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Getting Jev to play Monster Hotel
Testing Jev's typed decisions by giving it the controls to a browser game I made with the kids.
Polished essays sit alongside lab notes and standalone thoughts: experiments, implementation logs, useful rough edges, and ideas that do not need a project behind them.
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Testing Jev's typed decisions by giving it the controls to a browser game I made with the kids.
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I built a small comparison tool to test several models on the same real task, so I can make a more informed choice about which one to use next.
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I moved my voice agent off ElevenLabs' servers and into my own house: a speech-to-speech loop on a mini PC with no GPU. Deleting speech-to-text simplified it, and 25 versions of fixes taught me the client was never the problem.
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A read-only operations dashboard for my mini PC, built with OpenCode and GLM-5.3-Flash for under $0.70 of model cost. Python stdlib only, one page to see what needs attention.
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A practical build note on giving an ElevenLabs voice agent useful memory and tools through a guarded MCP server.
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A small Home Assistant lab note on comparing three Zigbee leak sensors from Sonoff, Moes, and Meian, then placing them under the sink and bath pump where I actually care about leaks.
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A practical build note on making private, age-appropriate Spider-Man stories for a Yoto card, with JSON scripts, ElevenLabs voices, renderer boundaries, and a lot of small judgement calls.
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I started building a parent activity-planning app, decided the product shape probably did not make sense, and salvaged the useful bit: 200 free at-home activities.
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The small clipboard fix that made text copy from OpenCode on minibot, through tmux and SSH, back to my Mac.
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How I moved coding agents onto a headless mini PC, with OpenCode, OpenChamber, tmux, SSH, and clipboard image paste from my Mac.
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A practical look at using an Obsidian vault as shared memory across different LLM tools, with folder rules, agent roles, frontmatter, credentials, and compounding notes.
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How I used Home Assistant history to turn Garmin battery drain from a vague annoyance into a measured debugging loop.
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How I use AI to turn noisy AI information streams into reading and listening material I will actually consume.