Feature Monkey – The feedback tracker for building better features

Hacker News

I build a lot of products in the past, all of them failed. I realised that we didn't listen to our users enough, Maybe not at all. Then thought why not something like HN, where people can post their feature requests and upvote. Did a bit of research and found a couple of product in the market. But all were ridiculously expensive for a feature tracker. I didn't want to take a loan to use a feature tracker Then last year I build a product using no code to track the feedback of customers and it helped a lot. (It won Product Hunt makers festival tools for teams). Later talking to other founders we found this as a common mistake. Hence we build this ground up a platform to collect feedback from customers. Please give it a try. Anyone who signs up gets the product for free please copy-paste into your browser. https://www.featuremonkey.com/

Visit product View on Hacker NewsMay 16, 2020

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

90%90% 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

81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Missing: https docs, excited, just released

57%57% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

47%47% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

19%19% 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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