Squabblr – A Twitter/Reddit hybrid platform

Hacker News

Hey guys, I made a post about this about a week ago and was told to create a Show HN post. So here we are! Squabblr is a hybrid that takes Twitter style posting and combines it with Reddit style commenting. It came up from a personal want where I liked using Twitter to see what people I'm interested in are up to, but found that the reply system left a lot to be desired. It's very hard to have or follow conversations with their UI. While Reddit provided a great commenting system but didn't support following people very well (or had much usage of it at all). So I decided to create Squabblr. You can "tweet" random blurbs or have in-depth conversations in a single unified platform. You can follow people, and you can also have Reddit style communities for whatever niche subject you find interest in. If you find any interest in it, check it out here! https://squabblr.com Tech stack: - Laravel - Vue.js - MySQL - DigitalOcean Appreciate your time!

Visit product View on Hacker NewsAug 31, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: single, using · Missing: mac, agents, macos

71%71% 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

67%67% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

44%44% predicted probability of success on AppSumo, 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

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

nativeThis product was originally launched on this platform.

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

33%33% 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

15%15% 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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