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Marcel Mierzejewski

Marcel Mierzejewski

iOS Developer at Snowdog

Wander App Becomes Open Source

“In open source, we feel strongly that to really do something well, you have to get a lot of people involved.“ words of Linus Torvalds. Snowdog is thrilled to announce that Wander App is now open source. Wander app was designed to make travel simple. We aspired to create a platform for people to create and experience stories. As of today, we are going open source. We believe in the power of the community, so we want everyone to assist us in shaping tomorrow of this project. We have open-sourced our Android and iOS app. You can find them on our GitHub page.

Is Your Idea for an App Worth a Billion Dollars?

There are around 2 million applications ready to be downloaded on Apple’s App Store and at least thrice as many apps that failed to get there. How do you know if your idea is brilliant, has the power to stay on the market or is generally worth pursuing? I am not trying to discourage you from following your instincts. This blog post is aimed at helping you clarify your vision. I also love thinking about new and exciting ideas; my notepad is full of them.

Diffable Data Sources - First look

iOS Data Handling Evolution: An Introduction to Diffable Data Sources. Handling data source for table view or collection view in iOS 12 and earlier required conforming to UITableViewDataSource or UICollectionViewDataSource protocol and setting its delegate. However, this approach had its drawbacks, including computational power inefficiencies and the loss of animations. With the introduction of iOS 13 Diffable Data Sources, Apple addressed these issues and taught developers how to use insertions and removals while working with collection view. This eliminated the error-prone approach of allowing view and model data to be different and forced developers to handle all insertions and deletions. This blog post delves into the evolution of iOS data handling and how Diffable Data Sources have emerged as a game-changing solution for efficient data handling in iOS applications.

How Does a Machine Find an Object? An introduction to Machine Learning Computer Vision

The difference between how machines “see” and identify objects compared to humans may not be so obvious. We are able to instantly identify any object we see and sometimes even objects we’ve never seen before, because we are using our memories and collective experiences. Without conscious thought, we create a complex web of connections between any new object we see and through the process of comparison we can approximate its purpose. Sometimes we even can correctly guess the name of this object. Even with incredibly fast GPU’s, improved algorithms and an unbelievably huge amount of collected data, machines are not yet able to do this. That’s because it’s still extremely difficult for machines to think outside of the box.