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), and GitHub. You can also trigger on-demand scraping via the API or web interface to get the freshest data for any topic.

[Trippy] Expose AI usage analytics data endpoints

Expose AI usage analytics data endpoints for tracking API consumption, costs, and popularity metrics, including total requests, successful/failed, avg response time, popular destinations, and estimated OpenAI API cost. Also, per-user usage analytics.

Original Source

@@Syed1012 | 0 pts | 9d ago

## Description Expose AI usage analytics data endpoints for tracking API consumption, costs, and popularity metrics. ## Acceptance Criteria - [ ] `GET /api/ai/analytics/usage?period=month` returns: total requests, successful/failed, avg response time - [ ] `GET /api/ai/analytics/popular-destinations` returns top 10 most-requested destinations - [ ] `GET /api/ai/analytics/cost-estimate` returns estimated OpenAI API cost for period - [ ] Per-user usage: `GET /api/ai/analytics/usage/me` — personal AI usage stats - [ ] Usage data aggregated from `ai_schema.ai_request_logs` table (#4) - [ ] Caching: analytics endpoints cached 5 minutes (not real-time critical) - [ ] Admin-only: global analytics; users see only their own stats - [ ] Response includes comparison to previous period (trend arrows) - [ ] Unit tests for aggregation queries ## Technical Notes - Depends on: #4 (request logging), #39 (AI setup) - Use JPA `@Query` with GROUP BY for aggregations - Consider materialized view or sche

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