Awaaz – revolutionary public opinion app for understanding society

Hacker News

We’re building a real-time mirror of collective thinking. Awaaz is a social insights platform where people anonymously vote on meaningful questions—about society, culture, relationships, work, politics, and everyday life—and instantly see how others think. No followers. No performative opinions. Just honest signals. Unlike traditional social media that rewards the loudest voices, Awaaz is designed for quiet truth: structured questions, genuine responses, and clear visual insights that reveal patterns in human thought. What makes it different: Anonymity-first → reduces social pressure and virtue signaling Signal over noise → votes, not debates Psychological safety → opinions without backlash Collective intelligence → every vote improves the data At its core, Awaaz taps into a simple human drive: “How do others really think—and where do I stand?” We believe curiosity, self-reflection, and belonging are timeless needs. When designed well, they don’t get boring. Building this with a small team, strong conviction, and a long-term view. If you’re interested in social data, human behavior, or products that prioritize depth over dopamine—happy to connect.

Visit product View on Hacker NewsDec 29, 2025

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Actual performance

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Launch Intel predictions

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

75%75% 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 · Strong signals: ios · Missing: supports, reddit linkedin, podcasting

74%74% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

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 · Missing: plus, intuitive, reviews

46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released

33%33% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

18%18% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: reward · 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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