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Log expenses with Apple’s Natural Language

Hacker News

Log expenses with Apple’s Natural Language

Hey, sharing my first ever app for tracking expenses :) Key features - Expenses are auto-organized for you using NLP (type "Rent" --> purchase is placed under "Household") - Visual layout for spotting spending habits - Free, no account/ads/tracking Story - I'm a designer /w some education in coding. Swift kickstarted my iOS journey (vs. ObjC) - Took 4y of work and testers feedback --> most screens redone 2-3 times (if anyone's interested in previous versions) - Started freemium, now free coz seems like target market is more into saving than spending - Costs: $400 for having TestFlight during 4y of dev + ~$150 for cumbersome ASO tools + $100 in store ads - Not looking to make $$ but rev is ~1/4 of costs Would love to hear your thoughts! P.S. Got tips on "indie" promotion or getting featured on the App Store? Contacts at Apple told me the team isn't into financial apps :/ Thanks :)

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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: started, ios · Missing: supports, reddit linkedin, podcasting
86%86% 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: apple, apps, visual · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: ios, apps, education · Missing: mobile apps, personal, entrepreneurs
53%53% predicted probability of success on TrustMRR, 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
39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
33%33% 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
20%20% 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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