I was playing with Supabase and Next.js, after years of Postgres and Django, and some concepts were new for me. Working out what they meant took me through Postgres roles, privileges, policies, and a couple of things I had assumed for years without checking. Maybe what was new for me is new for you too :)
I lead a team, and the thing we come back to most often is not a technical problem. People know their work. What they usually do not know is how to communicate it: an idea nobody buys into, a question that does not get answered, a piece of work nobody realizes was hard, an opinion that never quite lands as an opinion, and feedback that was never given.
Every Django project I have worked on reaches the same moment. A request is too slow, someone moves the slow part into a Celery task, and the page gets fast. Quietly, the function has stopped being a function. It became a message, handed to a broker you do not control, to be run by a worker that might die halfway through. This post is my study guide for everything that can go wrong in that handoff, and what to do about each case.
Using seaborn and matplotlib to make great plots isn’t easy. I had been looking on the internet for complete tutorials on how to set up a good plot, but I only found small pieces of code. So here are a few tips for plotting data with the Google Global Mobility Report and matplotlib.
The idea here is to gather suggestions that might help if you want to, or are about to, give a course, a class or a talk, and you are worried about how to do it. I put together some things I noticed over these years working with PyLadies Brasil, and maybe some of it is useful to you :) The post came out of a conversation in the PyLadies Natal group, so building these suggestions was itself a collective thing.
A ideia aqui é juntar sugestões que podem te ajudar caso você queira/vá dar um curso/aula/palestra e esteja preocupada em como fazer isso. Juntei algumas coisas que observei ao longo desses anos trabalhando no PyLadies Brasil, e talvez você possa aproveitar um pouco disso :) A ideia do post surgiu de uma conversa no grupo do PyLadies Natal, então a própria construção dessas sugestões foi algo coletivo.
When I started at my new job, the first task was: reports. It was a good way to learn the codebase, but when I opened the file that generated the PDFs, I found something really messed up. Every element (columns, rows, title…) had its position defined by hand, in centimeters. So a task that should have been quick was going to take much longer.
GitHub Actions was introduced last year, and it’s an API for cause and effect on GitHub. You can orchestrate any workflow, based on any event, you can have your workflow run on push events to master and release branches, or only run on pull_request events that target the master branch, or run every day of the week at Friday 02:00. The workflows are code in a repository, as you will see in this project, so you can create, share, reuse, and fork.