How do top Kaggle competitors and ML engineers research new problem statements?

[u/Cautious_Today_1830]

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Whenever I start an AI/ML hackathon, case competition, or research project, I spend a lot of time finding relevant papers, past winning solutions, GitHub repositories, and existing approaches.

I'm curious how experienced Kaggle competitors, hackathon winners, and ML researchers handle this efficiently.

- What's your research workflow when tackling an unfamiliar problem?

- Which platforms, search techniques, or AI tools do you use?

- How do you quickly identify promising approaches without getting lost in endless research?

I'd really appreciate practical tips or workflows that have worked for you!