Metrics SQL by Rill vs qmd

Metrics SQL is a SQL-based semantic layer from Rill Data that solves a specific and painful problem: AI agents that query your data warehouse tend to hallucinate aggregation logic, producing metrics that look plausible but are mathematically wrong. Metrics SQL lets analysts define business metrics once — revenue, MAU, conversion rate, ROAS — in a governed definition layer, and then exposes those definitions as queryable SQL tables. Every dashboard, notebook, and AI agent resolves from the same source. The technical approach is elegant: rather than inventing a new DSL, Metrics SQL extends SQL itself. An agent that knows SQL can query `SELECT * FROM metrics.weekly_revenue` and get correctly computed numbers without needing to know how revenue is defined, which tables it joins, or how edge cases like refunds are handled. The semantic layer intercepts the query, applies the governed definition, and returns correct results. The implications for AI-native data stacks are significant. Currently, one of the biggest failure modes for AI analysts and BI agents is inconsistent metric computation — different agents or dashboards produce different numbers for 'revenue' because they implement aggregation logic differently. Metrics SQL addresses this at the infrastructure level, not by improving agent prompting.

qmd is a lightweight local search engine built by Tobi Luetke, CEO of Shopify, for indexing and querying personal knowledge bases, documentation, and meeting notes — entirely offline. It combines three retrieval approaches in a single pipeline: BM25 full-text search for exact keyword matches, vector semantic search via ONNX-based embeddings, and LLM re-ranking using GGUF models through node-llama-cpp. All three stages run locally with no cloud dependency. The tool ships in multiple deployment modes: a CLI for ad-hoc queries, a Node.js library for programmatic use, an HTTP service for local API access, and — most useful for AI workflows — a native MCP server that lets Claude Code, Cursor, and similar editors query your local knowledge base directly during coding sessions. The hybrid retrieval approach means it handles both "find the exact error message from last week's standup notes" and "what was our decision about the auth architecture" equally well. What makes this notable beyond its technical approach is provenance: Luetke shipped it as a personal tool he actually uses, not a startup product. The GitHub history shows active iteration and he's been talking about it on X. It's a credible signal of where pragmatic AI-augmented knowledge management is heading for technical users who prefer local-first tools.