Insights and lessons learned from building production applications
Building a privacy-first system monitor that uses neural networks and the Apple Neural Engine to detect anomalies. Learn how CoreMetric combines PyTorch, Metal Performance Shaders, CoreML, and SwiftUI to create an intelligent monitoring system with <1% CPU overhead.
A personal roadmap through machine learning: Python foundations, NumPy, Pandas, scikit-learn, PyTorch with Apple Silicon MPS, deep learning architectures, MLOps, and production deployment. Lessons learned and the path forward.
Deep technical dive into GitSimulator (3rd Place, GitKon 2025 Game Jam): 135+ tests, automated CI/CD, interactive TUI, plugin architecture, and advanced conflict prediction. Learn how a production-ready CLI makes dangerous Git commands safe to explore.
Complete guide to achieving 100/100 Lighthouse score in Next.js. Real portfolio case study: WebP optimization, Three.js deferring, code splitting, accessibility, and security headers.
Open-source study companion: ingest course materials to generate summaries, flashcards, Q&A, and schedules — built with React, Next.js, and Supabase.
How I shipped a fast, secure, and low-cost static site on S3 + CloudFront: OAC, cache policies (max-age, s-maxage, stale-while-revalidate, stale-if-error), versioning, and concrete performance gains..
Real-world PostgreSQL optimization techniques from handling 50,000+ daily sensor readings. Indexing strategies, query planning, and monitoring tips that delivered measurable results.