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いい選択です 👍 `diskcache` は **ディスクにキャッシュを保存できるライブラリ** で、メモリを圧迫せずに大量のデータをキャッシュできます。しかも API がシンプルで、Webアプリや機械学習の前処理結果キャッシュなどにもよく使われます。 --- ## インストール ```bash pip install diskcache ``` --- ## 基本の使い方 ### キー・バリューのキャッシュ ```python import diskcache as dc cache = dc.Cache("./mycache") # 値を保存 cache["x"] = 42 # 値を取得 print(cache["x"]) # 42 # デフォルト値付き取得 print(cache.get("y", "default")) # default ``` --- ### 関数の結果をキャッシュ（デコレータ） ```python import time import diskcache as dc cache = dc.Cache("./mycache") @cache.memoize() def slow_function(x): time.sleep(2) return x * x print(slow_function(5)) # 初回は2秒かかる print(slow_function(5)) # 2回目はキャッシュから即時取得 ``` --- ### TTL（有効期限つきキャッシュ） ```python cache.set("key", "value", expire=10) # 10秒で消える print(cache.get("key")) # value ``` --- ### Webアプリ (Flask) での例 ```python from flask import Flask import diskcache as dc app = Flask(__name__) cache = dc.Cache("./flask_cache") @app.route("/square/<int:n>") @cache.memoize(expire=60) # 60秒キャッシュ def square(n): return str(n * n) if __name__ == "__main__": app.run(debug=True) ``` --- ## 特徴まとめ * **速い**（C実装が多い）。 * **永続化**：プロセスが終了してもキャッシュ残る。 * **柔軟**：TTL、サイズ制限、memoizeデコレータ対応。 * **スレッド/プロセス安全**なので並列処理にも強い。 --- 👉 `diskcache` をどんな用途で使いたいですか？ （例：APIレスポンスのキャッシュ、データ前処理結果の保存、Webアプリの応答高速化など） それに合わせてベストプラクティスを提案できます。

If by “MCP server” you mean a server implementing the Model Context Protocol (MCP) to allow LLMs / AI agents to interact with external tools/data sources, here are some of the best SDKs & frameworks — trade-offs, strengths & caveats — to help you choose one. If you meant something else by “MCP server,” happy to adjust. --- ## What is MCP The Model Context Protocol (MCP) is an open protocol by Anthropic to standardize how large language models (LLMs) can integrate with external tools, data sources, file systems, prompts, etc. ([ウィキペディア][1]) There are official SDKs in many languages, reference server implementations, and a growing ecosystem of tools. ([Model Context Protocol][2]) --- ## What to Look for in a Good MCP Server SDK Before picking one, consider: | Criterion | Why it matters | | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------- | | **Language ecosystem / community** | You’ll want one in a language you and your team are comfortable with; also availability of examples, integrations, debugging, etc. | | **Feature completeness** | Tools, prompt and resource exposure, transports (stdio, HTTP, SSE, etc.), good docs. | | **Security / sandboxing / permission control** | MCP servers often give access to file systems, external APIs, etc. You need to control what an agent can do. | | **Performance & latency** | Some tasks (web automation, file ops) need low latency; transport overheads matter. | | **Ease of deployment** | How easy is it to host, package, maintain (Docker, cloud, etc.)? | | **Interoperability** | Ability to connect to existing tools, integrate with LLM agents / clients, interface cleanly with other services. | --- ## Official SDKs & Languages Anthropic maintains official SDKs that support server and client building. Languages include: * **TypeScript** ([Model Context Protocol][2]) * **Python** ([Model Context Protocol][2]) * **Go** ([Model Context Protocol][2]) * **Kotlin / Swift / Java / C# / Ruby / Rust / PHP** ([Model Context Protocol][2]) These SDKs implement the core MCP protocol features such as: * Exposing “tools, resources, prompts” via MCP servers. ([Model Context Protocol][2]) * Building MCP clients to connect to servers. ([Model Context Protocol][2]) * Supporting different transports (local, remote) and ensuring protocol compliance. ([Model Context Protocol][2]) So using one of these “official” SDKs is usually the safest bet for compatibility & future support. --- ## Popular / Recommended Implementations & Servers Depending on what your MCP server needs to do, some reference / community servers are more mature or better suited. Some examples: | Use-Case | Good MCP Server / Implementation | | ------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | **Filesystem operations (read/write, project file context, etc.)** | Filesystem MCP servers (often official reference ones) are widely used. ([GitHub][3]) | | **Git / GitHub integration** | Git / GitHub MCP server tools are well supported. Useful for code review, CI, repo introspection. ([Digma][4]) | | **Browser / Web automation (UI testing / scraping)** | Puppeteer MCP, Playwright MCP are good choices. ([Digma][4]) | | **Memory / context preservation across sessions** | Memory Bank MCP, Knowledge Graph Memory MCP. ([Digma][4]) | | **Domain-specific / API / data integrations** | If you need database access (SQL, NoSQL), file storage, cloud services etc., look for MCP servers that already support those tools, or build using SDKs. ([LogRocket Blog][5]) | --- ## Pros & Cons **Pros:** * Easy extensibility: you can expose new “tools” or “resources” as MCP endpoints. * Standardization: once clients & servers follow MCP, tools become interoperable. * Rapid prototyping: you can spin up servers for local file access, APIs, etc. fairly quickly. **Cons / risks:** * **Security risks** — giving AI agents access to powerful tools (filesystem, OS commands, network) can be dangerous if permissions are too open. Must sandbox / control carefully. * **Complexity** as the number of tools/tools-APIs grows: tool discovery, versioning, schema drift, etc. * **Latency / overhead** in some scenarios (esp remote transport). * **Maintenance burden**, especially if you build custom servers. --- ## My Take: Best SDKs If I were to pick “best” SDKs / approach, here are what I’d recommend depending on scale / team size / urgency: * For a small team/prototype: **Python SDK** — very mature, many libraries/tools, quick to experiment. * For integration into existing large codebases or production, or if you need high performance/type safety: **TypeScript** / **Go**. Especially if you have a Node.js or cloud-native stack. * If your target is mobile / iOS / Android, using Kotlin or Swift SDKs may make sense. --- If you tell me your constraints (language(s), deployment environment, what tools you need, security/privacy concerns), I can recommend the *best specific MCP SDK + server implementation* for *your use-case*. Do you want me to do that? [1]: https://en.wikipedia.org/wiki/Model_Context_Protocol?utm_source=chatgpt.com "Model Context Protocol" [2]: https://modelcontextprotocol.io/docs/sdk?utm_source=chatgpt.com "SDKs" [3]: https://github.com/wong2/awesome-mcp-servers?utm_source=chatgpt.com "wong2/awesome-mcp-servers" [4]: https://digma.ai/15-best-mcp-servers/?utm_source=chatgpt.com "15 Best MCP servers for developers in May 2025" [5]: https://blog.logrocket.com/top-15-mcp-servers-ai-projects/?utm_source=chatgpt.com "The top 15 MCP servers for your AI projects"

# [2508.20722] rStar2-Agent: Agentic Reasoning Technical Report **URL:** https://www.arxiv.org/abs/2508.20722 **Captured:** 2025/9/6 17:39:22 --- Computer Science > Computation and Language [Submitted on 28 Aug 2025] rStar2-Agent: Agentic Reasoning Technical Report Ning Shang, Yifei Liu, Yi Zhu, Li Lyna Zhang, Weijiang Xu, Xinyu Guan, Buze Zhang, Bingcheng Dong, Xudong Zhou, Bowen Zhang, Ying Xin, Ziming Miao, Scarlett Li, Fan Yang, Mao Yang We introduce rStar2-Agent, a 14B math reasoning model trained with agentic reinforcement learning to achieve frontier-level performance. Beyond current long CoT, the model demonstrates advanced cognitive behaviors, such as thinking carefully before using Python coding tools and reflecting on code execution feedback to autonomously explore, verify, and refine intermediate steps in complex problem-solving. This capability is enabled through three key innovations that makes agentic RL effective at scale: (i) an efficient RL infrastructure with a reliable Python code environment that supports high-throughput execution and mitigates the high rollout costs, enabling training on limited GPU resources (64 MI300X GPUs); (ii) GRPO-RoC, an agentic RL algorithm with a Resample-on-Correct rollout strategy that addresses the inherent environment noises from coding tools, allowing the model to reason more effectively in a code environment; (iii) An efficient agent training recipe that starts with non-reasoning SFT and progresses through multi-RL stages, yielding advanced cognitive abilities with minimal compute cost. To this end, rStar2-Agent boosts a pre-trained 14B model to state of the art in only 510 RL steps within one week, achieving average pass@1 scores of 80.6% on AIME24 and 69.8% on AIME25, surpassing DeepSeek-R1 (671B) with significantly shorter responses. Beyond mathematics, rStar2-Agent-14B also demonstrates strong generalization to alignment, scientific reasoning, and agentic tool-use tasks. Code and training recipes are available at this https URL. Subjects: Computation and Language (cs.CL) Cite as: arXiv:2508.20722 [cs.CL] (or arXiv:2508.20722v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2508.20722 Focus to learn more Submission history From: Li Lyna Zhang [view email] [v1] Thu, 28 Aug 2025 12:45:25 UTC (1,217 KB) Access Paper: View PDF HTML (experimental) TeX Source Other Formats view license Current browse context: cs.CL < prev | next > new | recent | 2025-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)

# Daytona Sandbox：開発環境の新たな可能性 ## Daytona Sandboxとは Daytona Sandboxは、開発者がクラウド上で瞬時に開発環境を構築・共有できる革新的なプラットフォームです。従来のローカル開発環境の制約を取り払い、どこからでもアクセス可能な統一された開発体験を提供します。 ## 主な特徴 ### 1. 瞬時の環境構築 Gitリポジトリから数秒で完全な開発環境を立ち上げることができます。依存関係の解決やツールのセットアップは自動化されており、「動かない」というストレスから解放されます。 ### 2. チーム間での共有 作成した環境をURLで簡単に共有できるため、コードレビューやペアプログラミング、トラブルシューティングが格段に効率化されます。 ### 3. 一貫性の保証 開発者全員が同じ環境で作業するため、「私の環境では動く」問題が根本的に解決されます。 ## 開発ワークフローへの影響 Daytona Sandboxは特に以下のシーンで威力を発揮します： - **新しいプロジェクトの立ち上げ**：環境構築の時間を大幅短縮 - **オンボーディング**：新メンバーが即座に開発に参加可能 - **リモート開発**：デバイスに依存しない開発体験 ## まとめ Daytona Sandboxは開発環境の民主化を推し進める重要なツールです。クラウドネイティブな開発体験により、私たちはコードを書くことに集中し、環境の問題に悩まされることなく、より創造的な開発活動に時間を投資できるようになります。 現代の開発チームにとって、Daytona Sandboxのような標準化されたクラウド開発環境は、もはや選択肢ではなく必需品と言えるでしょう。

step-by-step E2B example in Python that shows stateful execution, installing packages, uploading a file, and doing a quick SQLite query—all inside a sandbox. --- ## 0) Install & set your key ```bash pip install e2b-code-interpreter python-dotenv export E2B_API_KEY="e2b_***" ``` E2B’s Python package is `e2b-code-interpreter`, and the SDK reads your `E2B_API_KEY` from env. ([PyPI][1]) --- ## 1) Minimal stateful sandbox script ```python # e2b_step_by_step.py import os from e2b_code_interpreter import Sandbox def main(): # Spins up an isolated VM ("sandbox"); auto-shuts down when the block exits with Sandbox() as sbx: # --- A) Stateful Python: variables persist across calls --- sbx.run_code("x = 41") out = sbx.run_code("x += 1; x") # reuses x print("x =", out.text) # -> 42 # --- B) Shell: install a package inside the sandbox --- sbx.commands.run("pip install --quiet pandas") # ok to pip-install at runtime # --- C) Upload a CSV into the sandbox filesystem --- csv = "name,age\nTaro,30\nHanako,28\n" sbx.files.write("/home/user/people.csv", csv) # --- D) Analyze the CSV in Python (pandas) --- out = sbx.run_code(r''' import pandas as pd df = pd.read_csv("/home/user/people.csv") df["age"].mean() ''') print("mean age:", out.text) # --- E) Quick SQLite session (persists objects across cells) --- sbx.run_code(r''' import sqlite3 conn = sqlite3.connect("/home/user/demo.db") cur = conn.cursor() cur.execute("CREATE TABLE IF NOT EXISTS t(a INT)") cur.executemany("INSERT INTO t(a) VALUES (?)", [(1,), (2,), (3,)]) conn.commit() ''') out = sbx.run_code(r''' cur.execute("SELECT sum(a) FROM t") cur.fetchone()[0] ''') print("sum =", out.text) # --- F) Peek at files (via Python, no shell needed) --- out = sbx.run_code('import os; print(os.listdir("/home/user"))') print("files in /home/user:\n", "".join(out.logs.stdout)) if __name__ == "__main__": main() ``` **Why this works / what to know** * `Sandbox()` starts an isolated cloud VM where you can **run code repeatedly and reuse variables** (`run_code` shares state across calls). The returned `Execution` has `.text` (last expression) and `.logs.stdout` for prints. ([e2b.dev][2]) * You can **run shell commands** like `pip install …` via `sandbox.commands.run(...)`. ([Hugging Face][3]) * You can **upload files** into the sandbox with `sandbox.files.write(path, data)` (string/bytes/IO). ([e2b.dev][4]) * By default, a sandbox has a short idle timeout (\~5 minutes) unless you keep using it. ([e2b.dev][5]) --- ## 2) (Optional) Pause & resume the same sandbox later If you want the *exact* process memory and filesystem to persist (even running kernels), E2B has **persistence** (public beta): ```python from e2b_code_interpreter import Sandbox sbx = Sandbox() # create sbx.beta_pause() # save full state (ID: sbx.sandbox_id) same = Sandbox.connect(sbx.sandbox_id) # resume later and continue ``` This preserves files **and memory** (variables, processes) between sessions. ([e2b.dev][6]) --- ## 3) Useful docs you’ll likely reference * **Quickstart** (API key, first sandbox). ([e2b.dev][7]) * **Python SDK: `Sandbox.run_code`** (stateful cells; result fields). ([e2b.dev][2]) * **Filesystem read/write** (Python `files.write`, `files.read`, etc.). ([e2b.dev][4]) * **Install custom packages / runtime installs** (`pip install` in sandbox). ([e2b.dev][8]) * **Commands API (shell in sandbox)**. ([e2b.dev][9]) * **Cookbook examples** (more end-to-end Python demos). ([GitHub][10]) If you want, I can tailor this to your exact use case (e.g., connecting to Postgres/MySQL from inside the sandbox, or wiring this into your LLM/tool-calling flow). [1]: https://pypi.org/project/e2b-code-interpreter/?utm_source=chatgpt.com "e2b-code-interpreter" [2]: https://e2b.dev/docs/sdk-reference/code-interpreter-python-sdk/v1.2.1/sandbox "E2B - Code Interpreting for AI apps" [3]: https://huggingface.co/docs/smolagents/en/tutorials/secure_code_execution?utm_source=chatgpt.com "Secure code execution" [4]: https://e2b.dev/docs/sdk-reference/python-sdk/v1.5.2/sandbox_sync?utm_source=chatgpt.com "E2B - Code Interpreting for AI apps" [5]: https://e2b.dev/docs/quickstart "E2B - Code Interpreting for AI apps" [6]: https://e2b.dev/docs/sandbox/persistence "E2B - Code Interpreting for AI apps" [7]: https://e2b.dev/docs/quickstart?utm_source=chatgpt.com "Running your first Sandbox" [8]: https://e2b.dev/docs/quickstart/install-custom-packages?utm_source=chatgpt.com "Install custom packages" [9]: https://e2b.dev/docs/commands?utm_source=chatgpt.com "Running commands in sandbox" [10]: https://github.com/e2b-dev/e2b-cookbook?utm_source=chatgpt.com "e2b-dev/e2b-cookbook: Examples of using E2B"

Agentic workflow patterns integrate modular software agents with structured large language model (LLM) workflows, enabling autonomous reasoning and action. While inspired by traditional serverless and event-driven architectures, these patterns shift core logic from static code to LLM-augmented agents, providing enhanced adaptability and contextual decision-making. This evolution transforms conventional cloud architectures from deterministic systems to ones capable of dynamic interpretation and intelligent augmentation, while maintaining fundamental principles of scalability and responsiveness. ###### In this section - [From event-driven to cognition-augmented systems](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/from-event-driven-to-cognition-augmented-systems.html) - [Prompt chaining saga patterns](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/prompt-chaining-saga-patterns.html) - [Routing dynamic dispatch patterns](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/routing-dynamic-dispatch-patterns.html) - [Parallelization and scatter-gather patterns](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/parallelization-and-scatter-gather-patterns.html) - [Saga orchestration patterns](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/saga-orchestration-patterns.html) - [Evaluator reflect-refine loop patterns](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/evaluator-reflect-refine-loop-patterns.html) - [Designing agentic workflows on AWS](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/designing-agentic-workflows-on-aws.html) - [Conclusion](https://docs.aws.amazon.com/prescriptive-guidance/latest/agentic-ai-patterns/conclusion.html)

# What's New at AWS - Cloud Innovation & News **URL:** https://aws.amazon.com/jp/about-aws/whats-new/2025/08/amazon-p5-single-gpu-instances-now-available/ **Captured:** 2025/8/26 15:02:09 --- Amazon EC2 Single GPU P5 instances are now generally available Posted on: Aug 12, 2025 Today, AWS announces new Amazon Elastic Compute Cloud (Amazon EC2) P5 instance size with one NVIDIA H100 GPU that allows businesses to right-size their machine learning (ML) and high-performance computing (HPC) resources with cost-effectiveness. The new instance size enables customers to start small and scale in granular increments, providing more flexible control over infrastructure costs. Customers developing small to medium Large Language Models (LLMs) such as chatbots or specialized language translation tools can now run inference tasks more economically. Customers can also use these instances to deploy HPC applications for pharmaceutical discovery, fluid flow analysis, and financial modeling without committing to expensive, large-scale GPU deployments. P5.4xlarge instances are now available through Amazon EC2 Capacity Blocks for ML in the following AWS Regions: US East (North Virginia, Ohio), US West (Oregon), Europe (London), Asia Pacific (Mumbai, Sydney, Tokyo) and South America (Sao Paulo) regions. These instances can be purchased On-Demand, Spot or through Savings Plans in Europe (London), Asia Pacific (Mumbai, Jakarta, Tokyo), and South America (Sao Paulo) regions. To learn more about P5.4xlarge instances, visit Amazon EC2 P5 instances.

## MCPサーバーとは？かんたん説明 **MCP（Model Context Protocol）サーバー**は、AI（とくに大きな言葉を扱うモデル：LLM）が、人間の代わりに外部の情報やツールとやりとりできる「通り道」をつくる仕組みです。たとえるなら「AI用のUSB-Cケーブル」です。 これはAnthropic（アンソロピック）という会社が2024年11月に公開した、誰でも使える（オープンな）仕組みです。([saketnkzw.com][1]) --- ## MCPの仕組み：関係者の役割 MCPは、以下3つの役割で成り立っています： 1. **ホスト (Host)** → AIアシスタントそのもの（例：Claude Desktop、IDEのAI機能） → ユーザーの命令を受けて、どの情報が必要か判断します。([Qiita][2]) 2. **クライアント (Client)** → ホストとサーバーをつなぐ“橋渡し役”。 → ホスト側にあって、適切な指示をMCPサーバーに送ります。([Qiita][2], [ウィキペディア][3]) 3. **サーバー (Server)** → 実際の情報やツールを持っていて、それをクライアント経由でAIに提供する“情報の倉庫”。 → 必要なデータや機能（たとえば天気情報やファイル読み込み）を返します。([Qiita][4], [ウィキペディア][5], [ウィキペディア][3]) つまり、ホスト → クライアント → サーバー → クライアント → ホストという流れで、データのやりとりをする構成です。 --- ## なぜ注目されているの？メリットまとめ * **共通ルールでつながる** → どのAIとも、どのデータとも同じ形式でやりとりできるようになり、作る側も使う側もラクになります。([ウィキペディア][5], [luenci.com][6], [ubsecure.jp][7]) * **拡張性が高い** → 新しいツールや機能を追加するのが簡単。道具を増やす感覚で、AIの能力を伸ばせます。([ubsecure.jp][7], [クラスメソッド発「やってみた」系技術メディア | DevelopersIO][8], [ウィキペディア][5]) * \*\*大手サービスでも採用中 \*\* → OpenAIやGoogle DeepMind、Microsoftなどの先進企業も採用しており、信頼性や注目度が高いです。([ウィキペディア][5]) --- ## &#x20;イメージで視覚化 > まるで「AI用のUSB‑Cケーブル」のような仕組み。 > USB‑Cで機器とつなげば、何でもデータがやりとりできるように、 > MCPを使えばAIが遠くのツールや情報にアクセスできるようになります。([GitHub][9]) --- ## 簡単な使い方の流れ（例：天気情報を取得するMCPサーバー構築） たとえばこんな使い方があります： 1. 天気を知らせるための「get\_forecast」や「get\_alerts」などの機能を持つMCPサーバーを用意する。([Model Context Protocol][10]) 2. クライアント（例：Claude Desktopなど）にそのサーバーを登録。 3. ユーザーが「今日の天気は？」と聞くと、AIがMCPを通じて情報を取得して答えてくれる。 --- ## 入門ポイントまとめ | 用語 | 説明（一言） | | ------ | ------------------ | | MCP | AIと情報をつなぐ共通ルール | | Host | ユーザーの命令を扱うAI本体 | | Client | HostとServerをつなぐ橋渡し | | Server | 情報や機能を持つ倉庫 | --- いいね！じゃあ「手を動かせる・非エンジニア向け」の超やさしい実例つきガイドいきます。コードは“貼って動かす”最短ルートにして、必要なところだけ軽く説明します。 --- # MCPサーバーを作って動かす：いちばんカンタンな道筋 ## 0) MCPって何をする？ * AI（Claudeなど）から**安全に外部ツールやデータに触るための共通ルール**。 * 仕組みの全体像と「Claude Desktopにサーバーをつなぐ手順」は公式クイックスタートが非常に分かりやすいです。 ([Model Context Protocol][1]) --- ## 1) どのやり方を選ぶ？ 初心者にやさしい順に3パターンあります。 1. **既製サーバーを入れる（最短）** 例：ファイル閲覧用の「Filesystemサーバー」をClaude Desktopに追加。数分で“体験”できます。 ([Model Context Protocol][2]) 2. **テンプレから自作（かんたん）** TypeScript（Node.js）やPythonの**雛形リポジトリ**を使うと、コピペ中心で自作できます。 ([GitHub][3]) 3. **クラウドに公開（応用）** 作ったサーバーを**Vercel**に配置すれば、URLでつないでどこからでも使えます。認証・スケールも面倒見てくれます。 ([Vercel][4]) > まずは①→②→③と段階を踏むのがおすすめです。 --- ## 2) まずは体験：既製サーバーをClaudeに入れてみる（数分） Claude Desktop を開き、\*\*Settings →（Developer または Extensions）**からMCPサーバーを追加します。公式の「ローカルMCPサーバー接続ガイド」が画面つきで丁寧です。最近は**ワンクリックの“Desktop Extension(.dxt)”\*\*にも対応してきています。 ([Model Context Protocol][2], [Anthropic Help Center][5], [Reddit][6]) --- ## 3) 実例①：超最小の“お天気”サーバー（Node.js/TypeScript） > 公式チュートリアルの流れを、初心者向けに**最短コード**に絞ったものです。 ([Model Context Protocol][1]) ### 準備 ```bash # 任意の空フォルダで npm init -y npm i @modelcontextprotocol/sdk fastmcp typescript ts-node npx tsc --init ``` ### サーバーコード（`server.ts`） ```ts import { Server } from "fastmcp"; // 1) サーバー作成（stdioで動かす） const app = new Server({ name: "weather-demo", version: "1.0.0" }); // 2) "get_weather" という道具（ツール）を1個だけ用意 app.tool("get_weather", { description: "都市名を入れるとダミーの天気を返すよ", inputSchema: { type: "object", properties: { city: { type: "string" } }, required: ["city"] }, execute: async ({ city }) => { // 本当はAPIを呼ぶが、まずは固定値で体験する return { summary: `${city} は 25℃ / 晴れ（デモ値）` }; }, }); // 3) 起動（stdio） app.startStdio(); ``` ### 実行 ```bash npx ts-node server.ts ``` --- ## 4) Claude Desktop とつなぐ（ローカル接続） Claude Desktop の設定で\*\*「カスタム（ローカル）MCPサーバー」**として今のプロセス（stdio）を登録します。手順は公式の「ローカルMCPをつなぐ」ドキュメントが確実です。登録後にClaudeを再起動すると、チャット画面のツール一覧に**get\_weather\*\*が出ます。 ([Model Context Protocol][2]) > もしURL経由の**リモート接続**をしたい場合は、**Custom Connector**からURLを追加する方式が用意されています。 ([Model Context Protocol][7]) --- ## 5) 実例②：同じことをPythonで（お好みで） Alex Mercedさんの**Pythonによる基本MCPサーバー**解説が読みやすいです。以下は「最低限の一個ツール」パターン。 ([Medium][8]) ### 準備 ```bash pip install fastmcp ``` ### サーバーコード（`server.py`） ```python from fastmcp import Server app = Server(name="weather-demo", version="1.0.0") @app.tool() def get_weather(city: str) -> dict: """都市名を受け取り、ダミーの天気を返す""" return {"summary": f"{city} は 25℃ / 晴れ（デモ値）"} if __name__ == "__main__": app.start_stdio() ``` ### 実行 ```bash python server.py ``` > あとは**手順4**と同様にClaude Desktopへ登録して使えます。 ([Model Context Protocol][2]) --- ## 6) 便利なテンプレ＆参考集 * **テンプレ（TypeScript / FastMCP入り）**： `mcpdotdirect/template-mcp-server` — stdio/HTTP両対応、ツールやリソース、プロンプトの雛形まで入っています。最短で“自分用サーバー”に化けます。 ([GitHub][3]) * **公式の参照実装とサーバーギャラリー**： いろんな用途のサーバーがまとまっており、「こう作るのか」のカタログとして便利。 ([GitHub][9]) --- ## 7) クラウド公開（応用）：Vercelにデプロイ → URLで接続 ローカルで動かせたら、次は**Vercel**に置いてURL接続してみましょう。**Functions**でスケールし、**OAuth/認可**もガイドあり。Claude側は\*\*Settings→Integrations（またはDeveloper）\*\*からURLを登録するだけ。 ([Vercel][4], [weavely.ai][10]) --- ## 8) 困ったときのヒント * **Claude Desktop の再起動**で拾われることが多い（ツールが見えない時）。 ([MESA][11]) * Windowsでの再起動や設定場所のコツをまとめたコミュニティTipsも参考に。 ([YouTube][12], [Reddit][13]) --- ## 9) 何ができるようになる？ MCPは\*\*「AIに手足を生やす」\*\*ようなもの。ファイル操作、社内API、データベース、SaaSなど、**一度“サーバー”として生やせば、Claudeなど複数のクライアントから共通ルールで使い回せる**のが最大の効用です。背景や事例の概観はこちらが読みやすいです。 ([The Verge][14]) --- # まとめ（今日やるアクション） 1. まずは**既製サーバー**をClaudeに入れて体験（1日目） ([Model Context Protocol][2]) 2. 次に**最小サーバー**を自作（TypeScriptかPythonのどちらかでOK） ([Model Context Protocol][1], [Medium][8]) 3. 気に入ったら**テンプレ**で整理し、**Vercel**に公開してURL接続（2〜3日目） ([GitHub][3], [Vercel][4]) --- 必要なら、あなたのユースケース（社内データ参照、RAG呼び出し、業務システム連携、など）に合わせて**具体的なツール設計**まで一気に作ります。やりたいことを教えてくれれば、上の最小サンプルをそのまま“本番の骨格”に育てます。 [1]: https://modelcontextprotocol.io/quickstart/server?utm_source=chatgpt.com "Build an MCP Server - Model Context Protocol" [2]: https://modelcontextprotocol.io/quickstart/user?utm_source=chatgpt.com "Connect to Local MCP Servers - Model Context Protocol" [3]: https://github.com/mcpdotdirect/template-mcp-server?utm_source=chatgpt.com "mcpdotdirect/template-mcp-server" [4]: https://vercel.com/docs/mcp/deploy-mcp-servers-to-vercel?utm_source=chatgpt.com "Deploy MCP servers to Vercel" [5]: https://support.anthropic.com/en/articles/10949351-getting-started-with-local-mcp-servers-on-claude-desktop?utm_source=chatgpt.com "Getting started with Local MCP Servers on Claude Desktop" [6]: https://www.reddit.com/r/ClaudeAI/comments/1ll88ay/local_mcp_servers_can_now_be_installed_with_one/?utm_source=chatgpt.com "Local MCP servers can now be installed with one click on Claude ..." [7]: https://modelcontextprotocol.io/docs/tutorials/use-remote-mcp-server?utm_source=chatgpt.com "Connect to Remote MCP Servers - Model Context Protocol" [8]: https://medium.com/data-engineering-with-dremio/building-a-basic-mcp-server-with-python-4c34c41031ed?utm_source=chatgpt.com "Building a Basic MCP Server with Python | by Alex Merced" [9]: https://github.com/modelcontextprotocol/servers?utm_source=chatgpt.com "modelcontextprotocol/servers: Model Context Protocol ..." [10]: https://www.weavely.ai/blog/claude-mcp?utm_source=chatgpt.com "How to add MCP servers to Claude (free and paid) - Weavely" [11]: https://www.getmesa.com/blog/how-to-connect-mcp-server-claude/?utm_source=chatgpt.com "Connect Claude to MCP Servers for Better AI Capabilities" [12]: https://www.youtube.com/watch?v=A151Nk_nN_U&utm_source=chatgpt.com "Easy connect Claude AI desktop to MCP servers (For Windows)" [13]: https://www.reddit.com/r/ClaudeAI/comments/1jf4hnt/setting_up_mcp_servers_in_claude_code_a_tech/?utm_source=chatgpt.com "Setting Up MCP Servers in Claude Code: A Tech Ritual for ... - Reddit" [14]: https://www.theverge.com/2024/11/25/24305774/anthropic-model-context-protocol-data-sources?utm_source=chatgpt.com "Anthropic launches tool to connect AI systems directly to datasets" [1]: https://saketnkzw.com/posts/mcp-get-started?utm_source=chatgpt.com "clineと無料で作るMCPサーバー入門 | tnkzw.sake's blog" [2]: https://qiita.com/sigma_devsecops/items/7c40266160262ecd5ed1?utm_source=chatgpt.com "【MCP入門】LLMとツールをつなぐ仕組みを理解する" [3]: https://zh.wikipedia.org/wiki/%E6%A8%A1%E5%9E%8B%E4%B8%8A%E4%B8%8B%E6%96%87%E5%8D%8F%E8%AE%AE?utm_source=chatgpt.com "模型上下文协议" [4]: https://qiita.com/takashiuesaka/items/49559b830366255d1216?utm_source=chatgpt.com "[MCP再入門]「MCPはAIアプリにとってのUSB-C」がしっくりこ ..." [5]: https://en.wikipedia.org/wiki/Model_Context_Protocol?utm_source=chatgpt.com "Model Context Protocol" [6]: https://luenci.com/en/posts/mcp%E5%85%A5%E9%97%A8%E6%A6%82%E5%BF%B5%E7%AF%87/?utm_source=chatgpt.com "MCP 入门（概念篇） - Luenci" [7]: https://www.ubsecure.jp/blog/20250612?utm_source=chatgpt.com "MCPでLLMがネットサーフィン!?～DifyとFetch Serverで作る ..." [8]: https://dev.classmethod.jp/articles/mcp-server-on-vercel/?utm_source=chatgpt.com "Vercelで始めるMCPサーバー構築入門 - DevelopersIO" [9]: https://github.com/liaokongVFX/MCP-Chinese-Getting-Started-Guide?utm_source=chatgpt.com "Model Context Protocol(MCP) 编程极速入门" [10]: https://modelcontextprotocol.io/quickstart/server?utm_source=chatgpt.com "Build an MCP Server"

Got it 👍 — let’s go step by step and make `dill` clear and practical. --- # 🔎 What is `dill`? * `dill` is a **Python library** that extends the standard `pickle` module. * `pickle` can serialize (“save”) Python objects into a file and later **deserialize** (“load”) them back. * But `pickle` has **limitations**: it cannot handle things like: * user-defined functions * lambdas * classes defined inside functions * some iterators, generators, closures 👉 `dill` overcomes many of these limitations. It can serialize almost anything in Python—including entire **interactive sessions**. --- # 🚀 How to Install ```bash pip install dill ``` --- # 🧪 Basic Usage ```python import dill x = 42 y = [1, 2, 3] def greet(name): return f"Hello {name}!" # ---- Save session ---- dill.dump_session("session.pkl") # (Restart Python...) # ---- Load session ---- dill.load_session("session.pkl") print(x) # 42 print(y) # [1, 2, 3] print(greet("AI")) # Hello AI! ``` ⚡ Notice: Even the function `greet` is restored — this wouldn’t work with `pickle`. --- # 🧩 What Can `dill` Save? ✅ Numbers, strings, lists, dicts (like pickle) ✅ Functions (including lambdas and closures) ✅ Classes, methods, and instances ✅ Generators, iterators ✅ Entire interactive sessions ❌ What it may still struggle with: * Open file handles, sockets, database connections (not serializable in principle) * OS-level resources (threads, processes) --- # 🧰 Core APIs 1. **Save one object** ```python dill.dump(obj, open("file.pkl", "wb")) ``` 2. **Load one object** ```python obj = dill.load(open("file.pkl", "rb")) ``` 3. **Save entire session (all variables)** ```python dill.dump_session("session.pkl") ``` 4. **Load entire session** ```python dill.load_session("session.pkl") ``` --- # 🆚 `pickle` vs `dill` | Feature | `pickle` | `dill` | | ------------------- | -------- | ------ | | Save basic types | ✅ | ✅ | | Save custom classes | ✅ | ✅ | | Save functions | ❌ | ✅ | | Save lambdas | ❌ | ✅ | | Save closures | ❌ | ✅ | | Save entire session | ❌ | ✅ | So: * If you just need **simple data** (numbers, arrays, dicts), `pickle` is fine. * If you want to checkpoint your **whole working environment** (like Jupyter does), `dill` is the tool. --- # ⚡ Practical Tip You can make a quick utility file `session_utils.py`: ```python import dill def save_session(filename="session.pkl"): dill.dump_session(filename) def load_session(filename="session.pkl"): dill.load_session(filename) ``` Then in any script: ```python from session_utils import save_session, load_session x = 123 save_session("my.pkl") # Later... load_session("my.pkl") print(x) # 123 ``` --- 👉 Would you like me to show you how to use `dill` specifically for a **step-by-step experiment workflow** (like Jupyter Notebook, where you checkpoint after each cell), or just for a one-shot "save everything at once" style?

Here’s a practical, up-to-date comparison of the main Python options for extracting tables from PDFs—what they’re best at, trade-offs, and quickstart code. # TL;DR — Which should you use? * **Digital PDFs with clear ruling/borders:** Start with **Camelot** (lattice/stream modes). ([Camelot][1], [Read the Docs][2]) * **Digital PDFs (simple/fast, Java OK):** **tabula-py** (wrapper of tabula-java). Great defaults, very fast. Requires Java. ([Tabula-Py][3]) * **Mixed/quirky layouts & you want control:** **pdfplumber** (fine-grained, tunable table finding). ([GitHub][4]) * **Scanned/image PDFs:** Pre-OCR with **OCRmyPDF**, then run Camelot/tabula/pdfplumber. ([OCRmyPDF][5], [GitHub][6], [Ubuntu Manpages][7]) * **Complex documents (forms, multi-span/merged cells) or images with no ruling:** ML approach like **Microsoft Table Transformer (TaTR)** via Hugging Face / layoutparser. Heavier, but most robust for detection/structure. ([GitHub][8]) --- ## Side-by-side comparison | Library | Best for | Accuracy | Speed | Setup friction | Notes | | ---------------------------- | --------------------------------------------- | ----------------------------: | --------: | ------------------------ | ----------------------------------------------------------------------------------------------------------------- | | **Camelot** | Digital PDFs with lines or consistent spacing | High (lattice); good (stream) | Fast | Pure Python | Two modes: *lattice* (uses ruling lines) and *stream* (white-space). Exports DataFrame/CSV easily. ([Camelot][1]) | | **tabula-py** | Bulk extraction from consistent PDFs | High on “regular” tables | Very fast | Needs Java (tabula-java) | Simple API (`read_pdf`, `convert_into`), good for pipelines. ([Tabula-Py][3]) | | **pdfplumber** | Tricky layouts & fine control | Good→High (with tuning) | Medium | Pure Python | Exposes characters/lines/rects; you can tune table settings per page. ([GitHub][4]) | | **OCRmyPDF** (pre-step) | Scanned/image PDFs | — | Medium | External CLI | Adds searchable text layer; then use one of the above. ([OCRmyPDF][5], [GitHub][6]) | | **Table Transformer (TaTR)** | Complex or image-only tables | High (best on hard layouts) | Slowest | ML model + deps | Detects table regions & structure; works on PDFs/images. ([GitHub][8]) | --- ## Minimal working examples ### 1) Camelot ```python import camelot tables = camelot.read_pdf("file.pdf", pages="1-3", flavor="lattice") # or flavor="stream" df = tables[0].df tables.export("out.csv", f="csv") ``` Camelot’s *lattice* mode relies on ruling lines; use *stream* for whitespace-based tables. ([Camelot][9]) ### 2) tabula-py ```python import tabula dfs = tabula.read_pdf("file.pdf", pages="all", multiple_tables=True) # returns list of DataFrames tabula.convert_into("file.pdf", "out.csv", output_format="csv", pages="all") ``` Requires Java because it wraps **tabula-java**. Great for batch jobs. ([Tabula-Py][3]) ### 3) pdfplumber ```python import pdfplumber, pandas as pd rows = [] with pdfplumber.open("file.pdf") as pdf: page = pdf.pages[0] table = page.extract_table( table_settings={"vertical_strategy":"lines", "horizontal_strategy":"lines"} ) df = pd.DataFrame(table[1:], columns=table[0]) ``` `table_settings` lets you adapt to non-standard layouts; you can also use edge/line tolerance strategies. ([GitHub][4]) ### 4) Scanned PDFs: OCRmyPDF → (Camelot/tabula/pdfplumber) ```bash ocrmypdf --deskew --clean input_scanned.pdf input_searchable.pdf ``` Then run Camelot/tabula/pdfplumber on `input_searchable.pdf`. ([OCRmyPDF][5], [GitHub][6]) ### 5) ML route: Microsoft Table Transformer (TaTR) * Typical stack: `layoutparser`/`transformers` + TaTR weights → detect tables → reconstruct structure → export CSV/DF. Best on complex images and messy scans; heavier to set up and slower. ([GitHub][8]) --- ## Recommended decision flow 1. **Is it scanned?** → Yes: **OCRmyPDF**, then try Camelot (lattice) → Camelot (stream) → pdfplumber. ([OCRmyPDF][5]) 2. **Digital with visible lines?** → **Camelot (lattice)** first; fall back to *stream*. ([Camelot][9]) 3. **Bulk and consistent layout, Java OK?** → **tabula-py** for speed and simplicity. ([Tabula-Py][3]) 4. **Weird/irregular layouts / need control?** → **pdfplumber** with tuned settings. ([GitHub][4]) 5. **Still failing (merged cells, rotated, camera shots)?** → **TaTR** (ML). ([GitHub][8]) --- ## Practical tips * Try **multiple modes** (Camelot lattice vs stream) per page; some pages switch styles. ([Camelot][9]) * For **rotated** pages, rotate first (pdfplumber can detect/rotate). ([GitHub][4]) * With **tabula-py**, you can pass `area` or templates for consistent cropping; great for reports where tables live in fixed regions. ([Tabula-Py][10]) * For **no ruling lines**, increase tolerance and rely on whitespace clustering (Camelot stream / pdfplumber strategies). ([Camelot][9], [GitHub][4]) * For **end-to-end pipelines**, standardize outputs to CSV/Parquet and add validation (row/column counts, headers present, totals match). If you share a representative PDF (one digital, one scanned), I can run a quick bake-off with 2–3 approaches and give you tuned code per page type. [1]: https://camelot-py.readthedocs.io/?utm_source=chatgpt.com "Camelot: PDF Table Extraction for Humans — Camelot 1.0.9 ..." [2]: https://readthedocs.org/projects/camelot-py/downloads/pdf/master/?utm_source=chatgpt.com "Camelot Documentation" [3]: https://tabula-py.readthedocs.io/?utm_source=chatgpt.com "tabula-py: Read tables in a PDF into DataFrame — tabula-py ..." [4]: https://github.com/jsvine/pdfplumber?utm_source=chatgpt.com "jsvine/pdfplumber - and easily extract text and tables." [5]: https://ocrmypdf.readthedocs.io/?utm_source=chatgpt.com "OCRmyPDF documentation — ocrmypdf 16.10.5.dev10+ ..." [6]: https://github.com/ocrmypdf/OCRmyPDF?utm_source=chatgpt.com "OCRmyPDF adds an OCR text layer to scanned PDF files ..." [7]: https://manpages.ubuntu.com/manpages/lunar/man1/ocrmypdf.1.html?utm_source=chatgpt.com "ocrmypdf - add an OCR text layer to PDF files" [8]: https://github.com/microsoft/table-transformer?utm_source=chatgpt.com "microsoft/table-transformer" [9]: https://camelot-py.readthedocs.io/en/master/user/quickstart.html?utm_source=chatgpt.com "Quickstart — Camelot 1.0.9 documentation - Read the Docs" [10]: https://tabula-py.readthedocs.io/en/latest/tabula.html?utm_source=chatgpt.com "tabula-py documentation"

# Xユーザーの上原 仁 /連続起業家さん: 「「2024年日本のスタートアップのEXITは60%がM&amp;Aになった」 とJVCA会長の郷治さんがスピーダスタートアップ資金調達動向2025H1の資料内で公式に仰った。これまで「日本のEXITの8割がIPO」「日本はM&amp;A exitが少ない」というステレオタイプがありましたが、業界全体でアップデートするのが良さそうです。」 / X **URL:** https://x.com/ueharajin/status/1959858887664009238 **Captured:** 2025/8/25 17:47:31 --- 上原 仁 /連続起業家 @ueharajin 「2024年日本のスタートアップのEXITは60%がM&Aになった」 とJVCA会長の郷治さんがスピーダスタートアップ資金調達動向2025H1の資料内で公式に仰った。これまで「日本のEXITの8割がIPO」「日本はM&A exitが少ない」というステレオタイプがありましたが、業界全体でアップデートするのが良さそうです。 最終更新 午後3:02 · 2025年8月25日 · 1.1万 件の表示 2 8 58 11

# XユーザーのRen Makino / 牧野連さん: 「スモールイグジットの先は、意外と過酷。 多くは事業を本格的にグロースさせる前に売却している。 だから大きな資金を扱った経験がないまま、大きな現金を手にしてしまう。 その結果、効率的なリソース配分の意思決定ができず、資本の論理を誤る。 多くは資金を溶かし、再び振り出しに戻っていく。」 / X **URL:** https://x.com/renvlog00/status/1959870031732551798 **Captured:** 2025/8/25 16:42:58 --- Ren Makino / 牧野連 @renvlog00 スモールイグジットの先は、意外と過酷。 多くは事業を本格的にグロースさせる前に売却している。 だから大きな資金を扱った経験がないまま、大きな現金を手にしてしまう。 その結果、効率的なリソース配分の意思決定ができず、資本の論理を誤る。 多くは資金を溶かし、再び振り出しに戻っていく。 ある程度のグロース経験までしておくのが良い気する。 午後3:46 · 2025年8月25日 場所: 大阪 大阪市 北区· 1,172 件の表示 1 2 19 5

# AWS Fargate vs. AWS Lambda vs. Azure Kubernetes Service (AKS): Which One Fits Your Cloud Needs? In the ever-evolving world of cloud computing, choosing the right platform for deploying and managing applications can feel overwhelming. AWS Fargate, AWS Lambda, and Azure Kubernetes Service (AKS) are three powerhouse options that cater to different needs—from serverless simplicity to container orchestration mastery. Whether you're a developer scaling a microservices architecture or a startup looking to minimize infrastructure headaches, this comparison will help you decide. In this post, we'll dive deep into each service, compare them across key dimensions, and provide practical insights to guide your choice. By the end, you'll have actionable advice to pick the best fit for your project. Let's get started! ## Understanding the Basics: What Each Service Offers Before we compare, let's break down what each tool brings to the table. These aren't one-size-fits-all; they're designed for specific workloads in the cloud ecosystem. ### AWS Fargate: Serverless Containers Without the Hassle AWS Fargate is a serverless compute engine for containers that runs on Amazon ECS (Elastic Container Service) or EKS (Elastic Kubernetes Service). It abstracts away the underlying servers, so you focus on your containers while AWS handles provisioning, scaling, and management. - **Key Features**: Pay-per-use pricing, automatic scaling, and seamless integration with other AWS services like ECR (Elastic Container Registry) for image storage. - **Ideal For**: Long-running applications like web servers or batch jobs that need consistent compute resources without managing EC2 instances. Think of Fargate as "containers as a service"—it's like running Docker containers in the cloud without worrying about the host machines. ### AWS Lambda: Event-Driven Serverless Functions AWS Lambda takes serverless to the function level. You upload code (in languages like Python, Node.js, or Java), and Lambda executes it in response to triggers like API calls, file uploads, or database changes. No servers to provision—it's all managed. - **Key Features**: Sub-second scaling, built-in fault tolerance, and integration with over 200 AWS services. Pricing is based on execution time and requests. - **Ideal For**: Event-driven apps, APIs, data processing pipelines, or microservices that run sporadically. Lambda shines in scenarios where you want to "write code and forget infrastructure." It's the go-to for building serverless architectures with tools like API Gateway. ### Azure Kubernetes Service (AKS): Managed Kubernetes Orchestration AKS is Microsoft's managed Kubernetes service on Azure. It simplifies deploying, managing, and scaling containerized applications using Kubernetes, the industry-standard orchestrator. Azure handles the control plane, leaving you to manage worker nodes (or use serverless options like Virtual Nodes). - **Key Features**: Auto-scaling, Azure Active Directory integration, and support for hybrid deployments. It works with Azure Container Registry and integrates deeply with Azure's ecosystem. - **Ideal For**: Complex, stateful applications requiring orchestration, like microservices architectures or machine learning workflows. AKS is perfect if you're already in the Azure ecosystem or need Kubernetes' flexibility without the full operational burden. ## Head-to-Head Comparison: Key Factors to Consider Now that we've covered the basics, let's compare them across critical areas. We'll look at use cases, pricing, scalability, management, and more to give you a clear picture. ### Use Cases and Workload Suitability - **Fargate**: Best for containerized apps that need persistent runtime, such as web apps, APIs, or background workers. Example: Running a Node.js app in a Docker container that processes user uploads continuously. - **Lambda**: Suited for short-lived, event-triggered tasks. Practical example: A function that resizes images uploaded to S3— it spins up only when needed, processes the image in seconds, and shuts down. - **AKS**: Excels in orchestrating multiple containers across pods, services, and deployments. Actionable insight: Use it for a multi-tier e-commerce app where you need rolling updates, load balancing, and persistent storage via Azure Disks. If your workload is stateless and bursty, Lambda wins. For container-heavy ops with orchestration needs, AKS is king. Fargate bridges the gap for simpler container runs. ### Pricing Models: Cost Efficiency Breakdown Pricing can make or break your choice—let's crunch the numbers. - **Fargate**: Charged per vCPU and GB of memory per second, plus data transfer. Example: A task using 1 vCPU and 2 GB RAM for 1 hour costs about $0.04 (vCPU) + $0.01 (memory) = $0.05/hour. No upfront costs, but watch for idle time. - **Lambda**: Pay per 1ms of execution time and per million requests. For a function running 1 second with 128 MB memory, it's ~$0.000002 per invocation. Free tier includes 1 million requests/month—great for low-traffic apps. - **AKS**: Free control plane, but you pay for virtual machines (e.g., $0.10/hour per node) and storage. Serverless add-ons like Azure Container Instances add flexibility. Total cost: Around $100–$500/month for a small cluster, scaling with usage. **Actionable Advice**: For cost-sensitive startups, start with Lambda for its granular billing. Use AWS Cost Explorer or Azure Cost Management to simulate scenarios—Fargate might edge out AKS for steady workloads due to no node management fees. ### Scalability and Performance All three scale automatically, but with nuances. - **Fargate**: Scales tasks horizontally; supports up to 100,000 tasks per region. Cold starts are minimal since it's container-based. - **Lambda**: Instant scaling to thousands of concurrent executions, but limited to 15-minute timeouts and 10 GB memory. Performance tip: Use Provisioned Concurrency to avoid cold starts in high-traffic APIs. - **AKS**: Kubernetes-native scaling with Horizontal Pod Autoscaler. Handles massive clusters (up to 5,000 nodes), ideal for enterprise loads. Example: Auto-scale pods based on CPU usage for a real-time analytics app. Insight: Lambda offers the fastest scaling for unpredictable traffic, while AKS provides fine-grained control for predictable, high-throughput needs. ### Management Overhead and Ease of Use - **Fargate**: Low overhead—no servers to patch or scale. Deploy via ECS console or CLI. However, you still manage container images and networking. - **Lambda**: Easiest for beginners; just upload code and set triggers. Monitoring via CloudWatch is built-in. - **AKS**: More hands-on—requires Kubernetes knowledge for YAML configs, Helm charts, etc. But Azure simplifies with managed masters and tools like Azure DevOps for CI/CD. **Practical Example**: If you're migrating a monolithic app to microservices, start with Fargate for quick containerization. For a team new to cloud, Lambda's simplicity reduces learning curves. ### Security and Integration Security is non-negotiable. All integrate with their cloud's IAM systems. - **Fargate**: Uses AWS IAM roles, VPC networking, and Secrets Manager. - **Lambda**: Fine-grained permissions per function; supports VPC for private access. - **AKS**: Azure RBAC, network policies, and integration with Azure Security Center. For multi-cloud setups, AKS's Kubernetes standard makes it portable. Fargate and Lambda lock you into AWS, but their ecosystems (e.g., Lambda with DynamoDB) boost productivity. ### Pros and Cons at a Glance - **Fargate Pros**: Serverless containers, cost-effective for steady loads. Cons: Less flexibility than full Kubernetes. - **Lambda Pros**: Ultimate simplicity, pay-per-use. Cons: Not for long-running tasks; cold starts can add latency. - **AKS Pros**: Powerful orchestration, open-source friendly. Cons: Steeper learning curve, potential higher costs for small apps. ## Real-World Examples and Actionable Advice Let's make this tangible with scenarios. **Scenario 1: Building a Scalable API** Use Lambda with API Gateway for a serverless API that handles variable traffic (e.g., a mobile app backend). Cost: Pennies for idle times. Advice: Monitor with X-Ray for bottlenecks. **Scenario 2: Running Containerized Microservices** Deploy to AKS for a system with databases, queues, and services. Example: A Netflix-like streaming app using Kubernetes for blue-green deployments. Tip: Leverage Azure's GitHub Actions for automated pipelines. **Scenario 3: Batch Processing Jobs** Fargate on ECS for scheduled jobs like data ETL. Insight: Combine with Step Functions for orchestration—cheaper than AKS for non-Kubernetes needs. **Choosing the Right One**: Assess your team's skills—Kubernetes pros? Go AKS. AWS shop? Fargate or Lambda. Start small: Prototype with Lambda, graduate to Fargate for persistence, or AKS for complexity. Tools like Terraform can help manage across clouds. ## Conclusion: Pick Your Cloud Champion AWS Fargate, Lambda, and AKS each excel in their niches: Fargate for hassle-free containers, Lambda for event-driven magic, and AKS for robust orchestration. The best choice depends on your workload, budget, and expertise. If you're just starting, experiment with free tiers—AWS offers generous ones for Lambda and Fargate, while Azure gives $200 credits. What are you building? Share in the comments—I'd love to hear your experiences! If this post helped, subscribe for more cloud comparisons.

了解。下に**Supabase**と、Vercel×Next.jsで相性の良い4構成（**Auth.js+Neon**、**Clerk+Neon**、**Firebase(Auth+Firestore)**、**Lucia+Turso**）を横並びで比較します。結論→**SQL/シンプル運用なら Auth.js+Neon**、**UI込みの速攻なら Clerk**、**全部マネージド&非SQL可なら Firebase**、**軽量&グローバル低レイテンシなら Turso**。 # サマリ比較 | 観点 | Supabase (Auth+PG+Storage+RT) | Auth.js + Neon (Postgres) + Drizzle/Prisma | Clerk + Neon | Firebase (Auth + Firestore) | Lucia + Turso(libSQL) | | -------- | ----------------------------- | ------------------------------------------ | ------------------ | --------------------------- | ---------------------- | | 方向性 | “全部入り”のOSS Supabase | 最小構成・低ロックイン | 認証UIごと外注 | 超マネージド（非SQL） | 超軽量・エッジ向き | | 学習コスト | 中（機能広い/RLS学習） | 低（Next.jsに直結） | 低（UI部品あり） | 中（NoSQL思考転換） | 低〜中（軽いが情報少なめ） | | 認証 | Supabase Auth（OAuth/OTP/MFA） | Auth.js（OAuth/Email等） | Clerk（MFA/組織/SAML） | Firebase Auth（豊富） | Lucia（柔軟・コード主導） | | DB | Postgres（RLS/拡張） | Neon Postgres（サーバレス） | Neon/PlanetScale等 | Firestore（ドキュメント） | Turso（libSQL/SQLite派生） | | Realtime | 内蔵（PGベース） | PG通知 or 外部（pusher等） | 同左 | 内蔵（Firestore RT） | レプリカ高速・RTは工夫 | | ストレージ | あり（S3互換API） | なし→Vercel Blob/S3 | なし→Blob/S3 | Cloud Storage | なし→Blob/S3 | | エッジ適性 | 可（一部制約） | 良（Neon+DrizzleはEdge相性◎） | 良 | 可（SDK多い） | 良（地理レプリカ強） | | 権限/RLS | 強い（DBポリシーで表現） | アプリ側ミドルウェアで実装 | ClerkのRBAC+DB側実装 | セキュリティルール学習必要 | アプリ側実装 | | ロックイン | 中（Supabase機能依存度次第） | 低（標準PG/OSS中心） | 中（認証はSaaS依存） | 中〜高（GCP思想） | 低（シンプル構成） | | コスト感 | 低〜中（無料枠◎） | 低（使った分だけ） | 中（有償開始早め） | 低〜中（スケールで増） | 低（PoC安価） | | 可観測性/DX | コンソール充実 | Next.js標準の流儀 | 管理画面+UIが最強 | コンソール強力 | シンプル、自前で足す | | 向いてる用途 | SQLで一気通貫・RT/Storage要 | SQL×Next.jsの王道最小構成 | 早く綺麗な認証UIを出す | RT/モバイル/Push/匿名Auth | PoC/軽量SaaS/多リージョン | --- ## どっちを選ぶ？（場面別の指針） * **「SQLで素直に作りたい、ロックイン避けたい、Vercel/Edgeも使いたい」** → **Auth.js + Neon + Drizzle**（おすすめの基本形） * **「ログイン/ユーザー設定UIをすぐ出したい、組織管理/MFA/将来SAML」** → **Clerk + Neon**（早い＆エンタープライズ拡張◎） * **「Realtime・Push・匿名Auth・モバイル強め、SQL不要でもOK」** → **Firebase Auth + Firestore** * **「とにかく軽くて世界中で速いPoC、ランコストも抑えたい」** → **Lucia + Turso + Drizzle** --- ## Supabase と比べたメリデメ（要点） ### Supabaseの強み * **一体型**：Auth/DB/Storage/Realtime/Edge Functionsが一箇所に揃う * **Postgres×RLS**：DBレベルで堅牢なデータアクセス制御 * **OSS & 互換性**：SQL資産や拡張が活きる ### Supabaseの弱み / 複雑に感じやすい点 * **学習曲線**：RLS/ポリシー、Edge Functions、RTチャネルなど把握量が多い * **構成の重さ**：小さく始めたい時に“全部”を理解する必要が出がち * **ロックイン**：Storage/RT/Functionsまで深く使うと脱出が手間 ### 代替構成の強み * **Auth.js+Neon**：Next.js標準思考で薄く始められる（**最小構成**、Edge親和） * **Clerk**：**UI・多要素認証・組織**を丸ごと外注できる（B2B向けが早い） * **Firebase**：**RT/Push/匿名Auth**を最速で。モバイル・クライアントSDKが強い * **Turso**：**グローバル低レイテンシ**と軽さ。PoCや地理分散に最適 --- ## 「Supabaseからの乗り換え」実務のコツ * **DB**：`pg_dump` → **Neon**にインポート（Postgres間は移行が容易） * **RLS/ポリシー**：アプリ層（Next.js middleware + サーバールート）で**明示チェック**に置換 * **Auth**：OAuthのRedirect URLを**Auth.js/Clerk**側へ再設定、ユーザーテーブルと紐付け * **Storage**：Supabase Storage→**Vercel Blob** or **S3**へ寄せる（CDN/署名URLで代替） * **Realtime**：必要な箇所だけ**pusher/Ably/pg\_notify**等でピンポイント実装（やり過ぎない） --- ## すぐ試せる最小テンプレ（Next.js App Router） * 認証：**Auth.js**（GitHub/Google） * DB：**Neon** * ORM：**Drizzle**（Edge互換が軽い） * ストレージ：**Vercel Blob**（必要なら） * レート制限/ジョブ：**Upstash Redis** > 希望があれば、あなたの要件（B2B/SAML要・マルチテナンシ・国産IDP連携・料金上限など）を前提に、**環境変数例・`route.ts`・DBスキーマ・ミドルウェア**をそのままコピペで動く形で出します。

a detailed comparison between AWS EC2 **G6**, **G6e**, and **G6f** instance families, based on the most recent official specifications: --- ### **Overview Table** | Feature | **G6** | **G6e** (latest) | **G6f** (fractional GPU variant of G6) | | -------------------------- | ------------------------------------------ | ----------------------------------------------------- | ---------------------------------------------- | | **GPU Type** | NVIDIA L4 Tensor Core | NVIDIA L40S Tensor Core | NVIDIA L4 Tensor Core (fractionalized) | | **GPU Memory** | 24 GiB per GPU; fractional as low as 3 GiB | 48 GiB per GPU | 3 GiB (1/8 GPU) up to full GPU (24 GiB) | | **CPU** | 3rd-gen AMD EPYC 7R13 | 3rd-gen AMD EPYC 7R13 | Same as G6 | | **vCPU / RAM Range** | Up to 192 vCPU, 768 GiB RAM | Up to 192 vCPU, 1,536 GiB RAM | Similar to G6, tailored for fractional GPUs | | **Network Bandwidth** | Up to 100 Gbps | Up to 400 Gbps | Up to 100 Gbps | | **Local NVMe Storage** | Up to \~7.52 TB | Up to \~7.6 TB | Same capabilities as G6 | | **Performance Advantages** | \~2× inference & graphics boost vs G4dn | Up to 2.5× better than G5; high memory bandwidth | Same as G6; fractional for cost optimizations | | **Primary Use Cases** | ML inference, real-time graphics/rendering | Large LLM inference, generative AI, spatial computing | Lightweight inference, graphics cost-effective | --- ### **Key Highlights by Family** #### **G6** * Features NVIDIA L4 Tensor Core GPUs (24 GiB each) and fractional GPU options (down to 1/8 GPU, or 3 GiB) for cost-effective, scaled usage.([Amazon Web Services, Inc.][1], [Amazon Web Services, Inc.][2], [Databricks][3], [Amazon Web Services, Inc.][4]) * Offers up to 192 vCPUs, 100 Gbps networking, and \~7.52 TB NVMe storage.([Amazon Web Services, Inc.][1]) * Delivers roughly twice the performance for deep learning inference and graphics compared to G4dn instances.([Amazon Web Services, Inc.][1]) #### **G6e** * The latest iteration, leveraging NVIDIA L40S Tensor Core GPUs with 48 GiB memory per GPU and up to 8 GPUs per instance.([Amazon Web Services, Inc.][2]) * Provides up to 192 vCPUs, 400 Gbps networking, 1.536 TB system memory, and \~7.6 TB NVMe storage.([Amazon Web Services, Inc.][4]) * Offers **up to 2.5× better performance** compared to G5 instances and is targeted at large-scale inference, generative models, and spatial computing.([Amazon Web Services, Inc.][4]) #### **G6f** * Essentially a fractional GPU variant of G6: same NVIDIA L4 GPUs, but size options include fractions like 1/8, 1/4, etc.([Amazon Web Services, Inc.][2]) * Ideal for workloads that don’t need full GPU power and aim for cost efficiency.([Amazon Web Services, Inc.][1]) * Same CPU, memory, and bandwidth capabilities as G6 fully accompanied options.([Vantage][5]) --- ### Summary Insights * **Choose **G6** for balanced GPU compute & graphics workloads**, especially if you're working with smaller ML inference tasks or real-time rendering, and want flexibility in scaling down GPU usage. * **Opt for **G6e** when you need maximum GPU memory and bandwidth**, e.g., running large LLM inference or generative AI tasks, or spatial computing that benefits from higher throughput and memory. * **Use **G6f** when you're optimizing costs** and your workload can function with fractional GPU capacity—great for light inference workloads with lower memory requirements. Would you like a size-level breakdown (e.g., xlarge, 12xlarge) or price estimates in your region? Happy to dig deeper! [1]: https://aws.amazon.com/ec2/instance-types/g6/?utm_source=chatgpt.com "Amazon EC2 G6 Instances" [2]: https://aws.amazon.com/ec2/instance-types/?utm_source=chatgpt.com "Amazon EC2 Instance Types - Compute" [3]: https://www.databricks.com/blog/aws-ec2-g6?utm_source=chatgpt.com "Announcing Databricks Support for Amazon EC2 G6 ..." [4]: https://aws.amazon.com/ec2/instance-types/g6e/?utm_source=chatgpt.com "Amazon EC2 G6e Instances" [5]: https://instances.vantage.sh/aws/ec2/g6.xlarge?utm_source=chatgpt.com "g6.xlarge pricing and specs - Vantage"

CLS pooling（\[CLS] pooling）についてわかりやすく整理しますね。 --- ## 基本アイデア * **BERTやModernBERTなどのTransformer系モデル**は、入力文の先頭に特別なトークン `[CLS]` を付けます。 * モデルがエンコードを終えると、各トークン（単語）の隠れ状態ベクトルが得られます。 * そのうち **先頭の `[CLS]` トークンのベクトル**を、「文全体の表現（sentence embedding）」として利用する方法を **CLS pooling** と呼びます。 --- ## どうして `[CLS]` を使えるのか？ * 学習時に「この `[CLS]` ベクトルを使って最終的に分類タスクを解く」ように設計されているため、モデルは自然と `[CLS]` に文全体の意味を集約するようになります。 * 例えば BERT では、文の感情分析や文ペア関係判定（自然言語推論など）のタスクで `[CLS]` の出力が使われます。 --- ## 他のPooling手法との比較 * **CLS pooling** → `[CLS]` のベクトルをそのまま使う。 長所: 高速・シンプル。 短所: 文埋め込みの質がタスクによっては不安定になる。 * **Mean pooling（平均プーリング）** → 全トークンのベクトルを平均する。 長所: 安定した意味表現になりやすい。 短所: CLSに比べると計算コストはわずかに増える。 * **Max pooling（最大プーリング）** → 各次元ごとに最大値を取る。 特徴が際立ちやすいが、情報が偏ることもある。 --- ## 実用的な注意点 * 「文ベクトルとして汎用的に使いたい」場合（例: 検索・クラスタリング）は、CLS pooling よりも **Mean pooling** の方がよいケースが多い（Sentence-BERT もこちらを採用）。 * ただし、**分類タスクの下流 fine-tuning**では CLS pooling が最適化されやすい。 * ModernBERT など長文対応モデルでは、CLS pooling だと先頭バイアス（文末情報を取り込みにくい）になることもあるので、平均プーリングや階層的プーリングが研究されている。 --- 👉 まとめると： * **CLS pooling = `[CLS]` の出力ベクトルをそのまま文表現として使う方法** * **分類タスク**には有効だが、**汎用的な文埋め込み**では Mean pooling の方が安定することが多い。 --- ご希望なら、PyTorch / Hugging Face Transformers で **CLS pooling と Mean pooling の実装例**をコードで書きますか？