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I made a simple Dropbox clone

Hacker News

I made a simple Dropbox clone

I made a simple site that allows you to upload files. It requires no sign up and 100% free. The biggest advantage is that it returns you the direct URL to your file. So you can use it to store images for MD-based forum posts. Or put in your site's <img src>. I use the Cloudflare R2 storage which offers zero egress fees. So I pay $0 of transfer costs. I added a simple anti-nsfw files check and a simple anti-malware check. Please do not try to break it. Thanks! (>‿◠)

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Actual performance

8points
1comments
Made the leaderboard

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
71%71% 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.
Hacker NewsMay not resonate with HN audience · Missing: https docs, excited, just released
49%49% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
43%43% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
42%42% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
20%20% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
19%19% 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
7%7% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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