Execution is the typing: turning a decided approach into a working diff. It decays fast. Context fills, compaction runs, and a session that wrote clean code an hour ago starts producing sludge. The fix is cheap: throw it away and open a fresh one. Judgment is the why: the approaches
"When change is easy, the need for it cannot be foreseen; when the need for change is apparent, change has become expensive, difficult, and time-consuming." David Collingridge wrote that in 1980, in a book on controlling technology that leaned on cases like nuclear power and heavy industry.
The code passed every test. It worked in staging. It worked in the demo. It shipped clean. Six hours later it took down checkout, on an input nobody had thought to try, on a path nobody had thought to look at, because the person who wrote it had been moving
Your vibe-code velocity slowing down is not the warning sign everyone says it is. The dominant take treats the plateau as debt coming due: you deferred the architecture, the system can no longer absorb change safely, and every small edit now risks breaking something you no longer understand. Sometimes
Most knowledge tools remember what you read. Almost none of them keep a track record of whether you were right. You can retrieve any fact you have ever filed. You cannot retrieve whether you were right about it. That second gap is the expensive one, and most knowledge systems are
I am a sucker for good words, and one from Tamil literature keeps surfacing the deeper I go into AI: மதக் களிறு, the bull elephant in musth, the periodic testosterone surge that turns a beast of burden into a force of nature. It breaks through fortress gates, shrugs off
Last week Claude Code shipped dynamic workflows. The first thing I pointed one at was the design decision I was least sure of, and verifying it took a prompt instead of a harness. Dynamic workflows are easy to underrate. Claude Code can now write its own multi-agent harness on
Anthropic's CEO used part of his own developer conference to explain why writing the code is no longer the hard part. His framing was Amdahl's Law: when you speed code generation up several times over, the work you did not speed up becomes the constraint. Review,
When AI was a private tool, PMs wrote for two audiences: business stakeholders and developers. Deck and PRD. That world is closing. PMs now write for three — and the third audience fails in a way the first two never did, in a way that's invisible at the moment
Our position for months has been that the harness around a model, its scaffold, prompts, and configuration, does more work than its weights on whether the product holds up in production. The AI market is still mispricing this. Procurement and investment decisions anchor on model capability: benchmarks, parameter counts, training
A frontier AI model read nine labeled debugging traces — three from itself, three from each of two peer models, all on the same bug. In a single API call it wrote 101 words describing what the peers did that it didn't. Those 101 words, pasted verbatim into its
AI agents can already think overnight. They monitor research, evaluate evidence, run scheduled analyses. The compute is async. The delivery isn't. When an agent discovers something at 3am, it has no native way to reach you in the medium where you'd actually act on it. The