Gis.chat – a Geospatial Community

Hacker News

Hi folks! I'm excited to show you gis.chat, a geospatial chat platform in both senses: a platform about geospatial topics and a geospatial platform itself, referencing the location of our communities. The setup is fairly simple and reproducible: a plain Zulip instance and a homepage with geospatial search capabilities. It seems almost trivial but it has some very nice features. I guess you should be familiar with Zulips stream/topic model to follow along ( https://zulip.com/help/streams-and-topics ). The core idea is that there are city-specific streams (currently represented by a pin), but there could just as well be streams about points of interest, line geometries (e.g. a river) or polygons (e.g. national park). - Every local stream can have the same topics, e.g. "general", "news", "meetups", "jobs" etc. - With Zulip's search you can either search for a particular topic, e.g. "news" in a local stream or instead in all streams and have some kind of news feed of the community with "topic:news" - Once more communities are added, specific filters could be added, e.g. country-wise or by drawing your own area of interest - Eventually, for the ones who like, users could associate themselves with a local community in their profile or add there main location so one could not only search for the local communities but instead also for individuals There are many nice features in Zulip's pipeline that would foster gis.chat: - Further nesting of streams/topics - Semantic search If for example Zulip would allow for saving coordinates (or better an entire geometry) in the Postgres DB, with the help of PostGIS, Zulip's search could allow for bounding boxes (or custom geometries). Let me know if you have any kind of other ideas or feedback!

Visit product View on Hacker NewsMay 23, 2023

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

63%63% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

54%54% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting

48%48% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

28%28% 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

16%16% 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 · Missing: web3, crypto, cryptocurrency

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

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