The example of releasing a Poetry-built package to PyPI is using manual CLI commands, which should not be the recommended approach IMO. Releases should be managed through some kind of CI workflow (Jenkins, GitHub Actions, Travis, GitLab CI, or others). Using manual uploads from a developer laptop is error prone and should be discouraged as you can end up with "dirty" (uncommitted code) releases on PyPI, which are immutable once published. Automated pipelines can perform validation checks prior to publishing to ensure tags, versions, and package metadata is in sync. They can also streamline releasing to GitHub, PyPI, and Conda all at once, which is critical for consistent release management between ecosystems.
The example of releasing a Poetry-built package to PyPI is using manual CLI commands, which should not be the recommended approach IMO. Releases should be managed through some kind of CI workflow (Jenkins, GitHub Actions, Travis, GitLab CI, or others). Using manual uploads from a developer laptop is error prone and should be discouraged as you can end up with "dirty" (uncommitted code) releases on PyPI, which are immutable once published. Automated pipelines can perform validation checks prior to publishing to ensure tags, versions, and package metadata is in sync. They can also streamline releasing to GitHub, PyPI, and Conda all at once, which is critical for consistent release management between ecosystems.