# AI is killing the spirit of hackathons if we keep letting LLMs > Source: > Published: 2026-08-17 09:39:50+00:00 # AI is killing the spirit of hackathons if we keep letting LLMs 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. [Is jailing anti-AI protesters actually a sign that we're losing 20h ago](/en/news/6570/) [AI is making code reviews the new bottleneck in the SDLC 4d ago](/en/news/6084/) [Lovable just hit a $13.3 billion valuation after a $400M raise 4d ago](/en/news/6060/) [Claude Code actually solved a math problem I'd been stuck on for 5d ago](/en/news/5956/) [AI is a tool for efficiency but a disaster when it starts making 7d ago](/en/news/5664/) [Typing is actually a thinking process that voice input can't 8d ago](/en/news/5622/) [Next Big Tech is spending way more on AI than the balance sheets →](/en/news/6657/)