Lately I’ve been thinking a lot about the role of AI in python-docs-es project. On the one hand, it feels like a no-brainer: AI can speed things up dramatically, improve consistency, and even suggest phrasing on the fly. Imagine a bot that asks whether certain paragraphs are okay and then automatically updates the repository. Pretty amazing, right?
But while translation itself is the core of the project, I believe there are two other aspects that are almost just as important:
1. Building connections between “Spanishes”
Every translation effort sparks conversations about perspective, nuance, and regional differences. We all remember the debate about how to translate “slice.” These moments aren’t just nitpicking—they build bridges among Spanish speakers worldwide and lower the entry barrier for those who don’t master English. Having Python “speak your language” is a powerful invitation into the community.
2. Learning Python from the source
I think the project highlights the value of going back to the original sources: beyond the console error, the YouTube tutorial, or the blog post, to ask “What does the documentation actually say this does?”. That exercise builds a habit I see as essential. Good docs + the discipline of consulting them = two key ingredients for professional growth.
AI vs. the “artisanal” approach
Of course, translating everything with AI in five minutes is spectacular. It strengthens the first pillar (translation) brilliantly. But those other two pillars—connection and learning—thrive best through human, artisanal work. Effort with meaning. Work that’s not just about computational efficiency, but about building understanding.
So, where does that leave us?
I’m 100% in favor of using AI. If it’s useful, we should leverage it. The real question is: how do we also foster connection and learning alongside efficiency? Do we need to do that within this project specifically? Honestly, I don’t know.