Engineering
The New Failure Modes in AI-Assisted Development
Amazon's AI coding incidents weren't caused by bad tools. They were caused by development processes that weren't redesigned to match the new risk profile.
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Insights
Notes on AI architecture, agent systems, latency, and building production AI products.
Voice AI
Why Voice AI Is Harder Than Chat AI
Voice AI sits at the extreme end of the latency spectrum. The pipeline, the UX, and the failure modes are fundamentally different from chat.
Performance
Reducing Latency in LLM Systems: How We Got an Agent Pipeline Under 500ms
Most agent systems feel slow because they are architected to be slow. The model is rarely the bottleneck anymore, and measuring everything else is where the real gains are.
Production AI
Why AI Demos Fail in Production
The gap between a compelling demo and a reliable production system is where most AI projects die. The problems are predictable, and the fixes are systematic.
Architecture
The Difference Between AI Workflows and AI Agents
Most teams asking for an agent really need a workflow. Autonomy has a cost, and the default should be simpler than you think.
Testing
Testing AI Systems Is Harder Than Testing Software
Traditional testing gives you pass or fail. AI systems live in a probabilistic space where correctness is a spectrum and ground truth itself is unreliable.
Architecture
Beyond RAG: Architectures for Real Knowledge Systems
RAG was a breakthrough, but its limitations are clear. When your knowledge base is complex, dynamic, or multi-modal, you need architectures that go further.