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408 posts on AI agents, the laws and asymmetries that shape how we build, and lunch-break physics. Search, filter by tag, or start with a series.

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2026

  1. Your Voice Clone Is Only as Good as the Reference Clip

    šŸŽ¬ Also available as a blog walkthrough video with narrated diagrams. TL;DR: A voice clone built by conditioning on a reference clip, Qwen3-TTS, in my case, inherits the clip’s cadence, pitch, and timb…

    VoiceCloning Ā· TTS Ā· AmazonBedrock Ā· SageMaker

  2. MCP Strategies on AWS, Part 2: Tool Design in Code

    TL;DR: The AWS Prescriptive Guidance paper on MCP gives the most useful, checkable rules in its tool-design section, so I wrote code to test them. A token-tax counter on a realistic 20-tool GitHub ser…

    MCP Ā· AgenticAI Ā· StrandsAgents Ā· AmazonBedrock

  3. From Columbo to Coworker

    šŸŽ¬ Also available as a blog walkthrough video with narrated diagrams. TL;DR: Running many interactive AI agents at once feels like Lieutenant Columbo lives in your terminal: every few minutes one turns…

    AgenticAI Ā· Productivity Ā· CognitiveLoad Ā· OperatingModel

  4. AI Content Pipeline Deep Dive (5/5): Publishing

    TL;DR: Publishing isn’t just ā€œclick deploy.ā€ It’s image generation guided by performance data, LinkedIn teasers written from analytics insights, one-command deployment that handles auth, build, deploy…

    AI Ā· Content Ā· AIAgents Ā· Productivity

  5. MCP Strategies on AWS, Part 1: Reading the Whole Guide

    šŸŽ¬ Also available as a blog walkthrough video with narrated diagrams. TL;DR: AWS published a Prescriptive Guidance paper, ā€œModel Context Protocol strategies on AWS,ā€ that organizes MCP into three pilla…

    MCP Ā· AgenticAI Ā· AmazonBedrock Ā· AgentCore

  6. CLI vs MCP, Part Two: The First Gap Just Closed

    TL;DR: In March I argued the CLI vs MCP debate was the wrong debate, and that the CLI’s advantages were a temporary artifact of training data, not a law of physics. One of those advantages was multi-a…

    AgenticAI Ā· MCP Ā· DeveloperTools Ā· Kiro

  7. AI Content Pipeline Deep Dive (4/5): Editing

    TL;DR: Every post goes through five automated editing passes before publishing: challenging questions that stress-test the argument, a FAQ that tests whether the post explains what it claims, an AI sm…

    AI Ā· Content Ā· AIAgents Ā· Productivity

  8. From a Generic Voice to My Own: Self-Hosting a TTS Model on Amazon SageMaker

    TL;DR: Last time, the demo video for my agentic-payments post was narrated by Amazon Polly: a clean, managed, recognizably synthetic voice. This time the same demo is narrated in my own voice, cloned …

    Publishing Ā· SageMaker Ā· TTS Ā· VoiceCloning

  9. Claude Fable 5 on Bedrock: A Hands-On Comparison, and the Data-Retention Switch You Set First

    TL;DR: Claude Fable 5 went GA on Amazon Bedrock yesterday (June 9, 2026), so within a day I ran it head-to-head against Opus 4.8 and Sonnet 4.6 (all three invoked from Frankfurt through EU Geo inferen…

    AmazonBedrock Ā· Anthropic Ā· ClaudeFable5 Ā· DataResidency

  10. AI Content Pipeline Deep Dive (3/5): Collaborative Writing

    TL;DR: The agent never writes the first draft. It studies your voice from previous posts, assists during drafting, and systematically reviews what you wrote. The result reads like you, not like ChatGP…

    AI Ā· Content Ā· AIAgents Ā· Productivity