InterPlanetary Fetcher – Fetch all resources of a hash to any gateway

Hacker News

This project is about a better distribution of resources on the InterPlanetary File System (IPFS) by allowing developers and end-users to prefetch all resources of a hash on a specific public gateway. Before this tool, only requested subfolders and subfiles would be fetched. With this tool, you can fetch all of them and check their availability on the specified gateway. This can be used when we need to publish something, or distribute something on another gateway, or even check the persistence of all resources of a hash. Example: I'm on planet Mars, I need to open a website on IPFS, but before, I check if all its resources are available on the Mars gateway. It is available here: https://fetchipfs.netlify.com/ You can test it with the neocities hash: QmcKi2ae3uGb1kBg1yBpsuwoVqfmcByNdMiZ2pukxyLWD8 https://steemit.com/utopian-io/@hazae41/interplanetary-fetch...

Visit product View on Hacker NewsMay 15, 2019

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3points

Did not reach leaderboard

Launch Intel predictions

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

60%60% 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 · Missing: supports, reddit linkedin, podcasting

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

Product HuntUnlikely to reach the leaderboard · Strong signals: user, open · Missing: mac, agents, macos

45%45% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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 · Strong signals: users, way · Missing: mobile apps, ios, personal

38%38% 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

12%12% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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