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

Описание и применение

Автор за 48 часов создал список из 3 000 открытых AI‑проектов, используя curated‑list, поиск по темам GitHub, граф зависимостей и поиск кода по статьям ArXiv.

Задача

Discovering the breadth of open‑source AI projects efficiently.

Результат

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.

Рабочий процесс

• 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.

Доказательства

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

Источник

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.

Источник

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

Источник

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

Источник

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

Источник