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Gripeless – Capture Complaints

Hacker News

Gripeless – Capture Complaints

Hey everyone, We all see these amazing ideas being posted on Show HN but let's be real - every product has some gripes that could be improved. The gripes could be things like broken flows, unintuitive UX behaviors, or some interactions being too slow - you name it. Everybody has their favourite products, and each of those products (awesome as they may be) have a couple of small things that could be improved to make them even better. What's interesting, though, due to a variety of reasons we usually don't report those things at all - we might not think the complaint is solicited, we might not think that anything will be fixed after we report the thing, or even just writing and sending a semi-formal letter is too much friction for such a simple task. This is why we've built Gripeless, an all-in-1 solution to make the process as smooth as possible for the user so that they don't have to write emails, don't have to think whether the product-owner is acting on those complaints or even requesting complaints in the first place. Gripeless can be installed onto any website and the provided script is only 20kB gzipped. Both the script and the management dashboard are built with Elm, which turned out to be an amazing choice for making things fast. We're specifically building a product that can capture complaints and efficient access to them, so Gripeless is not a replacement for: - Email support software - like Zendesk or Supportbee - Automated analytics - like Google Analytics or Mixpanel - Error reporting software - like Sentry or Rollbar Complaints, that's what we're all about. There's a lot of existing science behind complaints in the service industry which this product is based on, and we've linked a bunch of it on https://usegripeless.com. Let us know what you think. - [redacted] [1] [redacted] Create your project over at [redacted]

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
85%85% 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: google, user, email · Missing: mac, agents, macos
78%78% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
70%70% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: google · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: intuitive, efficient · Missing: plus, platform, reviews
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% 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.

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