Azartiz Contacts app

Hacker News

The Contacts app is the third demo app we've released this month that runs on any web server by uploading a single html file (contacts.html). These apps use the Azartiz.com CouchDB as the backend database. Azartiz is an experiment exploring the feasibility of a backend as a service and apps that can be run on any web server without installing any additional software. It takes about a minute to create a secure backend for your apps. After that you really don't have to think much about the "backend". There's example code for user authentication, CRUD, search, processing forms, displaying data, using embedded templates, and TinyMCE integration.

Visit product View on Hacker NewsJan 30, 2019

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

92%92% predicted probability of success on Product Hunt, 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

73%73% 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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: apps, month · Missing: mobile apps, ios, personal

56%56% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

30%30% 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

17%17% 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

3%3% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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