Timeline

I published Start the Chain: My Three-Agent Setup Is Now One Command, a follow-up to the main agent post. The main agent, secondary and worker setup is now the agent-chain skill in tmux-agents, started with one command, and the post covers what changed with a week of real use and how projects talk to each other. It also covers what happens after a restart: tmux-main-sequence brings back a chain for every project, and /resume brings back the conversations that matter.

I published How To Move Fast and NOT Break Things with AI, about the point where using an agent's name as its id stopped fitting tmux-agents. It covers the move to real ids in progress for v1.9.0, and the tests, decision records and release rules that grew out of real use. The lesson is to prototype fast, then add structure where the problems show up and leave guard rails for the next agent.

I published Your Sub-Agents Shouldn't Disappear: Meet tmux-agents, the introduction to tmux-agents that my last two posts kept pointing back to. It covers why I wanted visible sub-agent sessions, how I use them on my game and this blog, and how to try the tool. I also wrote down a few of the design choices and the limits I'm still working through.

I published Messy Chat with Your Agents? You Just Need One More. Really., about splitting the Claude agent I talk to from the one coordinating work on my Stone Age remake. With tmux-agents, the new main agent has one peer: the coordinator, which handles the main Codex agent and its sub-agents. The post shows the setup before and after, why my conversation got quieter, and the extra hand-off that comes with it.

I published I Gave My Coding Agents a Shared Worktree Pool, about the worktrees Claude and Codex left behind while I was building my Stone Age remake. I run those agents through tmux-agents, which I shipped the day before. I replaced fresh checkouts with reusable slots and shared asset folders, then made a general Python version available as a gist. The post covers the lease rules, the disk cleanup that prompted them, and what I have and haven't tested yet.

I published Two Small Wins That Turned Out to Be the Same Win, about moving my resume pipeline onto a Maestri canvas, teaching it to enforce its own two-page limit, and experimenting with local dictation. They looked like separate improvements until I realised each one removed the same mechanical tax between having an idea and acting on it.

I keep staring at the new dotfiles project card image, generated by GPT, and getting a little mind-blown. Not only is every bit of text actually real text rather than the usual AI gibberish, the content itself coheres: the Ghostty window on the left shows plausible git aliases, the tmux pane in the middle has a believable folder listing, a git log, and a btop-style stats block, and the Emacs frame on the right has elisp in init.el and YAML in config.yml that kinda parse as real config.

And on top of that, Ghostty, tmux, and Emacs are exactly the tools I actually use, even if I haven't reached for those particular git aliases in a long, long time.

Still huh. Genuinely surprised by how far this has come.

I published My AI-Assisted Terminal Setup: Subspace Emacs and a Tmux Layout Shortcut, the fuller write-up that ties together the tmux 70/20/10 layout and the move off Spacemacs into Subspace Emacs. It covers how Claude and Codex split the work between research and implementation, the tmux-as-TDD-harness approach that unblocked the layout binding, and the tmux/tmux#1839 discovery that finally let swap-pane preserve zoom state. This supersedes the two narrower notes from earlier today.

Published Where Do Codex's Cached Tokens Come From?, a note explaining why Codex can report millions of cached tokens after a run even when the actual prompt context is much smaller. The useful bit is that Codex's append-only agent loop keeps earlier messages as an exact stable prefix, so cache hits accumulate across repeated model calls in the same session.

Published AgentOS: The Agent Environment That Gets Smarter As You Build, a post about the project environment I am building around AI agents so a fresh session does not have to rediscover the same context every time.

It uses ProjectSpire as the working example: instructions as project memory, plans for intent, Captain Logs for collaboration taste, devlogs for technical history, and skills or workflows for repeated mechanical steps.

The useful idea is that the repo should accumulate context as it is used, so the human still supplies the judgment, but the surrounding system gets better at carrying that judgment forward.

After a few weeks working on ProjectSpire with Codex, I’m leaning toward it as my default for software engineering projects. The main frustration has been hitting the Pro account limit; otherwise the quality has been good, the interaction feels responsive, and the output gives me instant feedback while it works. Claude Code still feels more like a black box to me: it can disappear into minutes of research and thinking on its own, and the effective limit feels lower. That tradeoff matters, because for this kind of project I want a tight engineering loop more than a long silent reasoning pass.

I published The Brain That Grew — Then Shrunk: What I Just Discovered, a long-form note that starts from a Cleo Abram YouTube Short and then follows the research on human brain evolution, the unexpected Holocene shrinkage, and the idea that culture may have taken over part of the cognitive load. It is the kind of rabbit hole post I like writing here: one short video, a lot of reading, and a sharper takeaway than I expected.

I’ve been working on ProjectSpire’s iOS app (codename: Neow’s Cafe) in NicholasClooney/ProjectSpire as a 1:1 Slay the Spire 2 card library, and the useful part is not just the filtering UI and refactor cleanup, but the way I’m trying to work with AI.

I get better results when I lay down the foundations myself first, especially around quality, guard rails, and how the data is modeled, and then let AI work inside that framework instead of asking it to define the framework for me. It also helps a lot when I have AI propose higher-level API or contract changes before it starts making edits.

Here's a snapshot of the visual changes. There is also quite a bit of non-visual work too, like reorganizing the source files into clearer areas such as App, Components, Models, Views, Logic, and Dependencies, splitting the banner text into its own component, moving the app toward injected dependencies instead of hardcoded wiring, and a few other things.

...and the changes can be found here on GitHub

I shipped d60e0e1 in SwiftyBites with a new SwiftUIComponentsLibrary area for pickers, menus, search scopes and tokens, and width-showcase layouts.

The useful part is not just the snippets themselves, but that AI agents researched the best practices, produced the example code, organized the project, and left me with a runnable playground where I can compare equal-width stacks, GeometryReader, PreferenceKey, and some surprisingly similar results against my actual needs.

I published The Confident Lie: What AI Got Wrong About @ViewBuilder, a SwiftUI debugging note that came out of the ProjectSpire card view work. It captures a small but useful lesson: body gets @ViewBuilder from the View protocol, but a custom computed some View property needs the annotation explicitly if I want an if without an else. The compiler was right, the AI was overconfident, and now the mistake is written down somewhere I can find again.

Published The Accelerated Speed of Creation, a reflection on how much faster the path from thought to shipped artifact has become with coding agents handling the translation layer around writing, blog workflow, and routine Git operations. I also kept the earlier Encoding My Blog Workflow for Coding Agents draft as a note rather than a post, because it was useful and concrete but still did not meet my standard for what the real piece needed to be.

I published Smart AI Token Consumption as a note on matching model choice to task complexity instead of defaulting to the biggest model. It captures the split I keep using in practice: light models for mechanical work, stronger reasoning only when the problem actually needs it.

I published The Limits of AI and Where Humans Shine — a devlog about a tiny timeline sorting bug that turned into a useful comparison between AI confidence, runtime verification, and human judgement. It follows the fix from plausible-but-wrong timestamp changes to a quoted-date guard that keeps the timeline honest.

Published What's Worth Keeping: On Humanness in the Age of AI — a post on what I've been thinking about since attending a thought experiment session on AI and skill erosion. It's about the parts of humanness I think are genuinely worth protecting — junior skill-building, critical thinking, forming a view before outsourcing it — and what I actually try to do in my own daily use of AI tools.