From scattered feeds
to a readable daily destination.

Readers looking for positive reporting should not have to assemble it from separate feeds themselves. I built Just the Good News to gather stories into a readable site while keeping the original sources visible.

The site includes topic browsing, search and a Kids section. It identifies its content as AI-curated and AI-summarised.

I built the site and its automated publishing workflow: gather stories from RSS feeds, use AI to select and summarise them, then build and publish the resulting site.

A Python content pipeline feeds a Hugo site, published through GitHub Pages. The workflow is scheduled twice daily; successful runs depend on the source feeds and connected services.

Links to original reporting let readers go beyond a summary. Topic browsing and search make the collection easier to explore, while the AI disclosure explains how the content is prepared.

Publishing a static site separates the reading experience from the content-generation process. Readers can browse the published pages without waiting for a new AI response for each visit.

The public site and source code show both sides of the project: the reading experience and the workflow that prepares and publishes stories.

Status: live public project. AI selection and summaries can be imperfect; readers can follow the source links to check the original reporting. Source availability and workflow runs affect updates.

The transferable idea is a workflow that gathers information, transforms it and publishes a useful result. A business version would need its own source permissions, review points, data controls and monitoring.

The project shows how I connect separate tools into a usable system, from content processing through to the reading experience.

Explore the Prompter software case study →