AI is killing the spirit of hackathons if we keep letting LLMs

The "demo effect" at recent hackathons has become a joke because everyone is just wrapping a basic API call in a flashy frontend and calling it an innovation. At HackEurope 2026, the trend shifted from "solving a hard problem" to "who can prompt the fastest." We've reached a point where a team can generate a full-stack MVP in four hours using an LLM agent, spend the next twenty hours polishing the UI, and walk away with a prize without actually understanding how their own backend handles state or concurrency.

If we want to save the hackathon experience, we need to move toward a real-world AI workflow that prioritizes architectural decisions over raw code generation. When the barrier to entry for "working code" drops to zero, the only thing that actually matters is the logic and the edge cases. Most of the projects I saw were "happy path" demos—they work perfectly for the three minutes the judges are watching, but they crumble the moment you input something the prompt didn't anticipate.

For anyone actually trying to build something substantial during these events, I've found that a deep dive into the actual constraints of the model is more valuable than just iterating on prompts. Instead of letting the AI write the whole app, try this approach to keep your project grounded:

1. Map the State Machine: Before touching a keyboard, manually map out every possible state of your application. If you can't draw it on a whiteboard, the AI will eventually hallucinate a logic loop that you won't be able to debug in time for the presentation.
2. Isolate the LLM Logic: Keep your core business logic in a separate module from your AI calls. This makes it easier to swap models or tweak the prompt engineering without breaking the entire deployment pipeline.
3. Stress Test the Edge Cases: Spend at least two hours trying to break your own prompt. If a simple typo or an unexpected null value crashes the app, you don't have a product; you have a script.

We need to stop rewarding "fast" and start rewarding "robust." A project that solves a niche, difficult problem with a clunky UI is infinitely more impressive than a polished wrapper that does something a standard GPT-4o prompt could do in one shot. The real skill now isn't knowing how to code—it's knowing exactly what needs to be coded and why.

The goal should be to use these tools as a force multiplier for human intelligence, not as a replacement for the struggle of building from scratch. When you remove the struggle, you remove the learning. If you're heading into your next event, try restricting yourself to using AI only for boilerplate or debugging—force yourself to architect the system. That's where the actual growth happens.