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  • 関数名、メソッド名、変数名でよく使う英単語のまとめ

    プログラミングをしていると関数名、メソッド名、変数名をどうするか悩みます。 ロジックより命名に時間を費やすこともざらにあります。翻訳したり、一般的な命名規則なのかいつも検索して大変です。 よく使うサイトの内容をコピってメモしておく 関数名とメソッド名の違いについて よく使う英単語のまえに、いつもごっちゃにして使っているけど、定義はこんな感じ 「関数」と「メソッド」の違い 似ているところ どちらも何か(引数)を入れると処理をして何か(戻り値)を返してくれます。 違うところ やってること自体は大差ありません。概念としては違います。 メソッドはオブジェクト指向で登場する用語で、オブジェクトの動作を定義したものです。 まずオブジェクトありきなのですね。一方の関数は、オブジェクト云々は関係ありません。 個人的な使い分け Java で登場する関数は「メソッド」です。C 言語で登場する関数は「関数」と呼

      関数名、メソッド名、変数名でよく使う英単語のまとめ
    • research!rsc: Coroutines for Go

      This post is about why we need a coroutine package for Go, and what it would look like. But first, what are coroutines? Every programmer today is familiar with function calls (subroutines): F calls G, which stops F and runs G. G does its work, potentially calling and waiting for other functions, and eventually returns. When G returns, G is gone and F continues running. In this pattern, only one fu

      • Weird Lexical Syntax

        I just learned 42 programming languages this month to build a new syntax highlighter for llamafile. I feel like I'm up to my eyeballs in programming languages right now. Now that it's halloween, I thought I'd share some of the spookiest most surprising syntax I've seen. The languages I decided to support are Ada, Assembly, BASIC, C, C#, C++, COBOL, CSS, D, FORTH, FORTRAN, Go, Haskell, HTML, Java,

          Weird Lexical Syntax
        • 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

          • Solving Quantitative Reasoning Problems With Language Models

            Solving Quantitative Reasoning Problems with Language Models Aitor Lewkowycz∗, Anders Andreassen†, David Dohan†, Ethan Dyer†, Henryk Michalewski†, Vinay Ramasesh†, Ambrose Slone, Cem Anil, Imanol Schlag, Theo Gutman-Solo, Yuhuai Wu, Behnam Neyshabur∗, Guy Gur-Ari∗, and Vedant Misra∗ Google Research Abstract Language models have achieved remarkable performance on a wide range of tasks that require

            • MAI-Thinking-1: Building a Hill-Climbing Machine

              MAI-Thinking-1: Building a Hill-Climbing Machine The Microsoft AI Team 1 Abstract Progress in AI is driven not by a single model, but by the ability to continually improve upon the current state of models. Achieving this requires treating model development as a system-level optimization problem, for which the solution is building a hill-climbing machine for rapid improvement. Our process includes

              • The State of React and the Community in 2025

                Random musings on React, Redux, and more, by Redux maintainer Mark "acemarke" Erikson Detailed thoughts on how React has been developed over time, and explanations for common community confusion and concerns Introduction 🔗︎ Today, the state of React and its ecosystem is complicated and fractured, with a mixture of successes, skepticism, and contention. On the positive side: React is the most wide

                  The State of React and the Community in 2025
                • Dynamic Programming is not Black Magic - Quentin Santos

                  This year’s Advent of Code has been brutal (compare the stats of 2023 with that of 2022, especially day 1 part 1 vs. day 1 part 2). It included a problem to solve with dynamic programming as soon as day 12, which discouraged some people I know. This specific problem was particularly gnarly for Advent of Code, with multiple special cases to take into account, making it basically intractable if you

                    Dynamic Programming is not Black Magic - Quentin Santos
                  • 17 types of similarity and dissimilarity measures used in data science. | Towards Data Science

                    The following article explains various methods for computing distances and showing their instances in our daily lives. Additionally, it… Various ML metrics. Inspired by Maarten Grootendorst. "There is no Royal Road to Geometry." – Euclid Quick note: Everything written and visualized has been created by the author unless it was specified. Illustrations and equations were generated using tools like

                      17 types of similarity and dissimilarity measures used in data science. | Towards Data Science
                    • Type Parameters Proposal

                      Ian Lance Taylor Robert Griesemer August 20, 2021 StatusThis is the design for adding generic programming using type parameters to the Go language. This design has been proposed and accepted as a future language change. We currently expect that this change will be available in the Go 1.18 release in early 2022. AbstractWe suggest extending the Go language to add optional type parameters to type an

                      • NumPy 2.0.0 Release Notes — NumPy v2.5.dev0 Manual

                        Getting started What is NumPy? Installation NumPy quickstart NumPy: the absolute basics for beginners Fundamentals and usage NumPy fundamentals NumPy for MATLAB users NumPy tutorials NumPy how-tos Advanced usage and interoperability Using NumPy C-API F2PY user guide and reference manual Under-the-hood documentation for developers Interoperability with NumPy Extras Glossary Release notes 2.5.0 2.4.

                        • Unicode is harder than you think · mcilloni's blog

                          Reading the excellent article by JeanHeyd Meneide on how broken string encoding in C/C++ is made me realise that Unicode is a topic that is often overlooked by a large number of developers. In my experience, there’s a lot of confusion and wrong expectations on what Unicode is, and what best practices to follow when dealing with strings that may contain characters outside of the ASCII range. This a

                          • 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

                            • Renato Athaydes

                              Revenge of Lisp (Part 1⁄2) Background vector created by upklyak - www.freepik.com This may surprise you if you know me, but I’ve been learning Common Lisp for a few weeks now. It all started when I was reading, funnily enough, a blog post about another, much more hyped, language called Julia. The post was titled Julia and the reincarnation of Lisp, and in it the author lamented that despite his lo

                              • 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

                                • Large Text Compression Benchmark

                                   Large Text Compression Benchmark Matt Mahoney Last update: Mar. 25, 2026. history This competition ranks lossless data compression programs by the compressed size (including the size of the decompression program) of the first 109 bytes of the XML text dump of the English version of Wikipedia on Mar. 3, 2006. About the test data. The goal of this benchmark is not to find the best overall compress

                                  • std::flip

                                    std::flip is a little-known utility from the C++ standard library header <functional>: it is a higher-order function that accepts a Callable and returns an equivalent Callable with the order of its parameters reversed (or “flipped”). To understand how it can be useful, let’s start with a simple example. Consider the following tree node class: struct node { int value; node* parent = nullptr; node*

                                    • 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
                                      • SIMD-friendly algorithms for substring searching

                                        Introduction Popular programming languages provide methods or functions which locate a substring in a given string. In C it is the function strstr, the C++ class std::string has the method find, Python's string has methods pos and index, and so on, so forth. All these APIs were designed for one-shot searches. During past decades several algorithms to solve this problem were designed, an excellent

                                        • Simple Implementation of OpenAI CLIP model: A Tutorial | Towards Data Science

                                          Summary of CLIP model’s approach, from Learning Transferable Visual Models From Natural Language Supervision paper Introduction It was in January of 2021 that OpenAI announced two new models: DALL-E and CLIP, both multi-modality models connecting texts and images in some way. In this article we are going to implement CLIP model from scratch in PyTorch. OpenAI has open-sourced some of the code rela

                                            Simple Implementation of OpenAI CLIP model: A Tutorial | Towards Data Science
                                          • Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models

                                            Published as a conference paper at ICLR 2026 AGENTIC CONTEXT ENGINEERING: EVOLVING CON- TEXTS FOR SELF-IMPROVING LANGUAGE MODELS Qizheng Zhang1∗ , Changran Hu2∗ , Shubhangi Upasani2 , Boyuan Ma2 , Fenglu Hong2 , Vamsidhar Kamanuru2 , Jay Rainton2 , Chen Wu2 , Mengmeng Ji2 , Hanchen Li3 , Urmish Thakker2 , James Zou1 , Kunle Olukotun1 1 Stanford University 2 SambaNova Systems, Inc. 3 UC Berkeley {q

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