Anthropic’s agent containment pattern for Claude products

#1 📝 Anthropic Engineering

How we contain Claude across products - Anthropic engineers describe their approach to limiting the potential blast radius of increasingly capable agents by building containment systems across claude.ai, Claude Code, and Cowork. The post explains lessons learned and design decisions for safe deployment across products.

#2 📝 Simon Willison

Running Python code in a sandbox with MicroPython and WASM - Describes an approach to sandboxing code execution by bundling MicroPython as WebAssembly and releasing it as the alpha package micropython-wasm; the sandbox is being used to build a Datasette Agent plugin 'datasette-agent-micropython'.

#3 𝕏

Google Research launches 3DCodeBench at CVPR2026 booth #557 as part of Project Astra 3D, demonstrating Gemini models’ proficiency in generating diverse 3D objects via code execution. Lei Shu and Yipeng Gao present the demo at 5:30 pm.

#4 𝕏

Aravind Srinivas thanks @LipBuTan1 and @intel for partnering with Perplexity to bring on-device AI via local models and hybrid inference to Intel Ultra Series 3 laptops.

#5 ▶️

Hermes Agent Desktop: Full Setup + Real Use Cases

Greg Isenberg

#6 📝 PromptLayer Blog

How to design an LLM eval framework - This article argues that an LLM evaluation framework must answer practical release questions about whether a prompt, model, retrieval setup, or agent workflow is better for users, and warns that vague metrics or small example sets will fail in production. It emphasizes designing evaluations that reflect real user outcomes.

#7 𝕏

Madhu Guru explains that routing tasks to the right AI model demands detailed task-specific benchmarking to balance quality and cost.

#8 𝕏

Logan Kilpatrick proposes building a top-tier venture firm that drives short- and long-term investments through deep AI model benchmarking—spotting capability overhangs, pinpointing performance gaps, and tracking improvement trajectories.

#9 𝕏

Lenny Rachitsky says that amid growing marketplace noise, distribution has evolved into an increasingly powerful moat.

#10 𝕏

Guillermo Rauch emphasizes their new, highly scalable (albeit complex) architecture engineered to seamlessly handle both massive and tiny projects, noting it took a significant bake-time to perfect.

#11 𝕏

Madhu Guru questions why you’d stick with the same model for two years when you could switch to a cheaper alternative and hit the same total cost in just three months.

#12 📝 PromptLayer Blog

How to pilot an enterprise LLM visibility platform - An enterprise LLM visibility pilot must be connected to a real production workflow (not a toy chatbot), capture prompts, model calls, retrieval inputs, tool calls, agent steps, latency, cost, user feedback, evaluation results and prompt versions, and is best run on workflows that typically have 3–8 LLM calls per task such as support-ticket triage, sales call summarization, internal policy assistants, agentic data workflows, or code review assistants. Timebox the pilot to 30 days with days 1–3 to choose the workflow, define success criteria and data handling; days 4–10 to instrument traces (request ID, prompt template/version, model provider/settings, redacted inputs, retrieved doc IDs/scores, tool args/returns, outputs, latency, token counts, cost); days 11–17 to build an eval set; days 18–24 to run changes and configure alerts; days 25–30 to review and decide, using a cross-functional team (AI/app owner, platform engineer, security/privacy reviewer, product manager, support/domain expert, legal) and enforce redaction of emails, credentials, payment data and PHI — for example a trace might show classify_ticket (prompt v12, gpt-4.1-mini) returning “Billing issue” with confidence 0.82 in 620 ms, draft_response (claude-3-5-sonnet) in 2,140 ms, and a retry in 2,280 ms.