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.

A/B Testing Tools: Assist with Expected Value of Information (VoI) calculation inputs

Users struggle to apply the Expected Value of Information concept because they lack guidance on defining inputs like the cost of delay, the value they would pay for information, and realistic uplift ranges. A feature within A/B testing tools that helps users estimate or define these parameters would make VoI calculations more practical and actionable.

Original Source

@Ryan Lucht | 54 pts

Surely some things are not worth A/B testing... right?? I hear this all the time: "it's not worth A/B testing a bug fix, or some idea we're super confident in, or some tiiiny tweak we're making." Well, guess what? You can determine that empirically! Don't just stand there shrugging your shoulders at me all "idk man" If you can: - define the stakes (baseline metrics and impact), and - define a plausible range of possible outcomes (a prior), you can put a specific dollar value on A/B testing whatever bug fix or sure bet or tweak you're trying to weasel your way into just shipping. This is the Expected Value of Information. Calculate it, and you'll know "if you can test it for less than $X,XXX... you should test it." In my experience, "test everything" skeptics chronically underestimate this number, sometimes by an order of magnitude. So I built an easy-to-use calculator. Even has a PNG export button at the end so you can Slack your coworkers the outcomes and settle arguments with MATH. https://lnkd.in/dMAzEpTT (Feedback very much welcome!)

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