(For YC) Improvised Quora to Solve “First Impression Influence” Problem

Hacker News

Website: https://vaadit.com Problem: General world population is innocent enough to get influenced by first-impression it sees and the situation is worse on internet. User gets influenced by first answer it sees on Quora(or any other Q/A forum). It is impacting mass results too lately (Brexit, US elections, Indian Hindu-Muslim communal uprising etc.). Solution: Some web-platform, which shows not just one answer but multiple-perspective-based-answers right on first-impression. Then users can compare and reason better seeing different viewpoints right in front of them.

Visit product View on Hacker NewsMar 25, 2019

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2points

Did not reach leaderboard

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

65%65% 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.

AppSumoStrong fit for a featured deal · Strong signals: platform, users · Missing: plus, intuitive, reviews

59%59% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: io · 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.

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

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

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

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

12%12% 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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