Stars and mentions
Last 30 days, Oct 5 – Nov 3
Package downloads
Top repositories
curiousily’s repositories ranked by new stars, as on trending.
Contributes to
Repositories outside curiousily’s account where curiousily opened or reviewed pull requests in the past 365 days.
Who noticed curiousily
Everyone who starred or forked curiousily’s repositories since the archive began in 2011.
Notable accounts
Ordered by the stars their own repositories have received.
Biggest fans
Accounts that starred the most of these repositories.
Breakout weeks
Each repository’s best week of new stars, with the release published around it.
- Gold
- 01st place finishes
- Silver
- 02nd place finishes
- Bronze
- 03rd place finishes
- Awarded repositories
- 0Owned by curiousily now
No trending awards yet
None of the repositories curiousily owns has finished in the top three of a completed trending day or week, by stars, forks or PR authors. See who is trending now.
A repository earns an award for finishing first, second or third in a completed trending day or week, ranked by new stars, new forks or pull request authors. Awards follow a repository through renames and transfers and count toward its current owner. Weeks run Monday to Sunday, UTC.
Mentions
Stop piecing together your AI engineering curriculum from random tutorials. AI Bootcamp is a self-paced Generative AI learning repository for engineers who want an organized path from ML foundations to LLM applications. It helps you build a more deliberate learning path by grouping tutorials and videos into foundations, production systems, AI systems engineering, RAG/context engineering, and agents/workflows. Key features: • ML foundations – lessons cover Python, math, linear models, and PyTorch fundamentals • MLOps track – guides cover data exploration, validation and pipelines, experiment tracking, APIs, and cloud deployment • Local AI starting point – an Ollama quickstart walks through running models locally • RAG and context modules – covers document processing, chunking, embeddings, retrieval, and RAG pipelines • Agent workflow lessons – includes LangGraph, MCP, local-model agent workflows, and agentic RAG examples It’s open-source (MIT license). 🔗 GitHub: curiousily/AI-Bootcamp ⸻ ♻️ Share this with your network if you found it useful or insightful. ✉️ If you’re into AI, ML, agents, and building real systems, join my newsletter (it’s free): dankornas.substack.com