Eric Eaglstun · AI
A working notebook on AI & machine learning.
Almost nobody fully understands how modern AI works - not even the people building it. That makes it easy to fear, easy to hype, and hard to think clearly about.
I started this to fix that for myself - then figured it might help anyone else trying to make sense of AI. The heart of it is a glossary - short, plain-language definitions of the terms I kept tripping over while running models on my own machine, explained the way you’d explain them to a friend. Around it sit practice notes, blog posts, and deep dives into the machinery underneath. No hand-waving: because you can’t have a sane opinion about something you don’t understand.
Latest
- deep dive Part 6 - Profile, Don't Guess
Where the Metal backend actually gets fast, where it doesn't, and three times the intuition was dead wrong: a 16-bit op that was secretly 27× slower because it had never been on the GPU, the 'obvious' optimization that made things worse when measured, and a benchmark number that swung 2.7× between identical runs.
- blog The Weights Are Free. The Forklift Isn't.
Over two days in July, two labs released the largest open models the world had ever seen, and everyone cheered the word 'open.' Both are impossible to run at home. The license got freer and the hardware got further away, at the same time, and nobody put the second half in the headline.
- practice 172 Witnesses, Each One Half-Blind
Every page here carries 172 bits that are the meaning of the page, and the search box runs on nothing else. But nobody ever wrote the 172 questions those bits answer. So I interrogated the corpus to find out what they ask, guessed wrong about the answer, and got a better one.
Blog →
Notes and posts on AI/ML - local inference, models, and whatever I’m tinkering with.
Practice →
How I actually use AI day-to-day, and the techniques that have earned a permanent place in my workflow.
Deep Dives →
Longer, hands-on walkthroughs - how the pieces fit together, with the details left in.
Glossary
All 41 terms →Short, plain-language definitions of the AI/ML terms I keep bumping into - formats, GPU backends, architectures, and the concepts underneath.