a list of great startups to join. It's called the Breakout List. The list has 92 companies. These are the 20 with 25 or fewer employees: - Hone (@moritz_stephan, @CarloWillem, @oqbrady) - Normal (@ansonyuu, @hudzah) - Standard Intelligence (@G413N, @devanshpandey) - Tacit Labs (@ninklefitz, @AmDroste) - American Terawatt (@atroyn, @rslparker, @aranibatta) - Conduit (@clemvonstengel, @riopopper) - Convergent (Omkar Savant, Vivek Katara, @debnilsur) - Core Automation (@MillionInt, @_arohan_) - Engram (@dan_biderman, @EyubogluSabri, @realJessyLin) - Instinct (@noahrshinn) - Keenable (@styskin, Matthias Petri) - Lumaril (Mark Elliot, Ben Duffield) - Neion Bio (@Dimkell, Sam Levin) - Pangram Labs (@max_spero_, @bradley_emi) - Quadrillion (@echinaceous) - Re (@karnsaroya, @AnandDhillon, @thecliffwhite, @benaneesh) - Ricursive (@annadgoldie, @Azaliamirh) - Sail Research (@neilmovva, @blintzbase) - Trajectory (@rronak_, @michaelelabd, @QuantumArjun) - Watney Robotics (Sean Cheong, Ryan Gannon) Picks from Elad Gil, Charlie Songhurst, Keith Rabois, Mike Vernal, Alana Goyal, Sonya Huang, Ramtin Naimi, Marc Bhargava, Cory Levy, Aashay Sanghvi, Konstantine Buhler, John Luttig, Varun Gupta, Ray Tonsing and Avichal Garg. Disclosure: I'm a small investor in American Terawatt, Convergent, Standard Intelligence and Trajectory (in this post), and in Factory, Physical Intelligence and SF Compute (elsewhere on the list). I didn't vote. The full list is on Breakout List.

At Trajectory, we're constantly implementing and building upon the latest research ideas on the path to continual learning. We wish we had the time to share all of them, but here's a quick glimpse on our explorations with PiSSA, and choosing the right trainable geometries for agentic RL.

At Trajectory, we care about storytelling. The storytelling about continual learning, the storytelling about the research breakthroughs it’ll take to get there, and the storytelling about the product that we need to will into existence. Brand is part of how we tell it. Here’s a behind-the-scenes look at the work we did with @metalab to craft ours

Today we are announcing @KeenableAI, an AI-native index of the best human knowledge we have, starting with the open web. AI seems to know just about everything until you ask it about something you know deeply. The answer isn’t wrong, but it's just very average. We started Keenable to fix this problem: every model and every agent should be able to query, reason over, and continuously learn from the living web. Backed by a $26M Seed from @Accel and @conviction. Built by the team that took on Google at Yandex Search, with researchers and engineers from Amazon AGI, X, and Perplexity. The Web Search API and Web Query Language for AI are live now. Together they make the web queryable at AI scale. Both are free until end of September. Seven days of benchmarks, stories, and launches ahead. Let your agents search.

It's time to rethink RL. Translating real world use into model improvements requires redesigning post-training algorithms for non-verifiable, per token rewards. At @aiDotEngineer 's World Fair, we share our insights into scaling algorithms like SDPO for continual learning.

Intelligence and Experience are orthogonal vectors Terence Tao is perhaps the world’s smartest person, but drop him into an accounting firm or onto a construction site and on day one he’s not going to be very productive @trajectorylabs calls this The Experience Gap, and they have a way to close it @QuantumArjun explained how at our Sovereign AI event: 00:00 Introduction 00:12 Building the platform for continual learning 01:33 The experience gap: models have IQ but no tenure 02:52 Traceability → model spec → better models and harnesses 05:27 Four wishes for the agent ecosystem 06:34 Wish 1: Trace the whole tree — and capture the corrections 08:03 Wish 2: Evals from real traffic, graded in the real harness 09:26 Wish 3: Let the agents cook, and make tool responses informative 10:34 Wish 4: Get comfortable on open weights, experiment with routers 11:51 Why owning your intelligence shouldn't be consulted away 13:15 Demo: import a benchmark, train a model, deploy it 14:28 Q&A: What's the trainable object — weights, harness, or context? 16:08 Q&A: Continual learning without training on customer data 17:13 Q&A: Episodic memory and the hierarchy of feedback 19:37 Q&A: Where continual learning matters most

@KJHMiao and I held a post-training fireside chat with the @trajectorylabs team to discuss their vision for continual learning. I particularly liked the distinction between "experience" and "IQ". Another strong reason why so many firms these days are emphasizing the importance of AI that you own! 00:00 Intro 00:34 Where the continual learning vision came from 07:03 Why a platform instead of forward-deployed engineers 14:23 Where the name Trajectory came from 19:53 Labs optimize for IQ, we optimize for experience 25:14 Continual learning without touching the model 32:06 Hot take: the most underrated part of post-training 36:45 AI natives vs tech natives vs enterprises 42:58 The magic moment: wake up and it's smarter 45:09 Three possible worlds

Continual learning is a bet that the retraining loop will get cheaper over time. With larger models, you can maybe run this loop once every few weeks. But with smaller models, you can run it nightly, per customer. And it keeps recursing: a model per company, then a model per client that company serves, then per matter. We’re getting closer to intelligence cheap enough to meter. On the path to this, we received early access to, and post-trained @nvidia's Nemotron 3.5 Lightning on @harvey LAB. One click on the Trajectory platform, no new engineering. 0% to 8.3%, above Opus 4.6 at 6.6%.

Announcing Discovery Loop! I am very excited to announce that, along with my longtime friends and collaborators @Sanjay_Ghemawat, @OriolVinyalsML and @quocleix, we are founding Discovery Loop (@DiscoLoopAI), a Public Benefit Corporation whose mission is to automate machine learning, science, and engineering to accelerate discoveries and progress. The four of us have worked together for 14 to 30 years, and have helped build some of the world’s most used products, infrastructure and AI models, and we’re excited to turn our attention to this ambitious endeavor. ♾ Learn more at:

applications for Embed now open, due 8/10 Embed is a Schelling Point for the best early stage founders. cash, compute, community, & a catalyst past participants were dropouts, leading researchers and repeat founders alumni have raised more than a billion dollars links in🧵

We're excited to sign the call for Open Weights. We believe the best way to create something enduring is to start with the future you believe is coming, then work backwards. We think the future is one where every product has its own intelligence, shaped by its users, its workflows, and everything it learns after it’s deployed. We’re building the experience layer for that future, and the products to bring that control into everyone's hands. However, in almost every path we can imagine to that future, open weights play a major role. Not because every model will be open, but because they give builders ownership over one of the most important layers of the stack. The more capable open models become, the more ambitious the products built on top of them can be. We’re excited to do our part to help make that future happen.