Introducing @Sable. We're building Aidan — an AI employee that can see your screen, speak naturally, and operate any interface in real time to run the sales conversations that drive revenue. It's been a blast working together with @Nim_Ravid1 @chensterman and @itaprf towards this vision. We've brought together an insanely talent-dense team of engineers and researchers and are growing fast to meet customer demand. Companies like @Notion and @Decagon already rely on Aidan to run customer conversations at scale. We've raised $45M led by @Sequoia and @8VC, with the support of BoxGroup, Sabrina Hahn, SVA, and Valor. Grateful for all of them. We're just getting started, and we're hiring across engineering and research. Get in touch!

One of the biggest constraints on growth has always been the ability to scale human judgment. At Palantir, we saw firsthand why exceptional people matter in helping customers understand and deploy transformative technology. The hardest work wasn't explaining the technology. It was understanding context, reasoning through novel situations, and helping customers make better decisions. Until now, that hasn't been scalable. That's changing. @8vc led @withsableai's $45M raise alongside my friend @shaunmmaguire at @sequoia. I'm honored to be joining the board. The best founders recognize platform shifts early and execute even faster. Nim Ravid and the Sable team saw where multimodal AI was heading early and built with remarkable speed. Sable is building an AI employee that can see what a customer sees, interact with the product alongside them, and reason through complex problems in real time. What felt like science fiction a year ago is already working in production with some of the fastest-growing AI companies in the world. We’re excited to work alongside @Nim_Ravid1, Linda, Leon, Itamar, and the entire Sable team as they build one of the defining AI companies of this next era.

Everyone knows marketing, sales, and deployment convert better when personalized to the buyer and their specific needs. Previously you could only scale this personalization via more employees, but now you can scale it with Aidan at @withsableai. They've unlocked a level of personalization and real time engagement that couldn't exist before the latest advancements in computer use and vision models. We're excited to partner with them, and tell your front office teams to give them a try.

@sequoia blog post here So lucky to work w/ @Nim_Ravid1, @Lindaaa_He, Leon and the rest of this team! And of course my first time being on a board w/ fellow troublemaker @JTLonsdale sequoiacap.com/article/partn…

I trained an LLM from scratch on pre-1900 text to see if it could come up with quantum mechanics and relativity. While the model is too small to do meaningful reasoning, it has glimpses of intuition. When given observations from past landmark experiments, the model can declare that “light is made up of definite quantities of energy” and even suggest that gravity and acceleration are locally equivalent. I’m releasing the dataset + models and leave this as an open problem to the research community. I also include what this project has taught me about intelligence in a mini essay linked below. 🧵(1/n)

Introducing Catalyst, the agent layer for all of finance. Turn any natural language idea into a live strategy: research, backtesting & execution. Don’t get left behind. Waitlist open, join now.

In middle school, with money earned performing magic shows I opened my first investment account. At Stanford, captivated by the markets, I realized a paradigm shift was on the horizon. Today, I am excited to announce our $5.7M raise, led by @novaholdings. With the developments in AI, I knew I could no longer wait to start @vigillabs. At Vigil, we are building the engine to understand markets in realtime, designed for our own traders.

introducing chipmunk—a training-free algorithm making ai video generation 3.7x & image gen 1.6x faster! ⚡️ our kernels for column-sparse attention are 9.3x faster than FlashAttention-3 and column-sparse GEMM is 2.5x faster vs. cuBLAS a thread on the GPU kernel optimizations 🧵

🧬 Meet Lyra, a new paradigm for accessible, powerful modeling of biological sequences. Lyra is a lightweight SSM achieving SOTA performance across DNA, RNA, and protein tasks—yet up to 120,000x smaller than foundation models (ESM, Evo). Bonus: you can train it on your Mac. read our paper here: arxiv.org/abs/2503.16351

I taught an LLM to optimize proteins. It proposed a better carbon capture enzyme. Introducing Pro-1, an 8b param reasoning model trained using GRPO towards a physics based reward function for protein stability. It takes in a protein sequence + text description + previous experimental results, reasons over the information given in natural language, and proposes modifications to improve the stability of the given sequence. 🧵(1/n)

Read about how our RAG fine-tuning code assistant (fine-tuned Mistral-7B) can outperform both Claude 3 Opus and GPT-4o on some popular open source codebases! We get up to 19% more accuracy, 3.7x faster speed, and 150x cost reduction. together.ai/blog/rag-fine-tu…