rss-firehose

Aggregate Local RSS feeds into a lightweight page.

Example page: https://djdefi.github.io/rss-firehose/

Rendering:

To render the page:

ruby render.rb

Outputs to: public/index.html

Writing and Running Rendering Tests

To ensure the integrity of rendering changes, it's crucial to write and run rendering tests. Here's how:

1. Write new tests in test/render_test.rb when modifying rendering logic.
2. To run the tests, execute the following command:

ruby test/render_test.rb

This will verify that the output of render.rb matches the expected HTML structure or content.

Docker

To run the application using Docker, build the Docker image and then run the container:

docker build -t djdefi/rss-firehose .
docker run --rm -v rss-firehose:/usr/src/app/public -it djdefi/rss-firehose

Re-run the rss-firehose container to update the page.

Environment variables

Optional settings can be configured an Docker run time, or be set in your local Ruby environment:

## Docker:

docker run --rm -v rss-firehose:/usr/src/app/public -e "RSS_TITLE=My News" -e "RSS_URLS=https://url1/feed,http://url2/rss" -e "RSS_BACKUP_URLS=https://backup1/feed,http://backup2/rss" -e "ANALYTICS_UA=UA-XXXXX-Y" -it djdefi/rss-firehose

## Ruby:

export RSS_URLS="https://url1/feed,http://url2/rss"
export RSS_BACKUP_URLS="https://backup1/feed,http://backup2/rss"
ruby render.rb

Available environment variable options:

"ANALYTICS_UA=UA-XXXXX-Y"
"RSS_URLS=https://url1/feed,http://url2/rss"
"RSS_BACKUP_URLS=https://backup1/feed,http://backup2/rss"
"RSS_TITLE=My News"
"RSS_DESCRIPTION=My really awesome news aggregation page"
"GITHUB_TOKEN=your_github_token_for_ai_summaries"

AI-Powered Summaries

RSS Firehose can generate AI-powered summaries of your news feeds using GitHub's Models service. To enable this feature:

1. Set the GITHUB_TOKEN environment variable with your GitHub personal access token
2. Summaries are cached for 24 hours to minimize API usage
3. If no token is provided, the app gracefully falls back to displaying feeds without summaries

Summary Variation Features

The system generates two distinct types of summaries with different approaches:

Individual Feed Summaries

• Focus on unique aspects and specific details of each news source
• Use engaging language with varied sentence structure
• Highlight what makes each feed distinctive
• Higher creativity setting for more varied output

Overall Summary

• Provides big-picture analysis across all news sources
• Identifies themes, patterns, and trends
• Uses analytical language emphasizing implications and context
• Focuses on connections between stories rather than individual details

This dual approach ensures varied and interesting content rather than repetitive summaries.

Features

• Robust Error Handling: Feeds that are offline or unreachable are gracefully handled with placeholder content
• Smart Backup Feeds: Configure backup RSS feeds that are used when primary feeds are empty
• AI Summarization: Optional AI-powered news summaries using GitHub Models
• Caching: Intelligent caching of AI summaries to reduce API usage
• Input Validation: Automatic validation of RSS URLs and configuration
• Responsive Design: Mobile-friendly HTML output with accessibility features