Image converter built in Python and React (beta testing)

Hacker News

Hey guys, this is my first post on HN so please let me know if I did something incorrectly. I've been programming for a few months seriously now and have created my first full-stack application: an online picture converter. It features a full REST api, integrated payments through Stpipe, user conversion limits and tracking, and an overall a nice conversion experience (in my opinion). It was built with React for the front-end, and Python (Flask) for the back-end. Before I officially launch it, I would like to show it to HN for some feedback. As this is my first complete application, I fear I may not have discovered and fixed all of the bugs yet, hence why I would ask any one interested to give this app a test! For testing purposes, the payments have been turned off and any account registered will be given the highest conversion limits to play around with. At the end of the beta (no exact date) everything will be wiped and the site will launch officially (emails will be sent prior to warn beta users). I'm interested in bugs relating to converting images to different formats, security vulnerabilities, functionality of the API, and overall UX improvements to be made. Here's the link to the app: https://www.letsconvert.app

Visit product View on Hacker NewsJan 23, 2019

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

83%83% 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: user, email · Missing: mac, agents, macos

52%52% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

35%35% 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

17%17% 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.

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