--- title: "learn-claude-code" type: "tool" slug: "shareai-lab-learn-claude-code" canonical_url: "https://www.graphcanon.com/tools/shareai-lab-learn-claude-code" github_url: "https://github.com/shareAI-lab/learn-claude-code" homepage_url: "https://learn.shareai.run" stars: 70216 forks: 11445 primary_language: "Python" license: "MIT" categories: ["ai-agents"] tags: ["agent-development", "llm", "educational", "tutorial"] updated_at: "2026-07-07T19:00:29.732785+00:00" --- # learn-claude-code > A tool for understanding and building AI agents Learn Claude Code provides educational resources and tools to help developers understand and create real-world AI agents. It emphasizes the importance of both machine learning models and infrastructure in achieving intelligent behavior. ## Facts - Repository: https://github.com/shareAI-lab/learn-claude-code - Homepage: https://learn.shareai.run - Stars: 70,216 · Forks: 11,445 · Open issues: 54 · Watchers: 288 - Primary language: Python - License: MIT - Last pushed: 2026-06-26T19:36:35+00:00 ## Categories - [AI Agents](/categories/ai-agents.md) ## Tags agent-development, llm, educational, tutorial ## Related tools - [ECC](/tools/affaan-m-ecc.md) - The agent harness performance optimization system (★ 226,962) - [hermes-agent](/tools/nousresearch-hermes-agent.md) - The self-improving AI agent built by Nous Research (★ 210,880) - [AutoGPT](/tools/significant-gravitas-autogpt.md) - AutoGPT: Build, Deploy, and Run AI Agents (★ 185,417) - [ollama](/tools/ollama-ollama.md) - Get up and running with Kimi-K2.6, GLM-5.1, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. (★ 175,659) - [langflow](/tools/langflow-ai-langflow.md) - Langflow is a powerful platform for building and deploying AI-powered agents and workflows. (★ 151,298) - [dify](/tools/langgenius-dify.md) - Production-ready platform for agentic workflow development (★ 148,070) - [firecrawl](/tools/firecrawl-firecrawl.md) - The API to search, scrape, and interact with the web at scale. (★ 147,117) - [langchain](/tools/langchain-ai-langchain.md) - The agent engineering platform. (★ 141,211) ## README (excerpt) ```text [English](./README.md) | [中文](./README-zh.md) | [日本語](./README-ja.md) shareAI-lab%2Flearn-claude-code | Trendshift # Learn Claude Code -- Harness Engineering for Real Agents ## Agency Comes from the Model. An Agent Product = Model + Harness. Before we write any code, one thing needs to be clear. **Agency -- the capacity to perceive, reason, and act -- comes from model training, not from external code orchestration.** But a working agent product needs both the model and the harness. The model is the driver. The harness is the vehicle. This repository teaches you how to build the vehicle. ### Where Agency Comes From At the core of every agent is a neural network -- a Transformer, an RNN, a trained function -- shaped by billions of gradient updates on sequences of perception, reasoning, and action. Agency was never bestowed by the surrounding code. It was learned during training. Humans are the original proof. A biological neural network, refined by millions of years of evolutionary pressure, perceives the world through senses, reasons through a brain, and acts through a body. When DeepMind, OpenAI, or Anthropic say "agent," they all mean the same core thing: **a model that learned to act through training, plus the infrastructure that lets it operate in a specific environment.** The historical record is unambiguous: - **2013 -- DeepMind DQN plays Atari.** A single neural network, receiving only raw pixels and game scores, learned 7 Atari 2600 games -- surpassing prior algorithms and beating human experts in 3 of them. By 2015, scaled to [49 games at professional tester level](https://www.nature.com/articles/nature14236), published in *Nature*. No game-specific rules. One model, learning from experience. - **2019 -- OpenAI Five conquers Dota 2.** Five neural networks played [45,000 years of Dota 2 against themselves](https://openai.com/index/openai-five-defeats-dota-2-world-champions/) over 10 months, then defeated **OG** -- the TI8 world champions -- 2-0 in a live match. In the public arena, the AI won 99.4% of 42,729 games. No scripted strategies. Models learned teamwork through self-play. - **2019 -- DeepMind AlphaStar masters StarCraft II.** AlphaStar [beat a professional player 10-1](https://deepmind.google/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii/) in closed matches, then reached [Grandmaster rank](https://www.nature.com/articles/d41586-019-03298-6) on the European server -- top 0.15% of 90,000 players. An incomplete-information, real-time game with a combinatorial action space far exceeding chess or Go. - **2019 -- Tencent Jueyu dominates Honor of Kings.** Tencent AI Lab's "Jueyu" system [defeated KPL professional players in full 5v5](https://www.jiemian.com/article/3371171.html) at the World Champion Cup semifinal. In 1v1 mode, pros [won just 1 out of 15 matches, lasting under 8 minutes at best](https://developer.aliyun.com/article/851058). Training intensity: one day equaled 440 human years. A model that learned the entire game from scratch through self-play. - **2024-2025 -- LLM agents reshape software engineering.** Claude, GPT, Gemini -- large language models trained on the full breadth of human code and reasoning -- are deployed as coding agents. They read codebases, write implementations, debug failures, and coordinate as teams. The architecture is identical to every previous agent: a trained model, placed in an environment, given tools for perception and action. Every milestone points to the same fact: **Agency -- the ability to perceive, reason, and act -- is trained, not coded.** But every agent also needs an environment to operate in: an Atari emulator, the Dota 2 client, the StarCraft II engine, an IDE and a terminal. The model supplies the intelligence. The environment suppl ``` --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/tools/shareai-lab-learn-claude-code`](/api/graphcanon/tools/shareai-lab-learn-claude-code) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_