decision-making
10 essays on this topic.
- The TODO Intake Gate
Most TODO systems fail from too many items, not too few. A four-test intake filter for what deserves your attention.
- When to Think and When to Count
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.
- The Book That Tells You Not to Read It
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.
- The Heuristic Library
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.
- Your AI Is an Echo Chamber (And That's Sometimes Fine)
AI agrees with you by design. That's great for creative flow and dangerous at the decision point. Know when to switch modes.
- Taste Works for Small Bets
The 'ship and calibrate' loop works beautifully for reversible decisions. For the big ones, you're mostly guessing and then making the guess true.
- Shifting Priors Is Not Finding Truth
An experiment with AI deliberation revealed something uncomfortable: accumulating confident opinions feels like convergence on truth, but isn't.
- The Deliberation Format Is the Product
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.
- The Failure Mode of AI Advice Isn't Hallucination
The failure mode of AI advice isn't hallucination. It's that it agrees with you. Here's the architecture that fixes it.
- When to Build vs. When to Wait: The Recurrence Rule for AI Tooling
Most AI tooling debates are actually recurrence debates. The question isn't whether to build — it's how many times you'll need it.