Totalizer – P2P yes/no polling for meetings, no signup required

Hacker News

Totalizer is a real-time yes/no polling app that uses P2P (WebRTC) for instant sync across all participants. I built this because I wanted a quick way to play confession games at parties with friends – "Do you have a crush on someone here tonight?" "Are you actually pretty drunk right now?" – but every polling app I tried required accounts or had annoying delays. I just wanted to share a link and get everyone voting immediately. How it works: Create a room, share URL or QR code. Type your yes/no question. Everyone votes anonymously, results appear with confetti. Tech: React 19, WebRTC for P2P, Go + Redis backend. Questions and results travel peer-to-peer; the server only handles auth and atomic vote counting (Redis Lua scripts prevent duplicates). Great for party games, icebreakers, and quick group decisions. Feedback welcome!

Visit product View on Hacker NewsJan 27, 2026

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Did not reach leaderboard

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Hacker NewsStrong engagement from HN community · Strong signals: lua, 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

52%52% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

38%38% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

27%27% 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

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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