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Anthropic dropped a solid engineering post this week about containment across claude.ai, Claude Code, and Cowork. One of the more transparent writeups from a major AI lab about what actually broke. The core insight: model-layer defenses are probabilistic and will always have a non-zero miss rate. So the real answer is hard environmental containment, not just safer models. Three patterns they use: -claude.ai: ephemeral gVisor containers, fully server-side -Claude Code: OS-level sandbox with human-in-the-loop approvals (93% get approved anyway, so approval fatigue is real) -Cowork: full local VM, credentials never enter the guest Two incidents they disclosed: A red team phished an employee into running a prompt that exfiltrated AWS credentials. Succeeded 24 out of 25 times. The model had nothing to catch because the user was the one typing it. Only egress controls would have stopped it. A third-party found that Cowork’s egress allowlist passes traffic to api.anthropic.com. An attacker embedded an API key in a file in the user’s workspace, Claude followed hidden instructions, and uploaded files to the attacker’s Anthropic account. Sandbox worked perfectly and still leaked data. Their lesson: an allowlist isn’t a destination filter, it’s a capability grant. Every function reachable through an allowed domain is an attack surface. The section on persistent memory poisoning and multi-agent trust escalation at the end is worth reading too if you’re building anything agentic. submitted by /u/Direct-Attention8597 [link] [留言]

Parsing a papal proclamation.

Transform selected text anywhere on macOS Discussion | Link

Musk says drones used Starlink instead of Starshield, blames military contractor.

"We also obviously want to be very mindful of the Outer Space Treaty."

This is a submission for the GitHub Finish-Up-A-Thon Challenge What I...

Google Photos bulk editing: Enhance, Filter, Crop, and more Discussion | Link

Give your AI agent access to ~8k tools, APIs and services Discussion | Link

Local meeting transcription with no account needed Discussion | Link

CLI + companion App to spin up complete local LLM stacks Discussion | Link

Ad automation + budget control. Across every platform. Discussion | Link

Parse any PDF layout with SOTA accuracy for AI pipelines Discussion | Link

Not identifying people based on their use of Wi-Fi routers, but identifying people using Wi-Fi signals . This is accomplished through what is known as WiFi sensing , or the use of WiFi signals to infer information about a physical environment. When radio signals like WiFi travel through a space, they interact with the objects and people around them. Those signals can be reflected, scattered, or absorbed. By analyzing how the signal is expected to behave compared with how it is actually received, researchers can infer details about the surrounding environment...

Amid rapidly growing adoption of enterprise-level AI agents, there’s a disconnect emerging between ambition and execution. Although 85% of organizations say they want to be agentic within the next three years, 76% say their current operations and infrastructure can’t support that change. They cite a lack of readiness across people, processes, and workflows. The sticky…

A native local macOS network monitor Discussion | Link

Visualize cloud architecture with live traffic simulations Discussion | Link

This isn’t totally about AI. It’s about technical writing in the age of AI. I have some thoughts on this and I hope it’s helpful to you humans reading. Technical Writing in the AI Age originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

Validate agent-generated code before it ever reaches CI Discussion | Link

GoPro's Mission 1 Pro action cam has a big 1-inch sensor that offers sharp, color accurate 8K 60 fps and 4K 240 fps video.

This is the first post of series Building TinyAgent where we are going to build a small agent from...