Samuel Times deskAI
Models, agents, research, infrastructure, products, safety, and the AI economy.
Scaling Discovery through Test-Time Communication Jongho Parkb* Vasilis Kontonism Shivam Gargm Akshay Krishnamurthym Dimitris Papailiopoulosm bUC Berkeley mMicrosoft Research S…
Weekly · September 27, 20268 selected stories

multi-agent test-time communicationlinked documentSignal 8.7
Scaling Discovery through Test-Time Communication Jongho Parkb* Vasilis Kontonism Shivam Gargm Akshay Krishnamurthym Dimitris Papailiopoulosm bUC Berkeley mMicrosoft Research Science advances not in isolation but through collaboration, yet existing agentic systems capture little of this. Whether communicating agents help remains an open question with mixed prior results.…

AI sandbox infrastructurelinked documentSignal 8.4
Large-scale agentic training and evaluation with large language models (LLMs) rely on isolated, stateful execution environments in which models inspect repositories, invoke tools, execute commands, and interact with task-specific services.…

AI agent safety architecturelinked documentSignal 8.4
By going into detail about how Muse works under the hood, we hope to give you a sense of how — and how much — you can trust it in practice. Today we launched Muse — our personal agent. We’ve been working on and using Muse ourselves since early 2026.…

AI inference energy uselinked documentSignal 8.4
As artificial intelligence (AI) becomes widely used and new workflows scale token demand, questions about how much energy large language models (LLMs) consume are increasingly important for grid planning, AI development, and energy policy. Rapid expansion of AI data-center capacity further increases the importance of accurate energy estimates.…

Local LLM inference accelerationlinked documentSignal 8.3
Fast, exact LLM decoding on Apple Silicon (MLX) behind an OpenAI-compatible endpoint - ashhart/TensorFold TensorFold TensorFold serves a local LLM on Apple Silicon or NVIDIA GPUs at an OpenAI-compatible endpoint, fast and exact.…

AI-assisted performance engineeringlinked documentSignal 8.2
Inside our performance sprint: the benchmarks Claude built, the loop each Slack thread ran, and the guardrails that let us ship 3,000 changes safely. How we made claude.ai 3x faster in two weeks Once Claude can measure something, it can make it faster. So we kept finding more things to measure. This August, we made the core user experience of claude.ai and the Claude desktop app about 3x faster in a two-week sprint.…

AI agent evaluation benchmarkslinked documentSignal 8.2
OpenRSI Index turns influential open model-development projects into auditable environments where AI research agents iterate against fixed verifiers — and are scored against the human recipe they started from. Partnership The OpenRSI Index is co-led by the MIT-IBM Watson AI Lab and Amazon A-EVO Lab, built by and for the research community.…

AI-assisted biological discoverylinked documentSignal 8.1
In early results from our new life sciences research lab, Claude agents found an enzyme system whose function is still unknown. Claude discovers a novel enzyme system with CRISPR-like repeats We’re introducing a new life sciences research group and laboratory at Anthropic.…