go-go-web VS starter-workflows

[metadata] name = gogoweb version = 1.0.3 author = Katie Liu author_email = [email protected] description = Command-line tool that converts .txt or .md files into .html files. long_description = file: README.md long_description_content_type = text/markdown url = https://github.com/kliu57/go-go-web project_urls = Bug Tracker = https://github.com/kliu57/go-go-web/issues classifiers = Programming Language :: Python :: 3 License :: OSI Approved :: MIT License Operating System :: OS Independent [options] package_dir = = src packages = find: python_requires = >=3.7 [options.packages.find] where = src

For this lab exercise I had the opportunity to add unit tests to a classmate's project and experience their CI workflow. For this exercise I worked on go-go-web by kliu57. Go-Go Web is written in Python and uses the pytest testing framework. This was my first time writing tests for pytest, but I found the pytest docs helpful. However, more helpful was the information provided in the associated issue and the tests already written, which helped me write the test I needed in the author's preferences.

This week, I am using Continuous Integration to automatically lint and test my open source project whenever the code is pulled or pushed to GitHub!

This week I want to add a new feature to my text to HTML converter, go-go-web. The feature I plan to add is Markdown Front Matter support. When a user converts an .md file to .html using my program, my program would be able to convert Front Matter in the .md file into metadata tag content in the resulting .html file.

I chose to write my program in Python while she used Java. Aside from syntax differences, she has much more exception handling in her code compared to mine. It made me think that I should add more exception handling to my own code. Having another person test my code definitely helps to find more issues. She discovered that I missed implementing the spec for deleting the default output folder at the start of the run. Also, she uses a VS Code extension called Pylint which helped to find many problems with my code. Pylint found many issues with my code which were not best practice, such as not specifying an encoding when reading from a file, or using an f-string that does not have any interpolated variables.

CI/CD: GitHub Actions, GitLab CI, CircleCI, ArgoCD

In this post, I will share WebRTC.ventures' best practices in automating the deployment of AI-powered voice assistants for Amazon Connect, moving beyond manual, click-by-click setups to a robust, scalable Infrastructure as Code (IaC) approach. We’ll explore how to manage both static and dynamic resources, leverage tools like Terraform and AWS Serverless Application Model (SAM), and even set up an automated deployment pipeline using Github Actions to deploy your conversational IVR solutions efficiently.

Github Actions (Popular)

The Python Pulumi code is deployed with GitHub Actions. This leverages static credentials for AWS embedded as repository secrets. I have implemented two workflows:

When Microsoft announced the App Center shutdown last year, they recommended an array of alternative tools from elsewhere in their developer toolkit and beyond to replace its capabilities. Users seeking an alternative to App Center's hosted build automation, or App Store deployment, capabilities can look to Azure DevOps Pipelines or GitHub Actions. For cloud-based on-device testing, they recommend external tool BrowserStack. And for internal and test distribution, they simply steer users towards either TestFlight or Google Play Console.

We manage infrastructure with Terraform and lean heavily on GitHub Actions for CI/CD. Very simple but it works! Oh and a lot of bash scripts of course.

🔗 Check out AI-powered GitHub Actions

We're going to create some tokens and get some ids to get it all working together since we're going to use GitHub Actions to deploy our Worker to Cloudflare.

Github provides extensive documentation for you to tweak your ci.