How MindsHub Builds an Interactive App from Live Data

Updated

MindsHub turns a plain-language request into a working, shareable app that reads live data. You describe the KPIs and sources you want, connect each system through the credentials vault, and publish the result to a URL your team can open. The example on the site builds a KPI app that pulls MRR, signups, and NRR from PostgreSQL and Stripe, then wires Linear so a flagged row can become an assigned ticket. This works when your data lives in systems MindsHub can connect to and you can supply credentials for them.

What you describe

You write the goal in natural language, naming the metrics, the sources, and any actions you want inside the app. The site's sample prompt asks for an interactive app to explore MRR, signups, and NRR, pulling live from Postgres and Stripe, with the ability to open and assign a Linear ticket when signups look off, then publish it for the team.

The more specific the request, the more the agent can plan before it builds:

  • Metrics: name each one (MRR, signups, NRR) so the app knows what to compute.
  • Sources: name the systems (PostgreSQL, Stripe, Linear) so connectors can be set up.
  • Actions: say what a user should be able to do from the app, such as flag a row and create a ticket.
  • Output: say it should be published and shareable.

How the connections work

MindsHub connects to PostgreSQL through a credentials vault, so no raw key is exposed in the app. The same pattern applies to the other systems in the example: Stripe for MRR and signups, and Linear for one-click assignment.

System Role in the example What the agent did
PostgreSQL Source of KPI data Connected via the credentials vault, no raw key exposed
Stripe Subscription and signup data Queried for MRR and signups; 1,204 subscriptions reconciled
Linear Issue tracking Wired up so issues can be created straight from flagged rows

Because credentials are held in the vault rather than written into the app, you can share the published page without handing out database or payment keys.

What the build produces

The agent plans the analysis, connects the sources, queries them, and publishes a live page. In the sample run it planned three KPIs with live refresh and drill-down by segment, then reported the result: MRR up 9.4% week-over-week to $128.4k, with a flagged issue that signup checkout was erroring for EU accounts.

The output is a published file with a shareable URL (kpi-app.html in the example), so the team can open it, explore the numbers, and drill into segments. The flagged row is the bridge to action: it can be turned into a Linear ticket and assigned from inside the app.

Steps to reproduce the flow

  1. State the goal. Write the metrics, sources, and actions you want, as in the sample prompt.
  2. Connect each source. Add PostgreSQL through the credentials vault, plus Stripe and Linear. Expect the agent to confirm each connection before querying.
  3. Let the agent plan and query. It plans the KPIs, reconciles the data (for example, 1,204 subscriptions), and wires the action system.
  4. Publish. The agent produces a live, shareable page with a URL.
  5. Verify. Open the URL, check the headline numbers, and confirm that flagging a row creates and assigns a ticket.

Common snags

  • Vague metrics. If you don't name MRR, signups, and NRR explicitly, the app may compute something different from what you expect.
  • Missing credentials. A source that isn't connected through the vault won't return live data.
  • Action wiring. The Linear step only works if you asked for it and connected the system; otherwise the app is read-only.
  • Trusting the first number. The sample run surfaced an error condition (EU signup checkout failing) alongside the headline metric, so check flagged rows rather than only the top-line figure.

MindsHub also offers a free tier for its Air model with no credit card required, which is a reasonable way to try the flow before connecting production systems.