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.

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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.

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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.

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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.

[n8n] Build an AI observability node

Create a plug-and-play community node for AI observability that provides a dashboard with token usage, latency, errors by workflow/node, and alerts for token bleed.

Original Source

@u/Soft_Statement_9725 | 2 pts | 3/14/2026

**Anyone else flying blind with n8n AI workflows? Building a “Datadog for n8n.”** I’ve been building a lot of AI agent workflows in n8n and observability is a nightmare. Questions like: * Is an agent stuck in a loop burning tokens? * Which node is causing failures? * Are prompts quietly failing 20% of the time? I tried LangSmith, but it’s rough with n8n: * Hard to use on **n8n Cloud** (env var issues) * All traces go into one giant project * Hard to map traces back to specific visual nodes * Evals aren’t integrated into workflows So I’m building a **plug-and-play n8n Community Node for AI observability**. Idea: * Drop the node after AI steps * Add API key * Get a dashboard with **token usage, latency, errors by workflow/node**, alerts for token bleed, and automatic output evals. Works on **n8n Cloud** and requires no Docker setup. **Question:** If this existed today, would you use it? What features would make it a must-have?

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