Ne

New gamification SaaS ready for feedback

Hacker News

New gamification SaaS ready for feedback

After a little extra time this year (due to COVID-19), I've finally reached a good point with a gamification service that I've been thinking about for quite a while. I believe all of the MVP features have been sufficiently implemented and tested and a modest website and admin tool has been built up to support it. So, I believe it's ready for a wider audience to check out and provide feedback. Check it out: https://r4nkt.com I'd love to hear any kind of constructive criticism on the website and the service itself. For those who are interested in trying it out for some time for free, let me know. We can get you set up and then I'd love to hear about your experiences...good and bad. Pricing is yet to be determined, but I'd love to hear peoples' thoughts on it, too. So...I'm ready for more feedback...! Please be gentle. ;-) Thanks...! :-) P.S. I'm still a newbie here...and it looks like I messed up my first post. I hope this one's better. ;-)

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
72%72% 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: new · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, 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
50%50% 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 · Missing: plus, platform, intuitive
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: saas · 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.

Incorrect prediction on native model

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