Writing about AI architecture, front-end engineering, and the craft of building intelligent software.
From prompt engineering to multi-agent orchestration — how I designed a unified AI pipeline that handles code review, documentation, and testing.
A practical decision framework for choosing between RAG, fine-tuning, and hybrid approaches based on real production data and latency requirements.
Lessons from deploying autonomous agents at scale — error recovery, guardrails, observability, and why most agent demos fail in the real world.
A deep dive into the strategy, tooling, and hard lessons from migrating a massive enterprise application between frameworks.
Structured prompting techniques, chain-of-thought patterns, and tool-use design that turn LLMs from chatbots into reliable software components.
How to conduct code reviews that actually improve architecture, mentorship, and team velocity — not just catch typos.
Benchmarks, DX comparison, and real-world patterns for both reactivity models. Which one wins?
Benchmarking OpenAI, Cohere, and open-source embedding models across retrieval accuracy, cost, and latency for real-world RAG pipelines.
Lessons from building a 60+ component design system with WCAG AA compliance from day one.