Selected work

Website as working memory

I do not want my website to be a separate job. It should become a durable public memory that participates in my AI work: when an idea or investigation becomes useful, I can ask the assistant to turn it into a concise note, keep private details out, and create a review before anything goes public. The website is not the work itself. It records the proof and lessons from real work.

Built a small family AI workstation on PB62. A dedicated KVM guest named zsn runs OpenCode and OpenChamber, holds my wife's website checkout and development toolchain, and keeps her sessions, Git identity, and future provider credentials separate from mine. Each client pairs with its own revocable encrypted relay token; raw agent ports stay private. Git remains the recovery boundary, and deployment credentials stay outside the VM until they are explicitly needed.

I spent a night helping optimize my wife’s website. By morning, she was furious—not because it was broken, but because I had slightly changed her UI and logic. Predictable result: when working on someone else’s familiar system, preserving what they already recognize matters more than making it “better” without asking.

My daughter's grandma called while I was outside the house because she could not find the Roku remote and wanted me to stop a show. The Roku app refused to enable its remote over mobile data, even with Tailscale connected. ChatGPT suggested connecting my phone to any available Wi-Fi, then manually entering the Roku's home IP address. The Wi-Fi satisfied the app's check, manual entry bypassed discovery, and Tailscale carried the connection home. The official Roku remote then worked while I was outside.

I’m starting to optimize my Codex workflow. Instead of making every turn follow a heavy default sequence of pull, change, test, and review—even when a small change takes only a moment—I’m moving toward a hierarchy of iteration, review, and ship. The depth of the workflow should match the size and risk of the change, so a small task does not automatically take two or three minutes. I’m also starting to use Git worktrees so independent work can run in parallel.

I’m going to finish investigating the three TP-Link security cameras with the new ChatGPT Desktop as a hands-on engineering partner. One camera is already torn down and may need a main-chip replacement, so I’ll start with the other two: both still show working IR and PTZ movement at power-on. First I need a longer Ethernet cable. Then I’ll tear them down, connect interfaces, measure signals, form hypotheses, and test likely faults while ChatGPT helps identify serial pins, inspect U-Boot and Linux boot logs, and answer questions quickly. I’ll record the work on YouTube Live, then use the generated transcripts to summarize the investigation and update the website.

Coding, architecture, and other high-level work can be important, but they can also abstract the problem until I lose sight of what actually needs to be solved. I want to begin with the concrete problem, make the smallest useful thing, and only add abstraction when the work asks for it.

A part-time job might be a good fit for me. In theory, full-time employment is not always efficient for either the employer or the employee: people rarely work productively through every hour of a fixed schedule, while focused work often comes in intense bursts that can stretch beyond eight hours when the work is truly moving. Part-time work may leave more room to follow that natural rhythm.

Today I worked on my website while taking care of my daughter. Instead of separating work and life, AI let me fit small moments of engineering into ordinary parenting. It’s only the first day, but it feels like a different way to work.