From dense research papers to interactive, visual learning experiences.
PaperMap transforms landmark AI papers into beautiful, self-contained interactive explainers. Each paper becomes a rich, production-quality educational page featuring animations, live demos, architectural visualizations, and quizzes — all while maintaining a consistent, polished design system.
- Attention Is All You Need — The Transformer architecture (Vaswani et al., 2017)
- Language Models are Few-Shot Learners — GPT-3 and in-context learning (Brown et al., 2020)
- Training Language Models to Follow Instructions with Human Feedback — RLHF & InstructGPT (Ouyang et al., 2022)
- Highly Interactive — Token playgrounds, attention visualizations, training pipeline animations, and more
- Research-Accurate — Content is grounded in the original papers with clear references
- Static-First — Pure HTML, CSS, and vanilla JavaScript. No build step, no frameworks, no dependencies
- Consistent Design System — All explainers share the same high-quality UI components and visual language
- Mobile-First & Accessible — Responsive design, keyboard navigation, ARIA labels, and reduced motion support
- Easily Extensible — Add new papers while preserving one unified homepage and style
Most research papers are difficult to digest. PaperMap bridges the gap between academic writing and deep understanding by turning theory into interactive products.
PaperMap/
├── index.html # Homepage + paper catalog
├── 404.html
├── assets/
│ └── favicon.svg
├── paper/
│ ├── Attention_Is_All_You_Need.html # Interactive explainer
│ ├── Language_Models_are_Few_Shot_Learners.html
│ └── Training_Language_Models_to_Follow_Instructions_with_Human_Feedback.html
├── LICENSE
└── README.md- Create a new HTML file in
paper/using clearPascal_Case.htmlnaming (e.g.BERT.htmlorDiffusion_Models.html) - Use an existing explainer as a template to maintain visual and structural consistency
- Add a new card in
index.htmlunder the Paper Library section - Include proper metadata in the
<head>(title, description, Open Graph tags) - Test thoroughly on both desktop and mobile
The architecture is deliberately simple so contributors can focus on creating excellent educational content rather than fighting with tooling.
Option 1 (Simplest): Just open index.html in your browser.
Option 2 (Recommended):
# Python
python -m http.server 8080
# Node.js
npx serveThen visit http://localhost:8080
This project works on any static hosting platform:
- Vercel (current live site)
- Netlify
- GitHub Pages
- Cloudflare Pages
For GitHub Pages: Settings → Pages → Deploy from main branch (root folder).
- More foundational papers (BERT, ViTs, Diffusion Models, RAG, etc.)
- Search & filtering on the homepage
- Difficulty ratings and estimated reading time
- Expanded interactive components and quizzes
- Dark mode support
All explainers are educational derivatives of the original research papers. We greatly respect the work of the original authors.
Current papers:
- "Attention Is All You Need" — Ashish Vaswani, Noam Shazeer, et al.
- "Language Models are Few-Shot Learners" — Tom Brown, Benjamin Mann, et al.
- "Training Language Models to Follow Instructions with Human Feedback" — Long Ouyang, Jeffrey Wu, et al.
This project is open-sourced under the MIT License.
Built for the AI research and education community.