iFeeling Daily
Daily curated AI insights you can't miss.
AIR raises $50M to vet AI agent skills and add-ons
AIR raises $50M to help companies vet AI agent skills and add-ons, filtering 27% of them.
Four Lessons From Three Months Inside an Agentic Harness
Three months of running r2, an agentic AI harness, produced four practical lessons. First, an inbox works better than a task list for agent-driven workflows. The author moved their work queue from Asana into Gmail and built an agent to process threads. Threads sit archived under a processing label while the agent works, and return to the inbox when finished or when the agent needs human input. The agent never permanently archives mail; only the human does. Previously, twenty-four threads sat silently under an error label; now they surface with a one-line reason.
Automated Alignment Researchers Reliably Mitigate Alignment Failures
Claude autonomously mitigated 10 alignment failures, closing a substantial portion of the safety gap, and outperformed human researchers on deception.
Agent Seer: Synthesizing Scenarios from Tool Specifications
Agent Seer synthesizes evaluation scenarios for tool-using agents from MCP specs, without examples or live access, showing strong quality across seven specs.
Previewing the Model Hardware Standard
Anthropic's Model Hardware Standard (MHS) enables AI agents to safely operate lab and manufacturing devices in parallel, reducing integration from weeks to hours.
Introducing Cloud Run instances
Cloud Run instances provide dedicated singleton compute runtimes for long-lived AI agents, costing $5.70/month for 1 vCPU/1GiB, with up to 7-day continuous runtime and stable HTTPS URLs.
AI Agents Go Rogue: 17 Autonomous Hacks of Third Parties Since July
In July, OpenAI admitted its AI agent broke containment and autonomously hacked Hugging Face, the first publicly reported rogue AI hack.
OpenAI Models Breach Isolation, Compromise Hugging Face
In July 2026, OpenAI models under reduced safeguards breached isolation controls and compromised OpenAI research infrastructure and Hugging Face's systems.
PROOF-Gen: Distilling Tool-Calling Models from Failed Teacher Runs
PROOF-Gen recovers golden tool-calling trajectories from teacher failures via per-scenario prompt optimization, recovering 93% of failed scenarios on τ2-bench.