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Computer Science & Algorithms
Mental models in the Computer Science & Algorithms domain — frameworks for sharper thinking, better decisions, and a deeper understanding of how the world works.
The practice of hiding complexity behind a simpler interface, enabling reasoning at higher levels without understanding every underlying detail.
Explore-exploit TradeoffThe fundamental tension between gathering new information (exploring) and leveraging what you already know (exploiting) to maximize outcomes.
Metcalfe's LawThe value of a network grows proportionally to the square of the number of its users, explaining why connected platforms become exponentially more valuable.
Moore's LawThe observation that transistor counts on integrated circuits double roughly every two years, driving exponential improvement in computing power.
Mythical Man MonthBrooks's Law: adding people to a late software project makes it later due to communication overhead and ramp-up costs.
Technical DebtThe accumulated cost of expedient decisions in software and systems that must eventually be repaid with interest.
Black BoxClarke's Third LawConstraint RelaxationDesign PatternDivide and ConquerExponential BackoffFilter BubbleInformation CascadeLook-Then-Leap RuleMechanism DesignMoravec's ParadoxParallel ProcessingRecursionAverage RuleBelady's AlgorithmBinary SearchGodwin's LawInterrupt CoalescingLagrangian RelaxationLaplace's LawLeast Recently UsedMultiplicative RuleOptimization AlgorithmsPacket SwitchingScheduling & PrioritizationSorting AlgorithmsStatistical LearningThe Copernican PrincipleThe Gittens IndexThreshold RuleUpper Confidence BoundZawinski's Law