Insights on AI, architecture, and engineering
Most teams building AI solutions lean heavily on a single SaaS API. But when you care about cost, control, compliance, and differentiation, you need open-source tools. This post walks through practical ways to leverage open-source frameworks to build production-grade AI platforms.
Every production AI system eventually hits the same wall: 'Why are we sending 12,000 tokens to answer a question that only needs 400?' This post covers creative, battle-tested techniques to dramatically reduce tokens while increasing accuracy.
Every engineering team has made at least one decision that seemed 'fine at the time' but eventually became the most expensive technical mistake. This post outlines the most expensive decisions I keep seeing in real engineering teams, with concrete examples of why they hurt and how to avoid them.
As AI systems mature beyond prototypes, teams often mix technologies that operate at completely different levels of the stack. This post breaks down the AI stack from bottom to top, clarifying the role of each component.
For many teams, monitoring is treated as an afterthought. But in modern software systems, monitoring and observability are core product features that determine how quickly customers get help, how fast teams ship fixes, and how predictable sprints become.
For years, the handoff between design and development was a friction point. Figma's AI-powered features, combined with structured design systems and Git-integrated tooling, now make it possible to turn designs into production-ready components faster and more consistently.