Thoughts

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 dependency in my default machine setup.

I just kicked off Fable doing a bakeoff, training LoRA weights with a few image diffusion (DiT) models.

This is an area I know very little about.

I fed it images, briefs, and judging criteria for guidance, and now it is renting GPUs on @runpod, downloading models, and configuring+executing training runs, reviewing results, and adjusting.

It’s so crazy it can even attempt this … it’s a little nerve-racking waiting to see how it turns out. 🙃

result:

I am dazzled… Trained up LoRA weights with open source model, remotely, renting Nvidia chips, and generated an automated re-ranker based on human reviews, and can now generate on-style tiny icons both on remote CUDA, and local Mac w/MLX.

Thousands of them🤯

TIL: If you can keep a lot of the experiment organized in context and recorded thoughtfully in logs on disk, a good [llm] model can do an amazing job of summarizing it and explaining it from multiple perspectives. One area I discovered was weak, though, was follow-on and productionizing for repeatability. In service of meeting a /goal, model tended to ‘work around’ things and sometimes not clean up well.

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:

Here’s a tier ranking web app for sharing ranked preferences.

It uses Cloudflare (Pages, D1, R2, Turnstile, worker analytics engine, worker cron), Auth.js w/Google OAuth and Resend integration for passwordless sign in, an open source image diffusion model with LoRA weights for icon generation, and Playwright for E2E browser tests.

Feel free to poke at it if interested. Try it here

LOL. You keep using that word. I do not think it means what you think it means.

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:

Here’s Klondike Solitaire implemented in Babylon.js with custom shaders, 9 shuffle variations, 3D camera pans, responsive layout for desktop/tablet/mobile, solvability vetting and some fun easter eggs like being able to toss the cards in the fire when you are stuck lol.

There’s still no substitute for human taste judging how fun something is and fine-tuning.

Thus, a pre-LLM proven game concept is a huge leg up, not only because the LLM has seen similar code before to smooth implementing/iterating, but because the design of the game and its ruleset is proven to at least be interesting to people.

Try it here

Added dynamic music which moves with game progress using https://strudel.cc, because, why not?

New unlock found while stacking up AI capabilities:

Have the LLM generate a custom rich (HTML w/screenshots, videos, keyboard shortcuts, etc) comparison/review UI for providing feedback and decisions, w/embedded code and UI that can submit follow-up jobs back to orchestrator. 🤯

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.

“One of the challenges we will face in the next 2 years is the economics of tokens. The marginal cost of productivity improvement has to match the marginal cost of the token. That’s a management discipline. […] The business has to benefit from it.” — SN

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 within a directed graph.

Whoever can make that process durable, distributed, multiplayer, and multi-model is going to accelerate beyond all reason.

A lot of the current established industry players are trying to build ‘software factories’ on top of existing primitives.

IMHO, if there is one thing I have learned from prototyping my own local distributed job runner for AI to implement various strategies of how to form the work, it is that we could really use some new primitives, like yesterday.

Ohhhhh…

For fun, I’ve been building a little distributed agent scheduling system to explore different AI-related dev practices and workflows … with an eye towards stacking up primitives and finding compounding effects …

I just added Option ‘A’ here, which works sooooo much better than anticipated, I am dazed. There’s no substitute for a pro [human] designer, but with some taste (reject most ideas and adopt/try a select few), this raises the floor so much it feels crazy.

Here’s a little peek inside the engine for that first run and the models used, that I am still digesting…

Would have cost me ~$12 in tokens (if not for subscriptions).