Simor
Data Infrastructure for Production AI
Practical writing on AI data engineering, feature stores, and the infrastructure choices that determine whether AI systems work in production.
You stand at a hotel concierge desk. You want a table at the restaurant downstairs, a reservation at the spa, theater tickets, and a car to the airport. You do not want the concierge to do these thing
Most teams implement retrieval-augmented generation and call it a knowledge layer. Give the model access to a vector database, stuff in some documents, and ship. This approach works for demos. It fall
Four runners, one baton, four legs of a relay race. Runner A sprints the first leg, hands to Runner B, who sprints the second, hands to C, who hands to D, who crosses the finish line. None of them run
A financial services firm spent eight months building an AI-powered document analysis system. When it came time to deploy, they discovered their retrieval system had no governance layer, their agent h
Pack your bags. You are in Berlin with a US laptop and a German outlet. Your charger works fine, but the plug does not. You dig through your luggage for that travel adapter you bought years ago and fo
In school, one person whispers to two friends, they each tell two more, within hours everyone knows the cafeteria serves pizza tomorrow. The gossip protocol works identically: nodes randomly share inf
Friends writing a story together, each with their own copy. Alice adds a paragraph about dragons at the beginning while Bob deletes a sentence about knights in the middle and Charlie fixes typos at th
2025 was the year AI moved from experimentation to industrialization. While 2024 saw the explosion of generative AI capabilities, 2025 was about making those capabilities production-ready, cost-effect
Remote islands must agree on decisions—when to hold festivals, which trading routes to use, who leads the council. Messages travel by boat, boats sink, islanders leave for fishing trips. How reach agr