predict
Beyond Market Intelligence keeps predict in one place: 3 stories so far. The section currently leads with “Measuring clutch performance with analytics built for college sports”, “Can a 414K-parameter transformer learn to steer a flock?”, and “How a single bug fix sharpens BayesianRidge uncertainty estimates”. Measuring clutch performance has long felt more like folklore than fact. A 414,000-parameter transformer, trained on 12 simulated birds, learned to steer a flock with R² above 0.99, and then flew 50 birds it had never seen. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every predict story on Beyond Market Intelligence, newest first.
Measuring clutch performance with analytics built for college sports
Measuring clutch performance has long felt more like folklore than fact. NTangible is changing that with an analytics platform built specifically for college sports and club organizations, designed to quantify an athlete's ability to deliver under pressure. This isn't about gut feelings, it's about data that finally captures what happens when the stakes are highest. For a glimpse into how other innovators are rethinking legacy systems, see our coverage of how Cambridge Heavy Industries is quietly building a desktop fusion future.
Can a 414K-parameter transformer learn to steer a flock?
A 414,000-parameter transformer, trained on 12 simulated birds, learned to steer a flock with R² above 0.99, and then flew 50 birds it had never seen. The three classic boid rules were readable from its hidden states using a linear probe. That is a remarkably clean result for a model written from scratch and trained on a laptop CPU. It suggests something worth exploring: how much intelligence is actually hiding in simple attention mechanisms, and what else they might learn to simulate.
How a single bug fix sharpens BayesianRidge uncertainty estimates
A subtle bug in BayesianRidge's uncertainty calculation slipped into sklearn 1.8, and this notebook walks through the exact formulas behind `predict` on both versions. It's a sharp exercise in tracing what changed, and it's the kind of forensic digging that keeps models honest. If you've ever wondered how small shifts in regression internals ripple outward, this is worth your time. For a broader look at how data quality issues surface elsewhere, our piece on catching AI slop before it skews your model pairs nicely here.