- Ignition: first start: 0/5
- Chassis: car + fuel: 0/4
- Lift and diagnostics: 0/3
- Fuel system: model gateway: 0/5
- Navigator: task route: 0/5
- Glovebox: context and memory: 0/4
- Rain sensor and wipers: pipeline: 0/5
- Hitch and roof rack: MCP, Hooks, Skills: 0/6
- Convoy: subagents and orchestration: 0/6
- Road atlas: AI notebook: 0/5
- Dyno: engine tuning: 0/5 — now
- Onboard computer: second brain: 0/5
You're building: Dyno: engine tuning → Engine cutaway
Throughout the course you worked with the model from the outside: you phrased the task, held the context, plugged in tools, assembled agents. The model stayed a black box that sometimes answers brilliantly and sometimes confidently makes things up.
Recall the last time that happened. The agent named a function that doesn't exist, invented a link, or "remembered" a fact that never was.
What do you think: is this a glitch that will soon be fixed — or is it how the machine itself is built?
Getting it wrong is fine: it is part of learning. Guessing first helps the answer stick later, and the answer itself comes after the explanation.
Copy your personal prompt and paste it into your agent's learn mode — it picks up your context and takes you forward.
Honestly: with AI it feels like you can do more than you can without it. After practice, check yourself without AI. (Bastani et al., PNAS 2025)
Explains the material using the course method.