Samuel Times deskAI
Models, agents, research, infrastructure, products, safety, and the AI economy.
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 persona…
Daily · September 29, 20268 selected stories

AI agent safety architecturelinked documentSignal 8.6
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.…

Claude eval automationlinked documentSignal 8.2
Principles for designing evals and hillclimbing against them without fooling yourself, and how the claude-api skill's build-eval and hillclimb commands put them to work. Automating eval design and hillclimbing with Claude Principles for designing evals and hillclimbing against them without fooling yourself, and how the claude-api skill's build-eval and hillclimb commands put them to work.…

LLM and agent trendslinked documentSignal 8.2
On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose. I tied together the key trends from the past year into a chronological … Simon Willison’s Weblog 2026 in LLMs (so far) 27th September 2026 On Friday I gave the closing keynote at the WeAreDevelopers World Congress North America in San Jose.…

Agentic AI infrastructurelinked documentSignal 8.2
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 infrastructurelinked documentSignal 8.0
To understand where agentic AI stands today, consider the last seismic shift in technology: the rise of the internet in the 90s. It was new and full of possibilities. You could build a website over a… NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring Secure AI agents with a safety enforcement layer spanning software and hardware L T F R E AI-Generated Summary NVIDIA OpenShell…

AI inference energylinked documentSignal 8.0
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.…

AI agent safety infrastructuretweetSignal 7.4
Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come.…

AI agent infrastructure demandlinked documentSignal 7.4
An attempt to estimate the infrastructure required to serve 100M DAU Agent (Muse) Compute Demand An attempt to estimate the infrastructure required to serve 100M DAU There are obviously a lot of moving assumptions: how long agents stay active, how aggressively CPUs and memory can be oversubscribed, how many model calls an agent generates, and how efficiently those models are served.…