Ac

Acute – In-app widget to collect and analyze feedback (getacute.io)

Hacker News

Acute – In-app widget to collect and analyze feedback (getacute.io)

Hey guys, I've recently launched Acute (https://getacute.io), a service for collecting in-app feedback, analyzing it based on user data, and prioritizing it based on RICE factors. Acute offers an in-app widget that you can install with two lines of code, is customizable, and captures the feedback's user metadata (creation date, mrr, plan) so you can segment and analyze your feedback. I am looking for any feedback you might have regarding the service. Anything from landing page, value proposition, features, design to the overall concept. Would you find this useful? What are the reasons you wouldn't use the service? Any constructive criticism would be greatly appreciated, no matter how negative. Thanks a lot guys!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
70%70% 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: user, code · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
30%30% 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 · Strong signals: mrr · Missing: arr, revenue, profit
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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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