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How I made an iOS party game in just 3 days

Hacker News

How I made an iOS party game in just 3 days

Hey guys, this is my first Hacker News post. Happy to be here :) I recently created Imagine Storiez, where players are challenged to make a short story using 5 random words in under a minute. The game is safe to play with kids or at work. The important thing is that I made the first version of the game in just 3 days of coding. Here's how: 1) Mindset: Every idea can be condensed down into something that can be shipped in 24-72 hours. Even if it’s an extremely simplified version that doesn't have the features that the final app will, the core idea can almost always be conveyed using something simpler. [1] 2) Quantity: This is my 6th app. As I make more and more apps, I can see myself re-using parts of my code, which helps speed up development time. For example, in Imagine Storiez, I re-used a confetti extension I had found before [2], a press-able button animation I use frequently, and certain UI components I had written out from my previous apps. 3) Research: Before jumping into coding, I took some time to do design research, and I came across some great articles, one of which I used [3], and some of which I didn’t but could help me in the future [4]. 4) List of goals: After researching, I always lay out a list of EXACTLY what I am going to do for the first version to avoid getting carried away. If I get more ideas while coding, I write them in a separate “Ideas” list for later. For example, I have already taken another 3 days to add a mode with emojis since the first version of the game, and for the update I used the same approach by listing out exactly what I wanted to add so that I can stay focused. My Issue: I plan to add a lot more modes to the app with time, but before doing that I would love to know if the game I made is actually fun. I did test it with family and friends, and we had a great time seeing each other struggle to include all the words in one minute. If anyone would be interested in trying it out and giving me feedback, it would mean the world to me. I would also be more than happy to give away the app for free through promo codes to anyone who asks in the comments. Thank you for reading! If you found something of value please let me know in the comments! Links: [1] Marc Lou does a great job explaining this concept: https://www.youtube.com/watch?v=ebcakmRzl0I [2] Confetti Animation: https://medium.com/@jaredcassoutt/swiftui-tutorials-designin... [3] SwiftUI Gradients: https://cindori.com/developer/animated-gradient [4] Neuomorphic Design: https://sarunw.com/posts/how-to-create-neomorphism-design-in...

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Indie HackersFits the IH revenue-focused audience · Strong signals: created, para, ios · Missing: supports, reddit linkedin, podcasting
96%96% 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, new, using · Missing: mac, agents, macos
66%66% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: ios, apps, way · Missing: mobile apps, personal, entrepreneurs
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
34%34% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: hacker news, ide, io · Missing: https docs, excited, just released
28%28% 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 · Strong signals: arr · Missing: mrr, revenue, profit
13%13% 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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