A “library.json” bookshelf protocol to build a decentralized Goodreads

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Hey HN, I wrote up a draft sketch of a proposal for a "library.json" format that would allow you to build a kind of open decentralized GoodReads - powered by a thousand indie sites. The write-up is here: https://tomcritchlow.com/2020/04/15/library-json/ Some notes for HN in particular: 1. I'm not a developer so go easy on the json format etc. Could be better ways to do this - feedback welcome 2. I've specifically designed this to be easy to setup and use - I am aware of things like activitypub and indieweb stuff but frankly they are all too damn hard to use (and often start with hosting your own server...) 3. Would love suggestions on getting around the "book ID" problem. Feels like a major sticking point... Thanks!

Visit product View on Hacker NewsApr 16, 2020

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

87%87% 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: 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.

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

43%43% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

36%36% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

36%36% predicted probability of success on Product Hunt, 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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