Most AI workflows are linear. You build a prompt, run it, get output, done. The next time you need the same thing, you start from scratch. Maybe you save the prompt somewhere. Maybe you don’t. A flywheel is different. Every cycle … Read more
You don’t let a developer review their own pull request. The same principle applies to AI agents. When a single agent writes code and then checks its own work, it carries forward every assumption, shortcut, and blind spot from the … Read more
You know the pattern. You start a new Claude Code project, create a CLAUDE.md, and start dumping everything in there. Project context, coding preferences, API notes, team member names, database schemas. Two weeks later it’s 400 lines … Read more
The best way to make your AI agent smarter isn’t a better prompt. It’s putting the agent and your notes in the same folder. When your AI agent can read your knowledge base directly, you stop copy-pasting context into every … Read more
One AI agent can handle a task. A team of AI agents can run your operations while you sleep. But getting from one to the other is where most people stall, usually because they try to launch six agents on day one and everything falls apart … Read more
You write detailed instructions for your AI agent. You’re specific about the role, the format, the quality expectations. And then the agent does something completely different in the next session. The problem usually isn’t the … Read more
Most people use AI like a search engine with better answers. They type a question, get a response, and start over. Every task is isolated. Nothing builds on anything else. That’s fine for casual use. But if you’re spending hours … Read more
