Fix broken links

content/guides/hosting/iam/authentication/sso.md

@@ -101,7 +101,7 @@ Follow the procedure below to set up Okta for authorization:
     The Okta UI shows the company name under **Organization Contact**.
     {{< img src="/images/hosting/okta_identify_oidc_issuer_url.png" alt="" >}}
 
-The OIDC issuer URL has the following format: https://COMPANY.okta.com. Replace COMPANY with the corresponding value. Make note of it.
+The OIDC issuer URL has the following format: `https://COMPANY.okta.com`. Replace COMPANY with the corresponding value. Make note of it.
 {{% /tab %}}
 
 {{% tab header="Entra" value="entra"%}}

content/launch/integration-guides/dagster.md

@@ -121,7 +121,7 @@ The proceeding configuration options are used as settings on the W&B-specific Da
 
 * `wandb_resource`: Dagster [resource](https://docs.dagster.io/concepts/resources) used to communicate with the W&B API. It automatically authenticates using the provided API key. Properties:
     * `api_key`: (str, required): a W&B API key necessary to communicate with the W&B API.
-    * `host`: (str, optional): the API host server you wish to use. Only required if you are using W&B Server. It defaults to the Public Cloud host: [https://api.wandb.ai](https://api.wandb.ai)
+    * `host`: (str, optional): the API host server you wish to use. Only required if you are using W&B Server. It defaults to the Public Cloud host, `https://api.wandb.ai`.
 * `wandb_artifacts_io_manager`: Dagster [IO Manager](https://docs.dagster.io/concepts/io-management/io-managers) to consume W&B Artifacts. Properties:
     * `base_dir`: (int, optional) Base directory used for local storage and caching. W&B Artifacts and W&B Run logs will be written and read from that directory. By default, it’s using the `DAGSTER_HOME` directory.
     * `cache_duration_in_minutes`: (int, optional) to define the amount of time W&B Artifacts and W&B Run logs should be kept in the local storage. Only files and directories that were not opened for that amount of time are removed from the cache. Cache purging happens at the end of an IO Manager execution. You can set it to 0, if you want to turn off caching completely. Caching improves speed when an Artifact is reused between jobs running on the same machine. It defaults to 30 days.

content/ref/python/public-api/api.md

@@ -24,7 +24,7 @@ Most common way to initialize
 
 | Args |  |
 | :--- | :--- |
-|  `overrides` |  (dict) You can set `base_url` if you are using a wandb server other than https://api.wandb.ai. You can also set defaults for `entity`, `project`, and `run`. |
+|  `overrides` |  (dict) You can set `base_url` if you are using a W&B server other than `https://api.wandb.ai`. You can also set defaults for `entity`, `project`, and `run`. |
 
 | Attributes |  |
 | :--- | :--- |

content/ref/query-panel/_index.md

@@ -10,7 +10,7 @@ cascade:
             parent: qel
 ---
 Use the query expressions to select and aggregate data across runs and projects. 
-Learn more about Query Panels here: https://docs.wandb.ai/guides/models/app/features/panels/query-panel
+Learn more about [query panels]({{< relref "/guides/app/features/panels/query-panels/" >}}).
 
 ## Data Types
 

content/tutorials/integration-tutorials/huggingface.md

@@ -40,16 +40,16 @@ Install the Hugging Face and Weights & Biases libraries, and the GLUE dataset an
 - [Hugging Face Transformers](https://github.com/huggingface/transformers): Natural language models and datasets
 - [Weights & Biases]({{< relref "/" >}}): Experiment tracking and visualization
 - [GLUE dataset](https://gluebenchmark.com/): A language understanding benchmark dataset
-- [GLUE script](https://github.com/huggingface/transformers/blob/master/examples/run_glue.py): Model training script for sequence classification
+- [GLUE script](https://raw.githubusercontent.com/huggingface/transformers/refs/heads/main/examples/pytorch/text-classification/run_glue.py): Model training script for sequence classification
 
 
-```python
+```notebook
 !pip install datasets wandb evaluate accelerate -qU
-!wget https://raw.githubusercontent.com/huggingface/transformers/master/examples/pytorch/text-classification/run_glue.py
+!wget https://raw.githubusercontent.com/huggingface/transformers/refs/heads/main/examples/pytorch/text-classification/run_glue.py
 ```
 
 
-```python
+```notebook
 # the run_glue.py script requires transformers dev
 !pip install -q git+https://github.com/huggingface/transformers
 ```

content/tutorials/integration-tutorials/tensorflow.md

@@ -222,7 +222,7 @@ Use Weights & Biases Sweeps to automate hyperparameter optimization and explore
 ### Benefits of using W&B Sweeps
 
 * **Quick setup**: With just a few lines of code you can run W&B sweeps.
-* **Transparent**: We cite all the algorithms we're using, and [our code is open source](https://github.com/wandb/client/tree/master/wandb/sweeps).
+* **Transparent**: We cite all the algorithms we're using, and [our code is open source](https://github.com/wandb/sweeps).
 * **Powerful**: Our sweeps are completely customizable and configurable. You can launch a sweep across dozens of machines, and it's just as easy as starting a sweep on your laptop.
 
 {{< img src="/images/tutorials/tensorflow/sweeps.png" alt="Sweep result" >}}