Zero Student Debt Initiative – Change for Freedom

Hacker News

https://rounduptozero.com/ Today's our soft launch as a bootstrapped startup! Round-Up to Zero™ is a platform that allows users to round-up their daily purchases and automatically contribute the change to their linked student loan account. Those without student loans can sign up as a supporter to match the Round-Ups of family and friends. By making these monthly contributions users have the unique ability to reduce their principal loan amount automatically. The result is that these small, incremental payments greatly reduce the lifetime of your loan and the amount of interest you pay over the lifetime of the loan. We have a long to do list, but wanted to share in the excitement of what we've accomplished so far! Feedback would be appreciated! Our calculator gives you an idea of the impact small transactions can have over the years.

Visit product View on Hacker NewsNov 13, 2018

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Hacker NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released

62%62% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

56%56% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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

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

44%44% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

22%22% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: bootstrapped · 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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