How I use AI
I use a morning report to gather work from notes, messages and calendar events, and local models for small decisions inside command-line tools. The examples below use fictional inputs and simulated responses.
Setup guide: recipes and checks for adapting these workflows to your own tools.
A morning report that knows what to ask
From notes, messages and calendar events to a list of work I can actually act on. Walk through the evidence, the duplicates, and the decisions that stay with me.
Walk through a morning →A little judgment inside a CLI
A model picks a diagnostic. Code checks the answer and decides what can run. Try a useful answer, an invalid one, and no answer at all.
Try the decision demo →What happened between the goal and “done”
Why I made Agent Checkpoint public, and how recorded goals, sessions and verification results make agent work observable.
Follow a task across sessions →Giving an agent a useful health check
How a read-only CLI gathers homelab evidence and keeps missing data visible to an agent.
Explore the examples →From an idea to a shipped change
How I work with an agent from a rough problem through design, implementation and a verified release.
Explore the examples →AI on my own machine
How my local inference setup supports small CLI decisions, and what local execution does not guarantee.
Explore the examples →When the agent gets it wrong
A documented morning-report mistake: treating an old access assumption as fact, and the checks added afterward.
Explore the examples →