February 2, 2026
Import drama as drama
Al is killing programming and the Python community
Python meltdown: “AI slop” vs real skills as devs feud
TLDR: A longtime Python coder blasted AI chatbots for filling projects with flashy but fragile code. Comments split between “old problem, especially in Python,” “get ready for 99% AI slop,” and “blame people, not tools,” highlighting a growing fight over code quality and trust in the AI era.
A self‑taught Python veteran dropped a fiery rant claiming AI chatbots like ChatGPT are flooding the scene with shiny, 2,000‑line projects that look cool but fall apart on contact—copy‑paste code, boilerplate comments, chaotic performance, and devs who understand “maybe 30%” of what they shipped. His plea: use AI wisely, not as a fake senior dev, and get back to learning from the community. One commenter even kicked it old‑school with a link to old Reddit, which became its own mini‑meme.
The replies? A popcorn feast. Some rolled their eyes—“this panic is six months late”—while others went full hot take: one user said Python was already like this, a magnet for beginners copying other beginners (aka “cargo culting”), and suggested switching to a language with more gatekeeping. Another painted a dystopia where code libraries become “99% AI slop,” joking that the chaos might actually bring back human dev jobs. Then came the counter‑punch: stop blaming AI, blame people who choose to ship junk—tools don’t make decisions, humans do.
Between the jokes (“import drama as drama,” “more imports than ideas”) and the generational clash, the thread turned into a reality show for coders. The community is split between AI ruined everything, this was always a Python problem, and it’s not AI, it’s us.
Key Points
- •The author claims AI tools like ChatGPT have changed Python coding practices, reducing reliance on documentation and community learning.
- •Large projects from inexperienced developers are described as lacking version management and being poorly understood by their creators.
- •The article alleges widespread cloning of repositories, with limited original contribution or added value.
- •Technical issues cited include chaotic performance, security being treated as optional, misuse of multithreading, and poorly optimized SQL queries.
- •The author stresses that AI should be used critically and encourages learning from community resources rather than blindly trusting AI outputs.