Harvard Edge Computing
@harvard-edge • Open source projects from harvard-edge. Use this route to separate flagship concentration from portfolio breadth before you treat a publisher as broadly strong.
Portfolio concentration
99%
Top three share
Shows whether the organization is driven by one breakout repo or several visible projects.
Breadth
30 repos
Visible snapshot
7 repositories updated in the last 90 days.
Leading language
Python
Portfolio mix
Python (13), Unknown (6), HTML (4)
Average size
954
Stars per repository
Useful for distinguishing one flagship-heavy publisher from a repeatable portfolio.
Updated: 2026-09-05(1d ago)GitHub API fallback30 repositories
99%
of the visible star count comes from this organization's top three repositories.
954
stars per repository in this same snapshot.
Python
is the most common language here, with 7 repositories updated in the last 90 days.
Why this rank
This organization stands out because one flagship repo drives 98% of its visible star count.
Flagship share 98%Breakout repo: cs249r_book
Organization pages work best when you separate portfolio breadth from flagship concentration. In Harvard Edge Computing's case, the visible top three repositories account for about 99% 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 Python (13), Unknown (6), HTML (4). 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 | harvard-edge/cs249r_book Machine Learning Systems | Python | 28.2K |
| 2 | harvard-edge/multilingual_kws Few-shot Keyword Spotting in Any Language and Multilingual Spoken Word Corpus | Jupyter Notebook | 190 |
| 3 | harvard-edge/QuaRL QuaRL is an open-source framework for systematically studying the effect of applying quantization to reinforcement learning algorithms. | Python | 89 |
| 4 | harvard-edge/ML-Sensors | 35 | |
| 5 | harvard-edge/cs249r_book_dev Hosting the builds for the develop branch | HTML | 28 |
| 6 | harvard-edge/cs249r_fall2025 | Python | 25 |
| 7 | harvard-edge/Wake_Vision The code used to generate the Wake Vision dataset for person detection | Python | 17 |
| 8 | harvard-edge/dataperf-speech-example Example workflow for our data-centric speech benchmark | Python | 17 |
| 9 | harvard-edge/cs249r Repository for CS249r: Tiny Machine Learning | 16 | |
| 10 | harvard-edge/TinyML-Footprint | 13 | |
| 11 | harvard-edge/arch2 Architecture 2.0 public materials, including the synthesis lecture and preview site. | Python | 7 |
| 12 | harvard-edge/FlexiFlow | C | 7 |
| 13 | harvard-edge/Gladas | Jupyter Notebook | 7 |
| 14 | harvard-edge/Wake_Vision_Quickstart | Python | 4 |
| 15 | harvard-edge/Wake_Vision_Challenge_Data_Centric_Track | Python | 3 |
| 16 | harvard-edge/Wake_Vision_Challenge_Model_Centric_Track | Python | 3 |
| 17 | harvard-edge/Wake_Vision_Webpage A repository to host the source files for the wakevision.ai webpage | HTML | 3 |
| 18 | harvard-edge/physical-ai Physical AI Systems — open project course, book, and TinyAgents Kit curriculum | Python | 2 |
| 19 | harvard-edge/wake_vision_challenge_2_data_centric_track | Python | 2 |
| 20 | harvard-edge/arch2-seminar Website for Harvard Architecture 2.0 Seminar Series | SCSS | 1 |
| 21 | harvard-edge/lab | 1 | |
| 22 | harvard-edge/quarch-eval Evaluation harness for QuArch (ICML 2026) | Python | 0 |
| 23 | harvard-edge/arch2-dev | 0 | |
| 24 | harvard-edge/isca-26-arch-2-workshop | HTML | 0 |
| 25 | harvard-edge/asplos-26-arch-2-workshop | HTML | 0 |
| 26 | harvard-edge/wake_vision_challenge_2_model_centric_track | Python | 0 |
| 27 | harvard-edge/QuArch_Discussion | 0 | |
| 28 | harvard-edge/Architecture2_0 | CSS | 0 |
| 29 | harvard-edge/AVPerf | C++ | 0 |
| 30 | harvard-edge/tinyml_workspace | C++ | 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.