Conv911, a social-media platform to improve at texting

Hacker News

This is a web app we built for fun, basically a social-media platform for people to get help with their online interactions, like answering your boss, a colleague, or just handling any random conversation. I know some people were concerned with ethical issues with such website, which is why we made a clear guideline on the type of content which should be posted there. As an introvert it often occurred to me to spend a week before answering an email, or spend hours to answer a random discord conv, this is the kind of helpful use-cases we are aiming for. I know HN is usually interested in more technical topics, but any raw feedback is appreciated :) The website is built with React/Tailwind on the frontend and Rust for the backend

Visit product View on Hacker NewsOct 29, 2022

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1points

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

75%75% 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.

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

71%71% 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 · Missing: plus, intuitive, reviews

58%58% predicted probability of success on AppSumo, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Missing: mobile apps, ios, personal

50%50% predicted probability of success on TrustMRR, 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.

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

14%14% 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

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

Correct prediction on native model

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