Android development has come a long way since it emerged. New technologies and approaches are being developed, and old proven solutions are being improved.
Recently, Jetpack Compose has been gaining popularity among developers. This is a modern UI-building tool utilizing a declarative approach. In this context, the advantage of a declarative approach is that it requires less code compared to XML. This increases development speed and reduces the number of errors.
The framework itself works fine. But as usual, problems arise at the intersection of codebases. Suppose you already have a functioning application that has been around for many years, it is debugged and tested.
As a rule, in such cases, new technologies are tested in production as minor features to collect statistics and evaluate their value for the product. In such situations, the interoperability capabilities of the framework come to the rescue. Interoperability makes it possible to use multiple approaches to UI development within a single application.
In this article, I will cover two examples of working with the interoperability feature using the example of a side menu — in both the optimal and not-so-optimal ways. From the non-optimal scenario, we learn what to avoid and identify red flags that alert you if you are heading in the wrong direction. In the optimal scenario, we will take any drawbacks into account and correct them by analyzing prior experience.
If you want to build an efficient UI auto-testing process for your project, it’s essential to first consider how testable your application is generally, as well as how you can write effective UI tests.
Like many Android developers, we’re used to the notification bar being the secondary point of focus after the launcher. Yet with the release of Android 13, the rules of the game have changed.
In this article, we’ll discuss a common mistake beginners make when switching from Java to Kotlin for the first time. I often notice that people who have recently switched from Java to Kotlin become fascinated by its idiomaticity and start using it where they shouldn’t.
As your application grows, so does the flow of tasks for QA. And if you don’t reduce that amount of work in time, it will begin to threaten your releases. And just resemble a sprawling bush that’s not being looked after. I will tell you how we reduced the regression testing time from 5 to 3 days in a couple of months maintaining quality and relieving the level of stress for the QA team.
As your app’s audience grows, bots and scammers become increasingly interested in your app. When we needed a scalable tool for breaking bots and users apart, we found it way too expensive to cover all the bases with reCaptcha, which we were already using. So here is what we did.
In an ideal world, a perfect developer would find time to read professional articles, attend conferences, and work on pet projects. This is not always the case in the real world: fatigue at work, personal and family issues, going to a doctor — there are a million things to do and reasons to say “not today.”
That’s why our Android team created the “developing together” internal meetup format. We either prepare short presentations on new topics or watch professional videos together. We have time to do things after work, and our conscience isn’t burdened. Furthermore, we can discuss the applicability of what we heard immediately with our colleagues.
What is more critical in a push notification — the text or the picture? How does it depend on the mobile platform? When can sending push messages more frequently get better results? What mistakes can be made while you’re figuring it all out? Learn about it in this piece by the team developing the push notification system for FunCorp entertainment apps.
Since most of our users are from the USA, FunCorp decided to update its icon and splash screen for the Halloween holidays last year. Let’s recall that story. The task seemed trivial, but in the process, we found some more cases and grew our essential checklist from 8 to 13 items (attached).
I’ll explain how the Microbenchmark library works and show examples of how to use it. Perhaps this will help you evaluate performance and solve controversial situations during the code review.
Over the past 4 years, we’ve developed an adaptation process enjoyed even by ultra-experienced newcomers. Today, we’ll share its main stages and a basic checklist. You can easily adjust them to fit engineers of any level that you hire.
Most articles about the algorithms of the multi-armed bandit are too academic. They are filled with formulas and seem to imply that we have an immutable set of handles for pulling and n→∞ attempts. I will try to talk about these algorithms from the point of view of an ordinary developer, taking into account the real conditions in which our iFunny application for recommending memes works.
We share the zombie object detection mechanism we made for the iFunny app, and present tips for those who also want to get rid of this issue. So you turn on your laptop, open Crashlytics, and voila: EXC_BAD_ACCESS objc_release… What now?
Imagine how much memory space a 10k-pixel comic book takes up. And now imagine that you can’t compress it because if you did, it would lose too much quality and become unreadable. Curious how we at FUNCORP deal with this? Then read on!
We’ve been long working on improving the user experience in UGC products with machine learning. Here are our ten key lessons of implementing recommendation systems in business to build a really good product.
The iOS team of iFunny has gone from a completely ad-free model to using a variety of ad networks and formats in their popular entertainment app. In this article, we will discuss some of the less-apparent nuances of working with advertising SDKs that can affect the user experience and performance of your product and share the code that will help you fix them.
Overview of self-supervised methods.
While the demand for neural networks is growing, most state-of-the-art approaches to adapt them to business needs often lag, hindered by insufficient or absent markup. Supervised learning is hardly feasible in this situation, and standard unsupervised methods won’t work for most of your tasks. This is where self-supervised plans come to the rescue. Depending on the task, they require next to no markup or none at all.
A developer’s perspective (MLOps inside)
At iFunny, we are trying to compose the best possible feed of memes and funny videos. To rate our job, people use smile/dislike buttons and comments. Some of them even post memes about our efforts.
Recommendation systems will always stay relevant — users want to see personalized content, the best of the catalog (in the case of our iFunny app — trending memes and jokes). Our team is testing dozens of hypotheses on how a smart feed can improve user experience. This article will tell you how we implemented the second-ranking level of the model above the collaborative one: what difficulties we encountered, and how they affected the metrics.
Founded on years of work experience in testing
There is a tremendous variety of QA Engineer vacancies, ranging from junior to lead tester and even to principal QA Engineer. We’re often asked what qualities a senior-level tester should have compared to junior or middle-level ones. Let’s try to answer this.
Approximately 100,000 units of varying content come through our iFunny app daily, and every single one of them needs to be checked. We have already dealt with forbidden imagery by creating a classifier that automatically bans it. Next up — old memes, reuploads, and straight-up doubles that users try to sneak past the moderation.
To get rid of those, we have introduced a duplicate detection system. It had already gone through several iterations, but at some point, we realized it was impossible to put version-to-version improvements in proper perspective. And so we ventured into the Net, searching for books and articles that would allow us to examine currently existing approaches to duplicate detection and — most importantly — to their quality assessment. You can see what we’ve found below.
FUNCORP’s services and advertising infrastructure have recently undergone significant changes. In addition to the Prebid Mobile, we now also support and develop the Prebid Server to work with our apps.
We chose Prebid because we’ve been using its Software Development Kit for a long time and have enough experience and competence in working with it. This expertise allows us to improve and develop the service without compromising its stability. As a result, Prebid has become one of the critical services within our infrastructure.
It is pretty standard that as a project grows, so does the complexity of its build. Too many different technologies, third-party components, libraries, lints, server-side rendering, and project-specific nuances — as a result of all this, the configuration of a build may involve more than a thousand strings.
This article explains how you can automatically divide a dataset of images into clusters classified by qualitative contextual feature, thanks to embeddings from the much talked about neural network called CLIP created by Elon Musk’s company. I will give you an example using the content from our iFunny app.
There are different ways to remove a user’s personal information at the user’s request to make a product compliant with the CCPA or GDPR. The most basic method is to handle every request received by mail manually. The important thing is to make sure the process is as straightforward as it can be and clear to the user. So a little automation is not such a bad idea.
In 2020, for the first time since World War II, the world was faced with an epic, uncontrollable crisis. The overwhelming majority of the world’s commercial industries, including the tech industry, have had to deal with the negative impact of the pandemic.
iFunny users upload about 1,000,000 pieces of content to the app every day, including not only memes but also racism, violence, pornography, and other inappropriate material.
Previously, we checked all this manually, but now we are developing automatic moderation based on convolutional neural networks. We have already trained the system to divide content into three classes: it recognizes what can be included in user feeds, what needs to be removed, and what is hidden from the shared feed. To make the algorithms more accurate, we decided to add a specification for removing content that did not have such markup before.
Testing ad integrations is quite a tedious process. Since we work with third-party SDKs that can hardly be controlled, it can be challenging to automate the testing process. Nevertheless, to reduce testing time, we have developed and implemented a debug panel, which we will discuss in this article.
We plan to continue our growth and development by entering new markets and finding new business niches. Take a look at open positions. Perhaps there is one that is right for you!
If you know a passionate Software Developer who's looking for job opportunities, e-mail us at email@example.com. In case of successful recommendation you will get a $3000 reference fee.