Update the dense-python blog post

Author: w0rp

content/blog/post/2025-07-01-dense-python-template.md

@@ -3,31 +3,65 @@ type: post
 title: Introducing dense-python
 author: Andrew Wray
 subtitle: A convenient template for new Python projects
-date: 2025-07-01
+date: 2025-07-06
 summary: The dense-python repository provides a reusable template for starting Python projects quickly.
 tags: ["python", "template"]
 ---
 
-Dense Analysis has released **dense-python**, a template repository designed to help developers spin up new Python projects with minimal setup. The repository collects a set of best practices used across Dense Analysis projects and packages them into a starting point you can clone and modify.
+Dense Analysis has released
+[dense-python](https://github.com/dense-analysis/dense-python), a template
+repository designed to help developers spin up new Python projects with minimal
+setup. The repository collects a set of best practices used across Dense
+Analysis projects and packages them into a starting point you can clone and
+modify.
 
-Available on [GitHub](https://github.com/dense-analysis/dense-python), the template comes preconfigured with modern tooling for formatting, linting, static type checks, and continuous integration. By cloning the repository and customizing it for your work, you can skip much of the boilerplate configuration typically required when starting from scratch.
+Available on [GitHub](https://github.com/dense-analysis/dense-python), the
+template comes preconfigured with modern tooling for formatting, linting, static
+type checks, and continuous integration. By cloning the repository and
+customizing it for your work, you can skip much of the boilerplate configuration
+typically required when starting from scratch.
 
 ### Distroless Python containers
 
-For containerized deployments, dense-python includes a reproducible build pipeline that produces distroless images. Distroless containers ship only the Python runtime and your project code, resulting in smaller and more secure images than traditional distributions. The provided `Dockerfile` demonstrates how to compile wheels with the `uv` build tool and copy them into the final distroless image. Read more about the advantages in [this article on distroless Python images](https://pythonspeed.com/articles/distroless-python/).
+For containerized deployments, `dense-python` includes a reproducible build
+pipeline that produces distroless images. Distroless containers ship only the
+Python runtime and your project code, resulting in smaller and more secure
+images than traditional distributions. The provided `Dockerfile` demonstrates
+how to compile wheels with the `uv` build tool and copy them into the final
+distroless image. Read more about the advantages in [this article on distroless
+Python images](https://alex-moss.medium.com/creating-an-up-to-date-python-distroless-container-image-e3da728d7a80).
 
-### Testing with network isolation
+### Testing with Network Isolation By Default
 
-The template configures **pytest** so that tests run with socket access disabled by default. This makes it easy to catch accidental network calls in unit tests. Strict marker checking is also enabled, ensuring that every custom marker is declared in your configuration. Together, these settings help keep tests fast and deterministic.
+The template configures **pytest** so that tests run with socket access disabled
+by default. This makes it easy to catch accidental network calls in unit tests.
+Strict marker checking is also enabled, ensuring that every custom marker is
+declared in your configuration. You can decorate tests with
+`@pytest.mark.allow_network` to enable network access for them explictly.
+Together, these settings help keep tests fast and deterministic.
 
-### Pyright type checking
+### Pyright Type Checking
 
-Static type checking is handled by [**Pyright**](https://github.com/microsoft/pyright), the engine behind Pylance in VS Code and also used by Cursor. It offers quick feedback on type annotations without slowing down development. A ready-to-use `pyrightconfig.json` is included so that type errors are caught early.
+Static type checking is handled by
+[**Pyright**](https://github.com/microsoft/pyright), the engine behind
+[Pylance](https://marketplace.visualstudio.com/items?itemName=ms-python.vscode-pylance)
+in VSCode and also used by [Cursor](https://cursor.com). It offers quick
+feedback on type annotations without slowing down development. A ready-to-use
+`pyproject.toml` configuration is included so that type errors are caught early.
+As ever Dense Analysis recommends using [the
+DAFT](https://denseanalysis.org/projects/daft/) for programming over proprietary
+editors, but they may also be easily configured.
 
-### Package builds with uv
+### Package Builds with uv
 
-Instead of `pip`, dense-python relies on the lightning-fast `uv` tool to create virtual environments and build distribution packages. This speeds up the packaging process and ensures consistent builds across platforms.
+Instead of naked `pip`, `dense-python` relies on the lightning-fast
+[uv](https://github.com/astral-sh/uv) tool to create virtual environments and
+build distribution packages. This speeds up the packaging process and ensures
+consistent builds across platforms.
 
-### Open for contributions
+### Open for Contributions!
 
-Dense-python is open source and welcomes contributions. If you find a useful improvement or fix, open a pull request or start a discussion on the project page. We hope this template saves you time on setup so you can focus on writing code.
+`dense-python` is FOSS and welcomes contributions. If you find a useful
+improvement or fix, open a pull request or start a discussion on the project
+page. We hope this template saves you time on setup so you can focus on writing
+code.