Like many researchers, I found that staying current with the rapidly expanding volume of literature was becoming a fragmented, time-consuming mess. Between setting up broad keyword alerts that generate too much noise, checking individual journal homepages, and doomscrolling through generic feeds, the cognitive overhead was huge.
To fix this for my own workflow, I built a platform named daily-academic designed to sit somewhere between a developer feed (like daily.dev) and a traditional literature search. It's essentially a passive monitoring layer tailored specifically for life scientists, bioinformaticians, and translational researchers.
Here is how I set it up:
- Personalized, Freshness-First Feed: Instead of relying on exact keyword matches, the system encodes publication texts and your interest profile into 768-dimensional dense vectors. It uses FAISS (Facebook AI Similarity Search) to find semantic similarities, so you get a feed of highly relevant, recent papers even if they use different terminology than your standard queries.
- Consolidated Journal Tracking: You can follow specific high-impact journals or publication sources and view them in the same feed, reducing the need to check multiple sites manually.
- Low-Friction Workflow: The UI is built for rapid daily triage. Cards display clear metadata (impact factor, open access status, graphical abstracts), and we built in direct Zotero integration and "Groups" for sharing articles with colleagues.
- Dataset Linkage: As an add-on for those working with genomic data, the platform pings the Public Omics Explorer API to retrieve and display relevant GEO IDs and experiment types (like RNA-seq or ATAC-seq) right alongside the papers when they are available.
The Tech Stack (for those interested): The frontend is React + TypeScript. The backend is built with FastAPI (Python) and relies on a PostgreSQL database using the pgvector extension for unified querying over structured data and vector similarity scores. Background jobs and daily crawlers are handled via Celery and RabbitMQ.
Curious to hear if this workflow makes sense to others or what features you think are missing from standard literature alerts.