MMosaic Flow
AI coding workspace

Data dashboards

Let the dashboard tell the right story. Mosaic Flow helps you develop data views whose loading, empty and failed states remain distinct.

● ● ●Mosaic Flow●
A concrete starting point · Data dashboards ×
if (query.failed) return ErrorPanel();
if (query.loading) return LoadingPanel();
return query.rows.length
  ? DataTable(query.rows) : EmptyPanel();
✳Build a dashboard that distinguishes zero results from a failed request.
✓ ChangesReview before applying ↗
Illustrative workspace
  1. Connection

    Base URL, credentials and a reachable server.

  2. Project

    Preserve the selected sort order when data refreshes and test the loading transition separately.

  3. Review before applying

    Compare actual behavior with the expected result. If a check fails, investigate it before applying more changes.

Workflows

Data dashboards

Workflows ↗
A concrete starting point
if (query.failed) return ErrorPanel();
if (query.loading) return LoadingPanel();
return query.rows.length
  ? DataTable(query.rows) : EmptyPanel();
AI coding workspace

Turn dashboard requirements into code you can examine

Mosaic Flow helps you work from a product requirement to the files that implement it. In this Windows coding workspace, you can explain what a dashboard should communicate and ask a connected model to inspect the current data flow. That conversation is useful when a screen looks finished but its messages leave users unsure whether a request succeeded.

For example, a table with no rows can mean that a filter matched nothing, while a failed request means the answer is unknown. Bring the query state and rendering code into the discussion and describe those outcomes separately. You can also specify that a refresh preserves the chosen sort order. These are requirements for your dashboard project, rather than prebuilt reporting features supplied by Mosaic Flow.

Review the suggested changes from the query result through to the visible message. Run checks for the transition into loading as well as the final screen, and verify that the table still follows the selected ordering. Model access uses a supported OpenAI-compatible or Anthropic endpoint and credentials when needed. An unrestricted model can be chosen where available; the label does not settle its tools, context limits or provider rules. The practical result to aim for is a dashboard change whose behavior you can explain and test.

Questions behind a clear data screen

Can I describe the dashboard without writing a specification?

Start with plain language about what someone should understand from each state. Identify the existing screen and data request, then refine ambiguous outcomes in the conversation before asking for a proposed implementation you can review.

Does the domain name imply code-free delivery?

The workflow produces source changes that need inspection and testing. You can discuss requirements in ordinary language, but you still need to understand the resulting behavior and use your project's development setup to verify it.

How should a zero-result screen differ from failure?

A zero-result screen confirms that a completed query found nothing under its criteria. Failure cannot confirm that conclusion. Decide which message and recovery action each case needs, then check both against the application code.

What can a sort-order regression reveal?

Refresh may replace data while accidentally resetting the user's choice. Select a nondefault ordering, refresh and inspect the returned rows and visible control. That checks continuity of the interaction beyond a single successful table render.

Get the Windows app ↗

Mosaic Flow · Updated 2026-10-25