CUVE, using tiles UI that can diagonalize and transform coordinates

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Hello everyone, We’ve been working on an online test prep service called CUVE. CUVE is a multi-dimensional, metro-UI-based coordinate map that transforms (in a relativistic way) based on user inputs. It helps users discover the shortest path to master a given subject. In addition, we’re planning to differentiate by developing educator/tutor-focused functions, such as questions editing and lessons curation. Now, we face two critical questions as we try to decide our focus while preparing to apply for accelerators this year. 1. what should we focus, user acquisition or more demo-grade functions? User acquisition would require enhancing our Discovery algorithm and completing admin-related functions. These functions will help us acquire more test-taker users. On the other hand, if we do more demo functions, we will develop tutor-focused use cases, such as questions editor. Given our time constraints, these will be demo-grade and we probably won’t be able to market them to acquire users. 2. what accelerator should we target, general (Y Combinator/Techstar) or education-focused (Imagine K12)? This is somewhat related to the first question because if we target education-focused accelerator, we would focus more on demo-grade functions for educators/tutors as they play a significant role in the ecosystem. FYI, we already tried for Y Combinator twice but never got invited for an interview so we’re thinking that we may not be the right fit...although we’ll probably try again. So given those considerations, we'd really appreciate if you experts can help us with comments. Thanks in advance. www.cuve.me

Visit product View on Hacker NewsFeb 12, 2014

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

73%73% 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.

TrustMRRFits verified-revenue profile · Strong signals: users, way, education · Missing: mobile apps, ios, personal

65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

63%63% 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

54%54% 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: users · Missing: plus, platform, intuitive

32%32% 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

11%11% 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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