FastAPI is a modern, fast (high-performance), web framework for building APIs with Python 3.6+ based on standard Python type hints.

The key features are:

• Fast: Very high performance, on par with NodeJS and Go (thanks to Starlette and Pydantic). One of the fastest Python frameworks available.

• Fast to code: Increase the speed to develop features by about 200% to 300% *.

• Fewer bugs: Reduce about 40% of human (developer) induced errors. *

• Intuitive: Great editor support. Completion everywhere. Less time debugging.

• Easy: Designed to be easy to use and learn. Less time reading docs.

• Short: Minimize code duplication. Multiple features from each parameter declaration. Fewer bugs.

• Robust: Get production-ready code. With automatic interactive documentation.

• Standards-based: Based on (and fully compatible with) the open standards for APIs: OpenAPI (previously known as Swagger) and JSON Schema.

I like to use Makefiles. I like to use Makefiles in Java. I like to use Makefiles in Erlang. I like to use Makefiles in Elixir. And most recently, I like to use Makefiles in Ruby. I think you, too, would like to use Makefiles in your environment, and the engineering community would benefit if more of us used Makefiles, in general.

Varlink is an interface description format and protocol that aims to make services accessible to both humans and machines in the simplest feasible way.

A varlink interface combines the classic UNIX command line options, STDIN/OUT/ERROR text formats, man pages, service metadata and provides the equivalent over a single file descriptor, a.k.a. “FD3”.

Varlink is plain-text, type-safe, discoverable, self-documenting, remotable, testable, easy to debug. Varlink is accessible from any programming environment. See the Ideals page for more. And everybody likes Screenshots.

Manage pods, containers, and container images.

In Python, a decorator is a design pattern that we can use to add new functionality to an already existing object without the need to modify its structure. A decorator should be called directly before the function that is to be extended. With decorators, you can modify the functionality of a method, a function, or a class dynamically without directly using subclasses. This is a good idea when you want to extend the functionality of a function that you don't want to directly modify. Decorator patterns can be implemented everywhere, but Python provides more expressive syntax and features for that.

The goal of this article is to run a C program bare metal on an ARM Cortex M3. We will go through the assembler code generated from a small program written in C and come up with the prerequisites that must be in place in order for it to run.

afeOBJ is a most advanced formal specification language which inherits many advanced features (e.g. flexible mix-fix syntax, powerful and clear typing system with ordered sorts, parameteric modules and views for instantiating the parameters, and module expressions, etc.) from OBJ (or more exactly OBJ3) algebraic specification language.

CafeOBJ is a language for writing formal (i.e. mathematical) specifications of models for wide varieties of software and systems, and verifying properties of them. CafeOBJ implements equational logic by rewriting and can be used as a powerful interactive theorem proving system. Specifiers can write proof scores also in CafeOBJ and doing proofs by executing the proof scores.
afeOBJ is a most advanced formal specification language which inherits many advanced features (e.g. flexible mix-fix syntax, powerful and clear typing system with ordered sorts, parameteric modules and views for instantiating the parameters, and module expressions, etc.) from OBJ (or more exactly OBJ3) algebraic specification language.

CafeOBJ is a language for writing formal (i.e. mathematical) specifications of models for wide varieties of software and systems, and verifying properties of them. CafeOBJ implements equational logic by rewriting and can be used as a powerful interactive theorem proving system. Specifiers can write proof scores also in CafeOBJ and doing proofs by executing the proof scores.

CafeOBJ has state-of-art rigorous logical semantics based on institutions. The CafeOBJ cube shows the structure of the various logics underlying the combination of the various paradigms implemented by the language. Proof scores in CafeOBJ are also based on institution based rigorous semantics, and can be constructed using a complete set of proof rules.
CafeOBJ has state-of-art rigorous logical semantics based on institutions. The CafeOBJ cube shows the structure of the various logics underlying the combination of the various paradigms implemented by the language. Proof scores in CafeOBJ are also based on institution based rigorous semantics, and can be constructed using a complete set of proof rules.

This tutorial provides a basic Python programmer’s introduction to working with gRPC.

By walking through this example you’ll learn how to:

Define a service in a .proto file.
Generate server and client code using the protocol buffer compiler.
Use the Python gRPC API to write a simple client and server for your service.

It assumes that you have read the Overview and are familiar with protocol buffers. Note that the example in this tutorial uses the proto3 version of the protocol buffers language, which is currently in beta release: you can find out more in the proto3 language guide and Python generated code guide, and see the release notes for the new version in the protocol buffers Github repository.

Elixir ❤️ Embedded

Nerves is an open-source platform that combines the rock-solid BEAM virtual machine and Elixir ecosystem to easily build and deploy production embedded systems.

“Cloud native” is a term used to describe applications designed specifically to run on a cloud-
based infrastructure. Typically, cloud-native applications are developed as loosely coupled
microservices running in containers managed by platforms. These applications anticipate
failure, and they run and scale reliably even when their underlying infrastructure is experiencing outages.
To offer such capabilities, cloud-native platforms impose a set of contracts and
constraints on the applications running on them. These contracts ensure that the applications
conform to certain constraints and allow the platforms to automate the management of the
containerized applications. Many organizations understand the necessity and importance of
becoming cloud native, but do not know where to start. Ensuring that cloud-native platforms
and the containerized applications that run on them work seamlessly together provides the
ability to anticipate failure and the reliability to run and scale even when the underlying
infrastructure experiences outages. This whitepaper describes a number of principles that
containerized applications must comply with in order to become good cloud-native citizens.

Adhering to these principles will help ensure that your applications are suitable for
automation in cloud-native platforms such as Kubernetes.

Wer kennt es nicht: Man startet ein neues Softwareprojekt oder steigt bei der Entwicklung eines bestehenden Projektes mit ein und muss erst mal zahlreiche Compiler, Interpreter, Editoren, Abhängigkeiten und Weiteres installieren. Dabei heißt es nicht selten, man soll davon die Version 1.2.24-50rc4 mit Bugfix-Patch 19 installieren, sonst funktioniert es nicht.

Six provides simple utilities for wrapping over differences between Python 2 and Python 3. It is intended to support codebases that work on both Python 2 and 3 without modification. six consists of only one Python file, so it is painless to copy into a project.

Six can be downloaded on PyPi. Its bug tracker and code hosting is on GitHub.

The name, “six”, comes from the fact that 2*3 equals 6. Why not addition? Multiplication is more powerful, and, anyway, “five” has already been snatched away by the (admittedly now moribund) Zope Five project.

A single distribution of libraries that automatically collects traces and metrics from your app, displays them locally, and sends them to any analysis tool.

The key features of OpenCensus include:

• Standard wire protocols and consistent APIs for handling trace and metric data.
• A single set of libraries for many languages, including Java, C++, Go, .Net, Python, PHP, Node.js, Erlang, and Ruby.
• Included integrations with web and RPC frameworks, making traces and metrics available out of the box.
• Included exporters for storage and analysis tools. Right now the list includes Zipkin, Prometheus, Datadog, Stackdriver, and Azure App Insights.
• Full open source availability for additional integrations and export options.
• No additional server or daemon is required to support OpenCensus.
• In process debugging: an optional agent for displaying request and metrics data on instrumented hosts.

The Language Server protocol is used between a tool (the client) and a language smartness provider (the server) to integrate features like auto complete, go to definition, find all references and alike into the tool

Elixir is a dynamic, functional language designed for building scalable and maintainable applications.

Elixir leverages the Erlang VM, known for running low-latency, distributed and fault-tolerant systems, while also being successfully used in web development and the embedded software domain.

To learn more about Elixir, check our getting started guide and our learning page for other resources. Or keep reading to get an overview of the platform, language and tools.