TL;DR: In March I argued the CLI vs MCP debate was the wrong debate, and that the CLIâs advantages were a temporary artifact of training data, not a law of physics. One of those advantages was multi-aâŠ
2026
CLI vs MCP, Part Two: The First Gap Just Closed
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 smâŠ
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 âŠ
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 EU-resident in Frankfurt) on a document-reconâŠ
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 ChatGPâŠ
Why Your Cheapest Model Should Write the Harness
TL;DR: A May 2026 paper separates two capabilities that self-improving agents usually conflate: writing harness updates and benefiting from them. Writing is flat across model tiers: a 9B open model prâŠ
Architecting Skills: How Code Makes AI Agents More Reliable Over Time
TL;DR: An agent skill starts life as a markdown file full of instructions. It works, sometimes. Then you watch it fail in ways that are hard to predict, and you notice a pattern: the steps that break âŠ
AI Content Pipeline Deep Dive (2/5): Research
TL;DR: AI agents are confidently wrong about 1 in 10 factual claims. The research phase of a content pipeline isnât âask the agent whatâs trueâ â itâs a system of constraints that physically prevent tâŠ
I Built the Agent That Pays â Here's What I Learned
TL;DR: A couple of weeks ago I wrote about HTTP 402 and why AI agents might finally activate the internetâs oldest unused status code. The post sparked a real discussion, so I built it: a research ageâŠ
AI Content Pipeline Deep Dive (1/5): Ingestion
TL;DR: The ingestion phase is not about reading more. It is about building a system that reads for you, files what matters, and surfaces connections between ideas you captured weeks apart â at near-zeâŠ