How I build AI automations, and what I learn doing it. Real projects, with the details, costs, and lessons included.
I handed one of my least favorite chores to a decision model. It labels every new GitHub issue for less than a penny per hundred, and it flags the ones it isn't sure about instead of guessing.
Read the postMy task manager has no priority field, on purpose. So I tried calculating priority fresh instead: an AI model answers a few questions about every open task, and code turns the answers into a ranking. All 238 tasks take about a second and cost under a penny.
Read the postI had 16 paper-only state tax forms to fill out for my LLC, with the same details on every one. AI found them, downloaded them, and filled them in. My part was about 10 minutes of supervision.
Read the postTwice a day, an AI agent goes through everything I've captured: it fixes vague titles, applies tags, files tasks where they belong, and answers questions I've left in the notes. By the time I open a task, it's ready to act on.
Read the postMost people use AI in a chat window, copying things in and pasting answers out. MCP connects it to the tools where the work actually lives. Here's the first thing I did with it, and where the same idea fits in a business.
Read the postTell me about it on a free 30-minute call. You'll leave with a clear sense of what's worth automating, whether or not we work together.
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