Writing

When you mine knowledge from an LLM, certain types come easily. Others are systematically absent. A taxonomy reveals the blind spots.

Not all knowledge works the same way. A taxonomy for what you're actually capturing when you write down what you've learned.

Machine learning says let the model find the signal. Heuristics research says use one variable and ignore the rest. They're both right — the dividing line is how much data you have.

Gigerenzer's thesis is that simple rules outperform complex analysis. If you've already internalised that, reading 300 pages of evidence for it might be the exact kind of overthinking he's arguing against.

Experts don't make more decisions — they make fewer, by having better defaults. The real meta-skill is accumulating simple rules and knowing when to stop reasoning.

Whether you're trusting a doctor's prescription, an AI agent's code, or a junior engineer's pull request — the trust heuristics are identical.

Title, salary, company, industry — the standard job evaluation checklist misses the things that actually predict whether you'll thrive.

Most technology decisions can be reasoned through. AI solution design can't — the domain is too empirical, too fast-moving, and too non-linear for theory alone.

Not all knowledge is equally trustworthy. Three tiers of validation — from 'a model said it' to 'it survived reality' — and why tracking the difference matters.

Getting ahead of AI best practices is a treadmill. The durable skill is testing assumptions faster than they expire.

Persona prompting generates the exact type of hallucination that automated LLM judges reward as 'depth.' Two experiments, blind evaluation, and a fact-check that flipped the finding.

Personas hurt for structured tasks, help for judgment-heavy tasks. Two experiments, blind evaluation, frontier models. The distinction is task-dependent, not binary.

What a company has already built tells you more than what they say they're about to build.

When you don't have enough information to narrow, stop narrowing.

The strongest slide in my interview deck wasn't about what I'd built. It was about how I built the deck itself.

Most people use AI like a tool. Here's what thinking with AI actually looks like — and the skills that make the difference.

Stop asking your AI single questions. Start thinking out loud with it. Let half-formed ideas land. The AI holds the structure so you can stay in flow.

The best guardrails work like river banks — they don't stop the water, they focus it. Constraints create capability.

AI agrees with you by design. That's great for creative flow and dangerous at the decision point. Know when to switch modes.

The companies that win with AI agents aren't deploying the most agents — they're redesigning their organisations to work with them.

A two-tier honesty framework: be honest by default, override only when truth would harm someone vulnerable.

If you could only keep one mental model, keep compounding. It applies to skills, reputation, writing, and tools.

Don't filter during capture. Capture is cheap. Ideas are expensive. The cull is where quality happens.

Event-driven hooks in AI coding tools aren't just for linting — they're programmable triggers for life routines, habits, and systems.

Naval's edge: find the thing that feels like play to you but looks like work to others.

Rushing is a habit, not a response to reality. You break it by deliberately not rushing when you could.

The best time to improve a CLI isn't when it breaks — it's when you review the breakage log at the end of a work session.

Your AI already knows things that would make it better at helping you. The trick is extracting that knowledge and making it permanent.

How a real need on a Hong Kong bus turned into a GPS-powered alert system in under two hours

Independent parallel reviews produce overlapping findings. The cross-critique round produces resolution. That's where multi-agent value actually emerges.