TL;DR: Goodhart’s Law says that when a measure becomes a target, it stops being a good measure. People optimize the number and quietly drop every goal you had in mind but never expressed. I once watch…
2026
Laws & Disorder #5 — Goodhart's Law: The Goal You Specify Is the Only One You Get
The AI That Only Watches
TL;DR: The whole industry is racing to give AI more permissions: more tools, more autonomy, more write access. I went the other way this month and noticed something uncomfortable: the assistant that o…
Self-Consistency Is Just Probability Amplification
TL;DR: “Self-consistency” — ask a model the same question several times at a non-zero temperature and take the answer they most agree on (a plurality vote) — gives large accuracy gains over greedy dec…
Laws & Disorder #4 — Gall's Law: Great Systems Are Built From Small Bricks, Not Big Plans
TL;DR: Gall’s Law says a complex system that works is invariably found to have evolved from a simple system that worked, and that a complex system designed from scratch never works. There are two read…
Understanding Is the New Bottleneck — and Your AI Agent Can Fix It
TL;DR: In AI coding the hard part is no longer writing the code, it is understanding the code the agent wrote well enough to keep steering. Geoffrey Litt’s framing is that the human job shifts from ve…
Generate-and-Verify: What P vs NP Teaches Us About AI Reasoning
TL;DR: The defining feature of the complexity class NP is polynomial-time verifiability: a candidate answer is cheap to check. The asymmetry that matters shows up in its hardest members, the NP-comple…
Laws & Disorder #3 — Tesler's Law: You Can't Delete Complexity, Only Choose Who Owns It
TL;DR: Tesler’s Law, the conservation of complexity, says a system has an inherent amount of complexity that can be reallocated between owners but never deleted. The useful move is to split that into …
Stop Guessing Which Coding Agent to Use — Benchmark It on Your Own Tasks
TL;DR: Generic coding-agent leaderboards don’t tell you which model or tool fits your work at a budget you’re willing to spend. So I built a small task set that mirrors my real work and ran an open-so…
The Quadratic Wall: The Algorithmic Race to Beat O(n²) Attention
TL;DR: Standard self-attention costs on the order of n² in the sequence length n, in both arithmetic and the attention-score memory it materialises. Almost every long-context architecture headline of …
Laws & Disorder #2 — Hyrum's Law: In the Age of Agents, Who Pays for a Broken API?
TL;DR: Hyrum’s Law says that with enough users, every observable behavior of your API becomes something someone depends on, whether you documented it or not. The implementation quietly becomes the int…