Architecture
The Compute Moat
Anthropic just paid for 220,000 of a competitor's GPUs because their own buildout can't be accelerated. Capacity, not capability, is the 2026 AI frontier.
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Insights
Notes on AI architecture, agent systems, latency, and building production AI products.
Architecture
Three New Knowledge Primitives
RAG was robust because the retrieval decision was removed from the model. Memory, Skills, and agentic search hand it back, and demand more in return.
Architecture
Every Agent Needs a Judge
Putting safety rules in the system prompt fails in production. The pattern that works: a written constitution, a separate judge, and a retry loop.
Architecture
Your Prompts Are Not Portable
A better model can make your system worse. Production prompts fossilize around old failure modes. A more literal model follows them too literally.
Architecture
Build for the Floor, Not the Peak
Every AI system has two performance curves. The peak is the demo. The floor is production. Most teams build for the peak and then spend six months firefighting. The alternative is simple, unglamorous, and what actually ships.
Evaluation
The Evaluation Gap
Building AI systems got dramatically easier. Evaluating them didn't. That asymmetry, and Goodhart's Law, explain why most agent deployments still fail.
Performance
Browser Agents at Human Speed
Most browser agent benchmarks measure whether a task completes. The more useful question is whether it completes faster than a human would — and what it takes to get there.
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.
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.