Forward Future Loop Library

Loop 018

The product update podcast loop

A scheduled editorial workflow that turns meaningful public product changes into a short, source-grounded podcast episode.

Contributed by Pierson Marks · Source

Ready-to-use prompt

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Each night, review any new publicly released features or changes from the repository and identify the ones most meaningful to users. Verify each selected change against the released product, documentation, or release notes. Use the Jellypod MCP to generate a short three-to-five-minute podcast episode explaining how users can take advantage of the new features, why they are important, and how to try them. Review the script and audio for accuracy, clarity, and pronunciation; fix or regenerate anything that does not match the source material. If there were no meaningful public changes, record that result instead of manufacturing an episode.

Verify / stop

The episode accurately covers every meaningful public update.

Finish with a review-ready three-to-five-minute episode, or a confirmed no-episode result when nothing meaningful shipped.

Context and guidance When to use it, steps, safety notes, and related loops
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Use this when

Use this when a product ships frequently enough that users would benefit from a short recurring audio explanation of what changed and how to use it.

How to run it

  1. Collect the release window's public product changes, documentation, and release notes.
  2. Select the changes most meaningful to users and verify what actually shipped.
  3. Generate a three-to-five-minute Jellypod episode covering the benefit, importance, and how-to for each selected change.
  4. Review the script and audio against the sources, then regenerate weak or unsupported passages before stopping.

Why it works

A fixed release window keeps coverage current, while editorial selection and source verification prevent the episode from becoming an automated reading of commit titles.

Implementation note

Use only publicly released information. Do not expose private repository context, customer data, security-sensitive details, or unreleased work in the generated episode.