Mirror Wikipedia, Gutenberg and Stack Overflow on Sandstorm.io with Kiwix

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Just published on the Sandstorm app market: https://apps.sandstorm.io/app/5uh349d0kky2zp5whrh2znahn27gwh... Note that I take no credit for Kiwix ( http://kiwix.org/ ) itself. As is common for Sandstorm, I merely created the Sandstorm package, and also the onboarding and uploading interface since Kiwix's web server had none. However I have been in contact with Kiwix and they seem to like the idea. Hopefully soon they will officially put this package under their umbrella. My hope down the line is to see Sandstorm as a one-stop shop for server needs in an "off the grid" network, be it in a low connectivity part of the world, or an independent mesh network. One thing that I felt was missing was an easy way to host a local copy of popular open data. (Kiwix-serve already existed on its own, but I thought Sandstorm could make it easier to administer). For now, anyone who just likes to have a copy of this stuff at home, now there's an option to do it on Sandstorm. Any feedback would be appreciated! (Particularly any UX pain points.)

Visit product View on Hacker NewsAug 6, 2017

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Indie HackersFits the IH revenue-focused audience · Strong signals: created · Missing: supports, reddit linkedin, podcasting

85%85% 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 NewsStrong engagement from HN community · Strong signals: exist, ide, 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.

Product HuntOn track for Day 1 leaderboard · Strong signals: apps, open · Missing: mac, agents, macos

64%64% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: apps, way · Missing: mobile apps, ios, personal

46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: host, interface, soon · Missing: plus, platform, intuitive

44%44% 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

13%13% 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.

Incorrect prediction on native model

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