AI agents write code fast, but the feedback loop usually can't keep up. In this tutorial, you'll see how to wire Chunk sidecars into your agent's hooks so basic failures get caught before they ever reach your CI pipeline. We'll walk through the two hooks that chunk init writes automatically: Both hooks return exit 2 on failure, blocking the commit or keeping the turn open so the agent can fix its own mistakes with no manual prompting required.
Claude Mythos is now available to the public through Claude Fable 5, released June 9, 2026. Claude Fable 5 pricing is $10 per million input tokens and $50 per million output tokens, exactly 2x Claude Opus 4.8 ($5/$25). Claude Mythos 5 (the restricted Project Glasswing version) has identical pricing. Prompt caching cuts input spend by 90%. Batch API pricing is $5/$25 (50% off). In April 2026, Anthropic announced a model it said was too dangerous to release.
I tested the new Anthropic model Fable 5 truly is using data, not vibes. Why does it feel faster? Does it actually cost double? Is it better at coding?
Can AI actually improve infrastructure operations? Without sacrificing control? In this webinar, see how teams use Puppet AI to understand infrastructure with natural language, reduce operational effort, and move from insight to action faster—all within trusted automation workflows. Watch a live demo of detecting and mitigating a real-world vulnerability, and learn how context-aware AI helps teams scale safely with built-in governance.
If there was a theme this month, it was making the hard parts of a telemetry pipeline less risky. For SIEM customers, we shipped an ASIM-native Microsoft Sentinel destination and automatic OCSF mapping in Pipeline Intelligence, two of the most-requested pieces for teams routing security data through Bindplane. On the platform side, we added config rollback, which turns "I changed something and now it is behaving differently" into a one-click trip back to a known-good version.
What does it actually take to thrive in an AI-driven world, not just survive it? In part two of his conversation with Karthik Ravindran, General Manager of Enterprise Data and AI at Microsoft, host Tom Stoneman digs into the human qualities that no model can replicate.
During the Toronto KCD (Kubernetes Community Days), I attended an insightful talk on AI resource optimization that highlighted a staggering Gartner study: “AI infrastructure is adding $401 billion in new spending this year alone. Yet, real-world audits tell a much darker story, revealing that average GPU utilization in the enterprise is stuck at a dismal 5%”. While many people in the audience were shocked by that number, the data didn’t come as a surprise to us.
AI is changing how teams work. Developers are generating code faster, security teams are automating investigations, and employees across the business are using AI tools to accelerate research, content creation, and decision-making. But this adoption comes with a catch. As usage explodes, it introduces a new set of security risks: a rapidly expanding attack surface, faster attack timelines, potential data exposure, and an alarming lack of visibility into how these tools are being used.
AI agents have moved beyond experimentation. Today, they schedule meetings, process invoices, respond to customers, analyze contracts, update records, and make decisions that directly affect business operations. As organizations race to automate more workflows, one critical question is often overlooked: Can you explain exactly what your AI agent did, why it did it, and how it reached that decision?