Fa

Fab Friends a crowdfunding site for Rwandan college students

Hacker News

Fab Friends a crowdfunding site for Rwandan college students

Hey HN, I'm a Rwandan-American software engineer. I was fortunate enough to make it to the US, build a career here, and have opportunities that changed the trajectory of my life. I've been thinking for a long time about how I could give something back. So I built joinfabfriends.org. The idea is pretty simple: think GoFundMe, but specifically for Rwandan college students who need help paying for things like tuition and textbooks. I'm starting small. I want to learn whether this model works, how to make the process trustworthy and transparent. And because just asking HN for money felt boring, here's my slightly unusual donor perk: If you become a donor(Good Samaritan), you're invited to come to Rwanda with me in summer 2027. You still have to pay your own way! But I'll be your guide. We'll see Rwanda, eat very well, meet some of the students we've helped(make sure to bring hiking shoes). I'd love your feedback, especially around: -what level of transparency you'd expect around donations -what would make you trust/distrust a platform like this -anything about the sitethat feels confusing And, of course, if one of the students' stories resonates with you, I'd be grateful if you can help. https://joinfabfriends.org Murakoze cyane!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
92%92% 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: model, using · Missing: mac, agents, macos
60%60% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
43%43% 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: platform · Missing: plus, intuitive, reviews
25%25% 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
14%14% 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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