Jack Hessel
@jmhessel
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@AnthropicAI. Seattle bike lane enjoyer. Opinions my own.
Seattle, WA
Joined March 2010
How to build agentic search systems for long-horizon tasks? Check out our new paper! - Simple design principles are efficient and effective - Error analysis and fine-grain analysis for search systems A 🧵 on SLIM, our long-horizon agentic search framework
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A few weeks ago, I made the difficult decision to move on from @samaya_AI. Thank you to my collaborators for an exciting 2 years!!❤️ Starting next month, I'll be joining @AnthropicAI. excited for a new adventure!🦾 (I'm based in Seattle 🏔️🌲🏕️; but in SF regularly)
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I’m excited to share our new @Nature paper 📝, which provides strong evidence that the walkability of our built environment matters a great deal to our physical activity and health. Details in thread.🧵 https://t.co/omO3YcHrvG
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TFW you're one of the experts in a mixture-of-experts model and a query comes up that is relevant to your expertise
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Of all the FLOPS being used for LLMs in the world, the ratio of training FLOPs (incl. RL rollouts) to inference FLOPs is closest to:
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Thrilled to finally share what we’ve been building past few months! Audio used to be a black box for me, now I’m deep in the box, with more out-of-the-box ideas cooking. Enough with the box..introducing Voxtral. Grateful for the intense and rewarding learning curve at @MistralAI.
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It is a major policy failure that the US cannot accommodate top AI conferences due to visa issues.
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Check out our LMLM, our take on what is now being called a "cognitive core" (as far as branding go, this one is not bad) can look like, how it behaves, and how you train for it. https://t.co/gxrDVSkcZE
arxiv.org
Neural language models are black-boxes--both linguistic patterns and factual knowledge are distributed across billions of opaque parameters. This entangled encoding makes it difficult to reliably...
The race for LLM "cognitive core" - a few billion param model that maximally sacrifices encyclopedic knowledge for capability. It lives always-on and by default on every computer as the kernel of LLM personal computing. Its features are slowly crystalizing: - Natively multimodal
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Pitch decks these days: Slide 2: "We're entering an era of maximum efficiency where thanks to AI the next billion dollar company will have 10 employees and will be incredibly profitable" Slide 15: "This is why I'm raising a $60M Series A to build a great team of [80] people"
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First ever (i think?) cli coding agents battle royale! 6 contestants: claude-code anon-kode codex opencode ampcode gemini They all get the same instructions: Find and kill the other processes, last one standing wins! 3... 2... 1...
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...CLAUDE.md; ...GEMINI.md ; ...CODEX.md (?) in every directory?🤔
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+1 for "context engineering" over "prompt engineering". People associate prompts with short task descriptions you'd give an LLM in your day-to-day use. When in every industrial-strength LLM app, context engineering is the delicate art and science of filling the context window
I really like the term “context engineering” over prompt engineering. It describes the core skill better: the art of providing all the context for the task to be plausibly solvable by the LLM.
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It's harder and harder to find simple tasks where llms fail; but this is a nice one! (my guess is this isn't a fundamental limitation of attention; rather, maybe this type of reasoning just isn't represented in pre/post/RL, but we'll see...)
LLMs excel at finding surprising “needles” in very long documents, but can they detect when information is conspicuously missing? 🫥AbsenceBench🫥 shows that even SoTA LLMs struggle on this task, suggesting that LLMs have trouble perceiving “negative space” in documents. paper:
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writing an MCP server is wild. mechanically it's basically REST. But your user is an LLM, so things are different. e.g., don't accept or return more than you need; do make "fake" loading bars to stream back; do adjust your API based on watching the LLM struggle/succeed/compose
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A bit late to announce, but I’m excited to share that I'll be starting as an assistant professor at the University of Maryland @umdcs this August. I'll be recruiting PhD students this upcoming cycle for fall 2026. (And if you're a UMD grad student, sign up for my fall seminar!)
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## The case for more ambition i wrote about how AI researchers should ask bigger and simpler questions, and publish fewer papers:
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.@KaiserKuo: “The soft power cost is immeasurable. For decades, a degree from a U.S. university was the golden ticket, and not just for the prestige…It was often the start of a lifelong affinity for America, its values, and its people.” https://t.co/GQe1CzOTBU
sinicapodcast.com
On Rubio, Student Visas, and America’s Strategic Folly
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