More control over how your charts look

Shipped a lot at Basedash this week.

The big one: Suggestions. Basedash now studies your data, your past chats, and your dashboards, then suggests the questions, dashboards, and automations worth doing next. Click one and it starts working. No more blank page.

A question we get from customers (and occasionally from investors) is why we don't expose a model picker the way Cursor does.

The short answer is that we're building for a different user. Cursor's users are developers; picking between GPT-5 and Claude is a meaningful choice they're equipped to make. Our users include CFOs, sales leaders, customer success reps. For them, model choice is friction at best, and at worst a new vector for self-doubt. A marketing director second-guessing a revenue number because they're not sure whether they picked the right model is exactly the failure mode we're trying to design out of analytics.

We had a comment in our codebase that said SQL Server doesn't handle pagination well, so we should just avoid pagination for now. And it did exactly that: shipped the entire result set to the browser and let it sort itself out. Same deal for Spanner.

A lot of Basedash is just "run the SQL the user wrote, show the rows." We support 10+ dialects (Postgres, MySQL, BigQuery, Snowflake, Athena, SQL Server, Spanner, and friends), and every query gets paginated because we can't stream a 12M row result set to a browser.

SQL definitions: reusable snippets across your queries

You can now create SQL definitions scoped to a database connection versioned, named SQL snippets that you can reference inline in any query with {{ definition("name") }}. Definitions get their own home in the Data page sidebar where you can create, edit, describe, and preview them, and the expansion is applied both for human-run queries and for queries the chat agent runs on your behalf.

The chat agent understands definitions too, so when it writes SQL it can reach for your team s definitions automatically. Admins can also let the agent propose and save new definitions when it spots a pattern worth keeping around so the SQL your team relies on becomes a real, reusable building block instead of something everyone re-types.