AI Engineering
From Demo to Deployment: Engineering Robust AI Features in Production
Building production-ready AI features requires more than a working prototype—here's how to validate inputs, manage latency, and ensure reliability at scale.
Blog
Thoughts on AI engineering, backend, frontend, and building modern software.
Cloud & Infrastructure
Private Networking for AI Services: Avoiding Latency Traps
Optimize AI service performance with private networking strategies to reduce latency and ensure secure, scalable deployments.
Software Architecture
Mastering Software Architecture: Real-World Tradeoffs and Design Decisions for AI Systems
Learn real-world software architecture lessons from a senior engineer building AI systems. Explore tradeoffs, failure modes, and design decisions that shape production-ready tech stacks.
Software Architecture
Practical Lessons in Software Architecture: Real-World Tradeoffs and Design Decisions
Discover actionable insights and real-world tradeoffs in software architecture for AI engineers and builders building production systems.
MLOps
Practical MLOps Lessons for Real-World AI Systems
Master MLOps challenges with real-world strategies and engineering judgment. Learn from my experiences building production AI systems.
LLMs
Practical Lessons in LLMs: Real-World Tradeoffs and Implementation Pitfalls
Building production systems with large language models requires balancing speed, cost, and reliability. Learn concrete strategies for deployment, optimization, and avoiding common pitfalls.
Prompt Engineering
Mastering Prompt Engineering: Real-World Lessons for AI Engineers
Real-world lessons on prompt engineering for AI engineers, software builders, and technical founders.
Security
Practical Security Lessons for AI Engineers in 2026
Learn real-world security strategies for AI systems, from authentication to data encryption. Essential for engineers building production-grade AI applications.
RAG & Retrieval
Practical Lessons in RAG & Retrieval: Building Reliable AI Systems in Production
Real-world challenges and design decisions in implementing Retrieval-Augmented Generation for production AI systems
Embeddings & Vector Search
Practical Lessons in Embeddings & Vector Search for Production Systems
Building robust AI systems requires more than models—here’s how to design, optimize, and debug embeddings and vector search in real-world scenarios.