Contents Command Line Interface Guidelines An open-source guide to help you write better command-line programs, taking traditional UNIX principles and updating them for the modern day. Authors Aanand Prasad Engineer at Squarespace, co-creator of Docker Compose. @aanandprasad Ben Firshman Co-creator Replicate, co-creator of Docker Compose. @bfirsh Carl Tashian Offroad Engineer at Smallstep, first e
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
OpenAI API ドキュメントの日本語訳をこちらでまとめます。文字量の多いドキュメントなので、セクションごとに記事を分割しています。 今回は「GET STARTED 」のセクションからLibraries 、Models、TutorialsそしてUsage policiesを抜粋した後編です。 基本 DeepLで翻訳して、気になるところだけ書き換えています(ほぼ気になるところがないのが、DeepLのすごいところ)。原文との突き合わせができるようにはじめに原文を入れてますので、間違いなど見つけられましたら、ぜひご指摘ください。ご指摘箇所は随時反映させていただきます。 原文のリンクが有効になってますので、それぞれ必要な場合は原文リンクの方を参照ください。 前回のおさらいはこちら Python library｜Python ライブラリWe provide a Python library, w
Announcing New Tools for Building with Generative AI on AWS | Amazon Web Services
Artificial Intelligence Announcing New Tools for Building with Generative AI on AWS The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses. Just recently, generative AI appli
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
January 30, 2023 In this post, we'll implement a GPT from scratch in just 60 lines of numpy. We'll then load the trained GPT-2 model weights released by OpenAI into our implementation and generate some text. Note: This post assumes familiarity with Python, NumPy, and some basic experience with neural networks. This implementation is for educational purposes, so it's missing lots of features/improv
PyTorch and TensorFlow are far and away the two most popular Deep Learning frameworks today. The debate over which framework is superior is a longstanding point of contentious debate, with each camp having its share of fervent supporters. Both PyTorch and TensorFlow have developed so quickly over their relatively short lifetimes that the debate landscape is ever-evolving. Outdated or incomplete in
A few months ago, I set myself the challenge of writing a C compiler in 500 lines of Python1, after writing my SDF donut post. How hard could it be? The answer was, pretty hard, even when dropping quite a few features. But it was also pretty interesting, and the result is surprisingly functional and not too hard to understand! There's too much code for me to comprehensively cover in a single blog
This post is the first in a three-part series. The remaining two: How does Google Authenticator work? (Part 2) How does Google Authenticator work? (Part 3) When you’re accessing services over the WEB – let’s pick GMail as an example – a couple of things have to happen upfront: The server you’re connecting to (GMail in our example) has to get to know who you are. Only after getting to know who you
Learn how Replit trains Large Language Models (LLMs) using Databricks, Hugging Face, and MosaicML IntroductionLarge Language Models, like OpenAI's GPT-4 or Google's PaLM, have taken the world of artificial intelligence by storm. Yet most companies don't currently have the ability to train these models, and are completely reliant on only a handful of large tech firms as providers of the technology.
LogLog Games
The article is also available in Chinese. Disclaimer: This post is a very long collection of thoughts and problems I've had over the years, and also addresses some of the arguments I've been repeatedly told. This post expresses my opinion the has been formed over using Rust for gamedev for many thousands of hours over many years, and multiple finished games. This isn't meant to brag or indicate su
What We Learned from a Year of Building with LLMs (Part I)
It’s an exciting time to build with large language models (LLMs). Over the past year, LLMs have become “good enough” for real-world applications. The pace of improvements in LLMs, coupled with a parade of demos on social media, will fuel an estimated $200B investment in AI by 2025. LLMs are also broadly accessible, allowing everyone, not just ML engineers and scientists, to build intelligence into
缶つぶし機とソフトウェア移行技術 - Refactoring to Rust の読書感想文 - じゃあ、おうちで学べる
はじめに ——あるいは、「知っている」と「理解している」の間 Rustのことは、知っていた。学習もしていた。実務でも使っていた。 でも、それは知っているつもりだった。 知ってるつもり 無知の科学 (ハヤカワ文庫NF) 作者:スティーブン スローマン,フィリップ ファーンバック早川書房Amazon 日々Rustで開発し、BoxとRcとArcを使い分け、tokio::spawnでタスクを生成し、?演算子を当たり前のように書いている。FFI？PyO3使えばいいでしょ。WebAssembly？wasm-bindgenがあるじゃない。技術的には、確かに「使える」レベルにはあった。 でも、心のどこかで感じていた違和感があった。 オートバイのエンジンを分解できる人と、エンジンが動く原理を理解している人は違う。コードが動くことと、なぜそう書くべきかを理解することも違う。私は前者だった。メカニックではあった
Gamedev in Lisp. Part 1: ECS and Metalinguistic Abstraction - cl-fast-ecs by Andrew
Gamedev in Lisp. Part 1: ECS and Metalinguistic Abstraction In this series of tutorials, we will delve into creating simple 2D games in Common Lisp. The result of the first part will be a development environment setup and a basic simulation displaying a 2D scene with a large number of physical objects. It is assumed that the reader is familiar with some high-level programming language, has a gener
Node.js
Notable changes built-in .env file support Starting from Node.js v20.6.0, Node.js supports .env files for configuring environment variables. Your configuration file should follow the INI file format, with each line containing a key-value pair for an environment variable. To initialize your Node.js application with predefined configurations, use the following CLI command: node --env-file=config.env
Update 1.69.1: The update addresses these issues. Update 1.69.2: The update addresses these issues. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the June 2022 release of Visual Studio Code. There are many updates in this version that we hope you'll like, some of the key highlights include: 3-way merge editor - Resolve merge conflicts wit
Update 1.99.1: The update addresses these security issues. Update 1.99.2: The update addresses these issues. Update 1.99.3: The update addresses these issues. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the March 2025 release of Visual Studio Code. There are many updates in this version that we hope you'll like, some of the key highligh
Agents
Intelligent agents are considered by many to be the ultimate goal of AI. The classic book by Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach (Prentice Hall, 1995), defines the field of AI research as “the study and design of rational agents.” The unprecedented capabilities of foundation models have opened the door to agentic applications that were previously unimaginabl
Prompt Engineering
Date: March 15, 2023 | Estimated Reading Time: 21 min | Author: Lilian Weng Prompt Engineering, also known as In-Context Prompting, refers to methods for how to communicate with LLM to steer its behavior for desired outcomes without updating the model weights. It is an empirical science and the effect of prompt engineering methods can vary a lot among models, thus requiring heavy experimentation a
AST vs. Bytecode: Interpreters in the Age of Meta-Compilation
233 AST vs. Bytecode: Interpreters in the Age of Meta-Compilation OCTAVE LAROSE, University of Kent, UK SOPHIE KALEBA, University of Kent, UK HUMPHREY BURCHELL, University of Kent, UK STEFAN MARR, University of Kent, UK Thanks to partial evaluation and meta-tracing, it became practical to build language implementations that reach state-of-the-art peak performance by implementing only an interprete
Rust is an appealing language for building user interfaces for a variety of reasons, especially the promise of delivering both performance and safety. However, finding a good architecture is challenging. Architectures that work well in other languages generally don’t adapt well to Rust, mostly because they rely on shared mutable state and that is not idiomatic Rust, to put it mildly. It is sometim
Parsing SQL - Strumenta
The code for this tutorial is on GitHub: parsing-sql SQL is a language to handle data in a relational database. If you worked with data you have probably worked with SQL. In this article we will talk about parsing SQL. It is in the same league of HTML: maybe you never learned it formally but you kind of know how to use it. That is great because if you know SQL, you know how to handle data. However
How I developed a faster Ruby interpreter | Red Hat Developer
In this article, I will describe my efforts to implement a faster interpreter for CRuby, the Ruby language interpreter, using a dynamically specialized internal representation (IR). I believe this article will interest developers trying to improve the interpreter performance of dynamic programming languages (e.g., CPython developers). I will cover the following topics: Existing CRuby interpreter a
Most of my readers are probably familiar with procedural programming, object-oriented programming (OOP), and functional programming (FP). The majority of top programming languages on all of the language popularity charts (like TIOBE) support all three to some extent. Even if a programmer avoided one or more of those three paradigms like the plague, they’re likely at least aware of them and what th
The Ultimate Interactive JQ Guide Learn how to search, query, and modify JSON data with 25 interactive jq examples and explanations Cover Photo by Pixabay Has this ever happened to you? You’ve just received a massive JSON file that looks like it was designed to confuse you. Or maybe you entered a command, and you got so much JSON that it looks incomprehensible. Important: Level up your jq skills w
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
Patterns for Building LLM-based Systems & Products [ llm engineering production 🔥 ] · 66 min read Discussions on HackerNews, Twitter, and LinkedIn “There is a large class of problems that are easy to imagine and build demos for, but extremely hard to make products out of. For example, self-driving: It’s easy to demo a car self-driving around a block, but making it into a product takes a decade.”
This post is an account of why I prefer using the attrs library over Pydantic. I'm writing it since I am often asked this question and I want to have something concrete to link to. This is not meant to be an objective comparison of attrs and Pydantic; I'm not interested in comparing bullet points of features, nor can I be unbiased since I'm a major contributor to attrs (at time of writing, second
The OpenSSL punycode vulnerability (CVE-2022-3602): Overview, detection, exploitation, and remediation | Datadog Security Labs
emerging threats and vulnerabilities The OpenSSL punycode vulnerability (CVE-2022-3602): Overview, detection, exploitation, and remediation November 1, 2022 emerging vulnerability On November 1, 2022, the OpenSSL Project released a security advisory detailing a high-severity vulnerability in the OpenSSL library. Deployments of OpenSSL from 3.0.0 to 3.0.6 (included) are vulnerable and are fixed in
はじめての自然言語処理 Fusion-In-Decoder でクイズに答えるモデルを作る | オブジェクトの広場
今回は Fusion-In-Decoder を使ってクイズに答えるモデルを作ります。以前から Wikipedia 等の外部情報を参照できるテキスト生成モデルを試してみたいと思っていました。Fusion-In-Decoder の発表は 2020 年なので少し前のモデルですが、T5 ベースで手軽に試せるサイズ感ですので、日本語で試してみましょう。 1. はじめに 今回紹介する Fusion-In-Decoder（以下、FiD ）1 は Meta AI (当時は Facebook AI Research) が発表した Open Domain question Answering タスクを解くテキスト生成モデルです。 じつは、以前から外部情報を参照できるテキスト生成モデルを試してみたくて2、 Google の RETRO3 の論文を読んでたんです。 なのですが、外部情報のサイズ感が 1000 B
Tier 4 Support § Support for these targets is entirely experimental. If this target is provided by LLVM, LLVM may have the target as an experimental target, which means that you need to use Zig-provided binaries for the target to be available, or build LLVM from source with special configure flags. zig targets will display the target if it is available. This target may be considered deprecated by
Update 1.97.1: The update addresses these security issues. Update 1.97.2: The update addresses these issues. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the January 2025 release of Visual Studio Code. There are many updates in this version that we hope you'll like, some of the key highlights include: Next Edit Suggestions (preview) - Co
It is December. That means annoying Christmas things are everywhere, including but not limited to the annual programming semi-competition known as Advent of Code. The problem with Advent of Code is that it is a waste of time. Most of the puzzles are in the realm of either string processing (somewhat applicable to programming), logic puzzles (not really applicable to most programming), or stupid go
How a simple Linux kernel memory corruption bug can lead to complete system compromise
In this case, reallocating the object as one of those three types didn't seem to me like a nice way forward (although it should be possible to exploit this somehow with some effort, e.g. by using count.counter to corrupt the buf field of seq_file). Also, some systems might be using the slab_nomerge kernel command line flag, which disables this merging behavior. Another approach that I didn't look
Date: June 23, 2023 | Estimated Reading Time: 31 min | Author: Lilian Weng Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerfu
VSeeFace
Contents About Download Terms of use Credits VSFAvatar Tutorials Manual FAQ Virtual camera Transparency Network tracking Special blendshapes Expressions VMC protocol Model posing iPhone tracking Perception Neuron ThreeDPoseTracker Troubleshooting Preview in Unity Translations Running on Linux Troubleshooting Startup Tracking/Webcam Virtual camera Model issues Lipsync Game capture Log folder Perfor
Join a VS Code Dev Days event near you to learn about AI-assisted development in VS Code. Update 1.66.1: The update addresses these issues. Update 1.66.2: The update addresses these security issues. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the March 2022 release of Visual Studio Code. There are many updates in this version that we ho
This paper reflects work done in late 2022 and 2023 to audit for vulnerabilities in terminal emulators, with a focus on open source software. The results of this work were 10 CVEs against terminal emulators that could result in Remote Code Execution (RCE), in addition various other bugs and hardening opportunities were found. The exact context and severity of these vulnerabilities varied, but some
My thoughts on writing a Minecraft server from scratch (in Bash) For the past year or so, I've been thinking about writing a Minecraft server in Bash as a thought excercise. I once tried that before with the Classic protocol (the one from 2009), but I quickly realized there wasn't really a way to properly parse binary data in bash. Take the following code sample: function a() { read -n 2 uwu echo
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
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Command Line Interface Guidelines
OpenAI API ドキュメント 日本語訳｜#2 GET STARTED 後編｜ゑぐみかるちゃあ
research!rsc: Coroutines for Go
GPT in 60 Lines of NumPy | Jay Mody
PyTorch vs TensorFlow in 2023
Writing a C compiler in 500 lines of Python
How does Google Authenticator work? (Part 1)
Replit — How to train your own Large Language Models
June 2022 (version 1.69)
March 2025 (version 1.99)
Xilem: an architecture for UI in Rust
Implementing Logic Programming
The Ultimate Interactive JQ Guide
Kalyn: a self-hosting compiler for x86-64
Patterns for Building LLM-based Systems & Products
Why I use attrs instead of pydantic
0.8.0 Release Notes ⚡ The Zig Programming Language
January 2025 (version 1.97)
Advent of Code on the Nintendo DS
LLM Powered Autonomous Agents
March 2022 (version 1.66)
"�[31m"?! ANSI Terminal security in 2023 and finding 10 CVEs
My thoughts on writing a Minecraft server from scratch (in Bash)