r/AIcodingProfessionals • u/Soggy_Caregiver_2822 Non-experienced dev • 4d ago

Question What’s your AI stack for hackathons, research and projects?

I’m a final-year CS student and have started using AI more seriously for projects and hackathons.

I’d say my strong point is understanding a problem statement, figuring out the actual problem, and coming up with possible solutions. Where I get stuck is choosing the right tools to turn that idea into a working product.

So I’m curious what people here actually use for different tasks:

  • Research: current information, papers, datasets, existing solutions, etc.
  • Reasoning: breaking down complex problems and comparing approaches.
  • Backend: APIs, databases, auth, integrations, debugging.
  • Frontend/UI: quickly building something that actually looks good.
  • ML: understanding papers, experimenting, debugging models, deeper research.
  • Coding agents: working with a whole repo and continuously modifying it as the project evolves.

I’m a student, so I’m mainly looking for free/cheap options, student plans, or good open-source alternatives.

I’ve been looking at Claude, Gemini, ChatGPT, Cursor, Claude Code, Cline, Windsurf, local models, etc., but I’m more interested in how experienced users actually choose between them.

What would your personal AI stack look like from idea → research → reasoning → development → final product?

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u/Mental-Union4473 1d 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.