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Recipe Costing application

Hacker News

Recipe Costing application

Hello everyone! I recently graduated from Udacity's Intro to programming program. I am excited to share my first made-from-scratch project (i.e with Flask/Bootstrap): a Recipe Costing application using Google Sheets as a database. Here is the link to the repo https://github.com/skarmen/recipe-costing-app. I have also deployed the app successfully on Heroku (auth access is required). Heroku http://recipekosting.herokuapp.com/, user: public, pass: public2020 Link to the Google Sheets: https://docs.google.com/spreadsheets/d/1rAQuEhSI4mWI0Ja7D00xXpbP7RcOJJbCBa2hht5JH-g/edit?usp=sharing I have been working part-time on this project for 3 months now, learning as I go. I'm eager to learn more so any feedback would be greatly appreciated! Please feel free to connect with me with any junior web dev opportunities in Toronto or remote!

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Hacker NewsStrong engagement from HN community · Strong signals: excited, io · Missing: https docs, just released, exist
74%74% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntOn track for Day 1 leaderboard · Strong signals: google, user, using · Missing: mac, agents, macos
54%54% 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, google · Missing: mobile apps, ios, personal
48%48% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
44%44% 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
16%16% 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.

Incorrect prediction on native model

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