AI-Assisted Engineering Series

Move fast with AI, and add proper engineering when real usage shows you need it. For tmux-agents, that meant letting each agent's name double as its id, until it couldn't, and then adding real ids and guard rails so the agents and I don't repeat the mistake.

Built-in sub-agents hand you a summary and the work behind it is gone. tmux-agents gives every delegated task its own Claude or Codex session in a tmux pane instead, so you can watch it, step in, and pick its conversation up later.

With up to ten agents working on one project, the Claude agent I talked to was also juggling replies, merges and servers, and I kept waiting for my turn. The fix was one more pane: a main agent whose only job is to talk to me, while the old one became a coordinator.

Claude, Codex and their sub-agents each got their own git worktree, and my disk quietly filled up with copies of an 8GB game project. The fix was a small script that makes agents borrow worktrees from a shared pool instead of creating new ones.

Moving a resume pipeline onto Maestri, teaching it to enforce its own two-page limit, and trying local dictation apps all removed the same thing: the mechanical tax between having an idea and acting on it.

A new style of working with AI has been clicking for me lately: keeping several projects open at once, letting the main agent spawn off sub-agents per project, then hopping between them as work lands.

The glue is AGENTS.md and CLAUDE.md in each repo, which keeps every spawned agent oriented to that project's conventions while I focus on the next handoff. The loop in each project stays the same: pick a feature, write tests, document progress and findings as it goes, commit atomically.

Two projects in one: a lightweight Emacs config I actually own (Subspace Emacs), and a tmux 70/20/10 vertical layout I can summon and reshape with a single keystroke. Built collaboratively with Claude and Codex.

The bottleneck isn't the model. It's whether your repo can explain itself to a fresh agent. Here's how I'm building AgentOS, a project environment that gets smarter the more it's used.

This past week I shipped across code and writing at a pace that would have felt unrealistic before AI. The surprising part was that it did not feel frantic; it felt like less friction between thought and artifact. It truly feels like working at warp speed, 😜.

Look at this. This is what I have shipped so far in a single week and growing (full list here):

I hit a bug that looked too small to be interesting: entries on my timeline page were not sorted correctly within the same day.

The page had a date, a time, and a custom Eleventy collection sort. That sounds like the whole problem space. Sort by date plus time, reverse the collection for newest first, done. Instead, April 12 was rendering in a strange order: 00:01, 10:11, 22:16, 15:49, 22:20.

I've been building a habit-logging iOS app called ProjectDawn. Not because the App Store needs another habit tracker, but because I wanted a personal project that was genuinely mine and open source, and a project that can answer this openly: what does it feel like to build a real, modular, native iOS app with AI as a primary collaborator?

This post is part personal log, part technical retrospective. It covers the tools I used, what surprised me, where the AI fell flat, and the biggest shifts in how I think about building things now.

I’ve had the Umami + Ansible post in my head for ages, but it touched three different repositories and a whole bunch of code snippets. Totally doable, but undeniably tedious — which is why it kept slipping down the backlog. You can read the finished article here: Private Analytics With Umami, Docker Compose, and Ansible.

The idea that finally nudged it forward was simple: why not let GPT (Codex) do the heavy lifting while I steer?

For years, I thought of coding as something tied to my desk — Mac in front of me, full keyboard, full IDE. But recently, I found myself dreaming: what if I could carry my entire creative coding studio in my pocket? Not just SSH access, but a true AI-assisted environment where I could code, commit, and preview my projects anywhere.

This blog is half technical walkthrough, half personal reflection. It’s the story of how I explored Cloudflare Tunnel, discovered Tailscale, refined my workflow with tmux and iTerm, and ultimately unlocked the freedom of having a fully fledged Mac in my pocket.

Building small websites with GPT-5 Codex turned out to be less about typing code and more about collaboration. From crude sketches to polished sites, the model took on the heavy lifting while I guided direction and design.

Along the way I discovered both the joy of fast iteration and the limits of relying on an AI partner. These projects became less about the sites themselves and more about exploring a new style of programming — conversational, creative, and sometimes flawed, but always eye-opening.

Last month, my girlfriend mentioned she needed a sleek portfolio site, and I realized I’d been meaning to start a personal tech blog.

I wanted something lightweight—easy to spin up and even easier to tweak. So I dove in: building a small 11ty + Tachyons site from scratch, pushing every iteration live in under a minute, and watching her face light up with each update even though she’s halfway across the country.