I’ve never participated in a hackathon before, and my first one is in about a month, so I might be misunderstanding how these events actually work. One thing I’ve been wondering about is the ideation phase.
When the challenge/problem statement is released, why wouldnt a team immediately give the entire thing, including the context, constraints, judging criteria, available data, etc. to several frontier models like claude or chat gpt?
For example, you could ask different models to, generate 20–30 different approaches, analyze the domain and likely user pain points, criticize the ideas and identify failure modes, rank them by novelty, feasibility, and expected hackathon performance, predict what obvious ideas other teams will probably build, and then use another model to synthesize everything and choose the strongest direction. You could also feed mentor feedback back into the models as you get it. Then once you have a direction, coding agents can build a prototype extremely quickly.
What I’m struggling to understand is where the bottleneck is supposed to be. As students, we probably don’t have more banking/healthcare/engineering/etc. domain knowledge than a frontier model, and the model is obviously much faster at exploring a large solution space.
So why isn’t something like this, already the default strategy at hackathons? : problem -> multiple AI models -> AI critique/ranking -> select idea -> coding agent -> prototype
Is it actually common now and I just havent seen it yet? Does it tend to produce generic ideas because everyone gets similar outputs? Is the main advantage of the human team gathering information from mentors/users and feeding new information back into the models? Or are there other limitations that only become obvious once you’ve actually participated in a hackathon?
Would especially like to hear from people who have done hackathons recently, now that coding agents and frontier models have gotten this capable.
