LLMによるコード補完の登場 (2021年) GitHub Copilotの登場 我々がよく知るLLMによる支援はまず GitHub Copilot から始まりました。これはGPT-3 (OpenAI Codex, 現在のCodexと名前が同じで本当にややこしい) をベースとしたコード補完システムで、タイピングをしていると自動的にその行の続きを予測してくれるものです autocompleteとの違い それ以前にもIntelliSenseのようなautocompleteがありましたし、より進歩したn-gramなどを用いたものはありましたが、LLMをベースとしたものはTabnine (Tabnineは初期はn-gramモデル) やCopilotからとなります チャットベースのコードアシストの時代 (2022-2023年) ChatGPTの登場 ChatGPT以降、チャットによる対話形式でコード
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
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
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.”
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 October 9, 2025. Automotive & Logistics Business & Professional Services Financial Services Healthcare & Life Sciences Hospitality & Travel Manufacturing, Industrial & Electronics Media, Marketing & Gaming Public Sec
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
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
GitHub - taishi-i/awesome-ChatGPT-repositories: A curated list of resources dedicated to open source GitHub repositories related to ChatGPT and OpenAI API
awesome-chatgpt-api - Curated list of apps and tools that not only use the new ChatGPT API, but also allow users to configure their own API keys, enabling free and on-demand usage of their own quota. awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT better. awesome-chatgpt - Curated list of awesome tools, demos, docs for ChatGPT and GPT-3 awesome-totally-open-chat
Kickstart your transition from vibe coding to agentic engineering. These resources will help you master the new paradigm of AI-assisted development, where agents become true collaborators that can handle entire codebases and ship production features. Each piece was chosen for its practical, real-world insights. I’ll keep this list updated as the field evolves. How to Use Claude Code Effectively Re
This is an all-in-one crosspost of a scenario I originally published in three parts on my blog, No Set Gauge. Links to the originals: A History of the Future, 2025-2027A History of the Future, 2027-2030A History of the Future, 2030-2040 Thanks to Luke Drago, Duncan McClements, Theo Horsley, and Bilal Chughtai for comments. 2025-2027Below is part 1 of an extended scenario describing how the future
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models Qizheng Zhang 1∗ Changran Hu 2∗ Shubhangi Upasani 2 Boyuan Ma 2 Fenglu Hong 2 Vamsidhar Kamanuru 2 Jay Rainton 2 Chen Wu 2 Mengmeng Ji 2 Hanchen Li 3 Urmish Thakker 2 James Zou 1 Kunle Olukotun 1 1 Stanford University 2 SambaNova Systems, Inc. 3 UC Berkeley ∗ equal contribution # qizhengz@stanford.edu, changran.hu@sa
GitHub - fr0gger/Awesome-GPT-Agents: A curated list of GPT agents for cybersecurity
MagicUnprotect: This GPT allows to interact with the Unprotect DB to retrieve knowledge about malware evasion techniques. GP(en)T(ester): A cybersec assistant for pentesting guidance. Threat Intel Bot: A specialized GPT for the latest APT threat intelligence. Vulnerability Bot: A specialized GPT on vulnerability, secure code, ransomware attacks SourceCodeAnalysis: Upload any project's source code
Paul: Today’s spotlight: Paolo Perrone, master of turning tech into scroll-stopping content. This one’s packed, let’s go 👀 ↓ I’m going to be honest with you. Most AI agent tutorials are garbage. They show you how to copy-paste LangChain code, build a demo that breaks the moment you try anything real, and leave you feeling like you learned something. Three months later, you try to build something
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Patterns for Building LLM-based Systems & Products
The AI-Native Software Engineer
Essential Reading for Agentic Engineers | Peter Steinberger
A History of the Future, 2025-2040 — LessWrong
Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
The Realistic Guide to Mastering AI Agents in 2026