Thoughts on AI, Engineering & Research
Technical deep-dives on AI/ML research, system design, and lessons from shipping production applications.
How bio-inspired computing architectures can achieve energy-efficient AI — from theory to implementation with Python and TensorFlow.
A deep dive into NLP preprocessing, feature engineering, and probabilistic classification — achieving high-accuracy spam detection from scratch.
How we designed a future-aware memory management framework for autonomous LLM agents — addressing context degradation in long-running AI systems.
Lessons learned from building and deploying 8 production web applications — architecture decisions, CI/CD workflows, and performance optimization.
Designing an NFC-based car ignition system with encrypted identity authentication — from Arduino prototyping to secure embedded C++ firmware.
Building a fake news detection pipeline using NLP — text vectorization, model selection, and the challenges of training on real-world data.
How I designed and built CloudSecure — a real-time cloud security monitoring platform with threat detection, compliance tracking, and automated incident response.
How to build an ML-based network intrusion detection system (NIDS) using Python — from feature engineering on network traffic data to real-time anomaly classification.