Pythonで仕事をする人のための書籍まとめ2021 - 学習, 業務効率化, アプリ開発からデータサイエンスまで - Lean Baseball
2020年も多くの素晴らしい技術書がたくさん出ました. その中でも（昨今のトレンド・流行りも手伝ってか）Python本の多さ・充実度合いは目立つものがあります. （このエントリーを執筆した12/19時点で）Amazonの本カテゴリで「Python」と検索すると1,000件以上出てきます*1. これだと目的の本にたどり着くだけで疲れそうです. このエントリーでは, 主にPythonを学びたい・現在使っている方 手元の業務を効率化したり, RPAっぽいことをやりたい方 エンジニア・データサイエンティストとして業務や趣味・個人開発をされている方 を対象に, 今そして来年2021年に読んでおきたいPython関連書籍（と抑えておきたいサービス） をエンジニアでありデータサイエンティストである私独自の視点で紹介します*2. なおこのエントリーはこのブログで例年執筆している「Python本まとめ」の2
Join the O'Reilly online learning platform. Get a free trial today and find answers on the fly, or master something new and useful. Learn more Betty Jean Jennings and Frances Bilas (right) program the ENIAC in 1946. Via the Computer History Museum Eventually, interpreted languages, which are much easier to debug, became the norm. BASIC, one of the first of these to hit the big time, was at first s
達人出版会
知識ゼロからノーコードではじめる Studio Webサイト制作入門 gaz 徹底攻略 データベーススペシャリスト教科書 令和7年度 株式会社わくわくスタディワールド 瀬戸美月 徹底攻略データサイエンティスト検定問題集［リテラシーレベル］対応 第2版 小縣 信也, 斉藤 翔汰, 森田 大樹, 田澤 賢, 小宮 寛季, 野口 敏久, 山田 弦太朗, 安… Proxmox VEサーバー仮想化 導入実践ガイド エンタープライズシステムをOSSベースで構築 青山 尚暉, 海野 航, 大石 大輔, 工藤 真臣, 殿貝 大樹, 野口 敏久 1週間でLPICの基礎が学べる本 第4版 中島 能和 Windowsで作る侵入検知システム 自作IDS/IPSで学ぶ実践セキュリティ dora シリコンに導入されたドーパントの物理 公益社団法人 応用物理学会 半導体分野将来基金委員会 Pythonを使った数値計算入
Developers are increasingly relying on AI coding assistants to accelerate our daily workflows. These tools can autocomplete functions, suggest bug fixes, and even generate entire modules or MVPs. Yet, as many of us have learned, the quality of the AI’s output depends largely on the quality of the prompt you provide. In other words, prompt engineering has become an essential skill. A poorly phrased
[Hacker News discussion, LinkedIn discussion, Twitter thread] Update: My upcoming book, AI Engineering (late 2024/early 2025) will cover building aplications with foundation models in depth. A question that I’ve been asked a lot recently is how large language models (LLMs) will change machine learning workflows. After working with several companies who are working with LLM applications and persona
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. ActionKit by Paragon - Connect to 130+ SaaS integrations (e.g. Slack, Salesforce, Gmail) with Paragon’s ActionKit API. Adfin - The only platform you need to get paid - all payments in one place, in
awesome-scalability
The Patterns of Scalable, Reliable, and Performant Large-Scale Systems View the Project on GitHub View On GitHub An updated and organized reading list for illustrating the patterns of scalable, reliable, and performant large-scale systems. Concepts are explained in the articles of prominent engineers and credible references. Case studies are taken from battle-tested systems that serve millions to
型安全かつシンプルなAgentフレームワーク「PydanticAI」の実装を解剖する - ABEJA Tech Blog
はじめに こちらはABEJAアドベントカレンダー2024 12日目の記事です。 こんにちは、ABEJAでデータサイエンティストをしている坂元です。最近はLLMでアプローチしようとしていたことがよくよく検証してみるとLLMでは難しいことが分かり急遽CVのあらゆるモデルとレガシーな画像処理をこれでもかというくらい詰め込んだパイプラインを実装することになった案件を経験して、LLMでは難しそうなことをLLM以外のアプローチでこなせるだけの引き出しとスキルはDSとしてやはり身に付けておくべきだなと思うなどしています（LLMにやらせようとしていることは大抵難しいことなので切り替えはそこそこ大変）。 とはいうものの、Agentの普及によってより複雑かつ高度な推論も出来るようになってきています。弊社の社内外のプロジェクト状況を見ていても最近では単純なRAG案件は減りつつあり、計画からアクションの実行、結果
変更ログ 21/09/04: 「ドメイン駆動設計について DroidKaigi 2017 で登壇しました。」のリンクを追加 -21/08/11: 書籍「the Jargon File」についてのリンクを追加 -21/08/06: C, アセンブリ言語についてのリンクを追加 前書き プログラムを学ぶとき、良質役立ちそうなサイトを探すのにかなりの時間を浪費した。 他の人にはそうなってほしくないので、今まで役立ったサイトを公開する。 なお、強くオススメしたいサイト順に並ばせる。 随時更新予定。 21/08/06: 追記 (この記事はもともと大量のブックマークを処分し依存を絶つのが目的で作成しました。 しかし、ブックマークが便利すぎるので結局依存は断てず、この記事を自分で使うこともほぼなかったため、更新は未定に変更します。) この記事を効率よく使う方法の例： ・リンクを実際に踏んでみて、ざっと吟味
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
I am a huge fan of Richard Feyman’s famous quote: “What I cannot create, I do not understand” I think it’s brilliant, and it remains true across many fields (if you’re willing to be a little creative with the definition of ‘create’). It is to this principle that I believe I owe everything I’m truly good at. Some will tell you to avoid reinventing the wheel, but they’re wrong: you should build your
If you’ve been following me on Twitter, you know I’m not a big fan of MCP (Model Context Protocol) right now. It’s not that I dislike the idea; I just haven’t found it to work as advertised. In my view, MCP suffers from two major flaws: It isn’t truly composable. Most composition happens through inference. It demands too much context. You must supply significant upfront input, and every tool invoc
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
2022振り返り
普段、こういったブログはあまり書かないが、いい機会だし、書き残してあると、のちのち見返すのに便利かなと思い、今年やったことを書き出してみる。ほとんど趣味の話です。 電子工作 競技プログラミング パソコン ゲーム 登山 コーヒー 論文読み 仕事 おわりに 電子工作去年、コンピュータシステムの理論と実装という本をすこし読んでハードウェアというか電子回路方面に興味が湧き、年末にブレッドボードやら抵抗やらを買い揃えて、LED光らせて遊んだりしていた。 電子工作入門する pic.twitter.com/F3MQLZiFlS — takuya-a (@takuya_b) December 15, 2021 My first project pic.twitter.com/vUo1ZI3ao3 — takuya-a (@takuya_b) December 15, 2021 そこからLTSpiceを勉強
Masato Hagiwara Open in Recto Pad Google Colab Github Recto Pad TL;DR Recto is a 2D programming language that uses nested rectangles as its core syntax, encoding structure and recursion directly in space instead of a linear stream of text. Recto explores new ways to write, parse, and reason about code—and even natural language—spatially. Introduction Open in Recto Pad Virtually all the languages w
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.”
Announcing Dapr v1.0
By Dapr project maintainers | Wednesday, February 17, 2021 Today we are excited to announce the v1.0 release of the Distributed Application Runtime (Dapr), which has achieved the stability and enterprise readiness to be designated production ready. Dapr is an open source, portable, event-driven runtime that makes it easy for developers to build resilient, microservice, stateless and stateful appli
Cognition | Introducing Devin, the first AI software engineer
Introducing Devin, the first AI software engineer Setting a new state of the art on the SWE-bench coding benchmark. Meet Devin, the world’s first fully autonomous AI software engineer. Devin is a tireless, skilled teammate, equally ready to build alongside you or independently complete tasks for you to review. With Devin, engineers can focus on more interesting problems and engineering teams can s
The Andrej Karpathy episode. Andrej explains why reinforcement learning is terrible (but everything else is much worse), why model collapse prevents LLMs from learning the way humans do, why AGI will just blend into the previous ~2.5 centuries of 2% GDP growth, why self driving took so long to crack, and what he sees as the future of education. Watch on YouTube; listen on Apple Podcasts or Spotify
GenAI Handbook
William Brown @willccbb | willcb.com v0.1 (June 5, 2024) Introduction This document aims to serve as a handbook for learning the key concepts underlying modern artificial intelligence systems. Given the speed of recent development in AI, there really isn’t a good textbook-style source for getting up-to-speed on the latest-and-greatest innovations in LLMs or other generative models, yet there is an
State of the Common Lisp ecosystem, 2020 🎉 - Lisp journey
NEW: 9 videos (86min) about CLOS on my Common Lisp course. Out of 7h+ of content. Rated 4.7/5. Learn more and stay tuned. 🎥 I also have cool Lisp showcases on Youtube . The last ones: how to build a web app in Common Lisp, part 1 and 2. This is a description of the Common Lisp ecosystem, as of January, 2021, from the perspective of a user and contributor. The purpose of this article is both to gi
My LLM codegen workflow atm
tl:dr; Brainstorm spec, then plan a plan, then execute using LLM codegen. Discrete loops. Then magic. ✩₊˚.⋆☾⋆⁺₊✧ I have been building so many small products using LLMs. It has been fun, and useful. However, there are pitfalls that can waste so much time. A while back a friend asked me how I was using LLMs to write software. I thought “oh boy. how much time do you have!” and thus this post. (p.s. i
I’m a fairly frequent Hacker News lurker, especially when I have some other important task that I’m avoiding. I normally head to the Active page (lots of comments, good for procrastination) and pick a nice long discussion thread to browse. So over time I’ve ended up with a good sense of what topics come up a lot. “The Bay Area is too expensive.” “There are too many JavaScript frameworks.” “Bootcam
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
Transformer models: an introduction and catalog — 2023 Edition January 16, 2023 52 minute read This post is now an ArXiV paper that you can print and cite. Update 05/2023 Another pretty large update after 4 months. I was invited to submit the article to a journal, so I decided to enlist some help from some LinkedIn colleages and completely revamp it. First off, we added a whole lot of new models,
Recently, Shopify’s CEO Tobi Lütke shared his thoughts on AI’s role in coding, stating that “reflexive AI usage is now a baseline expectation at Shopify.” The gist of his message was that AI is revolutionizing how we work, and everybody should jump on board this train or risk being left behind. I’m paraphrasing a bit, but not much – check out the post for complete context and content. This struck
21st February 2024 Last week Google introduced Gemini Pro 1.5, an enormous upgrade to their Gemini series of AI models. Gemini Pro 1.5 has a 1,000,000 token context size. This is huge—previously that record was held by Claude 2.1 (200,000 tokens) and gpt-4-turbo (128,000 tokens)—though the difference in tokenizer implementations between the models means this isn’t a perfectly direct comparison. I’
Welcome to the 18th issue of the Rust GameDev Workgroup’s monthly newsletter. Rust is a systems language pursuing the trifecta: safety, concurrency, and speed. These goals are well-aligned with game development. We hope to build an inviting ecosystem for anyone wishing to use Rust in their development process! Want to get involved? Join the Rust GameDev working group! You can follow the newsletter
In Praise of dhh
In Praise of dhh November 8, 2025 | #tech #politics A reflection on Ruby’s past, present, and future. This is a long essay. I strongly recommend you read it from the beginning, but to help navigate it I have created this table of contents. Prologue The Past How I Learned To Love Ruby A Breath Of Fresh Air A Shared Worldview The Present Tragedy Strikes Recent Conflict In The Community Strength and
I did this in 2020 and then again in 2021, but I’m in the mood to look around again. Let’s look through Are We GUI Yet? and see what’s up these days. The task today is to have a text label and an input field that can change the text in the label. In React, for example, this is basically free: const Demo = () => { let [state, setState] = useState("Hello, world!"); return ( <div> <p>{state}</p> <inp
10 Things Software Developers Should Learn about Learning – Communications of the ACM
The dashed box on the left contains exactly the same information as the awkward textual description in the dashed box on the right. But if a developer only received one of the two to create an SQL database, they are likely to find the diagram easier than the text. We say that the text here has a higher extraneous cognitive load. When faced with a task that seems beyond a person’s abilities, it is
Technology Trends for 2024
This has been a strange year. While we like to talk about how fast technology moves, internet time, and all that, in reality the last major new idea in software architecture was microservices, which dates to roughly 2015. Before that, cloud computing itself took off in roughly 2010 (AWS was founded in 2006); and Agile goes back to 2000 (the Agile Manifesto dates back to 2001, Extreme Programming t
Rustenstein 3D: Game programming like it's 1992 - NextRoll
Twice a year, NextRoll celebrates Hack Week, where employees get to work for a week on a project of their choice. It’s an excellent opportunity to experiment, learn new technologies and team up with people from across the company. You can learn all about Hack Week here. As NextRoll increasingly adopts the Rust programming language, it’s common for engineers to use Hack Week as an opportunity to ga
Plan 9 Desktop Guide
PLAN 9 DESKTOP GUIDE INDEX What is Plan 9? Limitations and Workarounds Connecting to Other Systems VNC RDP SSH 9P Other methods Porting Applications Emulating other Operating Systems Virtualizing other Operating Systems Basics Window Management Copy Pasting Essential Programs Manipulating Text in the Terminal Acme - The Do It All Application Multiple Workspaces Tiling Windows Plumbing System Admin
Software engineering with LLMs in 2025: reality check
Hi – this is Gergely with the monthly, free issue of the Pragmatic Engineer Newsletter. In every issue, I cover challenges at Big Tech and startups through the lens of engineering managers and senior engineers. If you’ve been forwarded this email, you can subscribe here. Two weeks ago, I gave a keynote at LDX3 in London, “Software engineering with GenAI.” During the weeks prior, I talked with soft
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
An AI-native software engineer is one who deeply integrates AI into their daily workflow, treating it as a partner to amplify their abilities. This requires a fundamental mindset shift. Instead of thinking “AI might replace me” an AI-native engineer asks for every task: “Could AI help me do this faster, better, or differently?”. The mindset is optimistic and proactive - you see AI as a multiplier
AI Homework
It happened to be Wednesday night when my daughter, in the midst of preparing for “The Trial of Napoleon” for her European history class, asked for help in her role as Thomas Hobbes, witness for the defense. I put the question to ChatGPT, which had just been announced by OpenAI a few hours earlier: This is a confident answer, complete with supporting evidence and a citation to Hobbes work, and it
Expert Generalists
As computer systems get more sophisticated we've seen a growing trend to value deep specialists. But we've found that our most effective colleagues have a skill in spanning many specialties. We are thus starting to explicitly recognize this as a first-class skill of “Expert Generalist”. We can identify the key characteristics of people with this skill - and thus recruit and promote based on it. We
Machine Learning in Production (17-445/17-645/17-745) / AI Engineering (11-695) Spring 2025 CMU course that covers how to build, deploy, assure, and maintain software products with machine-learned models. Includes the entire lifecycle from a prototype ML model to an entire system deployed in production. Covers also responsible AI (including safety, security, fairness, explainability) and MLOps. Fo
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The End of Programming as We Know It
The Prompt Engineering Playbook for Programmers
Building LLM applications for production
個人的におすすめしたいプログラムの技術サイト - Qiita
Writing Toy Software Is A Joy
Tools: Code Is All You Need
Recto — a truly 2D language
Patterns for Building LLM-based Systems & Products
Andrej Karpathy — AGI is still a decade away
Hacker News folk wisdom on visual programming
Transformer models: an introduction and catalog — 2023 Edition
Coding as Craft: Going Back to the Old Gym
The killer app of Gemini Pro 1.5 is video
This Month in Rust GameDev #18 - January 2021
A 2025 Survey of Rust GUI Libraries
The AI-Native Software Engineer
17-445 Machine Learning in Production / AI Engineering