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.

Improve Agentic AI execution speed

The user wishes for agentic AI systems to be 10x faster, which would enhance their workflow for exploring ideas and code paths by allowing multiple agents to run in parallel more efficiently.

Original Source

@@anvanvan | 6 pts | 7/21/2025

Agentic AI helps me to be more in the flow. Especially with exploring different ideas and code paths. I can let multiple agents run in parallel implementing the various ideas I had. Then evaluating all the outcomes, learn from them and choose the ones I like. Only wish agents would be 10x faster.

Discussion

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