I turn high-entropy, chaotic data into low-entropy insight — and ship it as systems people can use.
DiceTamer is the working name for a data science practice focused on predictive modeling, computer vision, and applied machine learning. The approach is deliberate: understand the stochastic nature of a problem first, then build the smallest reliable system that resolves it.
The selection that follows spans end-to-end ML pipelines, in-browser inference, conversational agents, and procedural systems — each built to reach a working, measurable result.
An end-to-end pipeline that collects second-hand listings from multiple marketplaces, normalizes noisy specifications, and trains a regression stack tuned for outlier resistance — exposed through a Streamlit interface for instant valuation checks.
A privacy-focused upscaler that runs inference entirely client-side, so image files never leave the browser. It streams tiles through ONNX Runtime Web to keep memory predictable while preserving print-grade detail.
A conversational assistant that orchestrates pharmacy workflows through retrieval-augmented prompts and guarded tool calls. It answers stock questions, drafts orders, and escalates edge cases to human staff.
A deep-learning system that detects players and ball trajectories frame by frame, reconstructs match phases, and outputs tactical heatmaps for coaching review — built to stay reliable under occlusion-heavy conditions.
A browser-based parliament simulator where 25+ historical philosophers — Aristotle, Machiavelli, Marx, Turing, Curie, Nietzsche and more — debate and vote on the decrees you draft, each persona driven by an LLM (local or cloud) grounded in their documented worldview. What began as a menu-driven simulator grew into a full 16-bit JRPG experience: a walkable overworld, branching NPC dialogue, battle-styled debate mechanics, and persistent local saves.
A self-hosted AI platform that gives every teacher back their evenings. From a single curriculum it writes homework tuned to each student's level — the hours of hand-differentiating worksheets, done in minutes. The work stays on paper where classrooms already live: sheets are printed, answered by hand, scanned back in, and graded against the teacher's own rubrics — with the teacher steering the AI at every step, never replaced by it.