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      <video:description>Enterprise field notes on the MCP 2026-07-28 revision: stateless HTTP, retry correctness, identity, state handles and the controls practitioners still own.</video:description>
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      <video:description>Tool discovery and tool permission are different problems. Field notes on deferred loading, runtime capability gating, trusted-state controls and model fallbacks.</video:description>
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      <video:title>Open Weights Are Good Enough. The Hard Part Is Everything After.</video:title>
      <video:description>Open models now match commercial ones on most enterprise work at a fraction of the cost. The differentiating skill is no longer picking a model. It is drawing the open-versus-commercial line well, and running the open side with discipline.</video:description>
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      <video:title>GPT-5.6: What Actually Changed on Your Bill</video:title>
      <video:description>Read the diff, not the demo. Field notes on the GPT-5.5 to 5.6 migration: the flagship price held, but a few things that used to be free now cost money and several defaults moved underfoot.</video:description>
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      <video:title>The Agentic Engineer [Part 3/3]: Into the Brownfield</video:title>
      <video:description>What changes when you point an AI coding agent at a large codebase you didn&apos;t write: mapping subagents, reverse-engineered configs, discovered-convention skills, and characterization tests.</video:description>
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      <video:description>The full pipeline for running an AI coding agent on a real feature: spec, data contracts, controlled fan-out, arbitrated review, and bounded autonomy.</video:description>
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      <video:title>The Agentic Engineer [Part 1/3]: Claude Code Is Not a Chatbot. It&apos;s an Engineer You Build Structure Around.</video:title>
      <video:description>Why the structure is the job and the prompting is secondary: how I run an AI coding agent like a production engineering practice.</video:description>
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      <video:description>Field notes on evaluating LLM systems in production: why deterministic checks beat an LLM judge, and the one ordering decision that decides whether your evaluation layer works.</video:description>
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      <video:description>Inside an AI agent, the line between data and instructions disappears. That single blur is the root of nearly every security risk in agentic AI.</video:description>
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      <video:description>Inside one week in April 2026, the two largest frontier labs independently shipped the same agent architecture: a clean split between harness and sandbox. The convergence matters more than either release.</video:description>
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      <video:title>Your Prompts Are Technical Debt: A Migration Framework for Production LLM Systems</video:title>
      <video:description>A prompt in production is a contract with a specific model version. When the model changes, the contract is voided. Here is a four-layer framework to migrate without the firefighting.</video:description>
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      <video:title>Your AI Coding Agent Is Winging It. Superpowers Makes It Stop.</video:title>
      <video:description>The most popular Claude Code plugin doesn&apos;t make your agent faster. It makes your agent think before it types: brainstorm, plan, test, review, then ship.</video:description>
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      <video:title>Making the Most of Claude Code: Best Practices &amp; Hidden Features</video:title>
      <video:description>A practical guide to the underused features and workflows that get the most out of Claude Code: context management, CLAUDE.md, hooks, skills, subagents, MCP, and more.</video:description>
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      <video:title>The Rise of Harness Engineering: Why the Code Around the Model Matters More Than the Model Itself</video:title>
      <video:description>One team moved a coding agent from the bottom 30 to the top 5 on Terminal-Bench 2.0. Same model, same weights, zero retraining. They only changed the harness.</video:description>
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