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A simple air quality app

Hacker News

A simple air quality app

I made this app for myself, but it's probably worth sharing. When I tried to find a nice app that I could use to check air quality multiple times a day, I was shocked. All of them looked boring with traffic-light coding and overcrowded with unnecessary data. On top of that, they either contained ads or required payment. I didn't want to use any of those apps every day, so I thought, "Well, how hard could it be?" and accepted the challenge. I expected to finish development in a month, but since I'm a designer and don't normally program apps, I had to learn Swift on the go. This required a month more than I expected. In my designs, I'm usually way too focused on efficiency. Since this was my personal project, I wanted to experiment, so I didn't use standard controls or color coding. Inspired by avant-garde style, I used forms and movement to convey air quality, as well as an asymmetrical layout and overflowing visualizations. I enjoy using the app, and I hope you will, too.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: apps, visual, using · Missing: mac, agents, macos
81%81% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
79%79% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: personal, apps, month · Missing: mobile apps, ios, entrepreneurs
61%61% 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
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: io · Missing: https docs, excited, just released
42%42% 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
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
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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