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.
Every team has their own definition of "revenue." The CFO calculates it one way, marketing another, and product a third. Each calculation is technically correct—they just use different definitions, ti
At a library entrance, a master directory directs you: "A-G: Left Wing, H-P: Center Hall, Q-Z: Right Wing." You head to the Right Wing where another sign says "Q-S: Aisle 1-3, T-V: Aisle 4-6." Followi
Picture a pizza shop on Friday night. Method one: single pizza cutter, cut one line at a time, eight cuts for eight slices. Method two: eight pizza cutters attached to one handle, perfect spacing, one
Human communication is multimodal: we gesture while speaking, draw diagrams while explaining, and understand meaning through the interplay of sensory inputs. Yet most AI systems operate in silos—compu
Instead of checking out books and carrying them home, imagine a reading room where you think about page 547 of "War and Peace" and it appears before you—not a copy, but the actual page visible through
At a family dinner, Grandma wants to pass mashed potatoes to Cousin Jim across the table. The inefficient approach: Grandma scoops potatoes onto her plate, passes to Uncle Bob, who scoops onto his pla
In spy movies, agents use elaborate handshakes to identify each other—specific sequences known only to legitimate members. One extends their hand a certain way, the other responds with the correct gri
Fine-tuning a 70B parameter model costs $50K+ and requires weeks of training on expensive hardware. This is the reality for teams building domain-specific language models. Traditional full-parameter f
Drop a rubber ball from shoulder height. It bounces back, but not as high. Each bounce is lower than the last—vigorous at first, then gradually settling, until it barely leaves the ground before final