~52,000 lines of C++ implementing a real relational database: slotted-page storage, ACID transactions with MVCC, a cost-based query optimizer, vectorized + parallel execution, ARIES crash recovery with point-in-time restore, and a TLS SQL wire protocol. Not a toy — a rigorously tested engine.
A 52,000-line database engine is not something I sat down and typed. I directed AI agents across 25 build phases — driving the architecture, calling the trade-offs, and reviewing the output through a rigorous test suite (110/110 green, fuzzing, sanitizers). This is the clearest proof of the point: with AI as the tool and the judgment to steer it, one curious person can ship something that used to take a team.
Pick a query and the engine parses it, considers access paths, and builds a physical plan — choosing an index scan over a seq scan, reordering joins by cost, pushing predicates down. This is a faithful sketch of Mînî's real planner behavior.
The optimizer picks plans by estimated cost from real table statistics — an index scan when selectivity is high, a hash join when tables are large, a decorrelated subquery when it can prove equivalence.
Building a database from the page layer up is the clearest proof that the AI work above it rests on real systems fundamentals — concurrency, memory, storage, recovery, and query planning. The same discipline that keeps a transaction ACID keeps an agent's answer grounded.
See that discipline in Îktomnî →