I made an app called Girl Math that makes saving money fun

Hacker News

Girl Math started as a weekend project when I realized my friend was terrible at saving money but amazing at justifying purchases with elaborate mental gymnastics. Then I thought: what if that same creative energy actually helped you save? The app turns "I didn't spend" moments into visible progress toward things you really want. Set goals, log tiny wins in seconds (skipped a latte, walked instead of Uber), and watch a satisfying progress bar fill up with haptics, confetti, and a playful receipt at 100%. Under the fun, it's a habit loop: immediate dopamine for the right behavior, one active goal for focus, and daily micro-wins that build real momentum. No spreadsheets, no guilt trips. Just making your small choices feel like they add up to something bigger. Runs entirely on-device with no accounts or tracking. Just you, your goals, and your wins. I'd love your feedback, and if you've got a favorite girl math moment, drop it below so I can add more examples to inspire users!

Visit product View on Hacker NewsSep 21, 2025

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4points

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, tiny · Missing: mac, agents, macos

69%69% 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 · Strong signals: started · Missing: supports, reddit linkedin, podcasting

61%61% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

42%42% 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

37%37% 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 · Strong signals: users · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: active · Missing: arr, mrr, revenue

25%25% 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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