Codedraft – learn CSS at your own pace

Hacker News

Hello! In the last few months I've worked on creating Codedraft, a mobile application that allows you to learn CSS. It is currently available on Android only: https://play.google.com/store/apps/details?id=com.codedraft The main idea of the app is to learn and practice at your own speed, that's why the information is split into very small pieces. It offers two courses, CSS Basics and Advanced CSS, each of them being split into several sections. Another feature of Codedraft is the ability to practice what you learn in the web app, available at https://app.codedraft.io. When learning in the mobile app, occasionally you have the option to scan a QR code on the web app - this action will automatically take you to a relevant coding exercise. The web app can also be opened from the "Scan Code" tab in the mobile app. I've always wanted to work on an educational software, however this is my first dive into the domain. :) I would be very happy to get any type of feedback possible. Let me know if you have any questions and thanks a lot for reading!

Visit product View on Hacker NewsJan 12, 2022

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

89%89% 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

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

nativeThis product was originally launched on this platform.

TrustMRRFits verified-revenue profile · Strong signals: apps, month, google · Missing: mobile apps, ios, personal

53%53% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Product HuntUnlikely to reach the leaderboard · Strong signals: google, apps, coding · Missing: mac, agents, macos

44%44% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

20%20% 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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