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Make Microdonations to Charities via Intuit Customer Data API

Hacker News

Make Microdonations to Charities via Intuit Customer Data API

Hi Everyone, I've been on Hacker News since 2008, the community demographics and philosophy has changed a lot but what has always stayed the same is the supportive and critical feedback people give to others on their startup's. But I've also become very jaded with the startup community over the years as I see a lot of people focus on passive income, applying to startup accelerators to network and ultimately hoping to get an exit, and an emphasis on social media/advertising ecosystem. But I was really excited to see Watsi, it was like a not-so-popular kid in high school following the same fads (a la Kickstarter) except it knew who it was and tried to do something good (take money out of the pockets of overpaid yuppies and into much needed cases of people who needed medical help). Two weeks ago, I left my old job at a e-commerce company to work full-time on a part-time project I had been working on and off for a year. It's a donation website where based off your future and current spending habits, you can decide to donate the "keep the change" or a micro-percentage of the amount to a charity of your choice. But honestly, I'm still not sure if I made the right decision. It was cool to test out the Intuit Customer Banking API, the same API that Mint uses to analyze your spending habits; as well as Network for Good API, which is like Stripe but payment processor for charities that allows people's donations be tax deductible. But I'm not sure if the "business-side" of my project is by any means viable, if you as a user would use it and see any value. The site is here: http://changefol.io I would appreciate any feedback, everything in terms of business, UX and tech. Thanks everyone for reading this, and thanks in advance for your help and criticism; Happy pre-Thanksgiving!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
94%94% 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: user, stripe, new · Missing: mac, agents, macos
69%69% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, hacker news, ide · Missing: https docs, just released, exist
56%56% 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 · Strong signals: way · Missing: mobile apps, ios, personal
35%35% 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
24%24% 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
15%15% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: paid · 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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