December 2023: the generative turn
The week the repositories changed character, from predictive models to generative AI.
- Status
- stable
- Trust
- human-reviewedhuman:rrizwan98 ·
- Approved
- human:rrizwan98 ·
For three years every repository was a model, a pipeline or a deployment of one. In the last two weeks of 2023 that stops.1
The turn, dated
| Created | Repository | Visibility | What it is |
|---|---|---|---|
| 2023-12-18 | (private) | private | A Gemini experiment |
| 2023-12-24 | Generative-Ai | public | First generative-AI repository |
| 2023-12-26 | flower-classification-app | public | A last classifier, shipped as an app |
| 2023-12-27 | (private) | private | An agriculture solution, end to end |
| 2023-12-31 | learn_GenAI | public | GenAI learning, started on the last day of the year |
Five repositories in thirteen days, three of them generative.2 The last classifier and the first generative repositories sit two days apart.
Why it matters
Nothing from the earlier eras was thrown away. The MLOps habits, the AWS deployment work and the API discipline are exactly what the next era needs, because a language model is only useful when it is wrapped in a service a business can call. The turn changed what was being built, not how it was shipped.
Next: 2024: GenAI APIs and microservices.
Footnotes
Sources
- [^github-repos]GitHub API, repositories owned by rrizwan98, read 26 August 2026
- [^journey-note]From Predictive AI to Forward Deployed Engineer: My 6-Year Journey, Raza Rizwan, July 2026
2022 to 2023: MLOps
Two years spent learning to ship models, with CI/CD, DVC, MLflow, YOLO object detection in production, SageMaker endpoints and the first private client work.
2024: GenAI APIs and microservices
The year LLM features were shipped as clean, documented APIs, and the first paid client app was built.