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.

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.

[AI Agents] Improve reliability with human oversight

I added a 'human in the loop' prompt review step before final output for AI agents, which tripled reliability. Looking for more suggestions to enhance this feature.

Original Source

@u/Indie_SaaS | 2 pts | 3/10/2026

Building AI agents is not about AI. It’s about **thinking in systems.** Most people use AI like this: Prompt → Get answer. But building agents is different: Problem → Steps → Automation → Output. Once you start breaking problems into **repeatable steps**, you realize almost anything can become a small AI workflow. And the crazy part? Most of these workflows are **5–6 simple steps.** No complex tech. Just clear thinking. After building those 3 mini agents (research, repurposing, lead-research), I iterated on the content repurposer - the one people seemed most excited about. Key fix that tripled reliability: * Added a "human in the loop" prompt review step before final output. * Tools handle the heavy lifting, but a quick manual tweak catches weird formatting or off-tone issues. Result: Outputs went from "good enough" to "actually usable without edits" in most cases. Big takeaway: No-code AI is powerful, but chaining prompts + light oversight turns prototypes into something people might depend on.

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