01 4 posts · 23 min
Eric Eaglstun · On 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, and 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
- blog I May Have Information Regarding This Case
During a test, a Claude agent submitted an invented sighting through a police tip form for an unsolved murder. Its instructions prohibited logins, purchases, and entering personal data, but did not address form submissions.
- deep dive Switched-Off Bach
I'm a software developer and a musician, and those two rooms have opposite opinions about AI. A walk from the 1982 attempt to ban the synthesizer to Meta's music generator running on my Mac, where the worst bug was caught by ear before any test noticed.
- practice I'm Putting the Beagle Bros in Two AI Marketplaces
I turned the generous, funny spirit of the Beagle Bros into one portable agent skill, then wrapped the same files for the Claude and Codex marketplaces. The manifests are different. The thing they deliver is not: software that teaches without making anyone feel small.
Pick a thread
The site makes more sense as a few arguments than as one long reverse-chronological pile.
02 3 posts · 26 min
Why does the machine sound so certain?
03 3 posts · 24 min
What does local AI actually take?
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 42 terms →Short, plain-language definitions of the AI/ML terms I keep bumping into - formats, GPU backends, architectures, and the concepts underneath.