schemafreedb.com - new, dream database service for web developers?

Hacker News

Hello, I'm Eric, developer and co-founder of Xornet, Inc. the company behind our new service, SchemafreeDB. This is a 2nd submission... we removed some obscurity from the original submission title. SchemafreeDB was designed from the ground-up to enable Rapid Database Development for web developers. SchemafreeDB's unique combination of features include: * Creation of free-form data structures on the fly at insert-time. e.g. Simply add an attribute 'phone-extension' to any phone object, without disturbing your existing phone objects, all without visiting a schema design stage. * Full SQL support for querying across disparate object structures. * You don't have to worry about issuing UPDATE TABLE on large data sets. In fact, we did away with the entire concept of UPDATE TABLE. * Support for complex, nested data structures. Objects can contain simple attributes (String, Int, etc) or other Objects. * Familiar SQL query language. * Simple join-free, dot-notated syntax (e.g. WHERE $s:person.address.city='Rochester' AND $i:person.income>50000). This is familiar to the ease of working in an ORM, but without the impedance mismatch between in-memory objects and database objects. * Free indexing. Delivers fast index-like queries without index configuration, AND at the same time delivers inserts at fast no-index speeds. * Lightweight, consistent, platform-neutral connection library - JSON over HTTP. Access your database the same way from from any network-enabled platform. We are currently in Alpha. Please check us out at http://schemafreedb.com

Visit product View on Hacker NewsJun 1, 2012

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

77%77% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

76%76% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, existing, 000 · Missing: https docs, excited, just released

67%67% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

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

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

16%16% 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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