Software licenses assume a human model of creation: a person reads code, internalizes patterns, and might reproduce them. Clean-room reverse engineering exists to prove contamination didn't happen. AI dissolves this framework entirely. When Claude writes a Z80 emulator from the Zilog manual without ever seeing another emulator's source code, the concept of "copying" loses its meaning. Heidegger saw this coming in 1954.
Jason Fried argues that AI won't create a bespoke software revolution because most people don't want to build software. He's right. What AI will do is let people produce bad software at a rate that outpaces the fixed supply of human judgment needed to catch the failures. The result won't be a bespoke software revolution but a Jevons expansion in software that nobody qualified has reviewed.
LTX-Video 2.3 is a 22 billion parameter video generation model. It was never meant to run on decade-old GPUs. I got it running across four Tesla P40s using manual pipeline parallelism, float32 monkey-patching, and a lot of iterative debugging with Claude Code. Here's how, and what the results look like.
Les Orchard identifies a split among developers that AI tools have revealed: the craft people versus the results people. He's observing something real, but the line is drawn in the wrong place. The split isn't between two kinds of developers. It's between two kinds of tasks. The same person crosses that line constantly, and where the craft actually lives might not be where anyone expects.
I own $5,500 worth of GPU hardware for local inference and I use a Claude Max subscription for nearly everything that matters. Both are correct decisions. This piece explains why, with real power measurements, real electricity costs, and an honest accounting of what local inference is and isn't good for.
Merriam-Webster named "slop" its 2025 Word of the Year. This essay examines what distinguishes useful AI-generated content from hollow output, drawing on Clement Greenberg's kitsch theory, the MINT Lab's taxonomy, and the author's daily experience shipping real work with AI tools. The test is simpler than you'd expect: does the text commit to a claim that could be wrong?
