a tumblelog
week 51, 2020

Building a simple neural net in Java

In this post we will tackle Artificial Intelligence with baby steps and try to build a very simple neural net in Java.

Source: Building a simple neural net in Java, an article by Victor Parmar and Fabian Dreier.

An Introduction to Lock-Free Programming

In this post, I’d like to re-introduce lock-free programming, first by defining it, then by distilling most of the information down to a few key concepts. I’ll show how those concepts relate to one another using flowcharts, then we’ll dip our toes into the details a little bit. At a minimum, any programmer who dives into lock-free programming should already understand how to write correct multithreaded code using mutexes, and other high-level synchronization objects such as semaphores and events.

Source: An Introduction to Lock-Free Programming, an article by Jeff Preshing.

Fresh macOS setup

These are the apps and utilities I installed to get my machine ready for daily work (almost exclusively iOS development in Xcode).

Source: Fresh macOS setup, an article by Aleksandar Vacić.

Migrating to Ubuntu LTS: six facts for CentOS users

Considering migrating to Ubuntu from other Linux platforms, such as CentOS?

Think Ubuntu- the most popular Linux distribution on public clouds, data centre and the edge. Since its inception, Ubuntu consistently gains market share, as of today reaching almost 50%.

Wondering why Ubuntu is so popular?

Source: Migrating to Ubuntu LTS: six facts for CentOS users, an article by Lech Sandecki.

Box of Mangoes

At the end of the afternoon a friend of Esme brought us a box of 10 mangoes.

A box of 10 mangoes
A box of 10 mangoes.

Centering in CSS

Follow 5 centering techniques as they go through a series of tests to see which one is the most resilient to change.

Source: Centering in CSS, an article by Adam Argyle.

Python at Scale: Strict Modules

Welcome to the third post in our series on Python at scale at Instagram! As we mentioned in the first post in the series, Instagram Server is a several-million-line Python monolith, and it moves quickly: hundreds of commits each day, deployed to production every few minutes.

We’ve run into a few pain points working with Python at that scale and speed. This article takes a look at a few that we imagine might impact others as well.

Source: Python at Scale: Strict Modules, an article by Carl Meyer.

Bayesian Decision Theory Explained

Bayesian Decision Theory is the statistical approach to pattern classification. It leverages probability to make classifications, and measures the risk (i.e. cost) of assigning an input to a given class.

In this article we'll start by taking a look at prior probability, and how it is not an efficient way of making predictions. Bayesian Decision Theory makes better predictions by using the prior probability, likelihood probability, and evidence to calculate the posterior probability. We'll discuss all of these concepts in detail. Finally, we'll map these concepts from Bayesian Decision Theory to their context in machine learning.

Source: Introduction to Bayesian Decision Theory, an article by Ahmed Fawzy Gad.

Commits are snapshots, not diffs

Git has a reputation for being confusing. Users stumble over terminology and phrasing that misguides their expectations. This is most apparent in commands that “rewrite history” such as git cherry-pick or git rebase. In my experience, the root cause of this confusion is an interpretation of commits as diffs that can be shuffled around. However, commits are snapshots, not diffs!

Source: Commits are snapshots, not diffs, an article by Derrick Stolee.

DRY is a Trade-Off

DRY, or Don't Repeat Yourself is frequently touted as a principle of software development. "Copy-pasta" is the derisive term applied to a violation of it, tying together the concept of copying code and pasta as description of software development bad practices (see also spaghetti code).

It is so uniformly reviled that some people call DRY a "principle" that you should never violate. Indeed, some linters even detect copy-paste so that it can never sneak into the code. But copy-paste is not a comic-book villain, and DRY does not come bedecked in primary colors to defeat it.

It is worthwhile to know why DRY started out as a principle. In particular, some for some modern software development practices, violating DRY is the right thing to do.

Source: DRY is a Trade-Off, an article by Moshe Zadka.

Go on ARM and Beyond

The industry is abuzz about non-x86 processors recently, so we thought it would be worth a brief post about Go’s support for them.

Source: Go on ARM and Beyond.

Foo to Bar: Naming Conventions in Haskell

Developers spend most of their time reading code, understanding it and exploring other ways to use existing solutions. Frankly, in our profession, there is very little time on actually writing new libraries and creating new interfaces in real-life development. So it is quite important to have some help in the most common activities. Naming conventions is one such thing that improves readability and eases the usage cost if agreed upon and spread worldwide.

Some languages have their own special naming conventions that make sense. Haskell is among them. There are a bunch of naming patterns that are commonly used everywhere in the ecosystem (including the standard libraries) that may help you to recognise the function’s meaning without looking at its documentation and even its type! This ability is especially relevant because naming is one of the hardest development problems, so having some help and no-brainer rules to guide in this area improves everyone’s life.

In this post, we will explore common naming conventions in Haskell together. It is going to be useful for both creators (library and API developers) and consumers (library users), as it establishes norms accepted in the libraries’ APIs.

Source: Foo to Bar: Naming Conventions in Haskell, an article by Veronika Romashkina and Dmitrii Kovanikov.

Mastering Postgres indexes in 10 minutes

Enough about the insides of Postgres indexes to impress your coworkers at the coffee machine or recruiters at a job interview 🤓.

We’ll have a look at B-Tree, Hash, GIN, GiST, BRIN indexes and focus on demystifying them.

Source: Mastering Postgres indexes in 10 minutes, an article by Fabien Herfray.

Getting Started With Nix: Introduction

Nix is a powerful, purely functional package manager designed to be a reliable and reproducible package-management system.

Nix is also the primary package manager for NixOS and can also be installed as an additional package manager on Linux and Mac OS X.

It also offers the following features:

  • Atomic Upgrades and Rollbacks;
  • Multiple versions of a package;
  • Multi-user package management, the ability to install certain packages for certain users only;
  • Effortless setup of build environments for a package, providing functional builds;
  • and many more.

Source: Getting Started With Nix: Introduction, an article by Nasir Hussain.

Minimal safe Bash script template

Bash scripts. Almost anyone needs to write one sooner or later. Almost no one says “yeah, I love writing them”. And that’s why almost everyone is putting low attention while writing them.

I won’t try to make you a Bash expert (since I’m not a one either), but I will show you a minimal template that will make your scripts safer. You don’t need to thank me, your future self will thank you.

Source: Minimal safe Bash script template, an article by Maciej Radzikowski.

Also of interest is the resulting Hacker News discussion.

Exhaustiveness Checking with Mypy

Mypy is an optional static type checker for Python. It's been around since 2012 and is gaining traction even since. One of the main benefits of using a type checker is getting errors at "compile time" rather than at run time.

Exhaustiveness checking is a common feature of type checkers, and a very useful one! In this article I'm going to show you how you can get mypy to perform exhaustiveness checking!

Source: Exhaustiveness Checking with Mypy, an article by Haki Benita.

ProRAW on iPhone 12 Pro

Today Apple officially released their new image format for creative professionals, Apple ProRAW. It marks a monumental leap forward in digital imaging on iPhone and I can’t wait to share a bit more about it.

I’ll cover why ProRAW matters, how to shoot ProRAW, and some of the best tools and apps for your iPhone ProRAW workflow.

If you follow my work, you know I’m a travel photographer and I’m usually testing my camera gear in extreme environments, so I designed a few tests for ProRAW in this same vein and that’s where I saw this image format really shine.

Source: ProRAW Is Here!, an article by Austin Mann.

Web crawling with Python

Web crawling is a powerful technique to collect data from the web by finding all the URLs for one or multiple domains. Python has several popular web crawling libraries and frameworks.

In this article, we will first introduce different crawling strategies and use cases. Then we will build a simple web crawler from scratch in Python using two libraries: requests and Beautiful Soup. Next, we will see why it’s better to use a web crawling framework like Scrapy. Finally, we will build an example crawler with Scrapy to collect film metadata from IMDb and see how Scrapy scales to websites with several million pages.

Source: Web crawling with Python, an article by Ari Bajo.

My evolution writing JSON-REST APIs

I've been writing JSON-REST APIs in Python for a number of years, and over that time I've found the tooling has greatly improved. To show you how you can benefit I'm going to show you how I've evolved. However, if you want to skip to the tooling I use today take a look at Quart-Schema.

Source: My evolution writing JSON-REST APIs, an article by Philip Jones.

Pattern matching

I first started writing Haskell about 15 years ago. My learning curve for the language was haphazard at best. In many cases, I learnt concepts by osmosis, and only later learned the proper terminology and details around them. One of the prime examples of this is pattern matching. Using a case expression in Haskell, or a match expression in Rust, always felt natural. But it took years to realize that patterns appeared in other parts of the languages than just these expressions, and what terms like irrefutable meant.

It's quite possible most Haskellers and Rustaceans will consider this content obvious. But maybe there are a few others like me out there who never had a chance to realize how ubiquitous patterns are in these languages. This post may also be a fun glimpse into either Haskell or Rust if you're only familiar with one of the languages.

Source: Pattern matching.