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From No CS Background to a Diet Tracking App

Hacker News

From No CS Background to a Diet Tracking App

As a person who graduated from a business school, I've never taken any Computer Science classes. Before I got started, I doubted that I could even make this Diet-Tracking app. Hope you guys can give me some feedback about this app. Appreciate it! Language: SwiftUI Data and Authentication: Firebase About this app: You can track your daily calories, carbs, protein, and fat. You can add your food by meals and one tap to add yesterday log. It also supports custom food and saves favorite foods. Some thoughts from this experience. Start earlier: I spent half a year learning Storyboard. It turned out to be completely useless when I knew I could use SwiftUI. Spend more time to find the correct API: I tried 3 different APIs and wasted too much time on the two that I don't use. Design an MVP first: I had too many ideas when I was writing the UI part, but then I realized I couldn't finish the app if I built everything I wanted Don't rely too much on ChatGPT: When you copy a lot of code at once from ChatGPT, it's very likely to crash What I don't know: No Idea how to advertise it: I couldn't even find the App if I only searched "Keto" in AppStore even though it's called "Keto Spell" Some performance issues: It takes about 2 seconds to open the first food details view. Then it becomes normal to open the other views. It also takes 2 seconds to upload the image to Storage and send back the image URL to Firestore. I use a listener to update the UI. Usually, the loading time isn't that long when I use other apps. I have absolutely no idea about how to solve these issues Please let me know what you think about this app or the tools I used. Thanks a lot!

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Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Strong signals: supports, started · Missing: reddit linkedin, podcasting, created
87%87% 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: apps, computer, new · Missing: mac, agents, macos
83%83% predicted probability of success on Product Hunt, 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
44%44% 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: apps · Missing: mobile apps, ios, personal
41%41% 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
32%32% 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
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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