Show HN: I built an open-source AI data layer that connects any LLM to any data(github.com)
18 pointsby y147 days ago2 comments
  • y146 days ago
    The hardest problems building this weren’t in the LLM logic, but in everything around it —-observability, access control, and managing context across dbt, Tableau, and code. Finding the balance between a strict semantic layer and LLM agency was tricky. Too rigid and it loses llm magic, too loose and reliability breaks

    What worked for me and my users was leaning on instructions + AGENTS.md + metadata as a lighter abstraction layer — structured enough for trust, but flexible enough to keep the model useful.

    If you’ve been exploring similar ideas or trying to productionize AI analysts, I’d love to hear how you’re approaching it

  • ddmdd6 days ago
    how do you make sure there's no context bloat?
    • y145 days ago
      Thanks for the question. Avoiding context bloat and overall engineering the context is (still) most of work. What’s been working:

      - Role scoped calls: data modeling, code gen, are separate calls where each gets its own tailored context

      - Context is divided into sections (tables, dbt, instructions, code) and each is getting a hard limit budget (required some experimentation, liked Cursor’s priompt project)

      - agentic retrieval: agents can call tools to fetch or search data/metadata when needed

      - summaries for different objects: messages, widgets; reports, data samples/profiles.

      I wrote some more about how the agent and context work in the docs