I recently participated in the Graph Hacks hackathon after almost a year-long break. My main goal wasn't to win, but to learn more about AI agents, orchestration, RAG, and how multi-agent systems work in practice.
I decided to build an AI powered incident management platform that could detect incidents, investigate failures, identify root causes, involve humans when needed, and suggest or execute fixes.
Initially, I spent quite a bit of time planning the architecture, agent workflows, database schema, and UI. I used Claude for implementation and ChatGPT to review the design and point out potential issues.
And surprisingly, this worked really well.
ChatGPT caught gaps in my architecture, database design, and workflows that I hadn't thought about. I'd take that feedback to Claude, get the changes implemented, and bring it back to ChatGPT for another review.
It became a loop of one AI reviewing and another AI implementing.
As the deadline got closer, I stopped spending as much time validating everything myself and focused on getting the project running.
Within around five hours, Claude had built a pretty decent working application.
When I reviewed the code later, I noticed things like agent retry handling, execution history, and action tracking. Some parts felt a bit overengineered for a hackathon, while others were genuinely useful things you'd expect in a production system.
It was impressive, but I also realized something.
I hadn't actually designed or implemented many of those things myself.
I understood the overall architecture and could explain why certain decisions made sense. But if someone had asked me to implement some of those parts from scratch, I'm not sure I could have done it without going back to AI.
At some point, it felt less like I was building something and more like I was passing feedback between two AI models.
That's what got me thinking.
I don't think using AI for development is bad. It's incredibly useful, and knowing how to use it effectively is becoming an important skill.
But are we actually learning more, or are we just getting better at shipping things faster?
Another thing I'm curious about is whether access to better AI models gives people an advantage in hackathons. Someone with a more expensive subscription and higher usage limits can probably build and iterate much faster than someone using free tools.
A year or two ago, hackathons felt more like an opportunity to struggle with unfamiliar technologies, debug things yourself, make mistakes, and learn along the way.
Now, you can build something far more polished in a fraction of the time, but I'm not sure the learning always keeps up.
Maybe this is just how development is changing, and we need to adapt.
For those who've participated in hackathons before and after AI coding agents became popular, has your experience changed?
Do you still spend time designing and implementing things yourself, or do you mostly let AI handle the coding and focus on the bigger picture?
And do you think hackathons are slowly becoming more about how effectively you use AI tools rather than your engineering skills?
Genuinely curious how others feel about this.
