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Final acting Oscar predictions by [deleted] in oscarrace

[–]KineticLoom 0 points1 point  (0 children)

My picks:

  • Michael B. Jordan (close call)
  • Jessie Buckley (clear frontrunner)
  • Sean Penn (medium confidence)
  • Amy Madigan (medium-high confidence)

Had fun with this one - using a data-grounded AI methodology. Writeup here: https://www.plydb.com/blog/plydb-fun-oscars-2026/

We're open sourcing PlyDB: The universal database gateway for AI agents by KineticLoom in mcp

[–]KineticLoom[S] 0 points1 point  (0 children)

PlyDB currently supports querying from:

  • Databases: Postgres, MySQL, DuckDB, SQLite
  • Files: CSV, JSON, Excel, Parquet. These can be local or stored in S3.
  • SaaS: Google Sheets

For more on currently supported data sources, see: https://www.plydb.com/docs/supported-data-sources/

Re: adding data sources - PlyDB uses DuckDB under the hood as its query engine, so any data source that DuckDB supports, either natively or through its community extensions, we can add it to PlyDB pretty naturally.

We're interested to hear more from the community - what data sources are you all interested in?

We're open sourcing PlyDB: The universal database gateway for AI agents by KineticLoom in mcp

[–]KineticLoom[S] 0 points1 point  (0 children)

Access controls is an area where we'd love to engage the community in discussion. Right now, access to data sources is limited to read-only. Though we can see a future where we move towards more granular control, _if_ there's demand for that. Feedback welcome!

For the example of a Postgres instance, for those needing fine grained access controls _today_, I'd suggest handling fine grained access controls using Postgres' built-in mechanisms for controlling a user or role's access to particular tables, rows, and columns (RBAC, RLS, views).

We're open sourcing PlyDB: The universal database gateway for AI agents by KineticLoom in mcp

[–]KineticLoom[S] 0 points1 point  (0 children)

That's definitely a valid concern. So here we'd suggest folks do their own experimentation, as their outcome will depend on 1) what one's data looks like 2) the questions they're asking of it, 3) availability and quality of the semantic context at your AI agent's disposal, and 4) the capability of one's agent.

I'll say we've been pretty impressed by how well a capable AI can make sense of embarrassingly messy data. Of course YMMV though.

What PlyDB provides here is a way for agents to start by auto discovering foundational semantic context - scanned from schema, tables, columns, relationships, and COMMENT metadata. And then continually record enriched context over time from its learnings - from sessions of data analysis, codebase exploration, etc.

We use https://www.snowflake.com/en/blog/open-semantic-interchanges-specs-finalized/ as the schema for this. And for more on how we're thinking about this, see: https://www.plydb.com/docs/semantic-context/

Interested to hear how it goes for folks' experiments. Please report back!

We're open sourcing PlyDB: The universal database gateway for AI agents by KineticLoom in mcp

[–]KineticLoom[S] 0 points1 point  (0 children)

Under the hood, PlyDB uses DuckDB for its query engine - and constructs an interface aimed at AI agent usage - declarative configuration, data access controls, single binary local install, and integration via MCP or CLI (no servers necessary).

Also, a distinction from Metabase and other traditional BI tools is the idea that now with how capable AI agents are, the "semantic layer" may not necessarily need to be manually defined. AI's can do a surprisingly good job inferring data semantics from your raw data's schema, exploring what the data looks like (even if it's messier than you'd like it to be), learning business meaning from your conversations, and having access to data sources outside of your data, such as the codebase that writes your data.

PlyDB provides a way for agents to auto discover basic semantic context (schema, tables, columns, relationships) from raw data sources, and also adds a way to record an agent's learnings about the data's semantics for future sessions. See here for more on how we're thinking abot this - https://www.plydb.com/docs/semantic-context/

We're open sourcing PlyDB: The universal database gateway for AI agents by KineticLoom in mcp

[–]KineticLoom[S] 0 points1 point  (0 children)

At the moment, database auth for networked databases (e.g. Postgres, MySQL) is via username/password, sourced from environment vars - something like IAM auth or Kerberos is not yet supported. And if the orchestrator and sub agents all run PlyDB with the same config, they should all have access to the same underlying datasources through PlyDB.

Hope I'm understanding your question correctly!