fidzu : django

(One of my summaries of the 2026 Djangocon EU in Athens).

Full title: ATLAS: building a zero‑budget IT service management platform with Django in the public sector.

Georgios likes Django because it inspired him to think clearly about structure, workflow and architecting real-world systems.

Projects don't fail because of technology. They fail because workflows are not clear and because the system doesn't show what is happening. Clarity is what you need. Meaningful metrics. Maintainability.

The area for which he build the Atlas system was IT support. IT support originally was done by email and phone calls: completely unstructured. They now support 20500 employees... The problem: scaling without a system. Who owns what? Who's looking at what issue? The real problem was unstructured requests and lack of accountability. No visibility of the process, so you can only react.

Atlas solves it by having a central Django workflow system. The user starts with a single form they need to fill in. Simple: fewer choices, fewer mistakes. Predictablility counts for a lot in internal systems.

Workflow states are central. New/assigned/in progress/waiting/closed. Tickets move between those state based on explicit business rules. And timers to watch out for tickets being in the same state for too long: accountability. And: trust! Trust that your ticket will be handled.

Postgresql as the source of truth: tickets, state history, audit trail. Email is now only one of the interfaces, it isn't the system anymore.

Some lessons he learned:

• Prioritizing workflow modeling. You get more sustainable and effective internal systems.
• The Django admin helps a lot!
• Transparency and metrics: emphasise transparency and accountability.
• Pragmatic approach. Django really helps here.

Question from the audience about the "zero budget" mentioned in the title: he said that at the beginning it was just himself trying to improve the process by building something with Django next to his normal work. At the beginning, there was no budget to specifically hire people for the project.

Unrelated photo explanation: a recent trip to the "Modellbundesbahn" in Germany. View from the train window, there was still snow in February.

(One of my summaries of the 2026 Djangocon EU in Athens).

Paolo is very active in the Django community. He's seeing more and more problems crop up for maintainers, related to AI-assisted contributions.

He showed two quotes that seem to be about AI, but were really about something else:

• "It cripples the mind": Edsger Dijkstra (shortest path algorithm creator) in 1975, talking about the Cobol language.
• "You can't trust code that you didn't totally create yourself": Ken Thompson (Unix creator), talking about code within his own company.

New technologies aren't always welcomed with open arms.

He then showed an AI-adjusted/improved image of Django-the-guitar-player. With six fingers on his left hand and a too-modern guitar and amplifier. Amplification: that's what might happen with Django:

• More contributors, but also more low-value contributions.
• More code, but also more untested code.

What will happen? He showed some examples from other projects. Being swamped with low-quality pull requests, for instance. Within the Django community, the jazzband project is stopping: there were more reasons, but "AI slop" was one of them.

In matplotlib, an AI pull request was closed. Then the AI bot wrote a blog entry complaining about it and started attacking the open source maintainer. Automated conflict.

Some possible ways to react. What will we choose?

• You are responsible for any code you submit, including Ai-generated code. (Python guidelines).
• If the human effort is less than the effort to review it: don't submit it. (FastAPI).
• Unverified AI-generated contributions create unnecessary maintenance burden. Submissions that don't show human verification won't be considered. (Django) [Reinout: I might have mis-quoted it a bit, I can't quickly find the quote on the website during live-blogging ]
• Low-value AI-generated conributions will be closed to preserve maintainer time. (Matplotlib).
• No LLM-generated code. No AI-contributions. (ZIG)

AI is a tool that amplifies problems and possibilities. So responsibility and protection must scale. We need more human in the loop.

Unrelated photo explanation: a recent trip to the "Modellbundesbahn" in Germany. The old, disused locomotive shed at Ottbergen.