It’s that time of the year again. The time when you make yourself a cup of coffee, and go through the events of the past years, searching for a sign that it has been worth it. And judging by the numbers alone, it seems like a very eventful year: In 2018, I: Got engaged Changed my job Started new new side projects Attended 4 conferences and countless meetups Had my own chance to present in front of an audience (twice) Visited 5 countries and met many new people Read 15 books Wrote nearly 30 blog posts Ran 626 km, and finished 4 half-marathons Made between 5,500 and 6,000 push-ups Wow, quite a year, heh?

Good morning from Augsburg, Germany 👋🏼 Not many people around (expected for the second day of Easter), but we got a beatufil sunshine and a great view.

Meanwhile in Munich…

A few days ago, I was invited to give a talk at the Munich Kotlin Users Group meetup (check out my slides here) For quite some time, I have had an idea to promote Kotlin as the missing link between software engineering and Data Science. DuringKotlinConf 2018, I had the chance and the privilege to meet a couple of amazing people, whose talks only helped confirm my thoughts. Mathematical Modeling with Kotlin | Thomas Nield

This article is a follow-up transcription to a talk I recently gave at a local Munich machine learning meetup. Unlike my previous talk, this time I wanted to convey the idea of using an iPad for actively running data science experiments, as opposed to passively consuming information. I illustrated my point with a few example iOS applications I personally use on a daily basis, which I hope would be good starting points to get the audience interested in the idea.

Often, we will want to get to get a specific row, which marks the minimum or maximum of one of its columns. Let’s suppose we have the SF Salaries dataset from Kaggle. We want to find the employee name, with the largest total pay benefits. The experience with writing NumPy/Pandas filter conditions will quickly let us produce the following version: sal[sal['TotalPayBenefits'] == sal['TotalPayBenefits'].max()]['EmployeeName'] which is absolutely valid, but is it the only option?

Docker containers have proven themselves extremely useful in allowing developers to sandbox environments and ease the deployment of services. Have a complicated service setup? No worries. Simply, describe the steps in a Dockerfile and you should be able to replicate the process on every host OS that has Docker support. When I said services, I bet that the first thing you thought about was HTTP servers, or some sort of persistent, always running processes that send or accept requests to such services.

Good morning from Augsburg, Germany 👋🏼 Not many people around (expected for the second day of Easter), but we got a beatufil sunshine and a great view.

It is part of common sense to assume that the worthiest commit messages lie far from any extremes. Years ago, I often fell in the trap of hastily sending a commit after commit, annoyed by the fact that I have to add a summary to it (let alone a description). I’ve seen many projects whose commit summaries consist primarily of one-word verbs like [FIX], [CLEAN], [TEST], etc. They don’t tell much do they?

So you’ve been trying to wrap your heads around and decide between Grunt and Gulp? Yeah, me too, been there, done that. In my case, the decision wasn’t really hard. I’ve never had strong feelings for Grunt, and for a long time, I used a custom made ANT script. I used to copy it over to every new project, and it helped me with basic tasks — moving, copying, cleaning, linting, minifying, etc.

So you’ve been trying to wrap your heads around and decide between Grunt and Gulp? Yeah, me too, been there, done that. In my case, the decision wasn’t really hard. I’ve never had strong feelings for Grunt, and for a long time, I used a custom made ANT script. I used to copy it over to every new project, and it helped me with basic tasks — moving, copying, cleaning, linting, minifying, etc.

This article is a follow-up transcription to a talk I recently gave at a local Munich machine learning meetup. Unlike my previous talk, this time I wanted to convey the idea of using an iPad for actively running data science experiments, as opposed to passively consuming information. I illustrated my point with a few example iOS applications I personally use on a daily basis, which I hope would be good starting points to get the audience interested in the idea.

TL;DR! Just give me the code In your index.js file (or your platform-specific index file for that matter), simply add the following line to the top, directly after the import statements: XMLHttpRequest = GLOBAL.originalXMLHttpRequest ? GLOBAL.originalXMLHttpRequest : GLOBAL.XMLHttpRequest; You will also need a Chrome plugin that overrides CORS. I use CORS Toggle. You may also decide to skip the plugin and start Chrome with CORS disabled: chrome --disable-web-security then, as usually, start remote debugging from your app, and open: http://localhost:8081/debugger-ui on your computer.

So you’ve been trying to wrap your heads around and decide between Grunt and Gulp? Yeah, me too, been there, done that. In my case, the decision wasn’t really hard. I’ve never had strong feelings for Grunt, and for a long time, I used a custom made ANT script. I used to copy it over to every new project, and it helped me with basic tasks — moving, copying, cleaning, linting, minifying, etc.

Often, I would work on a Python module and add changes to it live, which I would then want to test immediately. Piece of cake! Add the changes and execute the script again. Well, that’s certainly a way, but as modules get bigger, they tend to import other modules, or do some preliminary setup work. With time, I have developed this practice of opening up a Python REPL (though I recommend IPython or the Jupyter Console for that), importing my in-progress module and singling out separate functions I would like to test.

I caught myself using my old Android phone more and more these days. A 5-year-old Nexus 5, that is. I dug it out a few weeks ago. After the fresh reset all the service updates, it works as if it hasn’t been two years since I last used it for real. Trinkering with Android like in the good old days felt exciting. In comparison, my experience with iOS has always been like living a comfortable, but relatively boring life.

Good morning from Augsburg, Germany 👋🏼 Not many people around (expected for the second day of Easter), but we got a beatufil sunshine and a great view.

A view to live for! 😍 Sendlinger Tor | Munich

Meanwhile in Munich…

A view to live for! 😍 Sendlinger Tor | Munich

Good morning from Augsburg, Germany 👋🏼 Not many people around (expected for the second day of Easter), but we got a beatufil sunshine and a great view.

Meanwhile in Munich…

A view to live for! 😍 Sendlinger Tor | Munich

I hate listicles, and I am highly hoping that this doesn’t become one of them. Yet, I somehow felt the need to write down the reasons why I am giving Go a second (or maybe, third 🤔) chance. To the Java developers out there, searching for a new weapon of choice, I am hoping that this will give you a bit of perspective. This is not a description of Go-specific features.

Here is a handy developer tool tip for today: DevDocs As developers, we spend a large portion of our time, searching for the “name of the function abc that did thing xyz”. Not surprisingly, sites like StackOverflow have become so popular. Yet, SO answers are often full of additional information, and what we need most of the time is a simple search on top of the docs themselves. Years ago, I used a tool for macOS, called Dash.

My company, KI labs recently held its first hackathon in our Munich office. My team and I went for a very cool mobile app idea that promotes healthy walking competitions among colleagues and friends. Though the app itself deserves a proper introduction, I will talk about it in a separate post. This time I will focus on one problem I experienced while working on the app’s backend part, written in Go.

Docker containers have proven themselves extremely useful in allowing developers to sandbox environments and ease the deployment of services. Have a complicated service setup? No worries. Simply, describe the steps in a Dockerfile and you should be able to replicate the process on every host OS that has Docker support. When I said services, I bet that the first thing you thought about was HTTP servers, or some sort of persistent, always running processes that send or accept requests to such services.

Last week, I made my first tentative foray into Go programming. My company uses Go in a couple of projects, but although I have heard many positive things about the language, so far I have tried to stay away from it. The mere thought that Go is a 21st Century version of C and has been explicitly designed from the start to not try to be much more than that, can make everyone coming from Kotlin and Swift pull a few hairs at first.

Hard-coding configuration is bad. This is one of the things that newbie programmers learn quickly after they realize that have committed sensitive information to a public GitHub repository. And it happens so easily. Often, you are in the spur of the moment, wanting to test something locally, when you figure out that you need some API key in order for your app to work. Adding the key to the code takes a second, and is just as easy to forget when committing.

Often, the seemingly simple things can make you spend hours chasing around your tail. Like trying to connect to a MongoDB instance using KMongo. Basically, if you have checked out the KMongo quick start, setting up a client and fetching some data should be extremely easy: val client = KMongo.createClient() //get com.mongodb.MongoClient new instance val database = client.getDatabase("test") //normal java driver usage val col = database.getCollection<Jedi>() //KMongo extension method The quick start tutorial assumes that your DB resides on mongodb://localhost:27017, but in reality, your MongoDB connection string looks more like this:

NOTE: This post was originally intended to provoke a discussion, and despite its length, the original goal is still the same. Please, share your feedback on Twitter, Reddit, or Dev.to. My experience with the JVM dates back from around 2010. Java in those days was a bit different from todays’ standpoint. Java 6 was the current standard at the time, but most projects I got to work on, were stuck between Java 4 and 5.

Dependency Injection (DI) in Python? Seriously? 🤔 At this point, I am expecting people to yell at me for thinking like a Java guy, but just bear with me for a moment, will you? Spoiler: I am going to mention Spring only once in this post, I promise. NOTE: If you are already familiar with Dependency Injection and want to see it in action, you might want to jump right to the example at the end.

This is the first part of a two-part series. In this post, I will introduce Flask-RestPlus and demonstrate how to start organising APIs around its simple REST-based conventions. Next time, I will address the topic of request/response marshalling (serialisation) and validation. A seasoned Spring developer, I felt a bit uneasy when designing and future-proofing a Flask-based API for the first time. I have recently started using Python way beyond my original intention to just play with data, and found Flask to be a super-easy micro-service alternative to Spring Boot or Ktor.

This article is a follow-up transcription to a talk I recently gave at a local Munich machine learning meetup. Unlike my previous talk, this time I wanted to convey the idea of using an iPad for actively running data science experiments, as opposed to passively consuming information. I illustrated my point with a few example iOS applications I personally use on a daily basis, which I hope would be good starting points to get the audience interested in the idea.

Often, I would work on a Python module and add changes to it live, which I would then want to test immediately. Piece of cake! Add the changes and execute the script again. Well, that’s certainly a way, but as modules get bigger, they tend to import other modules, or do some preliminary setup work. With time, I have developed this practice of opening up a Python REPL (though I recommend IPython or the Jupyter Console for that), importing my in-progress module and singling out separate functions I would like to test.

TL;DR! Just give me the code In your index.js file (or your platform-specific index file for that matter), simply add the following line to the top, directly after the import statements: XMLHttpRequest = GLOBAL.originalXMLHttpRequest ? GLOBAL.originalXMLHttpRequest : GLOBAL.XMLHttpRequest; You will also need a Chrome plugin that overrides CORS. I use CORS Toggle. You may also decide to skip the plugin and start Chrome with CORS disabled: chrome --disable-web-security then, as usually, start remote debugging from your app, and open: http://localhost:8081/debugger-ui on your computer.

Securing your ElasticSearch instances and keeping all the fun Often, we choose convenience over security. Many modern tools such as MongoDB and ElasticSearch, have grown in popularity, partly because of their easy-to-set-up-and-tinker-with nature. Just spin off an instance, point your browser to the right URL and you’re ready to start sending queries. Unfortunately, one thing comes for another, and as we have recently seen, ElasticSearch left in the open can be a vulnerable target, same as MongoDB was in its heyday.

It is part of common sense to assume that the worthiest commit messages lie far from any extremes. Years ago, I often fell in the trap of hastily sending a commit after commit, annoyed by the fact that I have to add a summary to it (let alone a description). I’ve seen many projects whose commit summaries consist primarily of one-word verbs like [FIX], [CLEAN], [TEST], etc. They don’t tell much do they?

So you’ve been trying to wrap your heads around and decide between Grunt and Gulp? Yeah, me too, been there, done that. In my case, the decision wasn’t really hard. I’ve never had strong feelings for Grunt, and for a long time, I used a custom made ANT script. I used to copy it over to every new project, and it helped me with basic tasks — moving, copying, cleaning, linting, minifying, etc.

Docker containers have proven themselves extremely useful in allowing developers to sandbox environments and ease the deployment of services. Have a complicated service setup? No worries. Simply, describe the steps in a Dockerfile and you should be able to replicate the process on every host OS that has Docker support. When I said services, I bet that the first thing you thought about was HTTP servers, or some sort of persistent, always running processes that send or accept requests to such services.

Hard-coding configuration is bad. This is one of the things that newbie programmers learn quickly after they realize that have committed sensitive information to a public GitHub repository. And it happens so easily. Often, you are in the spur of the moment, wanting to test something locally, when you figure out that you need some API key in order for your app to work. Adding the key to the code takes a second, and is just as easy to forget when committing.

Dependency Injection (DI) in Python? Seriously? 🤔 At this point, I am expecting people to yell at me for thinking like a Java guy, but just bear with me for a moment, will you? Spoiler: I am going to mention Spring only once in this post, I promise. NOTE: If you are already familiar with Dependency Injection and want to see it in action, you might want to jump right to the example at the end.

This is the first part of a two-part series. In this post, I will introduce Flask-RestPlus and demonstrate how to start organising APIs around its simple REST-based conventions. Next time, I will address the topic of request/response marshalling (serialisation) and validation. A seasoned Spring developer, I felt a bit uneasy when designing and future-proofing a Flask-based API for the first time. I have recently started using Python way beyond my original intention to just play with data, and found Flask to be a super-easy micro-service alternative to Spring Boot or Ktor.

This article is a follow-up transcription to a talk I recently gave at a local Munich machine learning meetup. Unlike my previous talk, this time I wanted to convey the idea of using an iPad for actively running data science experiments, as opposed to passively consuming information. I illustrated my point with a few example iOS applications I personally use on a daily basis, which I hope would be good starting points to get the audience interested in the idea.

Often, I would work on a Python module and add changes to it live, which I would then want to test immediately. Piece of cake! Add the changes and execute the script again. Well, that’s certainly a way, but as modules get bigger, they tend to import other modules, or do some preliminary setup work. With time, I have developed this practice of opening up a Python REPL (though I recommend IPython or the Jupyter Console for that), importing my in-progress module and singling out separate functions I would like to test.

TL;DR! Just give me the code In your index.js file (or your platform-specific index file for that matter), simply add the following line to the top, directly after the import statements: XMLHttpRequest = GLOBAL.originalXMLHttpRequest ? GLOBAL.originalXMLHttpRequest : GLOBAL.XMLHttpRequest; You will also need a Chrome plugin that overrides CORS. I use CORS Toggle. You may also decide to skip the plugin and start Chrome with CORS disabled: chrome --disable-web-security then, as usually, start remote debugging from your app, and open: http://localhost:8081/debugger-ui on your computer.

Today, I took part in the Sixtus Half-Marthon near lake Schliersee for the third time in a row. Unlike last year, this time I started with a completely different strategy - keep it cool and let everyone else pass by me during the first third of the race. Seeing everyone else’s backs can be demotivating and requires a lot of patience and training. After the first 5-6K, having realized that I was really the last of the HM group, I started slowly picking up the pace.

Hard-coding configuration is bad. This is one of the things that newbie programmers learn quickly after they realize that have committed sensitive information to a public GitHub repository. And it happens so easily. Often, you are in the spur of the moment, wanting to test something locally, when you figure out that you need some API key in order for your app to work. Adding the key to the code takes a second, and is just as easy to forget when committing.

Often, I would work on a Python module and add changes to it live, which I would then want to test immediately. Piece of cake! Add the changes and execute the script again. Well, that’s certainly a way, but as modules get bigger, they tend to import other modules, or do some preliminary setup work. With time, I have developed this practice of opening up a Python REPL (though I recommend IPython or the Jupyter Console for that), importing my in-progress module and singling out separate functions I would like to test.

TL;DR! Just give me the code In your index.js file (or your platform-specific index file for that matter), simply add the following line to the top, directly after the import statements: XMLHttpRequest = GLOBAL.originalXMLHttpRequest ? GLOBAL.originalXMLHttpRequest : GLOBAL.XMLHttpRequest; You will also need a Chrome plugin that overrides CORS. I use CORS Toggle. You may also decide to skip the plugin and start Chrome with CORS disabled: chrome --disable-web-security then, as usually, start remote debugging from your app, and open: http://localhost:8081/debugger-ui on your computer.

It is part of common sense to assume that the worthiest commit messages lie far from any extremes. Years ago, I often fell in the trap of hastily sending a commit after commit, annoyed by the fact that I have to add a summary to it (let alone a description). I’ve seen many projects whose commit summaries consist primarily of one-word verbs like [FIX], [CLEAN], [TEST], etc. They don’t tell much do they?