RequestHunt is a community-driven platform that collects and curates feature requests from X (Twitter), Reddit, and GitHub. We help product teams discover what features real users are asking for, making it easier to validate product ideas and prioritize feature development based on actual user demand.

How does RequestHunt collect feature requests?

RequestHunt uses AI-powered extraction to automatically collect feature requests from social media platforms including X, Reddit, and GitHub. The platform monitors discussions, issues, and posts to identify genuine user feature requests, extracting key information like the requested feature, product mentioned, and user sentiment.

Who uses RequestHunt?

RequestHunt is used by product managers, founders, developers, and product teams who want to discover what features real users are requesting. It's particularly valuable for startups looking to validate ideas, established companies prioritizing their roadmap, and developers building products that solve real user problems.

Is RequestHunt free to use?

Yes, browsing and searching feature requests on RequestHunt is completely free. We also offer API access for developers who want to integrate feature request data into their own applications. Check our pricing page for API plan details.

How often is RequestHunt data updated?

RequestHunt continuously collects feature requests from Reddit, X (Twitter), GitHub, YouTube, LinkedIn, and Amazon. You can also trigger on-demand scraping via the API or web interface to get the freshest data for any topic.

[User Feedback Tool] Centralize feedback collection

A user is facing challenges in tracking user feedback across multiple channels when testing new UI interactions. They suggest a need for a tool that centralizes feedback collection to identify patterns more effectively.

Original Source

@u/mathswiz-1 | 4 pts | 3/14/2026

Quick question for product teams here. When we test UI changes (animations, flows, micro interactions, etc.), the feedback ends up scattered everywhere: (a)Slack messages (b)support tickets (c)app reviews (d)survey responses (e)customer interviews For example, we were testing a few animation concepts created with Jitter, and suddenly we had dozens of comments across different channels. The hardest part wasn’t collecting feedback ,it was understanding patterns. A few loud users would complain about something minor, while a quieter usability issue appeared repeatedly but wasn’t obvious at first. We eventually started grouping similar feedback to track how often certain issues appeared over time. That made roadmap discussions way more objective. Curious how other teams handle this stage. Do you manually tag feedback or use some kind of analysis workflow?

Discussion

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