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O'Reilly Radar — AI/ML

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This Week in AI: Fable 5, the Clone Wave, and Uber’s AI Reality Check
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This Week in AI: Fable 5, the Clone Wave, and Uber’s AI Reality Check

This week, egghead.io cofounder John Lindquist joined host YK Sugi, founder of CS Dojo and developer experience manager at Eventual, to cover the latest AI news. First on the agenda was the contested release of Claude Fable 5. They also examined the financial shifts reshaping the technology industry, including the rising costs associated with agentic […]

O'Reilly Radar — AI/ML
The Case Against Building Your Own Agent Platform
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The Case Against Building Your Own Agent Platform

You know the meeting. The board wants an AI agent strategy by end of quarter. Someone on the leadership team has read a McKinsey report. You’ve been voluntold to build the platform. The slide deck says “AI-native.” The acceptance criteria are vague. Somebody mentions LangGraph, and somebody else says, “We’ll just wrap it ourselves.” You […]

O'Reilly Radar — AI/ML
This Week in AI: The Next-Gen Recommendation Experience
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This Week in AI: The Next-Gen Recommendation Experience

This week Miguel Fierro, a former Microsoft principal researcher who recently founded his own company, RecoMind, joined data and AI evangelist Christina Stathopoulos to talk about the state of recommendation systems. Christina also ran through the latest AI news she’s been watching, from Anthropic’s continued rise to responsible AI, announcements from Google’s I/O 2026 conference, […]

O'Reilly Radar — AI/ML
When Context Collapses: Teaching Agents to Detect and Recover from Lost Memory
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When Context Collapses: Teaching Agents to Detect and Recover from Lost Memory

This is the eighth article in a series on agentic engineering and AI-driven development. Read part one here, part two here, part three here, part four here, part five here, part six here, and part seven here. “640K ought to be enough for anybody.”—Bill Gates (allegedly) If you’re building AI agents that do complex, multistep work, you’re going to run into context […]

O'Reilly Radar — AI/ML
The PM’s Playbook for Shipping AI Features That Actually Work in Production
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The PM’s Playbook for Shipping AI Features That Actually Work in Production

The demo to production Death Valley If you’ve worked on an AI feature, you know the feeling. You start building something that you are excited about, set launch timelines. The model spits out a perfect response, the prototype works magically, and everybody in the room is mentally calculating how big this product will be when […]

O'Reilly Radar — AI/ML
Long-Running Agents
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Long-Running Agents

The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission. A long-running AI agent can keep making progress over hours, days, or weeks. It can do this across many context windows and sandboxes, recover from failure, leave structured artifacts behind, and resume where it left off. For […]

O'Reilly Radar — AI/ML
The Tidy House
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The Tidy House

DJ Patil has spent the past several months on a listening tour. Wherever he travels, he finds a local university, pings faculty and students and anyone else who wants to show up, and runs an AMA. He’s heard from grad students who can’t get callbacks, hospital administrators dealing with federal policy changes that land like […]

O'Reilly Radar — AI/ML
Predict, Don’t Enumerate
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Predict, Don’t Enumerate

A third of the way into a security-operations guide that Anthropic published in April 2026, wedged between a recommendation to patch CISA’s Known Exploited Vulnerabilities list and a suggestion to automate your deployment pipeline is a small recommendation: “Use EPSS to prioritize the rest.” For anyone who has worked on a vulnerability backlog in the […]

O'Reilly Radar — AI/ML