Technical deep-dives into the architecture of modern machine intelligence, retrieval systems, and neural optimization.
Why we stopped building one-off AI agents and built the framework that lets any team build them.
Why the last ten percent of making an AI feature reliable in production is harder than the ninety percent that made it impressive.
Notes on the problem of building an enterprise chatbot that stays coherent across turns, routes to the right assistant, and answers questions grounded in real business data.
Why turning messy, real-world documents into something an AI system can actually use is harder than it looks, and why it matters more than the model you pick.
A practitioner's rebuttal to the idea that giant context windows make retrieval obsolete.
The least glamorous part of shipping an LLM product is measuring whether it actually works, and it's the part that most teams skip until it hurts.
Why the real work of building with LLMs moved from crafting clever prompts to deciding what information the model gets to see at the moment it answers.