Frequently asked questions about thinking, epistemology, and cognitive tools. 1675 answers
Each improvement gets harder and smaller — know when further optimization is not worth the cost.
Each improvement gets harder and smaller — know when further optimization is not worth the cost.
Each improvement gets harder and smaller — know when further optimization is not worth the cost.
Each improvement gets harder and smaller — know when further optimization is not worth the cost.
Each improvement gets harder and smaller — know when further optimization is not worth the cost.
Select a system, habit, or process you have been actively trying to improve. Draw a simple chart: X-axis is total effort invested (hours, iterations, dollars), Y-axis is total improvement gained. Plot your best estimates for each round of optimization. Identify the inflection point — the moment.
Refusing to accept that the curve has flattened. The optimizer who cannot stop becomes the perfectionist — someone who spends four hours adjusting a slide deck that was already effective, who rewrites a paragraph eleven times when draft three was sufficient, who chases the last 2% of test coverage.
Each improvement gets harder and smaller — know when further optimization is not worth the cost.
The optimal amount of optimization is not infinite — there is a point where you should stop and move on.
Identify one thing in your life you are currently optimizing — a workflow, a habit, a project, a skill, a system. Write down the specific threshold at which it would be 'good enough' for its actual purpose. Then honestly assess: are you above or below that threshold? If you are above it, write a.
The most common failure is not refusing to stop — it is never defining when to stop in the first place. Without an explicit stopping criterion, optimization becomes open-ended by default. You keep refining because there is always something to refine, and each micro-improvement feels productive in.
The optimal amount of optimization is not infinite — there is a point where you should stop and move on.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Choose one agent, automation, or recurring process in your life — a morning routine, a writing workflow, an AI prompt you use regularly, a decision-making checklist. Design an A/B test for it. Write down: (1) The current version (A) and what you suspect could be improved. (2) A specific, single.
Changing multiple things between version A and version B, then attributing the result to whichever change you expected to matter most. This is the confounding variable problem. You modified the prompt, switched to a different model, and changed the output format simultaneously. Version B performed.
Run two versions of an agent simultaneously and let the data tell you which performs better.
Change one thing at a time so you can attribute improvements to specific changes.
Change one thing at a time so you can attribute improvements to specific changes.
Change one thing at a time so you can attribute improvements to specific changes.