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RT by @dair_ai: Increasingly, HTML Artifacts are becoming a core part of how I work with AI agents. Long-horizon agent sessions need a better way to surface insights about what work it has done. This may not be obvious right now, but as you start to let your agent work on dynamic workflows, large codebases, long-running loops (e.g., using /goal), and deep research tasks, you need a good way to present results. Chat window is not it. You also don't want to just trust everything the agents do. Artifacts help provide an important verification layer, which in turn enables important decision-making. I like HTML artifacts because I can just ask the agent to produce as many of them (and in whatever form) as I need to verify the work and make sense out of everything. I even built a nice tab system for my artifacts. They are great for continual learning and research. I use HTML artifacts for logging, tracking experiments, brainstorming, managing my inbox, code reviews, agent session management, deep research, writing, reading, and so much more. I believe @karpathy wrote about this somewhere: As we move on to more advanced applications of AI agents and outputs get more complex, we will start to find the need for even more advanced forms of interactions with AI, including interactive neural videos/simulations.

RT by @_akhaliq: We just adopted a super cool new space template for LocateAnything, made by @_akhaliq the great. Thank you AK! Try it out: https://huggingface.co/spaces/nvidia/LocateAnything Credit to AK's space example: https://huggingface.co/spaces/akhaliq/LocateAnything

RT by @dair_ai: In a few months, people will start to realize how fundamentally important MCP for agents is. It's not even about connecting tools. There are many ways to do that. It's about the types of abstraction it already enables. My new self-improving system, enabled through agent-to-agent interaction, is all powered by MCPs. This was not an accident. I ran my entire orchestrator through a self-improving loop with clear criteria/goal, and it came up with all kinds of interesting ways (mostly powered by MCP tools) on how to enable complex interactions, versioning, eval workflows, communications, tools, etc. Something new could always emerge, but I think the protocol itself will be crucial and necessary for all the advancements ahead. MCP is the future. And I am glad a lot of it is built in the open.

DynaFLIP Rethinking Robotics Perception via Tri-Modal-Dynamics Guided Representation

Pinned: Do proactive agents really need an LLM to decide when to wake? The default proactive agent calls an LLM on every event just to decide whether to wake up. That is a lot of expensive inference spent on a yes or no. New research from Microsoft and Purdue asks whether the trigger really needs a language model at all. Their answer is a 220MiB temporal-graph encoder that decides when to wake and what context to anchor. It gains +16.7 mean F1 across 14 backbones, runs 4 to 83x faster, and fits on-device at around 11ms per event. If you run an always-on agent loop, the polling decision is quietly the main cost driver. A tiny encoder removes it without giving up accuracy. Paper: https://arxiv.org/abs/2605.30152 Learn to build effective AI agents in our academy: https://academy.dair.ai/

R to @simonw: Here's more about Datasette Agent on my blog: https://simonwillison.net/2026/May/21/datasette-agent/ - and the announcement on the new Datasette project blog too: https://datasette.io/blog/2026/datasette-agent/

RT by @demishassabis: The results of the research happening in my team @GoogleDeepMind have convinced me that the next era of scientific discovery will be aided by AI agents acting as force multipliers for human ingenuity. That’s why I’m proud to introduce Gemini for Science - a collection of experimental science tools designed to support researchers at every stage of the research process. The tools include: 1️⃣ Literature Insights, built with Google NotebookLM, searches millions of scientific papers to synthesize findings and generate artifacts including data tables, slides, reports, and more. 2️⃣ Hypothesis Generation, built with Co-Scientist, simulates the scientific method via a multi-agent "idea tournament" to generate, debate, and rigorously evaluate research hypotheses. 3️⃣Computational Discovery, built with AlphaEvolve and ERA, is an agentic engine that generates and scores thousands of code variations in parallel, allowing researchers to test modeling approaches in fields like epidemiology in a fraction of the usual time. Read more: https://blog.google/innovation-and-ai/technology/research/gemini-for-science-io-2026/ Register for access here: http://labs.google/science

Gemini 3.5: frontier intelligence with action