Mayphus

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.

I am trying to develop a workflow that lets me work mostly on the go—with mobile devices, ChatGPT, AI, and Codex—while still being present and taking care of my daughter. With AI agents, I only need to prompt a few times; most of the work is waiting and thinking. A traditional desktop workflow often keeps me sitting at the computer even when I am mostly searching the web, browsing X, or watching YouTube. The better optimization is to let the agents work while I step away: think clearly, enjoy life, and care for my baby, then return when a decision is needed.

ChatGPT desktop is starting to feel like the AI version of Emacs: one extensible environment with a terminal, browser, code, files, and conversations together. The important part is not having every tool in one window. It is that my operations and working state can become shared AI context, so I spend less time switching apps and explaining where the work stopped.

Short ideas should have one quiet public home. They do not each need a new page, project, or polished conclusion. A dated thread can show the thought while it is still moving.