WhiteLotus – A Social App for Real People Only (AI-Verified, No Bots)

Hacker News

Hi HN I’m Arun, 17, and I built WhiteLotus — a social platform where every user is a verified human. No bots. No spam. No fake profiles. Just real people. Why I built it Over the last year I watched my friends slowly lose interest in “social” apps that felt anything but social — feeds of filters, fake accounts, and now AI-generated influencers. So I wondered: what would a social network look like if it was humans-only? What it does AI-powered face verification → one account per person Photo-based posting (“Moments”) → only real-time camera captures, no uploads “Kingdoms” (Fire / Water / Earth / Air) → light-hearted social tribes that compete weekly TrustNotes → friends can write short notes about you, building reputation Level system → shows contribution and trust within the community Right now we have ~40 users and I’m looking for feedback on: 1. The onboarding experience — does verification feel simple enough? 2. Whether the core idea (humans-only) feels valuable or gimmicky 3. Thoughts on privacy and verification ethics — what would make you trust a system like this? Tech stack Flutter (Android + iOS) Django + PostgreSQL backend What’s next Improving retention loops (Moments + Kingdom competitions) Open-sourcing the verification flow for transparency Possibly building a “Proof-of-Human” API for other apps If you’re curious, you can try it here: Play Store Link : https://play.google.com/store/apps/details?id=com.whitelotus... I’d love honest feedback — technical, ethical, or product-related. My goal is to make WhiteLotus a small step toward a human internet in an AI-flooded world. Thanks for reading, – Arun

Visit product View on Hacker NewsOct 25, 2025

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

87%87% 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: google, apps, user · Missing: mac, agents, macos

80%80% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

43%43% 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: platform, users · Missing: plus, intuitive, reviews

33%33% 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

29%29% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: real people · 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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