KZK Weekly — Issue #2
This week after the IO’17 we got a lot of new things to show! specially the new coding language for Android and a lot more stuff in the Android world.
Hope you find all this interesting and again if you want to share I will really appreciate it!
Session on the Google IO — 2017 to begin learning the tips and tricks of the Kotlin language.
A great cheatsheet to transfer the knowledge of swift to the new Kotlin language that is official supported for Android :)
Some more tricks that I found interesting about Kotlin, I know… this week there is a lot about kotlin but… not every year we got a new language on Android!
Google gives us new Components to have and easy way to handle all the lifecycle hell on Android. I really love the new Room sqlite wrapper for live updates.
Thats one of my favorites new features of Android, Instant Apps! Making apps much more easy to find and use. The user will not need to install the app, which is a big barrier and we will get much more users.
I am a cofounder of RethinkDB — an open-source distributed database designed to help developers and operations teams work with unstructured data to build real-time applications.
Totally agree with this blog, a lot of people still thinks that Jenkins has this horrible UI and that you have to configure shit of jobs with dependencies to do anything complicated. But the truth is that Jenkins 2.0 with pipelines is awesome, it’s more powerful than circleCI and almost as easy.
Each half year ThoughtWorks makes a collection with new technologies/tools etc.. and categorize them in a “you should use it” to “only early adopters” really great to know which technologies other people are using.
This was the course that make me in love with Deep Learning again after a few year, super well explained 18h of videos and notebooks. Try to see the first two videos they are amazing!
Really nice post about using RNN to make new MIDI music files. this is not state of the art but it’s really good to grasp how RNN works.
Google is opening the sketch dataset used to train autodraw, it consist of a large number of “sketches” made by humans for each category like “apple”, but not just the final image but all the pencil traces over time. See some example here.
Deep Learning with Emojis (not Math) — tech.instacart.com
Instacart is saving minutes per delivery by sorting shopping lists using deep learning. Emojis help to define the problem and outline both a simple and a more complex deep learning architecture.