Today, thanks to PyCoder's Weekly, I discovered this blog post and more generally learned about design by contract (DbC) methodology.
Inspired from it, I decided to write down a simple snippet to implement preconditions and postconditions through Python decorators.
I am still working on a legacy codebase, so even the smallest change feels slippery and made me fearful. When I was about to implement a not-so-small change in a data processing procedure taking place as one of the first steps of a bigger pipeline, as a painkiller I decided to ask a feature flag to come to the rescue!
appriseIn my current role I try to learn and experiment with side projects whenever I can. One of these regards the need to notify a developer as soon as an update has been made to internal tools (e.g. the latest release of a private Python library, a new page in the team's knowledge base, etc.).
After some research I came up with a simple solution based on a pub-sub architecture, Microsoft Teams webhook and the awesome Apprise.
I am working on a super-opinionated solution to assume AWS roles via STS, both programmatically and to log into AWS Console. While searching in the Python ecosystem utilities to build CLI and GUI with, I discovered three libraries that offer a great user experience both for the end users and the developers that build with them.
After having accidentally deleted every browser bookmark saved in more than 5 years of work, I decided to write down a simple backup script to prevent such a disaster from happening again.
mkdocstringsIn these days I am tidying up the documentation of a legacy codebase, where "legacy" stands for something in between spaghetti code and "things I wish I've done differently". Since I wish the output of the work to be as much complete as possible, I decided to include also the source code in the new docs built with mkdocs-material.
The go-to choice could have been mkdocstrings, but I wanted to avoid some setup complexity1 so I wrote down a custom source code collector.
Following my previous post, the quest for AWS Certifications continues.
In this episode, my goal is to quantitatively compare the three Associate certifications I'm focusing on: Solutions Architect, Developer and Data Engineer. So, I did some scraping to extract the exhaustive list of AWS services in scope for each certifications for the time being.
As a professional with more than 4 years of experience with AWS, I might attempt to get an AWS Certification sooner or later this year, so I'm evaluating all the possibilities out there.
AWS itself provides a useful guide to the recommended AWS Certification Paths, where all the top cloud job roles are listed together with a bried description of their responsibilities.
Even if such a resource is more than welcome, I find that the big picture is not as clear as it should be: I decided to help myself building a skill tree with the aid of the awesome diagrams.
Decorators are well known examples of Python syntactic sugar, which basically let you change the behaviour of a function without modifying its definition, thanks to a handy syntax based on the @ symbol followed by the decorator name, everything put just above your function definition.
Sometimes, however, you can't exploit this syntax, for example because you don't have the function definition at your disposal - let's think at the case in which the function you want to decorate lives in another codebase (the latter might be one of the libraries your project depends on).
Today I rediscover an amazing tool: pandoc, which correctly presents itself as
your swiss-army knife to convert files from one markup format into another.
In these days I am working on the redaction of a complex tender notice, which must be delivered as an editable docx and then exported (with all the fancy stuff and theming) into pdf. Unfortunately, I find the user experience of writing documents with Microsoft Word really unsatisfying and cluttered.