Figure: The 10× data student

Build one of two systems, NanoQuery (text-to-SQL) or NanoMem (agent memory). Both share the same arc: a foundations stage you build in stages with CA support that protects your grade, then a frontier stage where your own experiments earn credit even when the idea does not work, with extra points for the top five in each track.

Figure: Local memory: capture, store, retrieve

Local agent memory. A model captures records into one embedded database on your machine (claude-mem, SQLite). It retrieves selected records back into context two ways: keyword search with FTS5 finds exact terms, and an optional Chroma vector index finds related meanings. Selected records return to a later model context.

Figure: From a SQLite baseline to your own NanoMem engine

Phase 0 uses SQLite FTS5 and sqlite-vec to establish baseline results. A dotted vertical line separates it from Phases 1 through 6: implement a query planner, retrieval, ranking, and results, then benchmark against SQLite.

Figure: Anthropic's analytics stack

A skill routes Claude to roughly thirty governed domain files and the semantic layer before Claude writes and retries SQL against the warehouse. Evals gate each domain at about ninety percent. Governed sources are maintained across questions, and eval failures drive fixes to them. Without skills accuracy was at most twenty-one percent; raw query history moved it under one point; with skills it rose above ninety-five percent, and about ninety-nine percent in some domains.

Figure: NanoQuery project roadmap

Phase 0 prepares the data and proposal. T80 completes analysis, T87 builds a SQL library, T93 establishes ground truth, and T100 compares two text-to-SQL prompts against the answer key. Each stage links to its requirements.

Figure: Start this week

Figure: Plan ahead