Archive

The question I can't fully answer for my son

When AI conversations feel insightful because the language model is good at producing insight-shaped text

The things that matter most in production are the things that get benchmarked least

When the paper becomes more interesting than the answer you set out to find

The missing layer in enterprise AI isn't smarter models — it's structured memory that humans can actually review.

Prompt instructions are suggestions. Hooks are constraints. One survives a model swap.

The 'ship and calibrate' loop works beautifully for reversible decisions. For the big ones, you're mostly guessing and then making the guess true.

AI commoditises execution. What remains is taste — the 'that's the one' reflex. And the only way to sharpen it is to ship and see what reality says back.

The bottleneck in a world of AI tools isn't crafting the output — it's knowing which output is worth crafting.

Most task systems are binary, but a third class exists — tasks triggered by external notifications — and managing them like a backlog item is the wrong move entirely.

AI agents that require you to go looking for their results aren't agents — they're automation with better UX. The loop closes when results arrive, not when you remember to check.

Adding more AI tools doesn't make you faster if you're still the junction between every agent step.

Having great AI delegation tools and not using them isn't a tool problem — it's a pattern recognition problem, and that distinction changes everything.

The execution layer of an AI system is only half the infrastructure — the reporting layer is what determines whether anyone acts on the results.

The gap between AI assistants and AI agents isn't about reasoning capability — it's about whether the thing can survive your laptop closing.

The gap between 'agentic AI' hype and what actually ships in production turns out to be a workflow — and that's a feature, not a failure.

An experiment with AI deliberation revealed something uncomfortable: accumulating confident opinions feels like convergence on truth, but isn't.

I ran an experiment to find where multi-model deliberation adds value. The answer surprised me: it's the structured format, not the model diversity.

On the difference between eliminating friction and eliminating anxiety — and how to know when you've crossed the line.

Per-task model routing optimises cost per token. But at personal assistant scale, friction is the real cost.

In AI agent architecture, calling something a 'cross-cutting concern' without naming an owner and a gate is just a polite way of saying nobody owns it.

The planning/execution split is more useful than any benchmark comparison.

When red-teaming a document with multiple AI models, the second review — run on the edited version — consistently finds more than the first. Here's why, and what it means for how many rounds to run.

Evaluating LLM systems requires judgment, not statistics. That shifts who's qualified to do it — and where the gap is in most organisations.

The retrieval pipeline was built for systems that couldn't reason about their own information needs. Agents can.

An open-source AI agent framework became the fastest-growing project in GitHub history — mostly in China. The pattern is the same as last year. So is the security panic.

Most enterprises are optimising the wrong variable. The gap between 5% and 40% agent adoption won't be closed by better models.

I spent an afternoon researching AI tools for personal life management. The conclusion was that I should stop looking.

One of the architects of modern deep learning just raised $1B on the thesis that token prediction can't reach real reasoning. Here's what he's proposing instead — and why it matters even if he's wrong.

An AI agent ran unsupervised for two days and found twenty improvements to another model's training. Not an AGI claim. A rate claim.