One Weekend: How I Found 3,000 Open-Source AI Projects (and You Can Too)

说明与用途

作者在48小时内通过 curated 列表、GitHub 主题搜索、依赖图和论文到代码的方式，映射了3000个开源 AI 项目。

问题

Discovering the breadth of open‑source AI projects efficiently.

结果

The author produced a repeatable workflow that yielded ~3,000 open‑source AI projects in 48 hours, revealing that the local‑inference ecosystem is more active than API‑wrapper projects.

工作流

• Start with curated "awesome-*" GitHub repos to gather an initial list.
• Scrape entries, deduplicate, and compile ~1,200 unique projects.
• Search GitHub topics (e.g., llm, rag, local-llm) to surface maintained projects.
• Use topic:llm stars:>100 to find popular projects and drop star threshold for long tail.
• Explore dependency graphs of trusted tools to find related projects within one or two hops.
• Search GitHub for ArXiv paper titles or referenced repos to capture early implementations.
• Categorize the compiled list into ~30 real categories and identify ~200 actively maintained projects.

证据

I gave myself 48 hours to map the open-source AI landscape.

来源

These aren't just lists — each entry is a link with a one-line description. I scraped them into a single file and deduplicated. That alone gave me ~1,200 unique projects.

来源

Searching topic:llm stars:>100 gives you the projects people actually use.

来源

Pick a tool you already trust and look at what it depends on, or what depends on it.

来源

The 3,000 projects collapse into maybe 30 real categories, and maybe 200 that are actively maintained.

来源