Brieflet is a personalized briefing app for clinicians and researchers that filters articles from chosen sources into a single, focused brief. It combines source selection with user-defined interests and editable memory to deliver tailored summaries, with every item linking back to the original. The app is available on web, iOS, and iPadOS, with a 14-day free trial.
Funding
Funding not disclosed
Founders
Product
Problem
Clinicians and researchers face an overwhelming volume of articles from journals, RSS feeds, and alert services, making it difficult to keep up with relevant developments. Traditional tools like journal alerts and RSS readers simply collect items, leaving the user to filter and read through everything themselves, which is time-consuming and inefficient.
Solution
Brieflet is a personalized briefing app that filters articles from sources the user selects against their specific clinical or research interests, then writes a concise brief. Users describe their work and interests, and Brieflet uses this to curate a finite, focused summary of the most relevant items. The app allows users to import existing feeds via OPML, and it maintains an editable, reversible memory of user preferences. Every summary links directly to the source, and items that are skipped are listed in a separate section, allowing users to refine future filtering.
Target Audience
The primary users are clinicians and researchers in any field who need to stay current with professional literature, as well as individuals who want to separate work-related reading from optional, personal interests.
Features
- Filters articles from user-selected sources against a user's described work and interests
- Imports existing feeds via OPML file to maintain current source lists
- Editable and reversible memory that users can review, change, or remove
- Skipped items section that lists filtered-out content, with an option to stop skipping similar items
- Available on web, iOS, and iPadOS platforms
- Data privacy: user data is not sold, shared, or used to train models