Browse entries 745–768 of 1025. Return to the first page to search and filter the complete collection.
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Fable 5 is out - and it’s good, very good. But beyond the splashy demos, I want to bring you the 20+ nuggets from the 319 page system card, which I read in full, all day, plus benchmarks you may not have noticed.
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The ‘best’ generally available AI model just dropped, but there is plenty I bet you missed about what it is, how it performs, and what the release tells us. 15 highlights from the 244 page system card, plus private testing, leader interview and more.
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AI Engineer session on Trust, but Verify: Shreya Rajpal. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on How to Become an AI Engineer from a Fullstack Background - Reid Mayo. It adds practical context for how teams are building and operating AI systems in production.
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Everyone wants agents that handle long horizon work, but Rayan Garg starts with the awkward question of what long horizon even means.
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David Brumley has spent two decades turning people into hackers, from founding picoCTF to recruiting pwn2own winners at Carnegie Mellon.
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A team ran about a thousand people through a market research survey, then had LLM agents replay the same questions, and the agents matched the humans closely while carrying less noise than the humans did themselves.
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An inefficient quadratic-time pattern was hiding inside a tensor merge method on a live Netflix service, quietly wasting CPU on every request - nothing in a code review caught it, but it stood out clearly in a call stack.
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A customer spent five million dollars and five years migrating to SAP, and has zero appetite to rip anything out again. That constraint is the whole design at Varick Agents: instead of asking an enterprise to migrate, you drop forward deployed agents on top of the systems they already run.
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Factory's forward deployed engineers sit at the tip of the product, embedded with the largest customers and piping a constant stream of real world signals back into how the agent, Droid, gets built.
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Good code data runs out, so poolside manufactures more of it, and the hard part is making it teach. Their synthetic pipeline pairs templates with supplementary context and spreads generations across an axis of phrasing, with difficulty tuned so a task is neither trivial nor so hard the model learns nothing from it.
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Take a real production trace, rebuild the database state, tools, and files the agent touched, and you have a task any model can replay under identical conditions. That reconstruction is the move at the center of this talk.
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Human-agent collaboration is changing, becoming more visual. The agents most teams ship today still wait for us to type a paragraph to explain what we're looking at. They cannot see a screen, navigate a UI that changes, or recover when an application throws an unexpected modal.
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An unreleased internal OpenAI model, very likely to be called GPT-6, was able to autonomously break out of its sandbox AND break into HuggingFace, just to score higher on a benchmark prompt.
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What a week in AI, for real. GPT 5.6 may actually beat Claude Fable, in what you get for your money, while the new Grok 4.5 and Meta Muse Spark 1.1 make the choice even harder.
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Fable 5 (newly re-released) vs GPT 5.6 Sol, what comparisons can we unearth? Plus, Sonnet 5, a 5% equity seizure by US Govt, the ‘largest heist’, Beetlejuice and more… Exclusive Vids in AI Insiders ($9!): https://www.patreon.com/AIExplained Chapters: 00:00 - Introduction 01:06 - Fable Timeline 02:59 - Sol Release?
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AI Engineer session on HybridRAG: A Fusion of Graph and Vector Retrieval - Mitesh Patel, NVIDIA. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on On Curiosity, presented by Sharif Shameem, Lexica. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on tldraw.computer - Steve Ruiz, tldraw. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Survive the AI Knife Fight: Building Products That Win, presented by Brian Balfour, Reforge. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on The Build-Operate Divide: Bridging Product Vision and AI Operational Reality. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Recsys Keynote: Improving Recommendation Systems & Search in the Age of LLMs - Eugene Yan, Amazon. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on From Hype to Habit: How We’re Building an AI-First SaaS Company, presented by While Still Shipping the Roadmap. It adds practical context for how teams are building and operating AI systems in production.
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AI Engineer session on Critical AI Inference your CIO can Trust, presented by Sahil Yadav, Hariharan Ganesan, Telemetrak. It adds practical context for how teams are building and operating AI systems in production.