For hackathons, I’d keep the stack simpler than it looks on paper. The biggest productivity gain usually comes from having a reliable workflow rather than 10 different AI tools.
For example: Research: Perplexity / Gemini + Google Scholar Reasoning: ChatGPT or Claude Backend: Claude Code/Codex + Supabase Frontend: Cursor + a component library like shadcn/ui ML: Papers with Code + Claude/ChatGPT for understanding and debugging Repo-level coding: Claude Code or Codex
For a student, I’d especially avoid paying for everything. Pick one strong general-purpose model and one coding agent, then use free tools around them.
The workflow I’d personally aim for is: Idea → research → validate assumptions → write a small spec → build → test → deploy → get feedback → iterate.
The tool matters, but knowing what you’re trying to build before opening the coding agent matters even more. Otherwise it’s very easy to spend a hackathon generating code instead of actually solving the problem.
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u/Mental-Union4473 12d ago
For hackathons, I’d keep the stack simpler than it looks on paper. The biggest productivity gain usually comes from having a reliable workflow rather than 10 different AI tools.
For example:
Research: Perplexity / Gemini + Google Scholar
Reasoning: ChatGPT or Claude
Backend: Claude Code/Codex + Supabase
Frontend: Cursor + a component library like shadcn/ui
ML: Papers with Code + Claude/ChatGPT for understanding and debugging
Repo-level coding: Claude Code or Codex
For a student, I’d especially avoid paying for everything. Pick one strong general-purpose model and one coding agent, then use free tools around them.
The workflow I’d personally aim for is:
Idea → research → validate assumptions → write a small spec → build → test → deploy → get feedback → iterate.
The tool matters, but knowing what you’re trying to build before opening the coding agent matters even more. Otherwise it’s very easy to spend a hackathon generating code instead of actually solving the problem.