- Dataset
- 2026-10-27-catalog-ai-v2
- Ruleset
- b2e8889c66f7
- Scanned
- 2026-10-27
- Window
- February–September 15, 2026
Jupyter Notebook tutorials on solving real-world problems with Machine Learning & Deep Learning using PyTorch. Topics: Face detection with Detectron 2, Time Series anomaly detection with LSTM Autoencoders, Object Detection with YOLO v5, Build your first Neural Network, Time Series forecasting for Coronavirus daily cases, Sentiment Analysis with BER
01Result
curiousily/Getting-Things-Done-with-Pytorch ↗curiousily/Getting-Things-Done-with-Pytorch AI coding statistics: 0%
No signed agent commits found
No agent signatures were found in the last 0 considered commits. That is a real result, not a failure to measure.
It does not mean no AI was used here. A developer can use an agent and commit by hand without leaving a detectable git signature.
02Drivers
What shapes this result
Mode, timing, and detected signatures show whether the result reflects broad adoption or one concentrated workflow.
03Evidence
Inspect the matches
Public commit samples let you inspect the signatures behind the aggregate result.
04Method
What entered the denominator
The result begins with commit history, removes merges and routine automation, and reports literal agent signatures as a lower bound.
Exclusion ledger
| Commits in window | 0 |
| Merge commits removed | −0 |
| CI / dependency bots removed | −0 |
| Considered | 0 |
Bots are excluded from the denominator entirely. Agent attribution requires a literal supported signature; no code-style inference is used.
Continue exploring
Related coding-agent evidence
Run the same detector locally.
Deterministic signature matching; no code-style inference. Squash merges can remove trailers, so this result remains a measured floor.
npx qmmit-cli curiousily/Getting-Things-Done-with-Pytorch