Insights
Real talk on shipping
software, AI, and teams.
Practical lessons from the work — staffing, AI in production, cloud operations, and everything in between.
Vibes Are Not an Eval: Building Real Quality Gates for AI
Every team says their AI works great until it doesn't. This is how to replace vibes-based testing with a real eval harness: golden datasets, unit and regression evals, LLM-as-judge, and a flywheel that compounds quality over time.
The Hidden Cost of Running AI at Scale
What costs $0.002 per call at demo scale can cost $200,000 a year in production — and most teams find out too late. A field guide to the four dimensions of LLM cost, the latency users actually feel, and the levers that cut spend before you scale.
9 min readRAG Done Right — What Nobody Tells You About Retrieval in Production
Most RAG implementations work fine on your test data but fall apart on real user questions, and the gap is almost always in retrieval quality rather than the LLM. The five silent failure modes, a quality ladder, and what a production eval pipeline actually measures.
9 min readAI Agents in Production: Why They Break and How to Fix Them
AI agents look magical in demos but loop, hallucinate, and call the wrong tool in production. The four failure modes that kill production agents, and the reliability stack — validation, loop detection, structured output, human checkpoints, and cost guardrails — that makes them dependable.
9 min readStaff Augmentation Done Right
The engagements that fail rarely fail because of raw talent — they fail because nobody defined what "done" looks like. The four pillars, three operating models, and 10-day ramp plan we use to get it right.
6 min readShipping AI that survives production: the Forward Deployed Engineer model
A demo that works in a notebook is not a product. Here is how embedding engineers directly with the customer turns AI prototypes into systems people actually rely on.
7 min readWhat NOC Command watches so your team can actually sleep
Alert fatigue is a people problem before it is a tooling problem. Here is how an AI-driven NOC turns a wall of noisy alerts into a short list of things that actually matter.
5 min read