Connecting ambitious readers to inspiring authors.
Reader is a platform for readers to find and connect with authors. We believe that the best way to learn is to learn from the best.
For a long time, the entire blogging ecosystem has been fragmented and focused on authors, not readers.
We want to change that. We want to make it easy for readers to find the best content and connect with the authors.
How it works
This is how our feed works:
- Fetch articles from more than 1000+ websites.
- Use machine learning to extract the most important sentences and categorize them into topics.
- Use this data to build a recommendation engine that recommends articles to users based on their interests.
This is how authors can use Reader:
- Create a new draft.
- Invite co-authors to collaborate on the draft.
- Publish the draft to Reader.
Why we built Reader
Readers fail to find the best content because of the following reasons:
There is too much content on the internet. We are bombarded with content from all directions. It is impossible to keep up with everything
- We solve this by fetching the best content from the internet and recommending it to you.
Content is fragmented. There are too many websites and blogs. It is hard to keep track of all of them
- We solve this by aggregating content from all the websites and blogs and presenting it to you in a single feed.
Newsletters are not always present - All websites do not have a newsletter.
- We solve this by allowing you to follow specific websites and blogs and ensuring that you never miss an article - while skipping the email chaos.
We are a team of 1 person - who does the development, design and marketing (he tries to do marketing).
We built Reader using an extremely scalable but simple tech stack.
- NextJS - For everything frontend.
- TailwindCSS - For styling.
- RadixUI - For components.
- React Query - For data fetching.
- Rust - For everything backend.
- Prisma - Database operations.
- RSPC - Frontend <> Backend communication.
- PostgreSQL - Database.
- Python - For teaching the computer how to do things.
- Pytorch - For training the model.
- Pandas - For data manipulation.
- Numpy - For data manipulation.
We are grateful to the open source community for building such amazing tools.