Research & active projects

Competing in ROGII's wellbore geology prediction competition — a machine-learning challenge from a Houston-based oil & gas software company. The task is to predict the geological position (True Vertical Thickness) along the lateral section of horizontal wellbores from sparse training data.

Why it matters (and why we're in it)

Subsurface prediction is the same class of problem that shows up in many of our own product areas: making confident judgments from sparse, irregular, high-stakes data. The skills that win a Kaggle competition like this — careful cross-validation, ensemble construction, calibration discipline — are the same skills that make our shipped products reliable.

Approach (high-level)

• Geometric reformulation. The problem reduces to predicting a 2D spatial surface, then projecting back to the original space — a reframing that simplifies what initially looks like a sequence-modeling task.
• Ensemble of disjoint method families. Spatial statistics, neural surface models, and sequence-attention models combined with careful per-row clipping to prevent catastrophic outliers from dragging the average.
• Variance reduction. Seed bagging across stochastic components and adaptive weighting based on local prediction confidence.
• Honest cross-validation. The visible local test set is a known false positive — all model selection is done via 5-fold GroupKFold across the full training population, calibrated against the public leaderboard.

What we're learning

The biggest insights from this competition — careful calibration of cross-validation vs. leaderboard metrics, variance reduction for ensembled neural models, and the discipline of resisting fragile-but-impressive single-method "wins" — apply directly to our shipped products. Reliability under distribution shift is a feature you don't notice until it breaks, and it doesn't break by accident.

Our first shipping product. An AI-driven Mac disk cleaner that uses contextual judgment instead of generic rules — knowing that the same iOS simulator runtime is dead weight for a writer and load-bearing for a developer.

The same posture that drives our Kaggle approach — careful judgment, honest calibration, resistance to over-confident single-method wins — drives MacJanitor's product behavior. Every proposed deletion is shown to you before it runs. Conservative by default. The conservative pass is the trust-building one.

Magpie is structured to ship a portfolio of focused utilities — each one applying contextual AI judgment to a chore that currently requires deep manual expertise. Specifics on what's next will be announced when there's something to actually show.

If you'd like to be notified when our next product or research result ships, email support@magpiestudios.app with subject "Magpie updates list."