MyUserFeedback – A Dead-Simple Way to Collect User Feedback

Hacker News

Hey HN, I built MyUserFeedback.com – an embeddable feedback widget for websites. It’s lightweight, easy to set up, and helps developers collect user feedback without overcomplicating things. Key Features One-line embed: Just drop an <iframe> into your site. Fully customizable: Toggle form fields like star ratings, name, and email. No bloat: Simple, fast, and privacy-friendly (no tracking). Free trial: Give it a shot and cancel before the trial period ends I wanted a straightforward tool that doesn’t require external JS or bloated third-party services. If you're building something and need quick feedback from users, give it a shot! Live demo video on the landing page: https://www.myuserfeedback.com Would love to hear your thoughts—what’s missing? What would make this more useful for you? I will continue to expand and improve on the product over time, increasing how customisable the form is and also providing a larger variation in feedback forms. For more regular updates on the progress or if you want to reach out directly, feel free to follow me on X @robert_watkin_ :) Cheers, Robert

Visit product View on Hacker NewsJan 30, 2025

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Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email · Missing: mac, agents, macos

86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

64%64% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoStrong fit for a featured deal · Strong signals: friendly, users · Missing: plus, platform, intuitive

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: video, users, way · Missing: mobile apps, ios, personal

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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