The Fed says this is a cube of $1 million. They're off by half a million. - Calvin Liang

Trust me, I counted.

P-Hacking in Startups | Briefer

When agile experimentation at startups becomes a p-hacking trap

The In-Memory C++ Leap in Blockchain Analysis - Caudena

Real-Time, Court-Admissible Crypto Intelligence at 1/400th the Cost of Inferior Legacy Systems The explosion of blockchain data isn't just a challenge; it's a crisis for conventional analytics. Financial institutions, investigators, and law enforcement agencies are hamstrung by tools that are too slow, expensive, and built on legacy database technologies incapable of keeping pace. Critical insights

The Missing 11th of the Month - David R Hagen

Personal website of David R Hagen, scientific software engineer

I Counted All of the Yurts in Mongolia Using Machine Learning | Monroe Clinton

Analyzing ipv4 trades with gnuplot.

No Silver Bullets: Why Understanding Software Cycle Time is Messy, Not Magic

Understanding factors that influence software development velocity is crucial for engineering teams and organizations, yet empirical evidence at scale remains limited. A more robust understanding of the dynamics of cycle time may help practitioners avoid pitfalls in relying on velocity measures while evaluating software work. We analyze cycle time, a widely-used metric measuring time from ticket creation to completion, using a dataset of over 55,000 observations across 216 organizations. Through Bayesian hierarchical modeling that appropriately separates individual and organizational variation, we examine how coding time, task scoping, and collaboration patterns affect cycle time while characterizing its substantial variability across contexts. We find precise but modest associations between cycle time and factors including coding days per week, number of merged pull requests, and degree of collaboration. However, these effects are set against considerable unexplained variation both between and within individuals. Our findings suggest that while common workplace factors do influence cycle time in expected directions, any single observation provides limited signal about typical performance. This work demonstrates methods for analyzing complex operational metrics at scale while highlighting potential pitfalls in using such measurements to drive decision-making. We conclude that improving software delivery velocity likely requires systems-level thinking rather than individual-focused interventions.