Capture the Shining Moment

A developer I've read for years shipped something this week: a modern, AI-native RSS reader. It's genuinely nice — a couple hundred curated sources, eighteen thousand articles, a clever in-app assistant that reads the article you're looking at and answers questions about it. Keyboard shortcuts, the works. A real product, built over months on top of Claude Code and Codex, with a backend, an account system, a sync layer, the lot. I'd built the same thing months ago. Except mine isn't an app. Mine is a 640-line Python script that writes a Markdown file, plus a prompt that turns my AI assistant into the reader. No backend. No database. No account. No UI. And sitting here comparing the two, I don't think I cut corners. I think I found the floor — the actual minimum the problem requires once you stop assuming the answer has to be an application. That gap is worth pulling apart, because it's not really about RSS. It's about a whole category of software that...

  Two months ago I wrote about giving my AI a library — a persistent, file-based long-term memory so Claude could remember 161 conversations instead of waking up with amnesia every morning. That post was about building the thing. This one is about a problem nobody warned me about: A memory you don't tend rots faster than no memory at all. The Disease Has a Name: Entropy Here's what they don't tell you when you set up persistent AI memory. The first week feels magical. The AI remembers your projects, your preferences, the bug you fixed on Tuesday. You feel like Tony Stark. Then the rot sets in. You rename a project but the memory still points to the old path. You kill a side-project but its file lingers, and three weeks later the AI confidently references a tool you deleted. You migrate a folder from Desktop/ to Projects/ and now half a dozen breadcrumbs lie about where things live. Each individual lie is small. Collectively, they poison the well. I ran an audit this we...

The Problem Everyone Ignores Every time you open an AI assistant, it doesn't know you. It doesn't matter what you discussed yesterday, what decisions you made, what bugs you solved together. New session, blank slate. You have a brilliant colleague who gets total amnesia every morning. I've been using Claude Code for serious development work for about two and a half months now. 161 sessions. Over 6,000 message turns. Nearly 20 projects — from system architecture to WeChat mini-programs to writing a textbook. The equivalent API cost would be $5,800+. All of that context — the decisions, the reasoning, the dead ends, the breakthroughs — locked in isolated  .jsonl  files that Claude itself can never see. Then I saw Karpathy's tweet, and something clicked. Karpathy's Insight In April 2026, Andrej Karpathy shared a workflow he'd been using heavily:  LLM as knowledge base editor . Raw materials go in, the LLM "compiles" them into a wiki of interlinked  .md  f...

So here's what happened. I was coding with Claude Code and noticed these little status words that pop up while it's thinking: Crafting, Thinking, Roosting, Metamorphosing, Percolating... Pretty cool. I wanted to know what each one meant — like, do they represent different processing stages? So I asked ChatGPT. ChatGPT Came In Hot Instant response. Super detailed. Dripping with authority. It told me: These status words map to a  fixed state machine  with four distinct phases: Planning / Crafting → Thinking / Reasoning → Roosting / Finalizing → Metamorphosing / Formatting It's essentially a visualization of the Agent Loop. It even drew a flowchart: [Input] ↓ Planning / Crafting ↓ Thinking / Reasoning / Analyzing ↓ Tool Use (Executing / Fetching) ↓ Roosting / Finalizing ↓ Metamorphosing / Formatting ↓ [Output] And added: "The word pool contains approximately 10-20 tokens" "Weakly correlated with reasoning depth" "An abstraction built ...

The Human Conductor This is Part 3 of a 3-part series. ← Part 1: Stop Chatting, Start Conducting | ← Part 2: The Shared BrainProtocol The Story So Far In Part 1 , we split AI into three specialized roles — Architect, Tech Lead, and Engineer — because the AI that writes the code should never review its own code. In Part 2 , we solved AI’s amnesia problem with the Shared Brain Protocol — a structured state document that gives any AI instant full-project awareness. Now for the final piece: you. Because here’s the thing nobody tells you about AI-augmented development: the human’s job doesn’t get easier. It gets different . And arguably more important. You Are Not the Operator. You Are the Commander. In traditional AI usage, the human is the operator — hand-crafting every prompt, manually inspecting every output, doing the cognitive heavy lifting with AI as a fancy autocomplete. In the Trinity method, the human is the commander — making strategic decisions, routing tasks, and ...