Like back is a clever social networking application based on the idea earning maximum possible likes for any particular post. A user can earn likes in two different ways in the application. Either through in-app purchases or liking posts appearing in the feed posted by their friends. The application is integrated with instagram and allows users to connect to the app through their instagram accounts. Users posting their pictures on instagram appear in the feed for users to like them. This makes the app a potential medium to boost instagram posts and make them appear in the ‘explore’ section of the app.
The app needed to identify high value users and existing connections between different users. The app has set benchmarks for premium and normal users having differing rights. For example, only a premium user can follow other users and not vice versa.
Once a user logs into the application, his instagram details needed to be loaded and remembered by the application. It also caled for the likes generated from the app on the posts as well.
Instagram API Limit:
Instagram comes with a request limit of 5000 per hour and this could have put the app in jeopardy. We had to develop a method to overcome this and increase the request limit for each and every user.
App optimization was a significant challenge as large user activity indicated a lot of traffic. At such moments application would often hang. We had to make sure that the application runs smooth despite high traffic.
There are many in-app purchase provided within the app and the code had to be written carefully to get desired results.
To overcome the data synchronization challenges, we created an algorithm that identified the likes coming from normal and premium users. The algorithm can remember likes made by a user to any particular image and avoid the same user liking the same post again.
To maintain data consistency during login, our developers crafted an algorithm that fetched information with respect to each and every profile on the application. We stored the information files in a server that are fetched by the algorithm before the feed is presented for a particular user.
Instagram API limit was one of the most trickiest part of the project. The limitation of 5000 likes per hour was a limiting factor and we devised a technique through which each user account could get more likes than that.
For app optimization, our developers employed multi-threading and call back methods. The APIs available in the applications are processed in the background and it prevents the application from running slow or become laggy. Multi threading does not stop the user interaction within the app.
There are various inapp purchase packs available within the application and each of them come with a differing condition. Hence, in the event of a purchase, our developers had to be very cautious about the codes written for all the in-app purchase commands and their implied results too.
We made a beautiful and engaging app without any lags in the UI. The in-app purchase schemes worked smooth and performed as desired by the app owner. Instagram API limit was also overcome by our developers while multithreading methods kept the application running absolutely glitch free. The application segregated users into premium and non premium category successfully. The app emerged as a powerful medium to earn likes for instagram posts. Users could easily purchase likes or earn them through liking pictures of other users available within the application. The app has been able to attract a substantial lot of users regularly purchasing a lot of likes through in-app purchases.