ai

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

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.

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.

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.

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.

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

LangGraph is the SAP of agent orchestration — powerful at scale, overkill for most. Here's the line.

Most AI workflows are parallel-then-aggregate, not agent graphs. Knowing the difference saves you from framework theatre.

Why 'you are an expert' is the most popular and least useful prompt engineering technique

Not tools that search your notes — personalities that live in them, form opinions, and disagree with each other.

I benchmarked 10 memory backends and multiple embedding models on actual agent memory retrieval. The results challenge common assumptions about what matters.

Agent memory isn't knowledge management. It's performance infrastructure — and the gap between a stateless agent and one that accumulates context is measurable.

Enterprise AI keeps promising to capture institutional knowledge. The most valuable kind resists capture by design.

I built an AI landscape intelligence pipeline for zero marginal cost. Here's what it does and what it can't.

The scarce AI skill isn't building — it's knowing if what you built actually works.

AI can reproduce smart arguments on demand. I'm not sure that's different from thinking. But the uncertainty itself is worth sitting with.

The people who examine the system they're inside tend to make better decisions within it.

I use AI every day. I genuinely can't tell if it understands anything. That question is harder than it looks.

I work in AI in financial services. The honest list of where gen AI is real is shorter than the industry wants you to think.

My career looks like a plan in retrospect. It wasn't. It was a series of pushes, wrong calls, and adjustments.

The most successful piece of propaganda in modern economics is the idea that what you earn is what you deserve.

People confuse the LLM with the helpful AI assistant. They're not the same thing. The LLM is a prediction engine. The assistant is a role it's playing. The distinction changes how you use it.

An LLM can't feel accountability pressure. But structured role-play — simulated rejection, persona assignment, adversarial review — produces measurably better output. The mechanism is opaque; the effect is real.

AI's first 'I'm done' is almost never its best work. Simulated accountability pressure — just asking 'are you sure?' — surfaces blind spots that self-review misses.