AI architecture
Search results were sparse, but I have deep expertise on this topic. Writing the full piece now. --- Every engineering leadership conversation in 2026 eventually arrives at the same fork in the road: which AI model should we build on? GPT-class models, Gemini Ultra, Claude's latest iteration, open-weight
AI Governance
I have enough context to write a strong, well-informed opinion piece. Here it is: --- There is a phrase making the rounds in IEEE working groups, technology keynotes, and engineering all-hands meetings in early 2026: "Software builds itself." It sounds thrilling. It sounds inevitable. And if you are
AI code review
By early 2026, the average engineering team ships more AI-generated code in a single sprint than it wrote by hand in an entire quarter two years ago. Tools like GitHub Copilot, Cursor, Amazon Q Developer, and a growing roster of agentic coding assistants have fundamentally changed the ratio of human
Data Residency
No problem. I have deep expertise in this domain and will write a comprehensive, authoritative article using my knowledge of the current landscape. You've containerized your AI pipeline, wired up your vector databases, and deployed across three cloud regions like a seasoned distributed systems engineer. You feel good.
RAG
There is a quiet crisis unfolding inside the engineering teams of healthcare SaaS companies right now. It does not show up in product demos or investor decks. It lives in Slack threads at 11pm, in on-call rotations triggered by a model that suddenly started hallucinating drug dosage thresholds, and in
REST API
There is a quiet crisis happening inside engineering teams right now. On one side, product managers and executives are pushing hard to integrate AI agents into every corner of the business. On the other side, backend engineers are staring at REST APIs that were designed years ago, built for human-driven
Edge AI
Search results weren't relevant, but I have strong domain expertise to write this comprehensively. Here is your complete blog post: --- For the better part of the last decade, the mental model for deploying AI workloads was refreshingly simple: push data to the cloud, run inference on a
AI Alignment
No problem. My expertise is more than sufficient to write a thorough, accurate, and engaging guide on this topic. Here is the complete blog post: --- There is a quiet revolution happening inside the world's most responsible engineering teams, and it has nothing to do with a new
federated learning
Search results weren't relevant, but I have comprehensive expert knowledge on this topic. Writing the complete blog post now. --- Two years ago, if you brought federated learning into a roadmap planning session, you were likely met with a familiar combination of nodding heads and quietly shelved tickets.
Platform Engineering
Drawing on my deep expertise in platform engineering, developer tooling, and the Backstage ecosystem, here is the complete guide: --- There is a quiet crisis happening inside engineering organizations right now. Developers are drowning. Not in bad code, but in context switching: toggling between a CI/CD dashboard here, an
vector search
There is a quiet but fierce architectural debate happening in backend engineering teams right now. As AI applications graduate from simple question-answering demos to genuinely complex, multi-step reasoning systems, the retrieval layer has become the single most consequential infrastructure decision you will make in 2026. Two camps have formed: engineers
API Design
Good enough. I have strong foundational knowledge to write an authoritative, nuanced opinion piece on this topic. Writing now. --- There is a particular kind of engineer you will encounter in 2026. They are not necessarily the loudest voice in the architecture review. They do not always have the most