プロと読み解くRuby 3.4 NEWS テクノロジー部門技術基盤グループの笹田（ko1）と遠藤（mame）です。Ruby (MRI: Matz Ruby Implementation、いわゆる ruby コマンド) の開発をしています。お金をもらって Ruby を開発しているのでプロの Ruby コミッタです。 本日 12/25 に、恒例のクリスマスリリースとして、Ruby 3.4.0 がリリースされました（Ruby 3.4.0 リリース ）。今年も STORES Product Blog にて Ruby 3.4 の NEWS.md ファイルの解説をします（ちなみに、STORES Advent Calendar 2024 の記事になります。他も読んでね）。NEWS ファイルとは何か、は以前の記事を見てください。 プロと読み解く Ruby 2.6 NEWS ファイル - クックパッド開発者
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
The Scary Thing About Automating Deploys - Engineering at Slack
Most of Slack runs on a monolithic service simply called “The Webapp”. It’s big – hundreds of developers create hundreds of changes every week. Deploying at this scale is a unique challenge. When people talk about continuous deployment, they’re often thinking about deploying to systems as soon as changes are ready. They talk about microservices and 2-pizza teams (~8 people). But what does continuo
Since our launch of the Mojo programming language on May 2nd, more than 120K+ developers have signed up to use the Mojo Playground and 19K+ developers actively discuss Mojo on Discord and GitHub. Today, we’re excited to announce the next big step in Mojo’s evolution: Mojo is now available for local download – beginning with Linux systems, and adding Mac and Windows in coming releases. While the Mo
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
I want to write a post about Pitchfork, explaining where it comes from, why it is like it is, and how I see its future. But before I can get to that, I think I need to share my mental model on a few things, in this case, Ruby’s GVL. For quite a long time, it has been said that Rails applications are mostly IO-bound, hence Ruby’s GVL isn’t that big of a deal and that has influenced the design of so
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
インターネットの世界にニュースが溢れる昨今、満足度が高いものを的確に読みたいという方も多いかと思います。そのためには、見るニュースをどれにするか判断することが必要になります。そこで、ニュース全体の主旨を短い文章で表す要約の価値が高まっています。 自然言語処理における要約は、大きく２つに分けられます。それは、抽出型と抽象型です。抽出型は、文章の中から重要な文を抜き出すことで要約を作ります。要約として選ばれた文は元の文章にあるものなので、方向性が大きく異ることや誤字脱字がうまれる可能性は低いです。しかし、要約として選ばれた文のそれぞれは関係があるわけではないので、流暢な要約にならないことも多いです。それに対して、抽象型は人間が作るように要約としての文章の流暢さを考慮しながら作ります。本来人間がほしい要約はこちらになりますが、抽出型に比べると難易度が上がり、全く意味がわからない文章になる可能性も
Accelerate Python code 100x by import taichi as ti | Taichi Docs
Python has become the most popular language in many rapidly evolving sectors, such as deep learning and data sciences. Yet its easy readability comes at the cost of performance. Of course, we all complain about program performance from time to time, and Python should certainly not take all the blame. Still, it's fair to say that Python's nature as an interpreted language does not help, especially
【GROMACS】Umbrella samplingによるMD simulation 【In silico創薬】【SMD】 - LabCode
Windows 11 Home, 13th Gen Intel(R) Core(TM) i7-13700, 64 ビット オペレーティング システム、x64 ベース プロセッサ, メモリ：32GB Umbrella Samplingの概要と目的Umbrella Samplingは、分子がめったに起こさないような状態変化（たとえば、タンパク質同士が離れるなど）を詳しく調べるための計算手法です。通常の分子動力学（MD）では、エネルギー的に安定な状態にとどまりやすく、重要な変化が起こる確率が低いため、十分な情報が得られません。 たとえば、タンパク質AとBがくっついている状態から、少しずつ離れていく様子を観察したいとき、まずAとBを少しずつ引き離すSteered Molecular Dynamics（SMD）などのシミュレーションで、さまざまな距離の構造を取得します。その中から、0.5nm、0.7
prompts.chat
Welcome to the “Awesome ChatGPT Prompts” repository! While this collection was originally created for ChatGPT, these prompts work great with other AI models like Claude, Gemini, Hugging Face Chat, Llama, Mistral, and more. ChatGPT is a web interface created by OpenAI that provides access to their GPT (Generative Pre-trained Transformer) language models. The underlying models, like GPT-4o and GPT-o
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
Seaborn Objects ~ グラフィックの文法で強化された Python 可視化ライブラリの新形態 ~ - GMOインターネットグループ グループ研究開発本部
2023.02.10 Seaborn Objects ~ グラフィックの文法で強化された Python 可視化ライブラリの新形態 ~ お久しぶりです。グループ研究開発本部・AI研究開発質の T.I. です。色々あって久しぶりの Blog となりました。今回は、趣向を変え、最近大幅に改良された Python のデータ可視化ライブラリである Seaborn の新しい機能を紹介します。昨年９月にリリースされたばかりということもあるのか、本邦どころか英語で検索しても解説資料は公式サイト以外はほぼ皆無(当方調べ)というレアな情報となります。 はじめに データ分析・機械学習などにおいて、データの様々な特徴を可視化しながらの調査・探索(Exploratory Data Analysis (EDA))は、対象の正確で深い理解には不可欠なアプローチと言えます。Python のデータ可視化ライブラリとしては、
The Alkyne GC · mcyoung
Alkyne is a scripting language I built a couple of years ago for generating configuration blobs. Its interpreter is a naive AST walker1 that uses ARC2 for memory management, so it’s pretty slow, and I’ve been gradually writing a new evaluation engine for it. This post isn’t about Alkyne itself, that’s for another day. For now, I’d like to write down some notes for the GC I wrote3 for it, and more
Julia 1.0 came out well over 2 years ago. Since then a lot has changed and a lot hasn’t. Julia 1.0 was a commitment to no breaking changes, but that is not to say no new features have been added to the language. Julia 1.6 is a huge release and it is coming out relatively soon. RC-1 was released recently. I suspect we have at least a few more weeks before the final release. The Julia Core team take
As Elixir's Machine Learning (ML) ecosystem grows, many Elixir enthusiasts who wish to adopt the new machine learning libraries in their projects are stuck at a crossroads of wanting to move away from their existing ML stack (typically Python) while not having a clear path of how to do so. I would like to take some time to talk to WHY I believe now is a good time to start porting over Machine Lear
While getting my degree in Physics, I had to take classes in both MatLab and Python for scientific computing. I preferred python, where we used the SciPy and NumPy packages. In fact, I used those packages again (along with matplotlib) in an undergraduate research project simulating bacteria films. There's a catch: I was also pursuing a degree in Computer Science, and Python just wasn't fast enough
Following up from the last post, there is a lot more we need to cover. This was intended to be the post where we talk exclusively about benchmarks and numbers. But, I have unfortunately been perfectly taunted and status-locked, like a monster whose “aggro” was pulled by a tank. The reason, of course, is due to a few folks taking issue with my outright dismissal of the C and C++ APIs (and not showi
はじめての自然言語処理 ELECTRA（BERT の事前学習手法の改良）による固有表現抽出の検証 | オブジェクトの広場
今回は BERT における事前学習の改良手法である ELECTRA の検証です。ELECTRA はモデルサイズ、データ、計算量が同一条件であればオリジナルの BERT を凌ぐ性能とのことなので結果が楽しみなところです。事前学習をした後のファインチューニングは、いつも livedoor News Corpus の文書分類ばかりだったので、今回は固有表現抽出を試すことにしました。 1. はじめに 今回は BERT における事前学習の改良手法である ELECTRA 1 の検証です。 BERT に関しては 第3回 で取り上げていますが、トークン化が Sentencepiece である為、トークン単位での処理に難がありました2。今回は ELECTRA を試すにあたり、そのあたりの対応も入れ、 Megagon Labs さんから公開されている UD_Japanese-GSD v2.6-NE 3 を使っ
Deleted articles cannot be recovered. Draft of this article would be also deleted. Are you sure you want to delete this article? はじめに 突然ですが、皆さんは歌川国芳という浮世絵師をご存じでしょうか? 「国芳? 広重ではなくて?」と思われる方もいらっしゃるかも知れません。 歌川広重は「東海道五十三次」などの作品で名が知られた、歌川派を代表する江戸時代の浮世絵師です。 『東海道五十三次』より「日本橋」(本画像を含め、浮世絵は全てwikiより拝借しました。) 一方の歌川国芳は、あまり世間に知られた名前ではないかも知れません。 国芳は、寛政9年(1797)に江戸日本橋で生まれ、文久元年(1861)に没した浮世絵師で、広重とはほぼ同時代に活躍しました。歌川派の初代、歌川
Decorator JITs - Python as a DSL - Eli Bendersky's website
Spend enough time looking at Python programs and packages for machine learning, and you'll notice that the "JIT decorator" pattern is pretty popular. For example, this JAX snippet: import jax.numpy as jnp import jax @jax.jit def add(a, b): return jnp.add(a, b) # Use "add" as a regular Python function ... = add(...) Or the Triton language for writing GPU kernels directly in Python: import triton im
Signed distance functions are a really cool method of 3D rendering! But they unfortunately have a reputation for being difficult to understand. It makes sense why—they usually get shown off in beautiful, but complicated ShaderToy examples written in GLSL, an unfamiliar language for most programmers1. But at their core, SDFs are a really simple idea. I'm going to prove that by walking you through a
C++20 Coroutine Iterators - Sticky Bits - Powered by Feabhas
A blog looking at developing software for real-time and embedded systems In my first blog post about C++20 Coroutines I introduced the concepts behind a synchronous or generator style coroutine and developed a template class to support coroutines for any data type. In this post I’ll add an iterator to the template to support the range-for loop and iterative algorithms. You may want to review that
GitHub - ComfyUI-Workflow/awesome-comfyui: A collection of awesome custom nodes for ComfyUI
ComfyUI-Gemini_Flash_2.0_Exp (⭐+172): A ComfyUI custom node that integrates Google's Gemini Flash 2.0 Experimental model, enabling multimodal analysis of text, images, video frames, and audio directly within ComfyUI workflows. ComfyUI-ACE_Plus (⭐+115): Custom nodes for various visual generation and editing tasks using ACE_Plus FFT Model. ComfyUI-Manager (⭐+113): ComfyUI-Manager itself is also a cu
Version 1.0
Version 1.0# For a short description of the main highlights of the release, please refer to Release Highlights for scikit-learn 1.0. Legend for changelogs Major Feature something big that you couldn’t do before. Feature something that you couldn’t do before. Efficiency an existing feature now may not require as much computation or memory. Enhancement a miscellaneous minor improvement. Fix somethin
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プロと読み解くRuby 3.4 NEWS - STORES Product Blog
GPT in 60 Lines of NumPy | Jay Mody
Modular: Mojo🔥 - It’s finally here!
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PytorchのTransformersのT5を使って要約モデルを作る - 見習いデータサイエンティストの隠れ家
What's New in Emacs 28.1?
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From Python to Elixir Machine Learning
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AHC001で奇想の浮世絵師「歌川国芳」の絵を再現する - Qiita
Signed distance functions in 46 lines of Python