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  • How modern browsers work

    Note: For those eager to dive deep into how browsers work, an excellent resource is Browser Engineering by Pavel Panchekha and Chris Harrelson (available at browser.engineering). Please do check it out. This article is an overview of how browsers work. Web developers often treat the browser as a black box that magically transforms HTML, CSS, and JavaScript into interactive web applications. In tru

      How modern browsers work
    • Kalyn: a self-hosting compiler for x86-64

      Over the course of my Spring 2020 semester at Harvey Mudd College, I developed a self-hosting compiler entirely from scratch. This article walks through many interesting parts of the project. It’s laid out so you can just read from beginning to end, but if you’re more interested in a particular topic, feel free to jump there. Or, take a look at the project on GitHub. Table of contents What the pro

      • What's New in Emacs 28.1?

        Try Mastering Emacs for free! Are you struggling with the basics? Have you mastered movement and editing yet? When you have read Mastering Emacs you will understand Emacs. It’s that time again: there’s a new major version of Emacs and, with it, a treasure trove of new features and changes. Notable features include the formal inclusion of native compilation, a technique that will greatly speed up y

        • bytecode interpreters for tiny computers ⁑ Dercuano

          Introduction: Density Is King (With a Tiny VM) I've previously come to the conclusion that there's little reason for using bytecode in the modern world, except in order to get more compact code, for which it can be very effective. So, what kind of a bytecode engine will give you more compact code? Suppose I want a bytecode interpreter for a very small programming environment, specifically to minim

          • k-NN (k-Nearest Neighbors) in Supervised Machine Learning

            K-nearest neighbors (k-NN) is a Machine Learning algorithm for supervised machine learning type. It is used for both regression and classification tasks. As we already know, a supervised machine learning algorithm depends on labeled input data, which the algorithm learns to produce accurate outputs when input unlabeled data. k-NN aims to predict the test data set by calculating the distance betwee

              k-NN (k-Nearest Neighbors) in Supervised Machine Learning
            • Django for Startup Founders: A better software architecture for SaaS startups and consumer apps

              In an ideal world, startups would be easy. We'd run our idea by some potential customers, build the product, and then immediately ride that sweet exponential growth curve off into early retirement. Of course it doesn't actually work like that. Not even a little. In real life, even startups that go on to become billion-dollar companies typically go through phases like: Having little or no growth fo

              • Clojure's deadly sin

                This article is about laziness in Clojure. It is intended to be a comprehensive and objective (however possible) critique of lazy sequences as a feature. In no way do I want this to be a judgment of the decision to make Clojure lazy. Clojure the language is by no means formulaic; creating it involved making a plethora of impactful choices. We can judge by Clojure's longevity that the total package

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