GitHub - modelcontextprotocol/servers: Model Context Protocol Servers
Official integrations are maintained by companies building production ready MCP servers for their platforms. 21st.dev Magic - Create crafted UI components inspired by the best 21st.dev design engineers. 2slides - An MCP server that provides tools to convert content into slides/PPT/presentation or generate slides/PPT/presentation with user intention. ActionKit by Paragon - Connect to 130+ SaaS inte
Investigating three real-world incidents in our cybersecurity evaluations
Investigating three real-world incidents in our cybersecurity evaluations In a review of our cybersecurity evaluation transcripts, we found three incidents in which a Claude model reached the internet from within or while interacting with a third-party evaluation environment, and then gained unauthorized access to the real systems of three different organizations. Below we describe what happened,
GPT-neoxの学習用にマルチノード並列学習環境を整えた with DeepSpeed - ABEJA Tech Blog
1. はじめに 2. 並列学習環境を調べる 並列学習方法を調べる ネットワーク、コンピューティング周りを調べる 3. インフラ環境を構築する コンパクトプレースメントポリシーの作成 Compute Engine を起動する (Fast Socket と gVNIC を利用する) 4. まずはシングルノードで動かす 5. 次はマルチ環境で動かす w/ Docker リポジトリをクローン ssh/config を作成 authorized_keys を作成 hostfile を作成 Docker を build 6. つまずいたポイント 学習途中に出力したファイルを再利用するのでNFSが必要に NFSのリージョンを間違えて速度が出なかった 大量のGPUの調達はリソースを確保できないかもしれないので要サポート確認 コンパクトプレースメントポリシーは邪魔になりそうだった 7. 結果 8. まとめ
There are lots of posts trying to show how simple it is to get started with Kubernetes. But many of these posts use complicated Kubernetes jargon for that, so even those with some prior server-side knowledge might be bewildered. Let me try something different here. Instead of explaining one unfamiliar matter (how to run a web service in Kubernetes?) with another (you just need a manifest, with thr
G-gen の福井です。当記事では、AI エージェントツールに専門知識やワークフローを追加するためのオープンフォーマットである Agent Skills について、概念や動作の仕組み、スキルの構造、使い方などを解説します。 概要 Agent Skills とは Skills が可能にすること Skills の実体 Agent Skills の仕組み 基本的な動作 コンテキスト肥大化の防止 Skills と MCP の関係 Skills の構造 ディレクトリ構成 SKILL.md の中身 使い方 対応ツール Skills ファイルの配置場所 インストール Gemini CLI からスキルを呼び出してみる 概要 Agent Skills とは Agent Skills は、生成 AI エージェントツールに専門知識やワークフローを追加するためのオープンフォーマットです。Anthropic が策定
PyTorch discloses malicious dependency chain compromise over holidays
HomeNewsSecurityPyTorch discloses malicious dependency chain compromise over holidays PyTorch has identified a malicious dependency with the same name as the framework's 'torchtriton' library. This has led to a successful compromise via the dependency confusion attack vector. PyTorch admins are warning users who installed PyTorch-nightly over the holidays to uninstall the framework and the counter
Since I was unable to travel for a couple of years during the pandemic, I decided to take my new-found time and really lean into Rust. After writing over 100k lines of Rust code, I think I am starting to get a feel for the language and like every cranky engineer I have developed opinions and because this is the Internet I’m going to share them. The reason I learned Rust was to flesh out parts of t
Out of all the migrations I help teams with, Go to Rust is a bit of an outlier. It’s not a question of “is Rust faster?” or “does Rust have types?”, Go already gets you most of the way there. The discussion is mostly about correctness guarantees, runtime tradeoffs, and developer ergonomics. A quick disclaimer before we start: this guide is heavily backend-focused. Backend services are where Go is
Build AI-powered scripts with the fm CLI and Python SDK - WWDC26 - Videos - Apple Developer
Explore all the new ways to leverage Apple Foundation Models on macOS. The Foundation Models SDK for Python lets you integrate with popular tooling and evaluation packages in the Python ecosystem. Find out how to use the brand new fm command introduced in macOS 27 to streamline scripting, automate model workflows, and accelerate your development process. Chapters 0:00 - Introduction 1:22 - Introdu
Real-world gen AI use cases from the world's leading organizations | Google Cloud Blog
AI is here, AI is everywhere: Top companies, governments, researchers, and startups are already enhancing their work with Google's AI solutions. Published April 12, 2024; last updated April 22, 2026. We first published this list two years ago at Next ‘24, as the agentic era was just dawning. Watching this list grow — propelled by our customer’s enthusiastic commitment to AI — proves we are now fir
Bringing the power of AI to Windows 11 – unlocking a new era of productivity for customers and developers with Windows Copilot and Dev Home
Bringing the power of AI to Windows 11 – unlocking a new era of productivity for customers and developers with Windows Copilot and Dev Home The team and I are pumped to be back at Build with the developer community this year. Over the last year, Windows has continued to see incredible growth fueled by Windows 11 adoption. In fact, one of the most exciting areas driving that growth for Windows has
The Go Programming Language and Environment – Communications of the ACM
Go is a programming language created at Google in late 2007 and released as open source in November 2009. Since then, it has operated as a public project, with contributions from thousands of individuals and dozens of companies. Go has become a popular language for building cloud infrastructure: Docker, a Linux container manager, and Kubernetes, a container deployment system, are core cloud techno
CI/CD for Machine Learning in 2024: Best Practices & Tips | JFrog ML
CI/CD for Machine Learning in 2024: Best Practices to Build, Train, and Deploy Explore best practices for CI/CD in Machine Learning in 2024. Learn to build, train, and deploy ML models efficiently with expert strategies. Building and deploying code to production environments is a fundamental aspect of software development. This process is equally pivotal in the realm of production-grade Machine Le
The State of Python 2025: Trends and Survey Insights | The PyCharm Blog
This is a guest post from Michael Kennedy, the founder of Talk Python and a PSF Fellow. Welcome to the highlights, trends, and key actions from the eighth annual Python Developers Survey. This survey is conducted as a collaborative effort between the Python Software Foundation and JetBrains’ PyCharm team. The survey results provide a comprehensive look at Python usage statistics and popularity tre
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
agent loop x4檪 You are Manus, an AI agent created by the Manus team. You excel at the following tasks: 1. Information gathering, fact-checking, and documentation 2. Data processing, analysis, and visualization 3. Writing multi-chapter articles and in-depth research reports 4. Creating websites, applications, and tools 5. Using programming to solve various problems beyond development 6. Various ta
It’s safe to close your laptop now: Hosting coding agents on Amazon Bedrock AgentCore | Amazon Web Services
Artificial Intelligence It’s safe to close your laptop now: Hosting coding agents on Amazon Bedrock AgentCore There’s a habit going around. Walking from one meeting to the next with the laptop cradled half-open. Sitting through a 1:1 with the lid propped just enough to keep the screen alive. Riding home while holding your laptop because it must stay running. Anywhere except closed on a desk, becau
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
Automated Testing in Machine Learning Projects [Best Practices for MLOps]
Neptune Blog Automated Testing in Machine Learning Projects [Best Practices for MLOps] Automated testing in machine learning is a very useful segment of the ML project which can make some long-term differences. Probably underrated in the early stages of development, it gets attention only in the late stages, when the system starts to break apart with annoying bugs which only grow with time. To eas
![Automated Testing in Machine Learning Projects [Best Practices for MLOps]](https://cdn-ak-scissors.b.st-hatena.com/image/square/511179aad1767b982b51cabfdaca560c1afc33da/backend=imagemagick;height=288;version=1;width=512/https%3A%2F%2Fneptune.ai%2Fwp-content%2Fuploads%2F2022%2F07%2Fblog_feature_image_008809_5_4_4_3.jpg)
Pyrefly v1.0 is here! | Pyrefly
Today we are pleased to share that Pyrefly, our open source type checker and language server for Python, has reached stable version 1 status, meaning we are confident that Pyrefly is ready for production use. Pyrefly is a Python code analysis tool for helping you find bugs in your code, provide structure for your AI agents and give you faster navigation in your IDE. It was first released as an alp
宣伝こちらの記事は合成生物学大会iGEMの強豪校であるiGEM-Wasedaさん協力のもと執筆されました。ご協力誠にありがとうございます！ 【iGEM-Waseda】は合成生物学の研究を行う早稲田大学の学術サークルです。iGEMと呼ばれる合成生物学の世界大会の世界大会に出場するために日々研究に励んでいらっしゃいます。 本記事では、iGEM2024で日本Undergrad部門で史上初のTOP10に選ばれたプロジェクトの一環として、特にIn Silicoシミュレーションに関わる部分のツールの一部を紹介しています。プロジェクトの詳細については、iGEM-Wasedaの成果報告サイトをご覧いただければ幸いです。 MACEとはMACEは、機械学習ポテンシャル（Machine Learning Potential）の一種として開発されたツールで、材料内の原子間相互作用を高精度かつ高速に予測できるのが特
Update 1.60.1: The update addresses these issues. Update 1.60.2: The update addresses these issues. Downloads: Windows: x64 Arm64 | Mac: Universal Intel silicon | Linux: deb rpm tarball Arm snap Welcome to the August 2021 release of Visual Studio Code. There are many updates in this version that we hope you will like, some of the key highlights include: Automatic language detection - Programming l
Introducing Pedalboard: Spotify’s Audio Effects Library for Python | Spotify Engineering
Introducing Pedalboard: Spotify’s Audio Effects Library for Python We’ve just open sourced Pedalboard, Spotify’s framework for adding effects to audio in Python. Pedalboard makes it easy to use studio-quality audio effects in your code, rather than just in your digital audio workstation (DAW). If you ask any music or podcast producer where they spend most of their time, chances are they’ll say the
The Best GPUs for Deep Learning in 2023 — An In-depth Analysis
Deep learning is a field with intense computational requirements, and your choice of GPU will fundamentally determine your deep learning experience. But what features are important if you want to buy a new GPU? GPU RAM, cores, tensor cores, caches? How to make a cost-efficient choice? This blog post will delve into these questions, tackle common misconceptions, give you an intuitive understanding
This article is a translation, the original version is available here. I started using the OCaml language regularly around 2012, and since then, my interest and enthusiasm for this language have only grown. It has become my preferred choice for almost all my personal projects, and it has also influenced my professional choices. Since 2014, I have been actively participating in public conferences d
Exactly ten years ago today, we published "Why We Created Julia", introducing the Julia project to the world. At this point, we have moved well past the ambitious goals set out in the original blog post. Julia is now used by hundreds of thousands of people. It is taught at hundreds of universities and entire companies are being formed that build their software stacks on Julia. From personalized me
Awesome Terraform | Curated list of awesome lists | Project-Awesome.org
A curated list of resources on HashiCorp's Terraform. Your contributions are welcome! Terraform enables you to safely and predictably create, change, and improve production infrastructure. It is an open source tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned. Contents Legend Official Resources Com
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
o1 pro + AIエンジニアにチャットで指示しながら、研究的なことをさせてみる ｜Kan Hatakeyama
はじめに自律的にプログラミングをしてくれるAIエンジニアをいい感じに動かせるようになってきたので、今日はChatGPT + devinで研究的なことをさせてみます。 自動研究といえば、昨年の夏に話題になった、Sakana AIのAIサイエンティストが有名です。 ただ、研究のネタを考えるのはまだあまり得意でない気がしたので、今回は適宜、そこはスマホで指示を出しながら、human in the loopで進めていきます。 最初のセットアップを除いて、チャットをするだけで、基本的な研究作業をこなせそう感じでした。 下準備: リポジトリを作ってdevinに登録するはじめに、パソコンを使って設定をします。このセクションの作業以降は、スマホがあればOKです。 githubでレポジトリを作り、一つだけ、開発方針に関するファイルを作っておきます。 DevelopmentPolycy.md 開発､コメントな
Stable Diffusionで使えるLoRAを自分で作ってみたい なにを準備しておけばいい？ 学習の方法がさっぱりわからない こういったお悩みにお答えします。 自分好みのLoRAを作ろうと思っても、学習はなかなかハードルが高いもの。よくわからずに挫折してもらう人も多いです。 この記事では STEP1：LoRA学習のための事前準備 STEP2：sd-scriptsのインストール STEP3：学習用の素材を準備 LoRA学習開始 学習方式やキャプションなどについて もっとスムーズにイラストを生成したいなら… 生成した大量のイラストを簡単に管理する方法 LoRA学習時によく発生するエラー これらについて解説していくので、最後まで読むと3STEPで学習の準備を完了し、LoRAを自作していく方法がわかります。 初心者の方・Stable Diffusionに関してわからないことがある方は以下の記事
Lil' Fun Langs
LOC Host HM ADTs Match Cl. Target Hirrolot's CoC src ~70 OCaml ✗ ✗ ✗ ✓ Interpreter Harrop MiniML src ~100 OCaml ✗ ✗ ✗ ✗ LLVM → native Algorithm W src ~300 Haskell ✓ ✗ ✗ ✗ Type checker only tomprimozic/type-systems src ~300 OCaml ✓ ✗ ✗ ✗ Type checker only lambda-calculus-hs src ~200–900 Haskell ✗ ✓ ✓ ✓ Interpreter THIH src ~429 Haskell ✓ ✓ ✓ ✗ Type checker only Simple-sub src ~500 Scala ✓ ✗ ✗ ✓ Typ
Junie Playbook
Exploring Junie with You JetBrains IDE Welcome to Junie, the coding agent by JetBrains! We're here to help you hand off your tasks and let Junie handle routine or more complex tasks for you—check out this guide to get started. This playbook includes examples from different technologies for new Junie users so that you can explore it in action for your JetBrains IDE. As of September 2025, Junie is s
Look, I’m going to say something that might sound crazy to some of you: Java deserves to be better in the terminal. And 2026? That’s going to be the year we fix it! I’ve been watching people get absolutely amazed by AI terminal applications lately. LLM-powered CLI tools that help you write code, answer questions, generate content—all from your terminal. And you know what they’re all written in? Py
今回は Transformer 系のモデル、具体的には BERT, T5, GPT の推論を高速化してみます。高速化手法として FasterTransformer, Torch-TensorRT, AWS Neuron を用い、素 の transfomers に比べ、どの程度速くなるか（ならないか）、利点・欠点を確認してみましょう。 1. はじめに 今回は Transformer 系のモデル、具体的には BERT, T5, GPT の推論を様々な技術を使って高速化してみます。 高速化の元ネタは Hugging Face の transformers1 縛りとして、素の transformers で推論する場合に比べ、 どの程度速くなるか（ならないか）見てみましょう。 推論を高速化する技術としては FasterTransfomer2, Torch-TensorRT3, AWS Neuron(
[GUIDE] Use, create, and post LoRA. For beginners to intermediate level Japanese guide/LoRAを使う、作る、投稿する。 初心者～中級者向けの 日本語ガイド - v0.8 | Stable Diffusion Other | Civitai
LoRAを使い、作り、投稿する。まとめ更新中のため記述に誤りがあるかもしれません 2023/04/29 20：00 更新AI素人です 知識はそんなに深くないので難しい事はあまり書かないです download数が増えるとたぬきが喜びます good（💛）が増えるとたぬきのHPが回復します rating（☆）が増えるとたぬきのモチベーションが上がります 日記のようなものhttps://github.com/vladmandic/automatic fork版（派生版）の解説しようかと思ったら本家がdevを更新し始めたので どうしようか悩み中です 最近はLoRAモデルの学習よりもAI絵の生成の比率が高くなっていますが もともとやりたかったのはAI絵の生成でした V関係モデルの投稿はペースダウンしています そもそもLoRAって何？の説明ざっくりとした説明 stablediffuisonというソフト
![[GUIDE] Use, create, and post LoRA. For beginners to intermediate level Japanese guide/LoRAを使う、作る、投稿する。 初心者～中級者向けの 日本語ガイド - v0.8 | Stable Diffusion Other | Civitai](https://cdn-ak-scissors.b.st-hatena.com/image/square/240320c5fab03a8c72f23497f354a766c913383c/backend=imagemagick;height=288;version=1;width=512/https%3A%2F%2Fimage.civitai.com%2FxG1nkqKTMzGDvpLrqFT7WA%2F251dc50f-99e0-40db-0fa1-ecfbc0d4e400%2Fwidth%3D1200%2F251dc50f-99e0-40db-0fa1-ecfbc0d4e400.jpeg)
From Common Lisp to Julia
This post explains my reasoning for migrating from Common Lisp to Julia as my primary programming language, after a few people have asked me to elaborate. This article is the product of my experiences and opinions, and may not reflect your own. Both languages are very well designed, and work well, so I encourage you to do your own research and form your own opinions about which programming languag
MLOps roadmap 2024
The MLOps engineer role is different from an ML engineer role. Even though the role varies from company to company, in general, ML engineers focus more on bringing individual projects to production, while MLOps engineers work more on building a platform that is used by machine learning engineers and data scientists. To build such platforms, lots of different skills are required. Here is a roadmap
Vertex AI を利用して強化学習レコメンデーション アプリケーションをビルドする | Google Cloud 公式ブログ
※この投稿は米国時間 2021 年 8 月 18 日に、Google Cloud blog に投稿されたものの抄訳です。 強化学習（RL）は機械学習の形態の 1 つであり、エージェントが環境に対する行動を選択しながら、その一連の選択を通じて得られる目標（報酬）を最大化する方法を学習していくというものです。RL のアプリケーションの例として、学習ベースのロボット工学、自律走行車、コンテンツ配信などがあります。基本的な RL システムには、多くの状態、対応する行動、それらの行動に対する報酬が含まれています。これを映画のレコメンデーション システムで考えてみましょう。「状態」はユーザー、「行動」はユーザーにおすすめする映画、「報酬」は映画に対するユーザー評価に当てはめることができます。Applied ML Summit 2021 の基調講演 で Spotify が述べていたように、RL は ML
Noble Numbat Release Notes Table of Contents Introduction New features in 24.04 LTS Known Issues Official flavours More information Introduction These release notes for Ubuntu 24.04 LTS (Noble Numbat) provide an overview of the release and document the known issues with Ubuntu and its flavours. For details of the changes applied since 24.04, please see the 24.04.2 change summary. Support lifespan
Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads | Amazon Web Services
Artificial Intelligence Announcing new Jupyter contributions by AWS to democratize generative AI and scale ML workloads Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. The Jupyter Notebook, first released in 2011, has become a de facto standard tool used by millions o
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How Kubernetes Reinvented Virtual Machines (in a good sense)
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Ubuntu 24.04 LTS (Noble Numbat) Release Notes