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Pastebin clone on top of IPFS

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Pastebin clone on top of IPFS

I've created a quick application that stores data in IPFS (http://ipfs.io) which is a distributed web implementation. It's working as expected, paste some content, get a hash in the url and send to a friend. The content will be there forever and distributed to peers that asks for it. Demo: http://ipfsbin.xyz/#QmXdMvJaRrSTCAcDFMZLUFfm9uGK6Wq7qEVcwbvaQRxq8x This have a couple of advantages, mainly that the same content would have the same hash, meaning that things wouldn't be duplicated. Source is available for people interested in that here: https://github.com/victorbjelkholm/ipfsbin

Visit productView on Hacker NewsOct 20, 2015
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting
51%51% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos
50%50% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal
43%43% 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
36%36% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: arr · Missing: mrr, revenue, profit
16%16% 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
2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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