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Vitriol – a distributed, serverless web publishing platform

Hacker News

Vitriol – a distributed, serverless web publishing platform

Live app: https://vitriol.co Vitriol is an open source distributed publishing app which works in the browser without extensions or servers to set up, thanks to OrbitDB[1] and IPFS[2]. Read the intro article here: https://vitriol.co/QmccRaHCrUKZwZpjdJFiTTdgp8FG3ALFDZQexaYgi... And try the live web app here: https://vitriol.co Gitlab repo: https://gitlab.com/vitriolum/vitriol-web [1] https://github.com/orbitdb/orbit-db [2] https://ipfs.io

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30comments
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Launch Intel predictions

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Hacker NewsStrong engagement from HN community · Strong signals: open source, io · Missing: https docs, excited, just released
69%69% 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: open · Missing: mac, agents, macos
57%57% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
48%48% 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
37%37% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
33%33% predicted probability of success on BetaList, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting
29%29% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Correct prediction on native model

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