fast.ai
@fastai • Open source projects from fastai. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
80%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
29 repos
Visible snapshot
1 repositories updated in the last 90 days.
Leading language
Jupyter Notebook
Portfolio mix
Jupyter Notebook (16), Unknown (6), Python (3)
Average size
62
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2025-04-05(508d ago)GitHub API fallback29 repositories
80%
of the visible star count comes from this organization's top three repositories.
62
stars per repository in this same snapshot.
Jupyter Notebook
is the most common language here, with 1 repositories updated in the last 90 days.
Why this rank
This organization stands out because its public portfolio is relatively balanced across 29 repositories.
Balanced portfolio across 29 reposTop 3 share 80%
Organization pages work best when you separate portfolio breadth from flagship concentration. In fast.ai's case, the visible top three repositories account for about 80% of total stars in this snapshot, which helps explain whether the organization is known for one breakout project or for a broader repeatable portfolio.
The dominant language mix here is Jupyter Notebook (16), Unknown (6), Python (3). That makes this page useful not just for popularity checks, but also for seeing what technical shape an organization's public ecosystem actually has.
Top Repositories
| # | Repository | Language | Stars |
|---|---|---|---|
| 1 | fastai/diffusion-nbs Getting started with diffusion | Jupyter Notebook | 689 |
| 2 | fastai/course22p2 course.fast.ai 2022 part 2 | Jupyter Notebook | 523 |
| 3 | fastai/fastdiffusion Notes and plans for fastdiffusion course | Jupyter Notebook | 209 |
| 4 | fastai/course22-web Website for Practical Deep Learning for Coders 2022 | Jupyter Notebook | 102 |
| 5 | fastai/fastbook2e The fastai book, 2nd edition (in progress) | Jupyter Notebook | 59 |
| 6 | fastai/fastkaggle Kaggling for fast kagglers! | Jupyter Notebook | 56 |
| 7 | fastai/nbdev-vscode | TypeScript | 42 |
| 8 | fastai/apl-study fast.ai APL study group notes | Jupyter Notebook | 29 |
| 9 | fastai/jupytercon-2023 Materials for "Write, Document, Test and Distribute Python Packages With Jupyter & Quarto", JupyterCon 2023. | Jupyter Notebook | 15 |
| 10 | fastai/paperspace-setup Setup a paperspace instance for fastai | Shell | 15 |
| 11 | fastai/k-diffusion Karras et al. (2022) diffusion models for PyTorch | Jupyter Notebook | 14 |
| 12 | fastai/nbdev_cards A sample of how to create a deck of cards lib using nbdev | Jupyter Notebook | 5 |
| 13 | fastai/nbdev-spaces-demo A demo of how to create a Hugging Face Space with gradio within a nbdev project. | Jupyter Notebook | 4 |
| 14 | fastai/nbdev-demo | 4 | |
| 15 | fastai/tanishq_blog | CSS | 3 |
| 16 | fastai/nbdev-hello-world Hello world from nbdev | Python | 3 |
| 17 | fastai/fastdispatch Wrapper for plum dispatch to make it more compatible with fastcore's typedispatch | Jupyter Notebook | 3 |
| 18 | fastai/uvws A simple uv workspace | Python | 2 |
| 19 | fastai/fastsql Please see answerdotai/fastsql | 2 | |
| 20 | fastai/wandb-nbdev Demo of Weights & Biases documentation using nbdev | 2 | |
| 21 | fastai/getapt Get tweets for a single user | Jupyter Notebook | 2 |
| 22 | fastai/pynapl Dyalog APL ←→ Python interface | Python | 1 |
| 23 | fastai/quarto-web Quarto website | JavaScript | 1 |
| 24 | fastai/nbdev1 nbdev v1 (old version) | Jupyter Notebook | 1 |
| 25 | fastai/nb-thumb Extract thumbnails from Jupyter notebooks | Jupyter Notebook | 0 |
| 26 | fastai/docusaurus-html | 0 | |
| 27 | fastai/quarto-blog | Jupyter Notebook | 0 |
| 28 | fastai/nb2req | 0 | |
| 29 | fastai/doesitwork | 0 |
Next step after the organization read
Open a flagship repository, compare a couple of portfolio leaders, or return to the organization map when you want a broader concentration read.
Learn and methodology
Keep trust-building context reachable, but behind the first data read instead of ahead of it.
How to read this organization snapshot
Total stars are useful as a discovery signal, but they do not tell you whether a team maintains every repository equally. Pair this page with release cadence, maintainer activity, and the flagship concentration shown above before making adoption decisions.
For broader background on GitStar's ranking logic and editorial guidance, see Methodology & Editorial Standards.