I made 200 USD passively as a 16yo developer

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Excel Tutor is my second SaaS, in the past two weeks, where I have generated over 200USD passive income alone. Hey, I am a 16 year old developer based in Hong Kong and I've been trying to make successful SaaS's for the past 2 years. And it's only now worked. I built an MVP app called ExcelTutor.pro! ExcelTutor is an AI-driven assistant designed to supercharge productivity within Excel. It automates complex formula generation, accelerates data analysis, and delivers clear, actionable insights with ease. Built for professionals and teams, ExcelTutor integrates seamlessly into existing workflows to eliminate manual spreadsheet tasks and reduce errors—empowering smarter, faster decision-making.

Visit product View on Hacker NewsJun 21, 2025

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Actual performance

3points

2comments

Did not reach leaderboard

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

88%88% 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: tasks · Missing: mac, agents, macos

68%68% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, io · Missing: https docs, excited, just released

52%52% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

40%40% 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

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: saas · 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 · Strong signals: smart · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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