Storex – A modular and portable database abstraction for JavaScript

Hacker News

We'd like to show you Storex, a database abstraction layer that allows you to move your database interaction code between client- and server-side. By surrounding it with modular, recombinable packages for common problems such as schema migrations, it's possible to re-use logic touching the database across a wide variety of databases, allowing you to develop your code entirely in-browser in your daily development workflow, and then move your database to PostgreSQL/MySQL back-end once you're ready. Storex was designed modularly enough to easily be adapted to mBaaS like Firebase, which is coming up soon. Right now it's being used in WorldBrain's Memex (worldbrain.io) to provide you with a client-side full-text searchable database of every page you've seen online and your annotations on them (up to 5GB of data for some of our users.) We'd appreciate your thoughts very much, and don't hesitate to get involved! More info: https://medium.com/worldbrain/storex-a-modular-and-portable-...

Visit product View on Hacker NewsJan 25, 2019

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

66%66% 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

65%65% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

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

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

39%39% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

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