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I built the simple expense tracker I wanted to use
Hacker NewsI built the simple expense tracker I wanted to use
Hi HN! I built Spenso because I wanted a simple record of my spending, but never kept up with budgeting apps. I wanted to type or dictate something like "Starbucks 25" or "Uber Eats 25, Lidl 54.30 yesterday" and call it a day. Spenso understands the message, logs each expense, and automatically categorizes it. It’s built for iPhone, works offline-first, and uses AI only when an entry can’t be categorized on-device. I’d love honest feedback from anyone who has tried and abandoned expense trackers before.
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Did not reach leaderboard
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting82%82% 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.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal61%61% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: apps · Missing: mac, agents, macos49%49% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released30%30% 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, revenue21%21% 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, crypto0%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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