Introductory deep-dives into AI concepts
35 posts
A line in a stranger's bio sent me into a conversation about Hegel and Kant. The plain-English answer was accurate and I still couldn't follow it. The fix is the same one that teaches languages.
A model remembers nothing between sessions and cannot verify a word you say. The published rulebooks say more than the behavior does, and most workarounds are boring.
A large language model only ever produces text. Treating that text as a novel ethical category, rather than as speech, is where the argument goes wrong.
Four things to write down before you hand a job to an AI agent: what comes in, the rules, the action, and how you will check the work.
A token is a LEGO brick with a number on it, and almost none of the math that moves it is new.
A thought experiment exploring what happens when AI learns from narratives that prioritize meaning over facts - and the crucial difference between metaphor and false belief.
Edison metered electrons by the ampere-hour before anyone metered tokens, and the War of Currents was fought over the same problem the AI economy has now: getting a dead commodity to a device built to do something with it.
A taxonomist's view of Jev: the closed-category, confidence-scored work I built by hand for AT&T in the 90s, now manufactured to order by an LLM.
The man who coined 'artificial intelligence' invented the most elegant language ever designed for it. It was the wrong tool — because it was the wrong category.
Exploring whether AI systems like LLMs represent technological inventions or discoveries of fundamental patterns that exist independent of human design.