jeba-jebarsan/paperlab

PaperLab is a website that helps people understand famous AI research papers by letting them interact with the ideas instead of just reading a dense PDF. Rather than summarizing papers into plain text, it turns each one into an animated story, a working simulation you can play with in your browser, and a chatbot tutor that knows about that specific paper. The project covers ten well known AI papers, including AlexNet, GANs, ResNet, YOLO, the original Transformer paper, BERT, GPT-3, Vision Transformers, RLHF, and LoRA. Each paper's page includes a short animated story explaining the idea with no reading required, three levels of explanation for beginners, developers, and researchers, a problem and solution breakdown, an interactive diagram of the model's architecture you can explore, and math explained with plain language analogies. There is also a short mini course with quizzes and saved progress, plus an AI tutor chat that answers questions grounded in that paper's content. Beyond the paper pages, there are twelve separate interactive labs covering concepts like attention, convolution, gradient descent, backpropagation, and digit recognition, where you can literally draw a digit and watch a small neural network try to read it. The project is upfront about honesty: some labs run real, verified math and training, while others use curated, labeled example data rather than live computation, and the README states clearly which is which. Getting started is simple: install the dependencies and start the development server, with no API keys or database required out of the box, since the project runs on built in content and browser based math. There is also a clear upgrade path if someone wants to connect a real AI model or live paper search later, by adding API keys for a language model provider. The project is built with Next.js and TypeScript and is released under the MIT license, a permissive license allowing free use, modification, and redistribution, including commercially, as long as the copyright notice is kept.