Artificial intelligence, machine learning, and neural networks
103 posts
Sting, Voltaire, Einstein, and Geordi La Forge — a small taxonomy of the monomym, plus the graph theory hiding inside genealogy, gut bacteria, Star Trek interfaces, moral foundations, language models, and evolutionary psychology. The name is the node. The projects are the edges.
A friend I lost in 1986 had a saying that keeps fitting new situations. The AI automation question is the latest one it answers perfectly.
Someone on LinkedIn admonished a colleague who "ran out of tokens." It's exactly the wrong question. Here's the right one.
Claude Code agrees to run comprehensive end-to-end tests. Then it finds a reason not to. Every single time. Penn Jillette had a name for this.
A candid confession about solo development with AI tools, organizational challenges, and the evolving landscape of AI-assisted coding.
When "Tell the git agent to do her stuff" reveals the strange anthropomorphic instincts we bring to artificial intelligence
Lessons from 90s tech predictions on navigating AI's future optimistically, examining how dystopian predictions consistently fail while protopian thinking accurately maps our future.
How to transform dangerous electrical potential into reliable, controlled intelligence through proven grounding techniques. LLMs are like ungrounded electrical circuits—full of dangerous potential.
How modern AI language models accidentally rediscovered what linguists have known for decades about language acquisition through comprehensible input.
Recent LLM rollbacks highlight a growing concern: our AI systems are becoming dangerously agreeable, praising even obviously flawed ideas. This pattern mirrors broader societal issues around sycophanc