awesome-readme
A curated list of well-made README files. It serves as inspiration for writing clear, attractive project documentation.
Pick a small issue you can reproduce, then fix it with your AI coding agent.
Share on XNo license declared
Overview
Awesome README is a curated list of projects with well-made README files. The list points out what makes each one work, such as a logo, a clear description, screenshots, GIFs, badges, a table of contents and step-by-step install instructions. It is meant as inspiration when you write documentation for your own project.
Key features
- Curated links to projects with strong READMEs
- Notes on what each README does well
- Covers images, screenshots, GIFs and text formatting
Best for
Maintainers who are writing or improving a README and want concrete examples to learn from.
- Upstream
- rogersaloo/awesome-readme
- Fork on GitHub
- Guo-astro/awesome-readme
- Upstream stars
- 1
- Category
- Learning, interviews and curated lists
- License
- No license declaredWithout a license, the author keeps all rights. Ask the upstream owner before reusing the code.
- Forked
- 2024-01-20
- Sync status
- In syncLast synced 2026-10-19
More in Learning, interviews and curated lists
Open-source engineering handbooks for high-level system design and data structures and algorithms. It gives learners a structured, free curriculum with practical study materials and citations.
Forked 2026-10-18Last synced 2026-10-19License: CC-BY-SA-4.0Learning, interviews and curated listsGitHub
YouTube Digest turns captioned YouTube videos into study materials in a Chrome side panel, with transcripts, translations, AI summaries and timestamped notes. Bring your own API keys.
Forked 2026-10-18Last synced 2026-10-19License: MITLearning, interviews and curated listsGitHub
A knowledge graph and diagnostic resource for Chinese junior-high math, connecting curriculum topics to prerequisites and practice questions. It helps explore concept relationships and identify possible gaps before learning a topic.
Forked 2026-10-18Last synced 2026-10-19Custom license (see repository)Learning, interviews and curated listsGitHub
A personal collection of ML systems learning notes, focused on RL infrastructure, inference systems, and AI infrastructure fundamentals for readers studying how these systems work.
Forked 2026-10-17Last synced 2026-10-19License: Apache-2.0Learning, interviews and curated listsGitHub