No instructions from me mid-flight. It just ran.

Here's how I set it up:

Most people use LLMs like a stateless calculator -- you ask, it answers, session ends, it forgets you exist. That's not leverage, that's just autocomplete.

What I wanted was an AI agent with: - Persistent memory (knows my projects, preferences, goals across sessions) - A job description (knows what to work on without being told) - Scheduled autonomous tasks (cron jobs that run while I sleep) - Tool access (browser, file system, email, APIs) - A communication channel (Telegram alerts when something happens)

I used OpenClaw (self-hosted, runs on a Mac mini) with Claude as the underlying model. Total cost: ~$20/month in API calls.

The key insight: the difference between a useful AI and a useless one isn't the model -- it's the scaffolding. Memory files, a job description, tool access, and a persistent process change everything.

I documented the exact setup -- the file structure, prompts, job description template, and week-by-week implementation plan -- in a guide for people who want to replicate it.

Happy to answer questions about the technical setup, the OpenClaw config, or the memory/identity system.