#llm

Three weeks ago a model on my laptop designed a Japanese temple and never got to see it. On Sunday I gave it a third try. The temple appeared on my iPad on the first attempt — and by evening the model had looked at a photo of it and pointed at its own mistakes.

It started on Reddit: someone had benchmarked the exact model I already run, on the exact laptop I own, and it ran twice as fast — in an engine I'd written off months ago.

My weekly AI budget hit 99% with one part of the saga still unwritten. The replacement cost nothing, refused to say who built it — and by evening it had its own fan page.

What did I do this Sunday? I went public with my first homegrown model — and added a demo Space where anyone can try it out for free, thanks to Hugging Face.

The initial plan was simple: run a test over all 60,000 colors and see how good the new model really is. The plan didn't hold for long. By the end of the day two models were competing — and even the original dataset was on trial.

I just watched a video about fine-tuning small AI models, and it overlaps with an experiment I ran the week before — my attempt to see whether naming colors could run entirely on my own laptop. After watching the video, I had one question: how many parameters would it take to do the job?

I asked a 13-gigabyte model on my laptop to build me a Japanese temple. It designed something beautiful, and then spent all evening failing to show it to me. It still has never seen it.

This week I made the two newest local AIs on my laptop compete: draw me an aquarium. One finished in four minutes. The other one needed four tries and a pep talk — but still won.

Three years ago, I demoed a new customer's product catalog in our app. My scripts had pulled the category tree, the products, the prices. Only the colors couldn't be scripted: the data held just names.

I typed a three-letter command to run an AI code reviewer. It ran a screenshot reader instead. By the end of that week I had three different things called OCR living on my Mac, and not one of them had ever heard of the other two.

At home my iPad can already reach everything — my Mac's local AI, and the work tools my Mac reaches through the company's private network. RustDesk even beams the Mac's screen to my phone. But a screen you squint at through a café window isn't where you do real work. I wanted my iPad itself to reach what my Mac reaches — from anywhere.

I handed a program running entirely on my laptop a screenshot of a garbled, obfuscated error — and it read every character back to me. Then I asked it to sketch the screen in ASCII art, and it did that too.