Random topics, anything that interests me

The Greedy Algorithm Is Not the Problem

Insights on CMS

The Epistemological Void: Why Predictive Models Fail at Strategic Insight

The Deceiver’s Paradox: Why Deception is an Unreliable Strategic Foundation

PERT: Logical Paradox and Epistemological Futility in Estimation

On Scale, Throughput, and the Normalization of Waste Modern software systems routinely overestimate how much compute they need. This overestimation is not benign: it drives premature infrastructure scaling, masks inefficiencies, and eventually produces cost structures that feel inevitable rather than pathological. Benchmarks like HammerDB, when interpreted correctly, expose just how wide the gap is between actual hardware capability and typical business demand—and how often large cloud instances are late-stage symptoms rather than legitimate requirements. ...

Overfitting Reconsidered: An Epistemic Framework for Strategy Evaluation Introduction In quantitative finance, overfitting is usually defined in statistical terms. A model is said to overfit when it captures noise in historical data rather than persistent structure. This definition assumes that the past contains reliable information about the future and that the main risk lies in extracting too much of it. This essay adopts a stricter and more rigorous definition. Overfitting is treated as an epistemic failure rather than a statistical one. A model is overfit whenever it relies on information that is not known ex-ante or not known with certainty. This shift in definition changes which models are admissible and reverses many common intuitions about realism and sophistication. ...