Archive

Not productivity. Something stranger — the cognitive texture of days when the bottleneck shifts from execution to articulation.

The hardest phase of AI architecture isn't building the stack. It's the moment after the stack is built and eighteen teams start making independent decisions on top of it.

The comfort trap is about effort. This one is about epistemics — and it's harder to see.

The right test for any AI interaction isn't 'did it help me?' but 'am I more capable after it?'

AI coding agents didn't just make developers faster. They changed who gets to have a bespoke system.

The moment I stopped building scheduling into my tools, everything got simpler.

When building automation around a third-party app, the first question to answer is: what's the one thing this app does that nothing else can replicate? That feature becomes the tax you pay on everything else.

Running 10 real queries through 5 tools revealed that theoretical routing rules have systematic gaps — and the surprises were more useful than the confirmations.

AI agent results should be push, not pull. The feedback loop should close on mobile. Most tools miss all three — not from ignorance, but because dashboards photograph better.

Building tools to support your work can quietly become a substitute for the work itself.

Personal AI infrastructure should report results to you, not wait for you to go looking. A small architecture shift changes the whole dynamic.

When you catch yourself setting a reminder to check something later, that's usually a signal that a tool is failing to report what it should.

The difference between a rule that works and a rule that doesn't is usually not the content of the rule — it's where it lives.

Upgrading to a more capable model made my tool sixty times slower. The lesson isn't about models — it's about the difference between capability and fit.

Hong Kong's QDAP annuity is sold on a real tax benefit. But the HK$60K deduction cap is shared with MPF top-ups — and that changes everything.

Andrej Karpathy's autoresearch project is being read as a demo of what H100s can do overnight. It's actually a discipline for doing rigorous work on anything measurable.

Why the structure of an output matters more than the instructions that produce it.

Three hours of debugging revealed two non-obvious behaviours about GPT-5.4-Pro that aren't in the docs: a minimum token budget requirement and a wall-clock timeout gap in Rust async code.

A coding assistant quietly made three productivity apps redundant. Not by replacing them — by making context collapse the boundaries between them.

The resistance to AI agents in banking isn't mostly cultural. It's infrastructure — and the gap is more interesting than the politics.

On why the blank page stopped being the hard part.

WeChat is supposed to be a walled garden. Exa didn't get the memo.

The most sophisticated solution to a problem is usually a sign you haven't found the right abstraction yet.

Configuration that belongs to the data shouldn't live in the tool. .gitignore figured this out thirty years ago.

The real ROI of AI coding isn't doing the same work faster. It's doing work that wasn't worth doing before.

The simplest agent architecture might already be the right one: give the agent a file explaining how to do something, and let it read when needed.

Peter Steinberger stopped reviewing AI-generated code entirely. That works for indie software. In regulated environments, it can't. Here's how to think about where you sit.

When behaviour emerges from both code and model responses, reading source files isn't enough. You debug by examining execution traces.

The difference between writing down a rule and making the system enforce it — illustrated by a 15-line hook.