Seeking theoretical perfection or maximum efficiency often makes things more fragile and prone to disruption in practice.

Overoptimization narrows tolerances, removes redundancies and streamlines away safety nets that provide resilience.

It is better to favor empirically antifragile systems and activities that gain from uncertainty over theoretically optimal but fragile ones.

Antifragility means having systems, processes and activities that actually improve when faced with volatility, shocks and stressors. It goes beyond resilience or robustness to exploit uncertainty.

We should engineer and participate in antifragile domains that dynamically learn and strengthen from disorder rather than merely withstand it.

True optimal solutions are often unknowable in complex domains with many variables. Don't chase fictional best-case scenarios drawn from models and assumptions.

Settle for what empirically seems to work well enough in practice, avoiding overoptimizations.

Seek nuance, avoid false dichotomies. Binary, black-and-white thinking and simplistic models ignore the organic interconnectedness and complexity of the real world.

Appreciate gray areas and overlaps rather than forcing false choices.

Randomness, uncertainty and unpredictability are unavoidable facts of life. Black swan events happen. We cannot accurately forecast or model everything, especially in nonlinear systems.

Accept the limits of prediction and prepare to adapt.

Some errors and guided trial-and-error are necessary features for antifragility. Systems that are overprotected from all shocks and stresses tend to weaken.

Allow imperfection to enable dynamic gains from stressors, learning and evolution.

Pursue activities and gather knowledge for their own sake rather than narrow utility. Curiosity, not optimization, is crucial for fulfillment, optionality and avoiding fragility.

Learn broadly guided by interest, not just specialized investment.