Building Business Systems with Domain-Specific Languages for NGINX & OpenResty

This post is adapted from a presentation at nginx.conf 2016 by Yichun Zhang, Founder and CEO of OpenResty, Inc. This is the first of two parts of the adaptation. In this part, Yichun describes OpenResty’s capabilities and goes over web application use cases built atop OpenResty. In Part 2, Yichun looks at what a domain-specific language is in more detail.

You can view the complete presentation on YouTube.

https://www.nginx.com/blog/building-business-systems-with-domain-specific-languages-for-nginx-openresty-part-1/
https://www.nginx.com/blog/building-business-systems-with-domain-specific-languages-for-nginx-openresty-part-2/

ANALYZING CRYPTOCURRENCY MARKETS USING PYTHON

A DATA-DRIVEN APPROACH TO CRYPTOCURRENCY SPECULATION

How do Bitcoin markets behave? What are the causes of the sudden spikes and dips in cryptocurrency values? Are the markets for different altcoins inseparably linked or largely independent? How can we predict what will happen next?

Articles on cryptocurrencies, such as Bitcoin and Ethereum, are rife with speculation these days, with hundreds of self-proclaimed experts advocating for the trends that they expect to emerge. What is lacking from many of these analyses is a strong foundation of data and statistics to backup the claims.

The goal of this article is to provide an easy introduction to cryptocurrency analysis using Python. We will walk through a simple Python script to retrieve, analyze, and visualize data on different cryptocurrencies. In the process, we will uncover an interesting trend in how these volatile markets behave, and how they are evolving.

Combined Altcoin Prices

This is not a post explaining what cryptocurrencies are (if you want one, I would recommend this great overview), nor is it an opinion piece on which specific currencies will rise and which will fall. Instead, all that we are concerned about in this tutorial is procuring the raw data and uncovering the stories hidden in the numbers.

 https://blog.patricktriest.com/analyzing-cryptocurrencies-python/

How I do Developer UX at Google

When people talk about User Experience (UX), they often talk about their beloved consumer products: a smartphone, a messaging app, or perhaps a pair of headphones.

User Experience also matters when you build something for developers. People tend to forget that developers are users too, and software development is an intrinsically human activity limited by not only how computers work, but also how programmers work. Admittedly, there are fewer developers than consumers in general, but the more usable developer tools are, the more energy developers can spend on delivering value to their users. Therefore, the UX of developer products is just as important as for consumer products. In this post, I am going to introduce the developer experience, explain one of the ways we assess it at Google, and share some lessons we learned from a specific study we conducted on Flutter, a new SDK for building beautiful mobile apps.

The idea of developer experience is not exactly new. Research on developer experience dates back to the early days of computing, since all users at the time were developers to some degree. “The Psychology of Computer Programming”, published in 1971, is a landmark book on the topic. When we talk about developer experience, especially applying the term to an SDK or library, we usually refer to three aspects of the product:

  • API Design, which includes the naming of classes, methods and variables, the abstraction level of the API, the organization of the API, and the way the API is invoked.
  • Documentation, which includes both the API reference and other learning resources such as tutorials, how-tos, and developer guides.
  • Tooling, which involves both the command-line interface (CLI) and GUI tools that help editing, debugging, and testing the code. For example, research has shown that autocomplete in the IDE has a large impact on how APIs are discovered and used in programming.

These three pillars of developer experience complement one another, so they need to be designed and assessed as a package.

 
https://medium.com/google-design/how-i-do-developer-ux-at-google-b21646c2c4df

A GUI toolkit for Common Lisp

What is McCLIM?

McCLIM is a FOSS implementation of the Common Lisp Interface Manager specification, a powerful toolkit for writing GUIs in Common Lisp. It is licensed under the GNU Library General Public License.

You can access the McCLIM manual draft PDF if you want, but it’s still a work in progress. To reach the developers you may either write to the mailing list or on the #clim irc channel.

Features

  • Mature yet modern CLIM II protocol implementation
  • Extensible GUI toolkit for applications
  • Sophisticated interface manager for Common Lisp
  • Portable between various Common Lisp implementations
  • Robust solution for creating end-user applications

Resources

Some external tutorials for CLIM may be found here:

https://common-lisp.net/project/mcclim/

Machine Learning for Humans

Simple, plain-English explanations accompanied by math, code, and real-world examples.

Roadmap

Part 1: Why Machine Learning MattersThe big picture of artificial intelligence and machine learning — past, present, and future.

Part 2.1: Supervised LearningLearning with an answer key. Introducing linear regression, loss functions, overfitting, and gradient descent.

Part 2.2: Supervised Learning IITwo methods of classification: logistic regression and SVMs.

Part 2.3: Supervised Learning IIINon-parametric learners: k-nearest neighbors, decision trees, random forests. Introducing cross-validation, hyperparameter tuning, and ensemble models.

Part 3: Unsupervised LearningClustering: k-means, hierarchical. Dimensionality reduction: principal components analysis (PCA), singular value decomposition (SVD).

Part 4: Neural Networks & Deep Learning. Why, where, and how deep learning works. Drawing inspiration from the brain. Convolutional neural networks (CNNs), recurrent neural networks (RNNs). Real-world applications.

Part 5: Reinforcement LearningExploration and exploitation. Markov decision processes. Q-learning, policy learning, and deep reinforcement learning. The value learning problem.

Appendix: The Best Machine Learning ResourcesA curated list of resources for creating your machine learning curriculum.

 
https://medium.com/machine-learning-for-humans/why-machine-learning-matters-6164faf1df12

How to make complex requests simple with RxJava in Kotlin

It is a common problem in Android development when your API is not sending you exactly the same data, what you want to show in your views, so you need to implement more complex requests. Possibly your app needs to make multiple requests, that wait for each other, or call multiple requests after the previous one finished. Sometimes you even need to combine these two approaches. This can be challenging in plain Java and will often result in unreadable code, what is also painful to test.

Today I’m going to show you in a simple example how this can be achieved in a clean way using RxJava. The example is written in Kotlin, what makes the code more concise and easy to read. If you are completely new to RxJava or Kotlin, I suggest you catch up on the basics. There are some great resources here as well.

https://blog.mindorks.com/how-to-make-complex-requests-simple-with-rxjava-in-kotlin-ccec004c5d10