Above you can see an example design of an app we’re currently building at HYPE4. As you can see it’s not ideal, as espec

I think coming back to the iPhone 5/iPad Pro style is a great choice and I personally love that design. I also like the pro features and the fact of having a smaller iPhone available as well. The magnetic charging can bring back hopes of a future Apple laptop (ARM based) charging the same way.,Another reason why Stripe blew up is because it was so universal. Platforms like Shopify and Lyft rely on payment simplicity — each additional step in account verification can mean millions of churned users. Stripe’s 7 lines of core code worked just as well in any environment, enabling billions of emerging platform users with same-week verification.,But the iPhone 12 Mini gets a downscaled resolution of 375 x 812 — same as the iPhone X. The problem with this approach is, that it’s not really a 3x ratio anymore but 2.88. And sure, with a smaller screen it doesn’t quite hurt so much, as most of the actual calculations on how to display objects are done in code.,Pandas is a powerful library with a lot of inbuilt functions for analyzing time-series data. This article saw how Python’s pandas’ library could be used for wrangling and visualizing time series data. We also performed tasks like time sampling, time-shifting, and rolling on the stock data. These are usually the first steps in investigating any time series data. Going forward, we could use this data in several ways. One way could be to perform a basic financial analysis by calculating the daily percentage change in stocks to get an idea about the stock price volatility. Another way would be to use this data to predict Maruti’s stock price for the next few days by employing Machine Learning Techniques. Whatever assignment you choose, the preliminary steps shown in this article would come in handy.,With the channel, each event is delivered to a single subscriber. An attempt to post an event without subscribers will suspend as soon as the channel buffer becomes full, waiting for a subscriber to appear. Posted events are never dropped by default.,With the shared flow, events are broadcast to an unknown number (zero or more) of subscribers. In the absence of a subscriber, any posted event is immediately dropped. It is a design pattern to use for events that must be processed immediately or not at all.,Know the difference and use both shared flows and channels appropriately. They are both useful and are designed to work well together. However, broadcast channels are obsolete artifacts of the past, they will be deprecated and removed in the future.,Both BroadcastEventBus , that is written with the SharedFlow in the first example, and this SingleShotEventBus, that is written with the Channel, publicly expose their events as Flow, yet they have an important difference.,Ok, so where did those 360x780 and 390x844 resolutions come from? Well, they’re simply the 1/3 of the main resolutions of these phones. But that would add A LOT of complexity, right?,On October 13th, Apple held an annual iPhone event and introduced four new iPhones. Most of the discussion was obviously about the new design and the features, so let’s get those out of the way:,class SingleShotEventBus { private val _events = Channel() val events = _events.receiveAsFlow() // expose as flow suspend fun postEvent(event: Event) { _events.send(event) // suspends on buffer overflow } },Know the difference and use both shared flows and channels appropriately. They are both useful and are designed to work well together. However, broadcast channels are obsolete artifacts of the past, they will be deprecated and removed in the future.,But channels also have their application use-cases. Channels are used to handle events that must be processed exactly once. This happens in a design with a type of event that usually has a single subscriber, but intermittently (at startup or during some kind of reconfiguration) there are no subscribers at all, and there is a requirement that all posted events must be retained until a subscriber appears.,Pandas is a powerful library with a lot of inbuilt functions for analyzing time-series data. This article saw how Python’s pandas’ library could be used for wrangling and visualizing time series data. We also performed tasks like time sampling, time-shifting, and rolling on the stock data. These are usually the first steps in investigating any time series data. Going forward, we could use this data in several ways. One way could be to perform a basic financial analysis by calculating the daily percentage change in stocks to get an idea about the stock price volatility. Another way would be to use this data to predict Maruti’s stock price for the next few days by employing Machine Learning Techniques. Whatever assignment you choose, the preliminary steps shown in this article would come in handy.,If you’re a designer working on mobile apps (or responsive websites) you probably know, that there has been a growing number of Apple mobile devices to choose from. This is what the Artboard presets looked like in Sketch and Figma so far.