Las tres áreas IT más demandandas (y mejor pagadas)

Idiomas en aplicaciones de móvil

One of the best ways to learn about Android is to checkout Android code in the wild. The following is good Android sample code to review:
CodePath Code Samples

Beginner Android Samples
Custom ArrayAdapter Demo
Book Library Search Demo
ImageLoading and SQLite Demo
Rotten Tomatoes Demo
Styled ActionBar Demo
Drawables and Styles Demo
Animations and Gestures Demo
Custom Views Demo
Services and Notifications Demo
Unit and Integration Testing Demo
Audio and Video Demo
Menus, Popups, and Fragments Demo
Contacts Loader Demo
Google Maps Demo
Master-Detail Demo
Crouton Alert Demo
Android Snake Game
Android Rest Client Multi-Service Demo

External Code Samples

Tons of examples from commonsguy
Novoda Android samples
HMKCode Android
Google Samples
Android-Examples
Easyweather - Retrofit + Dagger 2 + RxJava

Open-Source Apps

Tivi - Great open-source Kotlin + RxJava 2 + Architecture Sample
FOSSDroid - Open-source android apps repository
Github Android App
Simple Android App
Astrid Android's #1 Task Management Application
MobileOrg for the Android platform
Guag.es App
Quora on Best Open Android Apps
tumblrsnap
Quran android app
Vt - VK video app
DroidFeed - News App for Android Developers

Varias apps libres de Android

Adaptadores con banners

Países TIER 1,2,3

Países TIER

Jerarquización orgánica de registros semánticos para posicionamiento natural

API para comincar con la consola de Google Play

Navidad

Many recommendation systems must optimise for multiple objectives at the same time, such as relevance, popularity, or personal preferences. We formulated the multi-objective optimisation problem as a constrained optimisation problem: the overall objective is to maximise the expected value of a primary metric, subject to constraints in terms of expected values of secondary metrics. During online serving, the objectives may shift according to user’s needs – for example, a user that had previously been interested in housing search apps might have found a new flat, and so is now interested in home decor apps – so we worked toward a dynamic solution.

Rather than solving the problem offline and bringing a fixed model online, we solved this problem on-line, per-request, based on the actual values of the objectives during serving time. We define the constraints to be relative constraints, meaning we would like to improve the secondary objective by a percentage rather than an absolute value. This way, any shifts in the secondary objectives didn’t affect our solver.

The algorithm that we developed can be used to find tradeoffs between a number of metrics. Finding suitable points along the tradeoff curve, our algorithm can significantly raise secondary metrics with only minor effects on the primary metric.

Google en el punto de mira del monopolio de la patente.