Favorites System
What is the Favorites System?
Section titled “What is the Favorites System?”The favorites system lets you mark any article with a ⭐ to save it permanently. Unlike read/unread status, favorites are intentional. You’re saying “I want to come back to this.”
How to Favorite an Article
Section titled “How to Favorite an Article”There are three ways to favorite an article:
- Click the star icon (⭐) in the article list next to the headline
- Open an article and click the star in the reading toolbar
- Keyboard shortcut: press
Swhile an article is focused
The star turns golden instantly, and the article is persisted to the database immediately.
Accessing Your Favorites
Section titled “Accessing Your Favorites”Click “Favorites” in the sidebar to open the dedicated favorites view.
In this view:
- All favorited articles appear, sorted by when you starred them (newest first)
- You can filter by folder or feed
- Search within favorites only
- Unfavorite any article by clicking the ⭐ again
How Favorites Are Stored
Section titled “How Favorites Are Stored”Favorites are stored directly in the articles table:
UPDATE articlesSET is_favorite = 1WHERE id = ?;Because favorites are in your local SQLite database:
- They persist forever, even if you remove the feed
- They’re instantly available, with no server round trip
- They can be backed up by copying the
.dbfile - They’re yours. No cloud service can delete them
Favorites vs. Read Later
Section titled “Favorites vs. Read Later”SparkFeed’s favorites system is intentionally simple. It’s a single “star” without categories or tags.
Here’s how to use it effectively:
| Use case | Recommended approach |
|---|---|
| Want to read later | Star the article |
| Ongoing research reference | Star + use search to retrieve |
| Share with someone | Star + open original link |
| Archive important content | Star (it’s stored permanently) |
Tags, categories, and annotation features are on the roadmap for a future release.
Export Favorites (Coming Soon)
Section titled “Export Favorites (Coming Soon)”A future release will allow you to export your favorites as:
- JSON: structured data for processing
- Markdown: a reading list you can paste anywhere
- OPML: standard feed list format (for the feeds, not articles)