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MotherDuck is now a supported data source

You can now connect MotherDuck as a first-class data source and query it right alongside your other databases. Connect through your MotherDuck Postgres endpoint with an access token, point Basedash at the database you want to sync, and you’re ready to build charts and dashboards and run AI chats on top of it.

Because Basedash understands MotherDuck natively, the AI writes proper DuckDB SQL—including support for nested types like lists, structs, and maps—and the schema explorer and autocomplete know your tables from the moment you connect. It’s a straightforward way to bring serverless DuckDB analytics into the same workspace as the rest of your data.

Reference definitions like tables in SQL

Definitions—your saved, reusable queries—now behave like real tables. Instead of the old {{ definition("name") }} template syntax, you reference a definition with a simple schema-qualified name: select * from definitions.your_definition. It reads like normal SQL and composes naturally into joins, CTEs, and subqueries.

Definitions also show up as virtual tables throughout Basedash now. They appear in SQL editor autocomplete with their columns, and the AI sees them in its schema catalog, so it can discover and build on your definitions without any extra prompting. Existing definitions were migrated automatically, so nothing breaks.

Auto-refreshing dashboards keep up under heavy load

Dashboards that refresh on an interval—especially the always-on ones running on office TVs—now update far more reliably. Refresh work is fairly distributed so a single busy workspace can’t monopolize the queue and delay everyone else, and throughput is much higher, so charts update within seconds of their interval instead of lagging behind. When several screens show the same dashboard, they now share a single refresh per chart instead of each running its own live query, dramatically cutting the load Basedash puts on your database.

We also made individual queries more resilient. The query timeout is now 120 seconds, up from 60, to give heavier charts room to finish, and when a query does time out, Basedash now cancels it at the source across Postgres, Snowflake, Oracle, DuckDB, and ClickHouse instead of leaving it running against your warehouse.

Fixes and improvements

  • Improved voice-to-text in chat by moving to a newer, more accurate transcription model.
  • Fixed scheduled chart subscriptions failing to render for charts that rely on date ranges or default variables.
  • Fixed chat image attachments that wouldn’t load when the file’s name contained special characters.
  • Fixed the Slack channel picker returning an error when your Slack connection needed reauthorizing—it now prompts you to reconnect.
  • Fixed the upgrade flow for expired trials, which could appear to do nothing, so it now shows clear loading and retry states.
  • Fixed automation run history freezing or crashing when a report contained very large query results.
  • Fixed ClickHouse queries that could fail on read-only connections, which now retry automatically.
  • Renamed the command menu’s “New draft chart” to “New chart” and made it easier to find when searching for “SQL query.”
  • Added a public API endpoint to delete an organization.