One thread. Multiple AIs. Deliberation, not polling. Most people use AI like this: 🤦 Ask one model → get one answer Ask multiple models → compare results That’s not thinking. That’s polling. Not side by side. Not isolated. But in sequence — where each one reads what the previous one said before responding. Manual Council is the simplest form of that idea. No backend. No orchestration. No
The fork visible in 1.1.1 (simple query protocol on one side, extended on the other) is the subject of this section, one level deeper. 1.1.1 set the skeleton: simple is one message, extended is four. The job here is to show how that split translates into four distinct outcomes: plan reuse, parameter safety, pipelining, and error handling. Putting the message sequences side by side makes the differ
My first Cloudflare Worker deployed in 47 minutes. Three of those were spent staring at this exact error in a red GitHub Actions log: Authentication error [code: 10000]. I had the API token. I had the account ID. I had copy-pasted the workflow from the official docs. It still failed. The fix was one checkbox I never selected. That checkbox is the entire reason I'm writing this post, because every
Find a beginner-friendly issue. Fork the repo. Set up the dev environment. Read through the codebase. Start working. Then check the issue again and see a comment from 2 days ago: "Hey I'm working on this, should have a PR up soon." Two hours wasted. Every single time. The weird part? Almost every existing tool for finding open source issues - goodfirstissue.dev, up-for-grabs.net, codetriage - rely
Updated May 2026: Now covers virtual desktop (Spaces) restoration and iCloud sync across multiple Macs, both shipped in ShiftPlus 1.3. TL;DR A complete macOS workspace includes apps, window layouts, browser profiles, virtual desktops, and terminal state. Native macOS saves almost none of it. Most third-party tools cover one slice: Stay and Spencer handle window layouts, Shift handles browser profi
In July 2025, a developer's Claude Code instance hit a recursion loop and burned through 1.67 billion tokens in 5 hours, generating an estimated $16,000 to $50,000 in API charges before anyone noticed. The agent did not crash. It did not throw an error. It just kept calling tools, getting confused, calling more tools, and silently accumulating cost. Old software crashes. LLM agents spend. This is
Modern applications are fragmented by default. A typical stack today might use: SQL for transactions MongoDB for flexible documents Redis for realtime state Pinecone or Weaviate for vectors Firebase for sync Separate tools for analytics, permissions, and operations That works — until the complexity starts to hurt. You end up with duplicated data, inconsistent permissions, fragile pipelines, multip