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Create a Realistic Prototype with Live Data in Reddit Proto

Use Reddit's internal prototyping tool, Reddit Proto, to build a working prototype of a new feature, like search, and test it with your own signed-in Reddit data for more realistic feedback.

Illustrated workflow for building and testing a Reddit Proto prototype with signed-in data.

I commented here, 'Make relevant typeaheads trending recent communities, et cetera.' And then down here, I said, 'Overall direction using the signed-in Reddit data and real typeaheads as user types in the search box,' and then I sent that to Cursor, and it vibe coded it on the spot.

Before you start

What you need

  • A feature idea (e.g., improving search typeaheads)
  • Access to Reddit's internal Reddit Proto tool

What you’ll make

A shareable, interactive prototype URL that uses real, signed-in user data to demonstrate the feature.

Tools used

  • Reddit Proto

    Reddit’s internal prototyping harness for building and reviewing interactive prototypes with its design system and authorized data. Available to authorized Reddit teams; not a public product.

  • Cursor

    AI-first code editor

    VisitCursor

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

6 steps

Step 01

Launch and Scaffold Project

Start a new project in the Reddit Proto desktop app. The app handles cloning the repository and setting up the boilerplate with Reddit's design system, components, and UX patterns.

You do not need to use Git or set up a local environment manually. The launchpad app handles the scaffolding.

Step 02

Annotate the Desired Change

In the prototype view, write annotations directly on the UI to describe the desired modification. Add an overall direction for more complex changes.

Be specific about the element you are targeting and the overall goal. For example, add a second annotation like: 'Using the signed-in Reddit data and real typeaheads as user types in the search box.'

Step 03

Send Annotations to AI Assistant

Use the 'Send to Cursor' action in the prototype. This writes a file containing the code location, the targeted element, and your instructional annotations.

This step grounds the AI by giving it precise context, avoiding the need for the AI to guess which part of the UI you mean.

Step 04

Apply Annotations in IDE

Switch to your IDE (Cursor) and instruct the AI to apply the changes based on the file generated in the previous step.

The AI will use the combination of the specific annotation and the overall direction to generate the code.

Step 05

Test with Live Data

In the Reddit Proto controls, switch from mocked data to 'signed-in data' and log in with your Reddit account. Test the feature to see how it behaves with your personalized data.

Using your own data provides a much more realistic test than mocked data, especially for personalized features like search or feeds.

Step 06

Share for Review

Use the built-in deployment feature to generate a shareable URL. This URL can be shared internally for stakeholder review.

You can also generate a QR code to install the prototype as a PWA on a mobile device for testing.

What good looks like

  • The prototype renders correctly and can be switched from mocked to signed-in data.
  • The new feature behaves as expected (e.g., search returns relevant results).
  • The prototype is shareable with stakeholders via a URL.

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Runbook notes

How to recover when the loop fails and where human judgment helps.

If it goes sideways

The AI-generated code doesn't work or produces unexpected visual glitches.
Undo the changes in the prototyping repository. Refine your annotations with more specific instructions, or manually edit the code in the IDE. An isolated prototype repository reduces production-code risk; confirm repository scope and data permissions before applying changes.
The feature works, but the results from live data are not relevant or useful (e.g., poor search ranking).
This is a successful outcome for a prototype, as it identifies a core logic problem early. A designer or engineer must then iterate on the underlying logic (e.g., the LLM ranking model) rather than the UI.

Human checkpoints

Let routine work keep moving.

  1. Review the AI-generated code changes before deploying the prototype.
  2. Judge whether the feature's behavior with your own live data is actually useful and relevant.

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