Privacy

A carefully curated set of practical, highly reproducible machine learning examples spanning PyTorch model training, Hugging Face dataset tooling, NumPy fundamentals, and scikit-learn experiments; featuring MPS-aware hardware benchmarks and rigorous experiment hygiene for local-first development.

A highly effective, local-first email auditing and automated cleanup tool designed to definitively identify noisy senders and deliver actionable, strictly privacy-preserving recommendations.

Privacy-first design rules for autonomous AI agents; establishing local-first execution defaults, strict data minimization and redaction, explicit user consent flows, and transparent, auditable action logs.

A secure platform design for advanced federated learning pipelines; training models directly across edge devices without sharing raw telemetry, utilizing secure local aggregation and robust privacy safeguards.

A comprehensive local RAG stack (ChromaDB + Ollama) designed for strictly private, reproducible retrieval and LLM inference; heavily focusing on hybrid retrieval strategies and index versioning.

A high-performance search and retrieval engine architecture designed for extensive document and media collections; strictly ensuring low-latency ranking and horizontally scalable inverted indexing.