Gridtalk - Leave comments anywhere on Earth (Flask + MapTiler)

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I've created a Flask app that allows users to add and view comments tied to specific locations on a map. Here is how it works 1. Users can browse a world map and leave comments at any location. 2. Comments become visible when a user's cursor is within 50m of the comment's coordinates 3. Ideal for sharing local insights, tourist tips, restaurant recommendations, etc. It's also ideal for shitposting. I built this over the course of a few hours as demo to help me learn to how deploy a flask application using digital ocean droplets. I have listed the tech stack below - SQLite - Flask - AlpineJS - Digital ocean droplet. - MapTiler It's missing SSL so I will fix that at some point as I want to figure out how to do that. Bot protection would also be good to figure out as well as a better system for displaying comments. Overall droplets are a nice way to deploy stuff and much cheaper than using Vercel(Nothing new there!)

Visit product View on Hacker NewsSep 22, 2024

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

82%82% 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 HackersIH features products with proven revenue · Strong signals: created · Missing: supports, reddit linkedin, podcasting

49%49% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

45%45% 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: users · Missing: plus, platform, intuitive

39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

12%12% 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.

Incorrect prediction on native model

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