Fix three dead links in the category intros (Prophet docs, Django Ninja routers guide)

website/data/category_intros/machine-learning.md

@@ -27,7 +27,7 @@ LightGBM is designed for [faster training, lower memory use, and large-scale dat
 
 CatBoost aims for [great results with default parameters](https://catboost.ai/), and it takes non-numeric features without you turning them into numbers. Name the category columns in `cat_features`, and [don't one-hot encode them](https://catboost.ai/docs/en/features/categorical-features), which hurts both training speed and quality.
 
-Prophet fits an additive model of the trend, yearly, weekly, and daily seasonality, and holiday effects. It [works best with series that have strong seasonal effects](https://facebook.github.io/prophet/) and several seasons of history. It [follows the scikit-learn model API](https://facebook.github.io/prophet/docs/quick_start.html): pass `fit` a DataFrame with `ds` and `y` columns, then call `predict`.
+Prophet fits an additive model of the trend, yearly, weekly, and daily seasonality, and holiday effects. It [works best with series that have strong seasonal effects](https://meta.github.io/prophet/) and several seasons of history. It [follows the scikit-learn model API](https://meta.github.io/prophet/docs/quick_start.html): pass `fit` a DataFrame with `ds` and `y` columns, then call `predict`.
 
 StatsForecast offers [widely used univariate models](https://github.com/Nixtla/statsforecast), like automatic ARIMA, ETS, and Theta, optimized for speed, plus benchmark models for baselines. Data goes in as a long-format DataFrame with `unique_id`, `ds`, and `y` columns, and for many series the docs [recommend the `forecast` method](https://nixtlaverse.nixtla.io/statsforecast/docs/getting-started/getting_started_short.html).
 

website/data/category_intros/web-apis.md

@@ -16,7 +16,7 @@ How to choose:
 
 FastAPI is [based on standard Python type hints](https://fastapi.tiangolo.com/). Declare path, query, and body parameters as typed function arguments, and it validates them and serves interactive docs at `/docs`. As the app grows, split it into modules with [`APIRouter`, a "mini FastAPI"](https://fastapi.tiangolo.com/tutorial/bigger-applications/).
 
-Django Ninja was [heavily inspired by FastAPI](https://django-ninja.dev/) and integrates with Django's core and ORM. You declare parameter and body types once, as function arguments, and the interactive docs show up at `/api/docs`. For a bigger project, [create an `api.py` with a `Router` in each Django app](https://django-ninja.dev/guides/routers/).
+Django Ninja was [heavily inspired by FastAPI](https://django-ninja.dev/) and integrates with Django's core and ORM. You declare parameter and body types once, as function arguments, and the interactive docs show up at `/api/docs`. For a bigger project, [create an `api.py` with a `Router` in each Django app](https://django-ninja.dev/guide/routers/).
 
 Django REST framework [splits an API into](https://www.django-rest-framework.org/) serializers for the representation, viewsets for the behavior, and routers for the URLs. [Viewsets get you running quickly](https://www.django-rest-framework.org/api-guide/viewsets/) and keep URLs consistent across a large API, while plain views are more explicit. For OpenAPI schemas, its docs [recommend drf-spectacular](https://www.django-rest-framework.org/topics/documenting-your-api/).