QuickFeedback – generate QR codes and feedback forms

Hacker News

Last year I visited a hotel and kept noticing small inconveniences and didn't want to waste my holiday time complaining at the reception. I wished they had an easy way to give them feedback and let them know how guests felt. So I created QuickFeedback, a way for hotels to intercept and handle feedback before it ends up in negative public reviews or gets forgotten. I purposely kept the core concept very domain-agnostic as it is very fundamental and could be used for almost any physical product/service. For a while I was looking for an interesting side project. As a developer, once this idea came along I thought I could put something together quickly and cheaply. So here it is, I'm trying to follow advice and ship early so don't expect much! This is my first go at something like this so please go easy on me but I am looking for honest feedback! Thanks!

Visit product View on Hacker NewsMay 23, 2023

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

Product HuntOn track for Day 1 leaderboard · Strong signals: physical, code, plain · Missing: mac, agents, macos

65%65% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: reviews · Missing: plus, platform, intuitive

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

17%17% 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.

Correct prediction on native model

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