Claude, npm, and the shift from AI product to developer infrastructure — what it means for AI platform engineering
AI-powered cybersecurity: defending data infrastructure and ML systems at scale
Why AI is becoming infrastructure, not a product — and what that means for data and ML engineers
Context windows vs memory: the next bottleneck in LLMs and what it means for AI engineering
Black box optimization: how nature-inspired algorithms power modern ML search and tuning
Building production data pipelines in Python with Airflow, Great Expectations, and ETL best practices
From developer to engineering lead — leadership lessons from building data and ML platforms