A New Kind of Chat Room

Hacker News

I’ve developed an application that reimagines chat rooms by integrating them with a world map. Each user can claim a rectangular piece of land on the map, referred to as a "banner." Users within close proximity are grouped into a chat room by the app. The banner’s size can expand or shrink based on the density of users in the area and the number of coins in the user’s account. Key Features: Real-World Interaction Model: Users are pseudo-anonymous, akin to real-world interactions. Coin balance determines user size and visibility ("stature"), while endorsements and other activities form a unique endorsement chart, serving as a digital representation. This allows for interaction without revealing one’s entire social or professional network. Community Clusters: Users can form open communities based on real-world locations. New users can discover and join these clusters via heatmaps, without needing specific URLs or hashtags. Initial Coins: New users start with 1000 coins. These coins can be used to endorse other users' content, earning stakes in the endorsed banner. When others endorse that banner, the original endorser gains more coins. This endorsement economy is experimental, and could eventually involve crypto tokens. Useful Points: Banner Interactions: Clicking on a banner opens it fully. A button with a chart icon on the top right flips the banner to display the activity chart. Compatibility: The application currently works best on Google Chrome. Heatmap View: Zooming out on the map reveals a heatmap of banners. Side Panel Tabs: First Tab: Displays a feed of content from the visible map area. Second Tab: Contains the cluster chat. Current User Base: At the moment, only my friends and family are using the application, so banners are predominantly found in the Chicago area. For more details, refer to the white paper provided. I’m excited to share this application and look forward to your feedback!

Visit product View on Hacker NewsMay 28, 2024

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

79%79% 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 · Missing: supports, reddit linkedin, podcasting

78%78% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, 000 · Missing: https docs, just released, exist

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

nativeThis product was originally launched on this platform.

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

56%56% 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 · Strong signals: chat, crypto · Missing: web3, cryptocurrency, make money

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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