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fakernews builds a markov chain using the top 500 post titles on HN and generates fake HN posts.

This is an example program to demonstrate the capabilities of a Golang library to build Markov models.

I’ve almost never been able to write correct Python import statements on the first go. Behavior is inconsistent between Python 2.7 and Python 3.6 (the two versions that I test here), and there is no single method for guaranteeing that imports will always work.

This post is my dive into how to resolve common importing problems. Unless otherwise stated, all examples here work with both Python 2.7 and 3.6.

Examples of Python and Rust code to illustrate the concept of ownership.

Very good tutorial.

Decision trees are the fundamental building block of gradient boosting machines and Random Forests(tm), probably the two most popular machine learning models for structured data. Visualizing decision trees is a tremendous aid when learning how these models work and when interpreting models. Unfortunately, current visualization packages are rudimentary and not immediately helpful to the novice. For example, we couldn't find a library that visualizes how decision nodes split up the feature space. So, we've created a general package (part of the animl library) for scikit-learn decision tree visualization and model interpretation.

Experienced programmers often need to use online resources to pick up new programming languages. However, we lack a comprehensive understanding of which resources programmers find most valuable and utilize most often. In this paper, we study how experienced programmers learn Rust, a systems programming language with comprehensive documentation, extensive example code, an active online community, and descriptive compiler errors. We develop a task that requires understanding the Rust-specific language concepts of mutability and ownership, in addition to learning Rust syntax.

Our results show that users spend 42% of online time viewing example code and that programmers appreciate the Rust Enhanced package’s in-line compiler errors, choosing to refresh every 30.6 seconds after first discovering this feature. We did not find any significant correlations between the resources used and the total task time or the learning outcomes. We discuss these results in light of design implications for language developers seeking to create resources to encourage usage and adoption by experienced programmers.

Quickly find and open a pdf among a collection of thousands of unsorted pdfs through fzf (fuzzy finder).

The Fibonacci numbers are the sequence 1, 1, 2, 3, 5, 8, ..., and satisfy the recurrence F(n) = F(n – 1) + F(n – 2).

They also have a beautiful formula.

My favorite derivation of this formula entirely avoids algebraic manipulation.

Sharing files can be a pain. The larger the file is, the more difficulty it inevitably creates. If you want those files kept private, you're in for a real nightmare. Well, not exactly.

Onionshare is a relatively new application that allows you to share files of any size securely and relatively anonymously over the Tor network without the need for a "midde-man" website. It's completely free and open source, and it's actually easy for anyone to use.

Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.

It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements.

Classical approaches to the problem involve hand crafting features from the time series data based on fixed-size windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field.

Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks or CNNs have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering.

It’s hard for me to teach an in-depth Python class without discussing lambda expressions. I almost always get questions about them. My students tend to see them in code on StackOverflow or they see them in a coworker’s code (which, realistically, may have also come from StackOverflow).

I get a lot of questions about lambda, I’m hesitant to recommend my students embrace Python’s lambda expressions. I have had an aversion to lambda expressions for many years, and since I started teaching Python more regularly a few years ago, my aversion to lambda expressions has only grown stronger.

I’m going to explain how I see lambda expressions and why I tend to recommend my students avoid using them.

🙀 A cat explains how HTTPS works...in a comic! 😻

molten is a minimal, extensible, fast and productive framework for building HTTP APIs with Python.

Liquore che si ottiene da un blend di bucce di rare varietà di limoni, fiori di sambuco, anice stellato e bacche di sambuco, che gli conferiscono il particolare colore nero-violaceo, opalescente. Ha un gusto dal fresco sentore di anice, mentre l’essenza di limone, rinfrescando l’aroma dell’anice, lo rende un liquore dal gusto nuovo.
Servita ghiacciata nel bicchierino o con molto ghiaccio.
Ideale per cocktails e long-drinks.
GRADO ALCOLICO: 40%

The second article in our series on privacycoins looks at Monero. One of the oldest privacycoins on the market, it's based on the innovative CryptoNote protocol first used by Bytecoin.

Adapted from http://www.possibility.com/Cpp/CppCodingStandard.html and NetBSD's style guidelines.

Roughly speaking, Gödel’s Incompleteness Theorem states that there are true mathematical statements that cannot be proven. When I was in 11-th grade, my geometry teacher Mr. Olsen, my friend Uma Roy, and I spent five weeks reading through Gödel’s original proof of the theorem. Why did it take so long? Partly because Uma and I were high-school students. Partly because Gödel was a less-than-talented writer. But mostly because the proof is actually pretty hard.

Human activity recognition is the problem of classifying sequences of accelerometer data recorded by specialized harnesses or smart phones into known well-defined movements.

It is a challenging problem given the large number of observations produced each second, the temporal nature of the observations, and the lack of a clear way to relate accelerometer data to known movements.

Classical approaches to the problem involve hand crafting features from the time series data based on fixed-sized windows and training machine learning models, such as ensembles of decision trees. The difficulty is that this feature engineering requires deep expertise in the field.

Recently, deep learning methods such as recurrent neural networks and one-dimensional convolutional neural networks, or CNNs, have been shown to provide state-of-the-art results on challenging activity recognition tasks with little or no data feature engineering.

In this tutorial, you will discover the ‘Activity Recognition Using Smartphones‘ dataset for time series classification and how to load and explore the dataset in order to make it ready for predictive modeling.

In their hearts, computers are sequential beasts. Their power comes from being able to break down the largest tasks into tiny steps that can be performed one after another. Often, though, our users need to see things occur in a single instantaneous step or see multiple tasks performed simultaneously.

A typical example, and one that every game engine must address, is rendering. When the game draws the world the users see, it does so one piece at a time — the mountains in the distance, the rolling hills, the trees, each in its turn. If the user watched the view draw incrementally like that, the illusion of a coherent world would be shattered. The scene must update smoothly and quickly, displaying a series of complete frames, each appearing instantly.

Double buffering solves this problem, but to understand how, we first need to review how a computer displays graphics.

Meshbird create distributed private networking between servers, containers, virtual machines and any computers in different datacenters, different countries, different cloud providers. All traffic transmit directly to recepient peer without passing any gateways. Meshbird do not require any centralized servers. Meshbird is absolutly decentralized distributed private networking.

For example, user can create private network between DigitalOcean’s droplets in each datacenter and link it together by executing one command. All traffic will be encrypted with strong AES-256.

Tasky is a command-line interface to Google's Tasks API.

It is meant to parallel the functionality of Todo.txt.

See how a minor change to your commit message style can make you a better programmer.

Format: <type>(<scope>): <subject>

where <scope> is optional.

Example
feat: add hat wobble
^--^  ^------------^
|     |
|     +-> Summary in present tense.
|
+-------> Type: chore, docs, feat, fix, refactor, style, or test.

More Examples:

• feat: new feature for the user, not a new feature for build script
• fix: bug fix for the user, not a fix to a build script
• docs: changes to the documentation
• style: formatting, missing semi colons, etc; no production code change
• refactor: refactoring production code, eg. renaming a variable
• test: adding missing tests, refactoring tests; no production code change
• chore: updating grunt tasks etc; no production code change