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Cloudflare absolutely nailed the serverless function DX with Cloudflare Workers1. However, I feel like it’s yet to receive widespread popularity like AWS Lambda since as of now, the service only offers a single runtime—JavaScript. But if you can look past that big folly, it’s a delightful piece of tech to work with. I’ve been building small tools with it for a couple of years but never got around to writing about the immense productivity boost it usually gives me whenever I need to quickly build and deploy a self-contained service. ...

I recently gave my blog1 a fresh new look and decided it was time to spruce up my GitHub profile’s2 landing page as well. GitHub has a special3 way of treating the README.md file of your repo, displaying its content as the landing page for your profile. My goal was to showcase a brief introduction about myself and my work, along with a list of the five most recent articles on my blog. Additionally, I wanted to ensure that the article list stayed up to date. ...

After reading Simon Willison’s amazing piece1 on how he adds new features to his open-source softwares, I wanted to adopt some of the good practices and incorporate them into my own workflow. One of the highlights of that post was how to kick off a feature work. The process roughly goes like this: Opening a new GitHub issue for the feature in the corresponding repository. Adding a rough description of the feature to the issue. ...

Whether I’m trying out a new tool or just prototyping with a familiar stack, I usually create a new project on GitHub and run all the experiments there. Some examples of these are: rubric: linter config initializer for Python exert: declaratively apply converter functions to class attributes hook-slinger: generic service to send, retry, and manage webhooks think-async: exploring cooperative concurrency primitives in Python epilog: container log aggregation with Elasticsearch, Kibana & Filebeat While many of these prototypes become full-fledged projects, most end up being just one-time journies. One common theme among all of these endeavors is that I always include instructions in the readme.md on how to get the project up and running—no matter how small it is. Also, I tend to configure a rudimentary CI pipeline that runs the linters and tests. GitHub Actions and Dependabot1 make it simple to configure a basic CI workflow. Dependabot keeps the dependencies fresh and makes pull requests automatically when there’s a new version of a dependency used in a project. ...

Five traits that almost all the GitHub Action workflows in my Python projects share are: If a new workflow is triggered while the previous one is running, the first one will get canceled. The CI is triggered every day at UTC 1. Tests and the lint-checkers are run on Ubuntu and MacOS against multiple Python versions. Pip dependencies are cached. Dependencies, including the Actions dependencies are automatically updated via dependabot1. I use pip-tools2 for managing dependencies in applications and setuptools3 setup.py combo for managing dependencies in libraries. Here’s an annotated version of the template action syntax: ...