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.

Implement a manual process for early-stage validation

As a technical founder, I’m struggling with the urge to automate everything on Day 1. I’m doing the work manually for my first 3 users but feel friction about whether I should automate before fully understanding the customer's emotional pain point. I want to know if focusing on manual processes is a valid approach for early-stage validation.

Original Source

@u/Medical-Variety-5015 | 5 pts | 3/12/2026

I’m an engineer/data analyst currently working on a Micro-SaaS. I’m following the YC advice of "Do things that don't scale," but I’m finding a weird friction point: **Automating is my default language.** I’ve identified a high-intent pain point in **\[Niche, e.g., Logistics/Data Compliance\]**. Instead of building the full SaaS, I’ve been doing the work "manually" for my first 3 users. However, "manually" for me means I’ve already written a set of Python scripts and n8n workflows that handle 90% of the work in the background. **My Dilemma:** Am I cheating the "Validation" phase by automating the service before I’ve fully understood the customer's emotional pain point? Or is "Automation as a Service" a valid way to find Product-Market Fit in 2026? **I’d love to hear from YC alumni:** In the early days, did you focus on the "Human-in-the-loop" to learn the edge cases, or did you build the "Logic Engine" first and iterate on the feedback?

Discussion

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