ai-assisted coding

I'm winding down a very broad and deep tour of building lots of different things with AI, for fun. It involved everything from creating a distributed AI job/harness scheduling system to execute repeatable job chains with…

2026 collateral damage: I dropped fish as my preferred shell this year because generating zsh scripts with LLMs is just so good that for me, the benefits of using fish no longer justify the weight of the additional…

Fable and gpt-5.6-sol are like rocket fuel for prototyping fun things to put on old domain names I have sitting around otherwise collecting dust. Part 2:

Fable and gpt-5.6-sol are like rocket fuel for prototyping fun things to put on old domain names I have sitting around otherwise collecting dust. Part 1:

Finally got all the knobs turned right to record a demo of how this experimental tool I made for revising Markdown plans works in the loop of a Claude Code session.

LLMs enable a new brainstorm, review, revise, and select mechanism for prototyping and iterating toward a goal. It requires tracking intention, rules vs. guidance, optionality in forward paths, and optimizing review UIs…

It feels like something akin to the current momentum around people vibe-coding prototypes that they then try to hand to developers and ask them to support, enhance, and maintain in production forever.

Using AI models to surface potential improvements for your particular code and product along the dimensions that are important to you is a fun way to find things you might otherwise miss.

A fun thing you can leverage AI for is learning about and tuning your own harness and habits with long-running experiments using data, or derivable data, that only you have.