Mapname – anonymous social network for organising points of interest

Hacker News

Hello HN, with mapname you can create a unique name for any particular point of interest as in prefix(id).group(optional).suffix. Once a mapname created then this can be used as an address, discovered and socialised. Mapname is free and has no advertisements. At onboarding pick a unique prefix. That is all about onboarding. Users can also organise collection of places with just two words, for example coffee shops i like at NY is abc.1 or I have visited Melbourne last year where you can see at abc.melbourne Mapname designed as an anonymous Social Network where users particular perspective engage with like minded users, instead of their persona. Personal Images are not promoted. Here is one minute user guide https://www.youtube.com/watch?v=G42THV9YcY4 More mapnames are on twitter https://twitter.com/NameYourWay You can consider this as "Pinterest of coordinates" Another way to think this; mapname(s) to coordinate(s) is similar to domain name(s) to ip address(es). Instead of hard to remember IP Addresses we use domain names. Similarly instead of coordinates or map list urls a mapname can be in use. Between mapname and a coordinate 1->1 or N->1 or 1->N relationships are possible. Whenever a mapname visited, the most relevant what3words attached to it so that you may communicate whichever is more relevant. Mapname is trying to reduce friction while describing addresses and organising points of interests, for transportation, navigation, directions uses integrations. A mapname can be viewed in your favourite maps (Google Maps, Apple Maps, Yandex Maps, Bing Maps, Baidu Maps) or with what3words, citymapper, waze. it is available on android & ios, feel free play with it, any feedback or question is appreciated. What other location/navigation/map integration would you benefit?

Visit product View on Hacker NewsMay 20, 2023

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

76%76% 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.

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

66%66% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

13%13% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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