1kb JavaScript library for building frontend applications

HyperApp is a JavaScript library for building frontend applications.

  • Declarative: HyperApp’s design is based on the Elm Architecture. Create scalable browser-based applications using a functional paradigm. The twist is you don’t have to learn a new language.
  • Custom tags: Build complex user interfaces from custom tags. Custom tags are stateless, framework agnostic and easy to debug.
  • Batteries-included: Out of the box, HyperApp has Elm-like state management and a virtual DOM engine; it still weighs 1kb and has no dependencies.

https://github.com/hyperapp/hyperapp

Kotlin and Groovy JVM Languages with AWS Lambda

When most people hear “Java” they think of Java the programming language. Java is a lot more than a programming language, it also implies a larger ecosystem including the Java Virtual Machine (JVM). Java, the programming language, is just one of the many languages that can be compiled to run on the JVM. Some of the most popular JVM languages, other than Java, are Clojure, Groovy, Scala, Kotlin, JRuby, and Jython (see this link for a list of more JVM languages).

Did you know that you can compile and subsequently run all these languages on AWS Lambda?

AWS Lambda supports the Java 8 runtime, but this does not mean you are limited to the Java language. The Java 8 runtime is capable of running JVM languages such as Kotlin and Groovy once they have been compiled and packaged as a “fat” JAR (a JAR file containing all necessary dependencies and classes bundled in).

In this blog post we’ll work through building AWS Lambda functions in both Kotlin and Groovy programming languages. To compile and package our projects we will use Gradle build tool.

To follow along, please clone the Git repository available at GitHub here. Also, I recommend using an Integrated Development Environment (IDE) such as JetBrain’s IntelliJ IDEA, this is the IDE I used while working on these projects.

https://aws.amazon.com/pt/blogs/compute/kotlin-and-groovy-jvm-languages-with-aws-lambda/

Macaron – A high productive and modular web framework in Go

Package macaron is a high productive and modular web framework in Go. It takes basic ideology of Martini and extends in advance.

API Reference

The minimum requirement of Go is 1.3.

Features

  • Powerful routing with suburl.
  • Flexible routes combinations.
  • Unlimited nested group routers.
  • Directly integrate with existing services.
  • Dynamically change template files at runtime.
  • Allow to use in-memory template and static files.
  • Easy to plugin/unplugin features with modular design.
  • Handy dependency injection powered by inject.
  • Better router layer and less reflection make faster speed.

https://go-macaron.com/

How Reuters Replaced WebSockets with Amazon Cognito and SQS

The advantages of a serverless architecture are, at this point, not really a matter of debate. The question for every application or component becomes, “How can I avoid having to manage servers?” Sometimes you come across a roadblock: Perhaps you need a GPU; it takes 60 seconds just to load a machine learning model; maybe your task takes longer than the 300 seconds Amazon gives you for a Lambda process and you can’t figure out how to chop it up. The excuses never end.

Perhaps you want to push events into a browser or app through a WebSocket to create something similar to a chat or email application. You could use Nginx and Redis to create topics and have applications subscribe to them via a push stream; however, that means managing some long-running processes and servers. You can fake it and pound your backend once a second, butBut Amazon SQS and Cognito offer an easier way. Each user session can be paired with a Cognito identity and an SQS queue meaning applications can use SQS long-polling to receive events in real-time. At Reuters, we use this in production to support messaging in event-driven web applications and have open-sourced the underlying Serverless stack.

https://serverless.com/blog/how-reuters-replaced-websockets-with-amazon-cognito-and-sqs/

How Bayesian inference works

Bayesian inference is a way to get sharper predictions from your data. It’s particularly useful when you don’t have as much data as you would like and want to juice every last bit of predictive strength from it.

Although it is sometimes described with reverence, Bayesian inference isn’t magic or mystical. And even though the math under the hood can get dense, the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer.

Bayesian inference is based on the ideas of Thomas Bayes, a nonconformist Presbyterian minister in London about 300 years ago. He wrote two books, one on theology, and one on probability. His work included his now famous Bayes Theorem in raw form, which has since been applied to the problem of inference, the technical term for educated guessing. The popularity of Bayes’ ideas was aided immeasurably by another minister, Richard Price. He saw their significance, refined them and published them. It would be more accurate and historically just to call Bayes’ Theorem the Bayes-Price Rule.

https://brohrer.github.io/how_bayesian_inference_works.html

A grammar of graphics for Python

plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot.

Plotting with a grammar is powerful, it makes custom (and otherwise complex) plots are easy to think about and then create, while the simple plots remain simple.

To find out about all building blocks that you can use to create a plot, check out the documentation. Since plotnine has an API similar to ggplot2, where we lack in coverage theggplot2 documentation may be of some help.

https://github.com/has2k1/plotnine