{ "source": "AIMap", "schema": "learn/v1", "updated": "2026-10-10", "generatedAt": "2026-10-22T12:12:50.964Z", "note": "Lessons with status \"published\" have full text in en and zh-hans (see record). \"outline\" lessons are planned and not yet written. Other languages have machine-translated navigation only.", "stages": [ { "id": "foundations", "level": 1, "title": { "en": "Foundations", "zh-hans": "入门基础" } }, { "id": "core", "level": 2, "title": { "en": "How LLMs Work", "zh-hans": "大模型原理" } }, { "id": "build", "level": 3, "title": { "en": "Building with AI", "zh-hans": "工程实战" } }, { "id": "mastery", "level": 4, "title": { "en": "Mastery & Industry", "zh-hans": "精通与产业" } } ], "paths": [ { "id": "beginner", "title": { "en": "Beginner Path: Understand AI", "zh-hans": "初学者路线：看懂 AI" }, "description": { "en": "No coding needed. Understand what LLMs and agents are, how they learn, and how to use them safely.", "zh-hans": "不需要写代码。看懂大模型和 agent 是什么、怎么学会的、怎样安全地用。" }, "lessons": [ "what-is-an-llm", "ai-ml-dl-genai", "prompting-basics", "hallucination", "ml-basics", "neural-networks", "tokens-embeddings", "transformer-attention", "how-llms-learn", "inside-llms", "llm-inference", "local-llm", "what-is-rag", "how-agents-work", "tool-calling-mcp", "prompt-injection", "ai-governance", "llm-evaluation", "gpu-basics", "ai-for-science", "ai-in-education", "ai-coding", "open-vs-closed" ], "pages": { "en": "https://aimap.page/en/learn/track/t-everyone/", "zh-hans": "https://aimap.page/zh-hans/learn/track/t-everyone/" } }, { "id": "developer", "title": { "en": "Developer Path: Build AI Apps & Agents", "zh-hans": "开发者路线：做 AI 应用与 Agent" }, "description": { "en": "From model APIs to RAG, tool calling, MCP, sandboxes, evaluation and deployment.", "zh-hans": "从模型 API 到 RAG、工具调用、MCP、沙箱、评测与部署。" }, "lessons": [ "what-is-an-llm", "prompting-basics", "tokens-embeddings", "transformer-attention", "how-llms-learn", "fine-tuning-lora", "llm-inference", "serving-engines", "local-llm", "model-gateway", "what-is-rag", "vector-databases", "chunking-reranking", "rag-evaluation", "how-agents-work", "tool-calling-mcp", "planning-memory", "a2a-multi-agent", "agent-sandbox", "agent-evaluation", "speech-audio", "llm-evaluation", "build-evals", "prompt-injection", "ai-coding", "ai-product-design", "open-vs-closed" ], "pages": { "en": "https://aimap.page/en/learn/track/t-builder/", "zh-hans": "https://aimap.page/zh-hans/learn/track/t-builder/" } }, { "id": "researcher", "title": { "en": "Researcher Path: How Models Really Work", "zh-hans": "研究者路线：深入模型原理" }, "description": { "en": "Maths, architectures, scaling, training and alignment, interpretability and frontier evaluation.", "zh-hans": "数学、架构、缩放定律、训练与对齐、可解释性与前沿评测。" }, "lessons": [ "math-for-ai", "ml-basics", "generalization", "neural-networks", "training-deep-nets", "cnn-rnn-to-transformer", "tokens-embeddings", "transformer-attention", "scaling-laws", "llm-architectures", "how-llms-learn", "pretraining-data", "alignment-methods", "reasoning-models", "inside-llms", "circuits-sae", "probing-steering", "llm-inference", "vision-language", "diffusion-models", "llm-evaluation", "agent-evaluation", "frontier-risks", "distributed-training", "ai-for-science" ], "pages": { "en": "https://aimap.page/en/learn/track/t-researcher/", "zh-hans": "https://aimap.page/zh-hans/learn/track/t-researcher/" } } ], "modules": [ { "id": "ai-basics", "stage": "foundations", "title": { "en": "AI & LLM Basics", "zh-hans": "AI 与大模型入门" }, "description": { "en": "What large language models are, what they can and cannot do, and how to use them well.", "zh-hans": "大模型是什么、能做什么不能做什么，以及怎样用好它。" }, "pages": { "en": "https://aimap.page/en/learn/ai-basics/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-basics/" }, "lessons": [ { "id": "what-is-an-llm", "status": "published", "level": 1, "minutes": 15, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "What Is a Large Language Model?", "zh-hans": "大模型是什么？" }, "summary": { "en": "A plain-language explanation of LLMs: next-token prediction, parameters, training and limits.", "zh-hans": "用大白话讲清大模型：预测下一个词、参数、训练过程和能力边界。" }, "pages": { "en": "https://aimap.page/en/learn/ai-basics/what-is-an-llm/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-basics/what-is-an-llm/" }, "record": "https://aimap.page/api/learn/what-is-an-llm.json", "software": [ { "id": "gemini", "name": "Google Gemini", "record": "https://aimap.page/api/v1/software/gemini.json" }, { "id": "ollama", "name": "Ollama", "record": "https://aimap.page/api/v1/software/ollama.json" } ], "newsTopics": [ "llm" ], "sources": [ { "title": "Vaswani et al. (2017), Attention Is All You Need", "url": "https://arxiv.org/abs/1706.03762" }, { "title": "Brown et al. (2020), Language Models are Few-Shot Learners (GPT-3)", "url": "https://arxiv.org/abs/2005.14165" }, { "title": "Kaplan et al. (2020), Scaling Laws for Neural Language Models", "url": "https://arxiv.org/abs/2001.08361" }, { "title": "Ouyang et al. (2022), Training language models to follow instructions with human feedback", "url": "https://arxiv.org/abs/2203.02155" }, { "title": "Zhao et al. (2023), A Survey of Large Language Models", "url": "https://arxiv.org/abs/2303.18223" }, { "title": "3Blue1Brown, Large Language Models explained briefly (video)", "url": "https://www.3blue1brown.com/lessons/mini-llm" } ] }, { "id": "ai-ml-dl-genai", "status": "outline", "level": 1, "minutes": 10, "audiences": [ "beginner" ], "title": { "en": "AI, Machine Learning, Deep Learning and Generative AI", "zh-hans": "AI、机器学习、深度学习与生成式 AI 的关系" }, "summary": { "en": "How the four terms nest inside each other, with one example of each.", "zh-hans": "四个概念的包含关系，各举一个例子。" }, "outline": { "en": [ "Rule-based AI vs learned AI", "Supervised, unsupervised and reinforcement learning", "Why deep learning took over", "Generative vs discriminative models" ], "zh-hans": [ "规则 AI 与学习型 AI", "监督、无监督与强化学习", "深度学习为什么胜出", "生成式模型与判别式模型" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-basics/ai-ml-dl-genai/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-basics/ai-ml-dl-genai/" }, "record": null, "software": [], "newsTopics": [], "sources": [] }, { "id": "prompting-basics", "status": "outline", "level": 1, "minutes": 15, "audiences": [ "beginner", "developer" ], "title": { "en": "Prompting Basics: Getting Reliable Answers", "zh-hans": "提示词基础：让回答更可靠" }, "summary": { "en": "Clear instructions, examples, context and output formats — and where prompting stops helping.", "zh-hans": "清晰指令、示例、上下文和输出格式，以及提示词的边界。" }, "outline": { "en": [ "Instructions, roles and context", "Few-shot examples", "Structured output (JSON)", "When to switch to RAG or tools" ], "zh-hans": [ "指令、角色与上下文", "少样本示例", "结构化输出（JSON）", "什么时候该改用 RAG 或工具" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-basics/prompting-basics/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-basics/prompting-basics/" }, "record": null, "software": [ { "id": "chatbox", "name": "chatbox", "record": "https://aimap.page/api/v1/software/chatbox.json" } ], "newsTopics": [], "sources": [] }, { "id": "hallucination", "status": "outline", "level": 1, "minutes": 12, "audiences": [ "beginner", "developer" ], "title": { "en": "Why LLMs Hallucinate", "zh-hans": "大模型为什么会“幻觉”" }, "summary": { "en": "Where confident-but-wrong answers come from and practical ways to reduce them.", "zh-hans": "一本正经地胡说八道从哪来，以及实用的缓解办法。" }, "outline": { "en": [ "Probabilistic generation and missing knowledge", "Training incentives that reward guessing", "Grounding with retrieval and citations", "Checking answers with tools" ], "zh-hans": [ "概率生成与知识缺失", "训练目标为什么鼓励“猜”", "用检索和引用做事实锚定", "用工具核对答案" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-basics/hallucination/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-basics/hallucination/" }, "record": null, "software": [], "newsTopics": [ "llm" ], "sources": [] } ] }, { "id": "math-ml", "stage": "foundations", "title": { "en": "Math & Machine Learning Foundations", "zh-hans": "数学与机器学习基础" }, "description": { "en": "The minimum math and the core idea of learning from data: loss, gradients and generalisation.", "zh-hans": "够用的数学，以及“从数据中学习”的核心：损失、梯度与泛化。" }, "pages": { "en": "https://aimap.page/en/learn/math-ml/", "zh-hans": "https://aimap.page/zh-hans/learn/math-ml/" }, "lessons": [ { "id": "ml-basics", "status": "published", "level": 1, "minutes": 18, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "How Machines Learn: Loss Functions and Gradient Descent", "zh-hans": "机器是怎么“学习”的：损失函数与梯度下降" }, "summary": { "en": "The core loop of all modern AI training explained with one tiny example.", "zh-hans": "用一个小例子讲清所有现代 AI 训练的核心循环。" }, "pages": { "en": "https://aimap.page/en/learn/math-ml/ml-basics/", "zh-hans": "https://aimap.page/zh-hans/learn/math-ml/ml-basics/" }, "record": "https://aimap.page/api/learn/ml-basics.json", "software": [ { "id": "uv", "name": "uv", "record": "https://aimap.page/api/v1/software/uv.json" } ], "newsTopics": [], "sources": [ { "title": "Goodfellow, Bengio & Courville, Deep Learning (MIT Press, 2016), chapters 4–5", "url": "https://www.deeplearningbook.org/" }, { "title": "Google, Machine Learning Crash Course", "url": "https://developers.google.com/machine-learning/crash-course" }, { "title": "Kingma & Ba (2014), Adam: A Method for Stochastic Optimization", "url": "https://arxiv.org/abs/1412.6980" }, { "title": "Ruder (2016), An overview of gradient descent optimization algorithms", "url": "https://arxiv.org/abs/1609.04747" }, { "title": "Stanford CS229 Machine Learning lecture notes", "url": "https://cs229.stanford.edu/main_notes.pdf" } ] }, { "id": "math-for-ai", "status": "outline", "level": 2, "minutes": 30, "audiences": [ "beginner", "researcher" ], "title": { "en": "The Minimum Math for AI: Vectors, Probability, Derivatives", "zh-hans": "AI 需要的最小数学：向量、概率与导数" }, "summary": { "en": "Linear algebra, probability and calculus — only the parts LLMs actually use.", "zh-hans": "线性代数、概率和微积分里大模型真正用到的部分。" }, "outline": { "en": [ "Vectors, matrices and dot products", "Probability distributions and softmax", "Derivatives and the chain rule", "Cross-entropy and information" ], "zh-hans": [ "向量、矩阵与点积", "概率分布与 softmax", "导数与链式法则", "交叉熵与信息量" ] }, "pages": { "en": "https://aimap.page/en/learn/math-ml/math-for-ai/", "zh-hans": "https://aimap.page/zh-hans/learn/math-ml/math-for-ai/" }, "record": null, "software": [], "newsTopics": [], "sources": [] }, { "id": "generalization", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "developer", "researcher" ], "title": { "en": "Overfitting, Generalisation and Data Splits", "zh-hans": "过拟合、泛化与数据划分" }, "summary": { "en": "Why models that ace training data can fail in the real world, and how to measure it.", "zh-hans": "为什么训练集满分的模型到真实场景会失灵，以及怎么测。" }, "outline": { "en": [ "Train / validation / test splits", "Overfitting and regularisation", "Data leakage", "Double descent and large models" ], "zh-hans": [ "训练集、验证集与测试集", "过拟合与正则化", "数据泄漏", "双下降与大模型" ] }, "pages": { "en": "https://aimap.page/en/learn/math-ml/generalization/", "zh-hans": "https://aimap.page/zh-hans/learn/math-ml/generalization/" }, "record": null, "software": [], "newsTopics": [], "sources": [] } ] }, { "id": "deep-learning", "stage": "foundations", "title": { "en": "Deep Learning", "zh-hans": "深度学习" }, "description": { "en": "Neural networks, backpropagation and the building blocks that made large models trainable.", "zh-hans": "神经网络、反向传播，以及让大模型可以训练的关键组件。" }, "pages": { "en": "https://aimap.page/en/learn/deep-learning/", "zh-hans": "https://aimap.page/zh-hans/learn/deep-learning/" }, "lessons": [ { "id": "neural-networks", "status": "published", "level": 2, "minutes": 20, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "Neural Networks and Backpropagation", "zh-hans": "神经网络与反向传播" }, "summary": { "en": "Neurons, layers, activations and how errors flow backwards to update millions of weights.", "zh-hans": "神经元、层、激活函数，以及误差如何反向传播去更新海量权重。" }, "pages": { "en": "https://aimap.page/en/learn/deep-learning/neural-networks/", "zh-hans": "https://aimap.page/zh-hans/learn/deep-learning/neural-networks/" }, "record": "https://aimap.page/api/learn/neural-networks.json", "software": [ { "id": "uv", "name": "uv", "record": "https://aimap.page/api/v1/software/uv.json" } ], "newsTopics": [], "sources": [ { "title": "Rumelhart, Hinton & Williams (1986), Learning representations by back-propagating errors, Nature", "url": "https://www.nature.com/articles/323533a0" }, { "title": "Goodfellow, Bengio & Courville, Deep Learning, chapter 6 (Deep Feedforward Networks)", "url": "https://www.deeplearningbook.org/contents/mlp.html" }, { "title": "3Blue1Brown, Neural Networks (video series)", "url": "https://www.3blue1brown.com/topics/neural-networks" }, { "title": "Andrej Karpathy, micrograd: a tiny autograd engine", "url": "https://github.com/karpathy/micrograd" }, { "title": "Krizhevsky, Sutskever & Hinton (2012), ImageNet Classification with Deep Convolutional Neural Networks", "url": "https://papers.nips.cc/paper/2012/hash/c399862d3b9d6b76c8436e924a68c45b-Abstract.html" }, { "title": "He et al. (2015), Deep Residual Learning for Image Recognition", "url": "https://arxiv.org/abs/1512.03385" } ] }, { "id": "training-deep-nets", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "researcher" ], "title": { "en": "Training Deep Nets: Optimisers, Normalisation, Residuals", "zh-hans": "训练深层网络：优化器、归一化与残差连接" }, "summary": { "en": "Adam, LayerNorm, residual connections and learning-rate schedules.", "zh-hans": "Adam、LayerNorm、残差连接和学习率调度。" }, "outline": { "en": [ "SGD vs Adam", "Normalisation layers", "Residual connections", "Learning-rate warm-up and decay" ], "zh-hans": [ "SGD 与 Adam", "归一化层", "残差连接", "学习率预热与衰减" ] }, "pages": { "en": "https://aimap.page/en/learn/deep-learning/training-deep-nets/", "zh-hans": "https://aimap.page/zh-hans/learn/deep-learning/training-deep-nets/" }, "record": null, "software": [], "newsTopics": [], "sources": [] }, { "id": "cnn-rnn-to-transformer", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "researcher" ], "title": { "en": "From CNNs and RNNs to Transformers", "zh-hans": "从 CNN、RNN 到 Transformer" }, "summary": { "en": "A short history of architectures and why attention replaced recurrence.", "zh-hans": "架构简史：为什么注意力取代了循环结构。" }, "outline": { "en": [ "CNNs and images", "RNNs/LSTMs and sequences", "Sequence-to-sequence and early attention", "Parallelism: why Transformers scaled" ], "zh-hans": [ "CNN 与图像", "RNN/LSTM 与序列", "序列到序列与早期注意力", "并行性：Transformer 为什么能扩展" ] }, "pages": { "en": "https://aimap.page/en/learn/deep-learning/cnn-rnn-to-transformer/", "zh-hans": "https://aimap.page/zh-hans/learn/deep-learning/cnn-rnn-to-transformer/" }, "record": null, "software": [], "newsTopics": [], "sources": [] } ] }, { "id": "llm-core", "stage": "core", "title": { "en": "Transformers & LLM Principles", "zh-hans": "Transformer 与大模型原理" }, "description": { "en": "Tokens, embeddings, attention and the scaling laws behind today's models.", "zh-hans": "Token、Embedding、注意力机制，以及大模型背后的缩放定律。" }, "pages": { "en": "https://aimap.page/en/learn/llm-core/", "zh-hans": "https://aimap.page/zh-hans/learn/llm-core/" }, "lessons": [ { "id": "tokens-embeddings", "status": "published", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "Tokens and Embeddings: How Text Becomes Numbers", "zh-hans": "Token 与 Embedding：文字如何变成数字" }, "summary": { "en": "Tokenisation (BPE), vocabularies and the vector space where meaning lives.", "zh-hans": "分词（BPE）、词表，以及承载语义的向量空间。" }, "pages": { "en": "https://aimap.page/en/learn/llm-core/tokens-embeddings/", "zh-hans": "https://aimap.page/zh-hans/learn/llm-core/tokens-embeddings/" }, "record": "https://aimap.page/api/learn/tokens-embeddings.json", "software": [ { "id": "ollama", "name": "Ollama", "record": "https://aimap.page/api/v1/software/ollama.json" }, { "id": "lm-studio", "name": "LM Studio", "record": "https://aimap.page/api/v1/software/lm-studio.json" } ], "newsTopics": [ "llm" ], "sources": [ { "title": "Sennrich, Haddow & Birch (2015), Neural Machine Translation of Rare Words with Subword Units (BPE)", "url": "https://arxiv.org/abs/1508.07909" }, { "title": "Kudo & Richardson (2018), SentencePiece", "url": "https://arxiv.org/abs/1808.06226" }, { "title": "Mikolov et al. (2013), Efficient Estimation of Word Representations in Vector Space (word2vec)", "url": "https://arxiv.org/abs/1301.3781" }, { "title": "OpenAI Help Center, What are tokens and how to count them?", "url": "https://help.openai.com/en/articles/4936856-what-are-tokens-and-how-to-count-them" }, { "title": "OpenAI, tiktoken (BPE tokeniser)", "url": "https://github.com/openai/tiktoken" }, { "title": "Reimers & Gurevych (2019), Sentence-BERT", "url": "https://arxiv.org/abs/1908.10084" } ] }, { "id": "transformer-attention", "status": "published", "level": 2, "minutes": 25, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "Transformers and Attention, Explained", "zh-hans": "Transformer 与注意力机制详解" }, "summary": { "en": "Self-attention, queries/keys/values, multi-head attention and the full decoder block.", "zh-hans": "自注意力、Q/K/V、多头注意力，以及完整的解码器层。" }, "pages": { "en": "https://aimap.page/en/learn/llm-core/transformer-attention/", "zh-hans": "https://aimap.page/zh-hans/learn/llm-core/transformer-attention/" }, "record": "https://aimap.page/api/learn/transformer-attention.json", "software": [ { "id": "llama-cpp", "name": "llama.cpp", "record": "https://aimap.page/api/v1/software/llama-cpp.json" }, { "id": "mlx-lm", "name": "mlx-lm", "record": "https://aimap.page/api/v1/software/mlx-lm.json" } ], "newsTopics": [ "llm" ], "sources": [ { "title": "Vaswani et al. (2017), Attention Is All You Need", "url": "https://arxiv.org/abs/1706.03762" }, { "title": "Bahdanau, Cho & Bengio (2014), Neural Machine Translation by Jointly Learning to Align and Translate", "url": "https://arxiv.org/abs/1409.0473" }, { "title": "Jay Alammar, The Illustrated Transformer", "url": "https://jalammar.github.io/illustrated-transformer/" }, { "title": "Harvard NLP, The Annotated Transformer", "url": "https://nlp.seas.harvard.edu/annotated-transformer/" }, { "title": "Radford et al. (2019), Language Models are Unsupervised Multitask Learners (GPT-2)", "url": "https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf" }, { "title": "Su et al. (2021), RoFormer: Enhanced Transformer with Rotary Position Embedding", "url": "https://arxiv.org/abs/2104.09864" }, { "title": "Dao et al. (2022), FlashAttention", "url": "https://arxiv.org/abs/2205.14135" } ] }, { "id": "scaling-laws", "status": "outline", "level": 3, "minutes": 15, "audiences": [ "researcher", "developer" ], "title": { "en": "Scaling Laws and Emergence", "zh-hans": "缩放定律与“涌现”" }, "summary": { "en": "How loss falls with parameters, data and compute, and what Chinchilla changed.", "zh-hans": "损失如何随参数、数据和算力下降，以及 Chinchilla 改变了什么。" }, "outline": { "en": [ "Kaplan et al. power laws", "Compute-optimal training (Chinchilla)", "Emergent abilities and the debate", "Test-time compute scaling" ], "zh-hans": [ "Kaplan 等人的幂律", "算力最优训练（Chinchilla）", "涌现能力及其争议", "测试时算力扩展" ] }, "pages": { "en": "https://aimap.page/en/learn/llm-core/scaling-laws/", "zh-hans": "https://aimap.page/zh-hans/learn/llm-core/scaling-laws/" }, "record": null, "software": [], "newsTopics": [ "training" ], "sources": [] }, { "id": "llm-architectures", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "researcher", "developer" ], "title": { "en": "Modern LLM Architectures: Decoders, MoE, Long Context", "zh-hans": "现代大模型架构：解码器、MoE 与长上下文" }, "summary": { "en": "Decoder-only models, mixture-of-experts, positional encodings and long-context tricks.", "zh-hans": "纯解码器、混合专家（MoE）、位置编码与长上下文技术。" }, "outline": { "en": [ "Decoder-only vs encoder-decoder", "Mixture of experts", "RoPE and context extension", "Efficient attention variants" ], "zh-hans": [ "纯解码器与编码器-解码器", "混合专家", "RoPE 与上下文扩展", "高效注意力变体" ] }, "pages": { "en": "https://aimap.page/en/learn/llm-core/llm-architectures/", "zh-hans": "https://aimap.page/zh-hans/learn/llm-core/llm-architectures/" }, "record": null, "software": [], "newsTopics": [ "llm" ], "sources": [] } ] }, { "id": "training", "stage": "core", "title": { "en": "Pre-training, Data, Fine-tuning & Alignment", "zh-hans": "预训练、数据、微调与对齐" }, "description": { "en": "How base models are built and then turned into helpful, harmless assistants.", "zh-hans": "基座模型如何炼成，又如何被调教成有用、无害的助手。" }, "pages": { "en": "https://aimap.page/en/learn/training/", "zh-hans": "https://aimap.page/zh-hans/learn/training/" }, "lessons": [ { "id": "how-llms-learn", "status": "published", "level": 2, "minutes": 25, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "How LLMs Learn: Pre-training, Fine-tuning and RLHF", "zh-hans": "大模型如何学习：预训练、微调与 RLHF" }, "summary": { "en": "The three-stage recipe behind ChatGPT-style assistants, and newer methods like DPO.", "zh-hans": "ChatGPT 类助手背后的三段式训练，以及 DPO 等新方法。" }, "pages": { "en": "https://aimap.page/en/learn/training/how-llms-learn/", "zh-hans": "https://aimap.page/zh-hans/learn/training/how-llms-learn/" }, "record": "https://aimap.page/api/learn/how-llms-learn.json", "software": [ { "id": "mlx-lm", "name": "mlx-lm", "record": "https://aimap.page/api/v1/software/mlx-lm.json" } ], "newsTopics": [ "training" ], "sources": [ { "title": "Ouyang et al. (2022), Training language models to follow instructions with human feedback (InstructGPT)", "url": "https://arxiv.org/abs/2203.02155" }, { "title": "Christiano et al. (2017), Deep reinforcement learning from human preferences", "url": "https://arxiv.org/abs/1706.03741" }, { "title": "Hoffmann et al. (2022), Training Compute-Optimal Large Language Models (Chinchilla)", "url": "https://arxiv.org/abs/2203.15556" }, { "title": "Llama Team, Meta (2024), The Llama 3 Herd of Models", "url": "https://arxiv.org/abs/2407.21783" }, { "title": "Rafailov et al. (2023), Direct Preference Optimization", "url": "https://arxiv.org/abs/2305.18290" }, { "title": "Bai et al. (2022), Constitutional AI: Harmlessness from AI Feedback", "url": "https://arxiv.org/abs/2212.08073" }, { "title": "Hu et al. (2021), LoRA: Low-Rank Adaptation of Large Language Models", "url": "https://arxiv.org/abs/2106.09685" }, { "title": "DeepSeek-AI (2025), DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning", "url": "https://arxiv.org/abs/2501.12948" } ] }, { "id": "pretraining-data", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "researcher", "developer" ], "title": { "en": "Pre-training Data: Collection, Cleaning and Mixing", "zh-hans": "预训练数据：采集、清洗与配比" }, "summary": { "en": "Web crawls, deduplication, quality filters, data mixtures and licensing.", "zh-hans": "网页抓取、去重、质量过滤、数据配比与版权。" }, "outline": { "en": [ "Common Crawl and curated sources", "Deduplication and quality filtering", "Data mixtures and curricula", "Copyright, consent and opt-outs" ], "zh-hans": [ "Common Crawl 与精选数据源", "去重与质量过滤", "数据配比与课程", "版权、授权与退出机制" ] }, "pages": { "en": "https://aimap.page/en/learn/training/pretraining-data/", "zh-hans": "https://aimap.page/zh-hans/learn/training/pretraining-data/" }, "record": null, "software": [], "newsTopics": [ "training" ], "sources": [] }, { "id": "fine-tuning-lora", "status": "outline", "level": 3, "minutes": 25, "audiences": [ "developer" ], "title": { "en": "Fine-tuning in Practice: SFT, LoRA and QLoRA", "zh-hans": "微调实战：SFT、LoRA 与 QLoRA" }, "summary": { "en": "When to fine-tune, how parameter-efficient methods work and how to evaluate the result.", "zh-hans": "什么时候该微调、参数高效方法如何工作、怎样评估结果。" }, "outline": { "en": [ "Fine-tune vs prompt vs RAG", "Supervised fine-tuning data", "LoRA and QLoRA", "Evaluating a fine-tuned model" ], "zh-hans": [ "微调、提示词还是 RAG", "监督微调数据", "LoRA 与 QLoRA", "评估微调后的模型" ] }, "pages": { "en": "https://aimap.page/en/learn/training/fine-tuning-lora/", "zh-hans": "https://aimap.page/zh-hans/learn/training/fine-tuning-lora/" }, "record": null, "software": [ { "id": "mlx-lm", "name": "mlx-lm", "record": "https://aimap.page/api/v1/software/mlx-lm.json" }, { "id": "lm-studio", "name": "LM Studio", "record": "https://aimap.page/api/v1/software/lm-studio.json" } ], "newsTopics": [], "sources": [] }, { "id": "alignment-methods", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "researcher" ], "title": { "en": "Alignment Methods: RLHF, DPO and Constitutional AI", "zh-hans": "对齐方法：RLHF、DPO 与宪法式 AI" }, "summary": { "en": "Reward models, preference optimisation, AI feedback and their failure modes.", "zh-hans": "奖励模型、偏好优化、AI 反馈，以及它们的失效方式。" }, "outline": { "en": [ "Reward modelling", "PPO-based RLHF", "Direct preference optimisation", "RLAIF and constitutions", "Reward hacking and sycophancy" ], "zh-hans": [ "奖励建模", "基于 PPO 的 RLHF", "直接偏好优化", "RLAIF 与“宪法”", "奖励投机与谄媚" ] }, "pages": { "en": "https://aimap.page/en/learn/training/alignment-methods/", "zh-hans": "https://aimap.page/zh-hans/learn/training/alignment-methods/" }, "record": null, "software": [], "newsTopics": [ "safety" ], "sources": [] }, { "id": "reasoning-models", "status": "outline", "level": 4, "minutes": 20, "audiences": [ "researcher", "developer" ], "title": { "en": "Reasoning Models and Reinforcement Learning", "zh-hans": "推理模型与强化学习" }, "summary": { "en": "Chain-of-thought, RL with verifiable rewards and spending more compute at test time.", "zh-hans": "思维链、可验证奖励的强化学习，以及测试时多花算力。" }, "outline": { "en": [ "Chain-of-thought prompting", "RL with verifiable rewards", "Test-time compute", "Limits and faithfulness of reasoning traces" ], "zh-hans": [ "思维链提示", "可验证奖励的强化学习", "测试时计算", "推理过程的局限与忠实性" ] }, "pages": { "en": "https://aimap.page/en/learn/training/reasoning-models/", "zh-hans": "https://aimap.page/zh-hans/learn/training/reasoning-models/" }, "record": null, "software": [], "newsTopics": [ "llm" ], "sources": [] } ] }, { "id": "interpretability", "stage": "core", "title": { "en": "Inside LLMs: Interpretability", "zh-hans": "大模型内部机制与可解释性" }, "description": { "en": "What models represent internally and the tools researchers use to look inside.", "zh-hans": "模型内部表征了什么，以及研究者“打开黑箱”的工具。" }, "pages": { "en": "https://aimap.page/en/learn/interpretability/", "zh-hans": "https://aimap.page/zh-hans/learn/interpretability/" }, "lessons": [ { "id": "inside-llms", "status": "published", "level": 3, "minutes": 25, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "What Do LLMs Learn Inside? Representations and Interpretability", "zh-hans": "大模型内部学到了什么？表征与可解释性" }, "summary": { "en": "Features, superposition, circuits and world models — what we know and what we don't.", "zh-hans": "特征、叠加、电路与世界模型：我们知道什么、还不知道什么。" }, "pages": { "en": "https://aimap.page/en/learn/interpretability/inside-llms/", "zh-hans": "https://aimap.page/zh-hans/learn/interpretability/inside-llms/" }, "record": "https://aimap.page/api/learn/inside-llms.json", "software": [], "newsTopics": [ "interpretability" ], "sources": [ { "title": "Alain & Bengio (2016), Understanding intermediate layers using linear classifier probes", "url": "https://arxiv.org/abs/1610.01644" }, { "title": "Li et al. (2022), Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task (Othello-GPT)", "url": "https://arxiv.org/abs/2210.13382" }, { "title": "Gurnee & Tegmark (2023), Language Models Represent Space and Time", "url": "https://arxiv.org/abs/2310.02207" }, { "title": "Geva et al. (2020), Transformer Feed-Forward Layers Are Key-Value Memories", "url": "https://arxiv.org/abs/2012.14913" }, { "title": "Meng et al. (2022), Locating and Editing Factual Associations in GPT (ROME)", "url": "https://arxiv.org/abs/2202.05262" }, { "title": "Elhage et al. (2022), Toy Models of Superposition", "url": "https://transformer-circuits.pub/2022/toy_model/index.html" }, { "title": "Olsson et al. (2022), In-context Learning and Induction Heads", "url": "https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html" }, { "title": "Templeton et al. (2024), Scaling Monosemanticity: Extracting Interpretable Features from Claude 3 Sonnet", "url": "https://transformer-circuits.pub/2024/scaling-monosemanticity/index.html" }, { "title": "Gao et al. (2024), Scaling and evaluating sparse autoencoders (OpenAI)", "url": "https://arxiv.org/abs/2406.04093" }, { "title": "Lindsey et al. (2025), On the Biology of a Large Language Model", "url": "https://transformer-circuits.pub/2025/attribution-graphs/biology.html" }, { "title": "Turpin et al. (2023), Language Models Don't Always Say What They Think", "url": "https://arxiv.org/abs/2305.04388" } ] }, { "id": "circuits-sae", "status": "outline", "level": 4, "minutes": 25, "audiences": [ "researcher" ], "title": { "en": "Circuits and Sparse Autoencoders", "zh-hans": "电路与稀疏自编码器" }, "summary": { "en": "Induction heads, dictionary learning and attribution graphs in practice.", "zh-hans": "归纳头、字典学习与归因图的实际做法。" }, "outline": { "en": [ "Induction heads", "Superposition", "Sparse autoencoders", "Attribution graphs" ], "zh-hans": [ "归纳头", "叠加", "稀疏自编码器", "归因图" ] }, "pages": { "en": "https://aimap.page/en/learn/interpretability/circuits-sae/", "zh-hans": "https://aimap.page/zh-hans/learn/interpretability/circuits-sae/" }, "record": null, "software": [], "newsTopics": [ "interpretability" ], "sources": [] }, { "id": "probing-steering", "status": "outline", "level": 4, "minutes": 20, "audiences": [ "researcher" ], "title": { "en": "Probing, Editing and Steering Models", "zh-hans": "探针、知识编辑与激活引导" }, "summary": { "en": "Linear probes, causal interventions, knowledge editing and activation steering.", "zh-hans": "线性探针、因果干预、知识编辑和激活引导。" }, "outline": { "en": [ "Linear probes", "Activation patching", "Knowledge editing (ROME)", "Steering vectors" ], "zh-hans": [ "线性探针", "激活修补", "知识编辑（ROME）", "引导向量" ] }, "pages": { "en": "https://aimap.page/en/learn/interpretability/probing-steering/", "zh-hans": "https://aimap.page/zh-hans/learn/interpretability/probing-steering/" }, "record": null, "software": [], "newsTopics": [ "interpretability" ], "sources": [] } ] }, { "id": "inference", "stage": "build", "title": { "en": "Inference & Deployment", "zh-hans": "推理与部署" }, "description": { "en": "How models generate text fast and cheaply — from KV caches to local apps and gateways.", "zh-hans": "模型如何又快又省地生成文本：从 KV Cache 到本地运行与模型网关。" }, "pages": { "en": "https://aimap.page/en/learn/inference/", "zh-hans": "https://aimap.page/zh-hans/learn/inference/" }, "lessons": [ { "id": "llm-inference", "status": "published", "level": 2, "minutes": 20, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "How LLMs Generate Text: Decoding, KV Cache and Quantisation", "zh-hans": "大模型如何生成文本：解码、KV Cache 与量化" }, "summary": { "en": "Sampling, temperature, the KV cache, batching and quantisation in one lesson.", "zh-hans": "采样、温度、KV Cache、批处理与量化，一课讲清。" }, "pages": { "en": "https://aimap.page/en/learn/inference/llm-inference/", "zh-hans": "https://aimap.page/zh-hans/learn/inference/llm-inference/" }, "record": "https://aimap.page/api/learn/llm-inference.json", "software": [ { "id": "ollama", "name": "Ollama", "record": "https://aimap.page/api/v1/software/ollama.json" }, { "id": "llama-cpp", "name": "llama.cpp", "record": "https://aimap.page/api/v1/software/llama-cpp.json" }, { "id": "lm-studio", "name": "LM Studio", "record": "https://aimap.page/api/v1/software/lm-studio.json" }, { "id": "mlx-lm", "name": "mlx-lm", "record": "https://aimap.page/api/v1/software/mlx-lm.json" } ], "newsTopics": [ "inference" ], "sources": [ { "title": "Holtzman et al. (2019), The Curious Case of Neural Text Degeneration (nucleus / top-p sampling)", "url": "https://arxiv.org/abs/1904.09751" }, { "title": "Kwon et al. (2023), Efficient Memory Management for LLM Serving with PagedAttention (vLLM)", "url": "https://arxiv.org/abs/2309.06180" }, { "title": "Yu et al. (2022), Orca: A Distributed Serving System for Transformer-Based Generative Models (continuous batching), OSDI", "url": "https://www.usenix.org/conference/osdi22/presentation/yu" }, { "title": "Leviathan, Kalman & Matias (2022), Fast Inference from Transformers via Speculative Decoding", "url": "https://arxiv.org/abs/2211.17192" }, { "title": "Ainslie et al. (2023), GQA: Training Generalized Multi-Query Transformer Models", "url": "https://arxiv.org/abs/2305.13245" }, { "title": "Frantar et al. (2022), GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers", "url": "https://arxiv.org/abs/2210.17323" }, { "title": "Dettmers et al. (2022), LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale", "url": "https://arxiv.org/abs/2208.07339" }, { "title": "llama.cpp (GGUF format and quantisation types)", "url": "https://github.com/ggml-org/llama.cpp" } ] }, { "id": "serving-engines", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "developer" ], "title": { "en": "Inference Engines: vLLM, llama.cpp and Friends", "zh-hans": "推理引擎：vLLM、llama.cpp 等" }, "summary": { "en": "PagedAttention, continuous batching and choosing an engine for your hardware.", "zh-hans": "PagedAttention、连续批处理，以及按硬件选引擎。" }, "outline": { "en": [ "PagedAttention and continuous batching", "Speculative decoding", "GGUF and llama.cpp", "Choosing an engine" ], "zh-hans": [ "PagedAttention 与连续批处理", "投机解码", "GGUF 与 llama.cpp", "怎么选推理引擎" ] }, "pages": { "en": "https://aimap.page/en/learn/inference/serving-engines/", "zh-hans": "https://aimap.page/zh-hans/learn/inference/serving-engines/" }, "record": null, "software": [ { "id": "llama-cpp", "name": "llama.cpp", "record": "https://aimap.page/api/v1/software/llama-cpp.json" }, { "id": "ollama", "name": "Ollama", "record": "https://aimap.page/api/v1/software/ollama.json" }, { "id": "mlx-lm", "name": "mlx-lm", "record": "https://aimap.page/api/v1/software/mlx-lm.json" } ], "newsTopics": [ "inference" ], "sources": [] }, { "id": "local-llm", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer" ], "title": { "en": "Running LLMs on Your Own Computer", "zh-hans": "在自己电脑上运行大模型" }, "summary": { "en": "Memory math, quantised models and desktop apps for private, offline AI.", "zh-hans": "显存估算、量化模型，以及私密离线使用的桌面软件。" }, "outline": { "en": [ "How much memory a model needs", "Choosing a quantisation", "Desktop apps and CLIs", "Privacy and licences" ], "zh-hans": [ "一个模型需要多少内存", "怎么选量化版本", "桌面软件与命令行工具", "隐私与许可证" ] }, "pages": { "en": "https://aimap.page/en/learn/inference/local-llm/", "zh-hans": "https://aimap.page/zh-hans/learn/inference/local-llm/" }, "record": null, "software": [ { "id": "ollama", "name": "Ollama", "record": "https://aimap.page/api/v1/software/ollama.json" }, { "id": "lm-studio", "name": "LM Studio", "record": "https://aimap.page/api/v1/software/lm-studio.json" }, { "id": "jan", "name": "jan", "record": "https://aimap.page/api/v1/software/jan.json" }, { "id": "mlx-lm", "name": "mlx-lm", "record": "https://aimap.page/api/v1/software/mlx-lm.json" } ], "newsTopics": [], "sources": [] }, { "id": "model-gateway", "status": "outline", "level": 3, "minutes": 15, "audiences": [ "developer" ], "title": { "en": "Model Gateways, Routing and Cost Control", "zh-hans": "模型网关、路由与成本控制" }, "summary": { "en": "One API for many models: fallbacks, caching, rate limits and spend tracking.", "zh-hans": "一个接口调用多家模型：回退、缓存、限流与花费追踪。" }, "outline": { "en": [ "Why a gateway", "Routing and fallbacks", "Prompt caching", "Observability and budgets" ], "zh-hans": [ "为什么需要网关", "路由与回退", "提示词缓存", "可观测与预算" ] }, "pages": { "en": "https://aimap.page/en/learn/inference/model-gateway/", "zh-hans": "https://aimap.page/zh-hans/learn/inference/model-gateway/" }, "record": null, "software": [], "newsTopics": [ "inference" ], "sources": [] } ] }, { "id": "rag", "stage": "build", "title": { "en": "RAG & Vector Databases", "zh-hans": "RAG 与向量数据库" }, "description": { "en": "Give models fresh, private knowledge through retrieval instead of retraining.", "zh-hans": "不重新训练，用检索给模型补上最新、私有的知识。" }, "pages": { "en": "https://aimap.page/en/learn/rag/", "zh-hans": "https://aimap.page/zh-hans/learn/rag/" }, "lessons": [ { "id": "what-is-rag", "status": "published", "level": 2, "minutes": 25, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "RAG Explained: Retrieval-Augmented Generation", "zh-hans": "RAG 详解：检索增强生成" }, "summary": { "en": "Chunking, embeddings, vector search, re-ranking and grounded answers with citations.", "zh-hans": "切分、向量化、向量检索、重排，以及带引用的回答。" }, "pages": { "en": "https://aimap.page/en/learn/rag/what-is-rag/", "zh-hans": "https://aimap.page/zh-hans/learn/rag/what-is-rag/" }, "record": "https://aimap.page/api/learn/what-is-rag.json", "software": [ { "id": "cherry-studio", "name": "cherry-studio", "record": "https://aimap.page/api/v1/software/cherry-studio.json" }, { "id": "obsidian", "name": "Obsidian", "record": "https://aimap.page/api/v1/software/obsidian.json" } ], "newsTopics": [ "rag" ], "sources": [ { "title": "Lewis et al. (2020), Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks", "url": "https://arxiv.org/abs/2005.11401" }, { "title": "Karpukhin et al. (2020), Dense Passage Retrieval for Open-Domain Question Answering", "url": "https://arxiv.org/abs/2004.04906" }, { "title": "Gao et al. (2023), Retrieval-Augmented Generation for Large Language Models: A Survey", "url": "https://arxiv.org/abs/2312.10997" }, { "title": "Liu et al. (2023), Lost in the Middle: How Language Models Use Long Contexts", "url": "https://arxiv.org/abs/2307.03172" }, { "title": "Malkov & Yashunin (2016), Efficient and robust approximate nearest neighbor search using HNSW graphs", "url": "https://arxiv.org/abs/1603.09320" }, { "title": "Anthropic (2024), Introducing Contextual Retrieval", "url": "https://www.anthropic.com/news/contextual-retrieval" }, { "title": "Es et al. (2023), RAGAS: Automated Evaluation of Retrieval Augmented Generation", "url": "https://arxiv.org/abs/2309.15217" }, { "title": "Edge et al. (2024), From Local to Global: A Graph RAG Approach to Query-Focused Summarization", "url": "https://arxiv.org/abs/2404.16130" }, { "title": "Gao et al. (2022), Precise Zero-Shot Dense Retrieval without Relevance Labels (HyDE)", "url": "https://arxiv.org/abs/2212.10496" }, { "title": "Zheng et al. (2023), Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena", "url": "https://arxiv.org/abs/2306.05685" } ] }, { "id": "vector-databases", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "developer", "researcher" ], "title": { "en": "Vector Databases and Similarity Search", "zh-hans": "向量数据库与相似度搜索" }, "summary": { "en": "Distance metrics, HNSW and IVF indexes, filtering and choosing a store.", "zh-hans": "距离度量、HNSW/IVF 索引、过滤，以及如何选型。" }, "outline": { "en": [ "Cosine, dot product, L2", "Approximate nearest neighbour (HNSW, IVF)", "Metadata filtering", "Choosing a vector store" ], "zh-hans": [ "余弦、点积与 L2", "近似最近邻（HNSW、IVF）", "元数据过滤", "向量库选型" ] }, "pages": { "en": "https://aimap.page/en/learn/rag/vector-databases/", "zh-hans": "https://aimap.page/zh-hans/learn/rag/vector-databases/" }, "record": null, "software": [], "newsTopics": [ "rag" ], "sources": [] }, { "id": "chunking-reranking", "status": "outline", "level": 3, "minutes": 15, "audiences": [ "developer" ], "title": { "en": "Chunking, Hybrid Search and Re-ranking", "zh-hans": "切分、混合检索与重排" }, "summary": { "en": "The engineering choices that decide most of RAG quality.", "zh-hans": "决定 RAG 效果的大部分工程选择。" }, "outline": { "en": [ "Chunk size and overlap", "BM25 + vectors", "Cross-encoder re-ranking", "Contextual retrieval" ], "zh-hans": [ "块大小与重叠", "BM25 + 向量", "交叉编码器重排", "上下文检索" ] }, "pages": { "en": "https://aimap.page/en/learn/rag/chunking-reranking/", "zh-hans": "https://aimap.page/zh-hans/learn/rag/chunking-reranking/" }, "record": null, "software": [], "newsTopics": [], "sources": [] }, { "id": "rag-evaluation", "status": "outline", "level": 3, "minutes": 15, "audiences": [ "developer", "researcher" ], "title": { "en": "Evaluating RAG and GraphRAG", "zh-hans": "RAG 评估与 GraphRAG" }, "summary": { "en": "Measuring retrieval and answer quality; when knowledge graphs help.", "zh-hans": "怎样衡量检索和回答质量；什么时候知识图谱有用。" }, "outline": { "en": [ "Retrieval metrics", "Faithfulness and answer relevance", "GraphRAG", "Long context vs RAG" ], "zh-hans": [ "检索指标", "忠实度与答案相关性", "GraphRAG", "长上下文还是 RAG" ] }, "pages": { "en": "https://aimap.page/en/learn/rag/rag-evaluation/", "zh-hans": "https://aimap.page/zh-hans/learn/rag/rag-evaluation/" }, "record": null, "software": [], "newsTopics": [], "sources": [] } ] }, { "id": "agents", "stage": "build", "title": { "en": "How AI Agents Work", "zh-hans": "AI Agent 工作原理" }, "description": { "en": "Tool calling, planning, memory, MCP/A2A, sandboxes and evaluation for agents that act.", "zh-hans": "工具调用、规划、记忆、MCP/A2A、沙箱与评估：让 AI 真正去做事。" }, "pages": { "en": "https://aimap.page/en/learn/agents/", "zh-hans": "https://aimap.page/zh-hans/learn/agents/" }, "lessons": [ { "id": "how-agents-work", "status": "published", "level": 2, "minutes": 25, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "How AI Agents Work: The Agent Loop", "zh-hans": "AI Agent 工作原理：智能体循环" }, "summary": { "en": "Model + tools + loop: observe, think, act, and the patterns that make agents reliable.", "zh-hans": "模型 + 工具 + 循环：观察、思考、行动，以及让 agent 靠谱的设计模式。" }, "pages": { "en": "https://aimap.page/en/learn/agents/how-agents-work/", "zh-hans": "https://aimap.page/zh-hans/learn/agents/how-agents-work/" }, "record": "https://aimap.page/api/learn/how-agents-work.json", "software": [ { "id": "claude-code", "name": "Claude Code", "record": "https://aimap.page/api/v1/software/claude-code.json" }, { "id": "codex", "name": "OpenAI Codex", "record": "https://aimap.page/api/v1/software/codex.json" }, { "id": "gemini-cli", "name": "Gemini CLI", "record": "https://aimap.page/api/v1/software/gemini-cli.json" }, { "id": "goose", "name": "goose", "record": "https://aimap.page/api/v1/software/goose.json" }, { "id": "opencode", "name": "OpenCode", "record": "https://aimap.page/api/v1/software/opencode.json" }, { "id": "cursor", "name": "Cursor", "record": "https://aimap.page/api/v1/software/cursor.json" } ], "newsTopics": [ "agents" ], "sources": [ { "title": "Anthropic (2024), Building effective agents", "url": "https://www.anthropic.com/research/building-effective-agents" }, { "title": "Yao et al. (2022), ReAct: Synergizing Reasoning and Acting in Language Models", "url": "https://arxiv.org/abs/2210.03629" }, { "title": "Schick et al. (2023), Toolformer: Language Models Can Teach Themselves to Use Tools", "url": "https://arxiv.org/abs/2302.04761" }, { "title": "Shinn et al. (2023), Reflexion: Language Agents with Verbal Reinforcement Learning", "url": "https://arxiv.org/abs/2303.11366" }, { "title": "Park et al. (2023), Generative Agents: Interactive Simulacra of Human Behavior", "url": "https://arxiv.org/abs/2304.03442" }, { "title": "Yang et al. (2024), SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering", "url": "https://arxiv.org/abs/2405.15793" }, { "title": "Lilian Weng (2023), LLM Powered Autonomous Agents", "url": "https://lilianweng.github.io/posts/2023-06-23-agent/" }, { "title": "Jimenez et al. (2023), SWE-bench: Can Language Models Resolve Real-World GitHub Issues?", "url": "https://arxiv.org/abs/2310.06770" } ] }, { "id": "tool-calling-mcp", "status": "published", "level": 2, "minutes": 25, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "Tool Calling and MCP (Model Context Protocol)", "zh-hans": "工具调用与 MCP（模型上下文协议）" }, "summary": { "en": "How function calling works and how MCP standardises tools for every AI app.", "zh-hans": "函数调用如何工作，MCP 如何让工具在所有 AI 应用里通用。" }, "pages": { "en": "https://aimap.page/en/learn/agents/tool-calling-mcp/", "zh-hans": "https://aimap.page/zh-hans/learn/agents/tool-calling-mcp/" }, "record": "https://aimap.page/api/learn/tool-calling-mcp.json", "software": [ { "id": "claude-code", "name": "Claude Code", "record": "https://aimap.page/api/v1/software/claude-code.json" }, { "id": "cherry-studio", "name": "cherry-studio", "record": "https://aimap.page/api/v1/software/cherry-studio.json" }, { "id": "peekaboo", "name": "Peekaboo", "record": "https://aimap.page/api/v1/software/peekaboo.json" } ], "newsTopics": [ "mcp" ], "sources": [ { "title": "OpenAI API docs, Function calling", "url": "https://platform.openai.com/docs/guides/function-calling" }, { "title": "Anthropic docs, Tool use with Claude", "url": "https://docs.anthropic.com/en/docs/build-with-claude/tool-use/overview" }, { "title": "Anthropic (2024), Introducing the Model Context Protocol", "url": "https://www.anthropic.com/news/model-context-protocol" }, { "title": "Model Context Protocol, Specification", "url": "https://modelcontextprotocol.io/specification" }, { "title": "Model Context Protocol, Architecture overview", "url": "https://modelcontextprotocol.io/docs/learn/architecture" }, { "title": "Linux Foundation, Formation of the Agentic AI Foundation", "url": "https://www.linuxfoundation.org/press/linux-foundation-announces-the-formation-of-the-agentic-ai-foundation" }, { "title": "A2A Protocol (Agent2Agent) documentation", "url": "https://a2a-protocol.org/" }, { "title": "Anthropic Engineering (2025), Writing effective tools for agents", "url": "https://www.anthropic.com/engineering/writing-tools-for-agents" }, { "title": "JSON-RPC 2.0 Specification", "url": "https://www.jsonrpc.org/specification" } ] }, { "id": "planning-memory", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "developer", "researcher" ], "title": { "en": "Agent Planning and Memory", "zh-hans": "Agent 的规划与记忆" }, "summary": { "en": "Task decomposition, reflection, short- and long-term memory and context engineering.", "zh-hans": "任务分解、反思、短期与长期记忆，以及上下文工程。" }, "outline": { "en": [ "ReAct, plan-and-execute", "Reflection and self-correction", "Memory stores", "Context engineering" ], "zh-hans": [ "ReAct 与先计划后执行", "反思与自我纠错", "记忆存储", "上下文工程" ] }, "pages": { "en": "https://aimap.page/en/learn/agents/planning-memory/", "zh-hans": "https://aimap.page/zh-hans/learn/agents/planning-memory/" }, "record": null, "software": [], "newsTopics": [ "agents" ], "sources": [] }, { "id": "a2a-multi-agent", "status": "outline", "level": 3, "minutes": 15, "audiences": [ "developer", "researcher" ], "title": { "en": "A2A and Multi-agent Systems", "zh-hans": "A2A 与多 Agent 协作" }, "summary": { "en": "Agent cards, task delegation and when several agents beat one.", "zh-hans": "Agent Card、任务委派，以及什么时候多个 agent 比一个好。" }, "outline": { "en": [ "The A2A protocol", "Agent cards and discovery", "Orchestrator–worker patterns", "Costs and failure modes" ], "zh-hans": [ "A2A 协议", "Agent Card 与发现", "编排者-执行者模式", "成本与失败方式" ] }, "pages": { "en": "https://aimap.page/en/learn/agents/a2a-multi-agent/", "zh-hans": "https://aimap.page/zh-hans/learn/agents/a2a-multi-agent/" }, "record": null, "software": [], "newsTopics": [ "agents" ], "sources": [] }, { "id": "agent-sandbox", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "developer" ], "title": { "en": "Sandboxes, Browsers and Computer Use", "zh-hans": "代码沙箱、浏览器与电脑操作" }, "summary": { "en": "Letting agents run code and drive GUIs safely: containers, VMs, permissions.", "zh-hans": "让 agent 安全地执行代码、操作界面：容器、虚拟机与权限。" }, "outline": { "en": [ "Code sandboxes", "Browser automation", "Computer-use agents", "Least privilege and approvals" ], "zh-hans": [ "代码沙箱", "浏览器自动化", "电脑操作型 agent", "最小权限与人工确认" ] }, "pages": { "en": "https://aimap.page/en/learn/agents/agent-sandbox/", "zh-hans": "https://aimap.page/zh-hans/learn/agents/agent-sandbox/" }, "record": null, "software": [ { "id": "docker-desktop", "name": "Docker Desktop", "record": "https://aimap.page/api/v1/software/docker-desktop.json" }, { "id": "cua", "name": "cua", "record": "https://aimap.page/api/v1/software/cua.json" }, { "id": "browseros", "name": "BrowserOS", "record": "https://aimap.page/api/v1/software/browseros.json" } ], "newsTopics": [], "sources": [] }, { "id": "agent-evaluation", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "developer", "researcher" ], "title": { "en": "Evaluating Agents: SWE-bench, OSWorld and Your Own Tasks", "zh-hans": "Agent 评估：SWE-bench、OSWorld 与自建任务" }, "summary": { "en": "How agent benchmarks work, what their failure analyses reveal and how to test your agent.", "zh-hans": "agent 基准怎么设计、失败分析说明了什么、怎样测自己的 agent。" }, "outline": { "en": [ "SWE-bench family", "OSWorld and computer use", "τ-bench and tool use", "Trace-based evaluation" ], "zh-hans": [ "SWE-bench 系列", "OSWorld 与电脑操作", "τ-bench 与工具调用", "基于轨迹的评估" ] }, "pages": { "en": "https://aimap.page/en/learn/agents/agent-evaluation/", "zh-hans": "https://aimap.page/zh-hans/learn/agents/agent-evaluation/" }, "record": null, "software": [], "newsTopics": [ "evaluation", "agents" ], "sources": [] } ] }, { "id": "multimodal", "stage": "build", "title": { "en": "Multimodal AI", "zh-hans": "多模态 AI" }, "description": { "en": "Models that see, hear, speak and generate images and video.", "zh-hans": "能看、能听、能说，还能生成图像和视频的模型。" }, "pages": { "en": "https://aimap.page/en/learn/multimodal/", "zh-hans": "https://aimap.page/zh-hans/learn/multimodal/" }, "lessons": [ { "id": "vision-language", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "developer", "researcher" ], "title": { "en": "How Vision-Language Models See", "zh-hans": "视觉语言模型如何“看图”" }, "summary": { "en": "Image encoders, patches, projection layers and multimodal training.", "zh-hans": "图像编码器、图块、投影层与多模态训练。" }, "outline": { "en": [ "ViT and image patches", "CLIP-style contrastive learning", "Connecting vision to an LLM", "OCR, charts and screens" ], "zh-hans": [ "ViT 与图块", "CLIP 式对比学习", "把视觉接入大模型", "OCR、图表与屏幕理解" ] }, "pages": { "en": "https://aimap.page/en/learn/multimodal/vision-language/", "zh-hans": "https://aimap.page/zh-hans/learn/multimodal/vision-language/" }, "record": null, "software": [], "newsTopics": [ "multimodal" ], "sources": [] }, { "id": "diffusion-models", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "Diffusion Models for Images and Video", "zh-hans": "扩散模型：图像与视频生成" }, "summary": { "en": "Noise, denoising, latent diffusion, guidance and video models.", "zh-hans": "加噪、去噪、潜空间扩散、引导与视频模型。" }, "outline": { "en": [ "Forward and reverse diffusion", "Latent diffusion", "Classifier-free guidance", "Diffusion transformers and video" ], "zh-hans": [ "前向与反向扩散", "潜空间扩散", "无分类器引导", "扩散 Transformer 与视频" ] }, "pages": { "en": "https://aimap.page/en/learn/multimodal/diffusion-models/", "zh-hans": "https://aimap.page/zh-hans/learn/multimodal/diffusion-models/" }, "record": null, "software": [ { "id": "invokeai", "name": "InvokeAI", "record": "https://aimap.page/api/v1/software/invokeai.json" } ], "newsTopics": [ "multimodal" ], "sources": [] }, { "id": "speech-audio", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer" ], "title": { "en": "Speech Recognition and Speech Synthesis", "zh-hans": "语音识别与语音合成" }, "summary": { "en": "How Whisper-style ASR and modern TTS work, and real-time voice agents.", "zh-hans": "Whisper 类语音识别、现代语音合成，以及实时语音 agent。" }, "outline": { "en": [ "Audio as tokens", "Whisper and ASR", "Neural TTS", "Real-time voice agents" ], "zh-hans": [ "把音频变成 token", "Whisper 与语音识别", "神经语音合成", "实时语音 agent" ] }, "pages": { "en": "https://aimap.page/en/learn/multimodal/speech-audio/", "zh-hans": "https://aimap.page/zh-hans/learn/multimodal/speech-audio/" }, "record": null, "software": [ { "id": "superwhisper", "name": "superwhisper", "record": "https://aimap.page/api/v1/software/superwhisper.json" } ], "newsTopics": [], "sources": [] } ] }, { "id": "evaluation", "stage": "mastery", "title": { "en": "Evaluation & Benchmarks", "zh-hans": "评测与基准" }, "description": { "en": "How to measure models and AI systems honestly — and how benchmarks mislead.", "zh-hans": "怎样诚实地衡量模型和 AI 系统，以及基准为什么会误导。" }, "pages": { "en": "https://aimap.page/en/learn/evaluation/", "zh-hans": "https://aimap.page/zh-hans/learn/evaluation/" }, "lessons": [ { "id": "llm-evaluation", "status": "published", "level": 2, "minutes": 20, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "How to Evaluate LLMs: Benchmarks, Arenas and Contamination", "zh-hans": "怎样评测大模型：基准、竞技场与数据污染" }, "summary": { "en": "What MMLU, SWE-bench and Chatbot Arena measure, and how to read leaderboards critically.", "zh-hans": "MMLU、SWE-bench、Chatbot Arena 各自测什么，以及如何批判地看排行榜。" }, "pages": { "en": "https://aimap.page/en/learn/evaluation/llm-evaluation/", "zh-hans": "https://aimap.page/zh-hans/learn/evaluation/llm-evaluation/" }, "record": "https://aimap.page/api/learn/llm-evaluation.json", "software": [], "newsTopics": [ "evaluation" ], "sources": [ { "title": "Hendrycks et al. (2020), Measuring Massive Multitask Language Understanding (MMLU)", "url": "https://arxiv.org/abs/2009.03300" }, { "title": "Rein et al. (2023), GPQA: A Graduate-Level Google-Proof Q&A Benchmark", "url": "https://arxiv.org/abs/2311.12022" }, { "title": "Chen et al. (2021), Evaluating Large Language Models Trained on Code (HumanEval)", "url": "https://arxiv.org/abs/2107.03374" }, { "title": "Jimenez et al. (2023), SWE-bench", "url": "https://arxiv.org/abs/2310.06770" }, { "title": "OpenAI (2024), Introducing SWE-bench Verified", "url": "https://openai.com/index/introducing-swe-bench-verified/" }, { "title": "Xie et al. (2024), OSWorld: Benchmarking Multimodal Agents for Open-Ended Tasks in Real Computer Environments", "url": "https://arxiv.org/abs/2404.07972" }, { "title": "Chiang et al. (2024), Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference", "url": "https://arxiv.org/abs/2403.04132" }, { "title": "Zheng et al. (2023), Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena", "url": "https://arxiv.org/abs/2306.05685" }, { "title": "Liang et al. (2022), Holistic Evaluation of Language Models (HELM)", "url": "https://arxiv.org/abs/2211.09110" }, { "title": "Phan et al. (2025), Humanity's Last Exam", "url": "https://arxiv.org/abs/2501.14249" }, { "title": "Singh et al. (2025), The Leaderboard Illusion", "url": "https://arxiv.org/abs/2504.20879" } ] }, { "id": "build-evals", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "developer" ], "title": { "en": "Building Evals for Your Own AI App", "zh-hans": "为自己的 AI 应用搭建评测" }, "summary": { "en": "Golden sets, LLM-as-judge, regression tests and online monitoring.", "zh-hans": "黄金测试集、大模型裁判、回归测试与线上监控。" }, "outline": { "en": [ "Defining success criteria", "Golden datasets", "LLM-as-a-judge and its biases", "Regression and online evals" ], "zh-hans": [ "定义成功标准", "黄金数据集", "大模型裁判及其偏差", "回归测试与线上评测" ] }, "pages": { "en": "https://aimap.page/en/learn/evaluation/build-evals/", "zh-hans": "https://aimap.page/zh-hans/learn/evaluation/build-evals/" }, "record": null, "software": [], "newsTopics": [], "sources": [] } ] }, { "id": "safety", "stage": "mastery", "title": { "en": "Safety, Security & Governance", "zh-hans": "安全与治理" }, "description": { "en": "Prompt injection, misuse, alignment risks and the rules being written for AI.", "zh-hans": "提示注入、滥用、对齐风险，以及正在成形的 AI 规则。" }, "pages": { "en": "https://aimap.page/en/learn/safety/", "zh-hans": "https://aimap.page/zh-hans/learn/safety/" }, "lessons": [ { "id": "prompt-injection", "status": "published", "level": 2, "minutes": 20, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "Prompt Injection and Tool Poisoning", "zh-hans": "提示注入与工具投毒" }, "summary": { "en": "Why agents that read untrusted content can be hijacked, and the defences that work.", "zh-hans": "读取不可信内容的 agent 为什么会被劫持，以及有效的防御办法。" }, "pages": { "en": "https://aimap.page/en/learn/safety/prompt-injection/", "zh-hans": "https://aimap.page/zh-hans/learn/safety/prompt-injection/" }, "record": "https://aimap.page/api/learn/prompt-injection.json", "software": [ { "id": "docker-desktop", "name": "Docker Desktop", "record": "https://aimap.page/api/v1/software/docker-desktop.json" } ], "newsTopics": [ "safety", "mcp" ], "sources": [ { "title": "Greshake et al. (2023), Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection", "url": "https://arxiv.org/abs/2302.12173" }, { "title": "OWASP GenAI Security Project, LLM01:2025 Prompt Injection", "url": "https://genai.owasp.org/llmrisk/llm01-prompt-injection/" }, { "title": "Simon Willison (2025), The lethal trifecta for AI agents", "url": "https://simonwillison.net/2025/Jun/16/the-lethal-trifecta/" }, { "title": "Invariant Labs (2025), MCP Security Notification: Tool Poisoning Attacks", "url": "https://invariantlabs.ai/blog/mcp-security-notification-tool-poisoning-attacks" }, { "title": "Model Context Protocol, Security Best Practices", "url": "https://modelcontextprotocol.io/specification/draft/basic/security_best_practices" }, { "title": "Debenedetti et al. (2025), Defeating Prompt Injections by Design (CaMeL)", "url": "https://arxiv.org/abs/2503.18813" }, { "title": "Debenedetti et al. (2024), AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents", "url": "https://arxiv.org/abs/2406.13352" }, { "title": "Hines et al. (2024), Defending Against Indirect Prompt Injection Attacks With Spotlighting", "url": "https://arxiv.org/abs/2403.14720" } ] }, { "id": "ai-governance", "status": "outline", "level": 2, "minutes": 20, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "AI Governance and Regulation", "zh-hans": "AI 治理与法规" }, "summary": { "en": "EU AI Act, China's generative-AI measures, NIST AI RMF and what they require.", "zh-hans": "欧盟 AI 法案、中国生成式 AI 管理办法、NIST AI RMF 及其要求。" }, "outline": { "en": [ "EU AI Act risk tiers", "China's Interim Measures for Generative AI", "NIST AI RMF and ISO/IEC 42001", "Practical compliance checklist" ], "zh-hans": [ "欧盟 AI 法案的风险分级", "中国《生成式人工智能服务管理暂行办法》", "NIST AI RMF 与 ISO/IEC 42001", "落地合规清单" ] }, "pages": { "en": "https://aimap.page/en/learn/safety/ai-governance/", "zh-hans": "https://aimap.page/zh-hans/learn/safety/ai-governance/" }, "record": null, "software": [], "newsTopics": [ "safety" ], "sources": [] }, { "id": "frontier-risks", "status": "outline", "level": 4, "minutes": 20, "audiences": [ "researcher" ], "title": { "en": "Alignment and Frontier Risks", "zh-hans": "对齐与前沿风险" }, "summary": { "en": "Specification gaming, deception evaluations and frontier safety frameworks.", "zh-hans": "规格博弈、欺骗评估与前沿安全框架。" }, "outline": { "en": [ "Specification gaming", "Dangerous-capability evaluations", "Responsible scaling policies", "Open problems" ], "zh-hans": [ "规格博弈", "危险能力评估", "负责任扩展政策", "开放问题" ] }, "pages": { "en": "https://aimap.page/en/learn/safety/frontier-risks/", "zh-hans": "https://aimap.page/zh-hans/learn/safety/frontier-risks/" }, "record": null, "software": [], "newsTopics": [ "safety" ], "sources": [] } ] }, { "id": "compute", "stage": "mastery", "title": { "en": "Compute & Hardware", "zh-hans": "算力与硬件" }, "description": { "en": "GPUs, memory bandwidth, clusters and the chips that power AI.", "zh-hans": "GPU、显存带宽、集群，以及驱动 AI 的芯片。" }, "pages": { "en": "https://aimap.page/en/learn/compute/", "zh-hans": "https://aimap.page/zh-hans/learn/compute/" }, "lessons": [ { "id": "gpu-basics", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "GPUs, Memory and FLOPs: Why AI Needs Them", "zh-hans": "GPU、显存与算力：AI 为什么需要它们" }, "summary": { "en": "Matrix maths, memory bandwidth and why inference is often memory-bound.", "zh-hans": "矩阵运算、显存带宽，以及推理为什么常常卡在内存上。" }, "outline": { "en": [ "Parallel matrix multiplication", "FLOPs vs memory bandwidth", "Unified memory on Apple silicon", "Estimating hardware needs" ], "zh-hans": [ "并行矩阵乘法", "算力与显存带宽", "Apple 芯片的统一内存", "估算硬件需求" ] }, "pages": { "en": "https://aimap.page/en/learn/compute/gpu-basics/", "zh-hans": "https://aimap.page/zh-hans/learn/compute/gpu-basics/" }, "record": null, "software": [], "newsTopics": [ "hardware" ], "sources": [] }, { "id": "distributed-training", "status": "outline", "level": 4, "minutes": 20, "audiences": [ "researcher" ], "title": { "en": "Distributed Training and Clusters", "zh-hans": "分布式训练与集群" }, "summary": { "en": "Data, tensor and pipeline parallelism, interconnects and failures at scale.", "zh-hans": "数据并行、张量并行、流水线并行、互联与大规模故障。" }, "outline": { "en": [ "Data parallelism and ZeRO", "Tensor and pipeline parallelism", "Interconnects", "Checkpointing and failures" ], "zh-hans": [ "数据并行与 ZeRO", "张量并行与流水线并行", "互联网络", "检查点与故障" ] }, "pages": { "en": "https://aimap.page/en/learn/compute/distributed-training/", "zh-hans": "https://aimap.page/zh-hans/learn/compute/distributed-training/" }, "record": null, "software": [], "newsTopics": [ "hardware" ], "sources": [] }, { "id": "ai-chips", "status": "outline", "level": 3, "minutes": 15, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "The AI Chip Landscape", "zh-hans": "AI 芯片格局" }, "summary": { "en": "GPUs, TPUs, custom accelerators and export controls.", "zh-hans": "GPU、TPU、定制加速器与出口管制。" }, "outline": { "en": [ "NVIDIA, AMD and the CUDA moat", "TPUs and custom ASICs", "Chinese AI chips", "Export controls" ], "zh-hans": [ "NVIDIA、AMD 与 CUDA 护城河", "TPU 与定制芯片", "国产 AI 芯片", "出口管制" ] }, "pages": { "en": "https://aimap.page/en/learn/compute/ai-chips/", "zh-hans": "https://aimap.page/zh-hans/learn/compute/ai-chips/" }, "record": null, "software": [], "newsTopics": [ "hardware" ], "sources": [] } ] }, { "id": "ai-industry", "stage": "mastery", "title": { "en": "AI Across Industries", "zh-hans": "AI + 行业应用" }, "description": { "en": "AI in science, finance, health, education and software development.", "zh-hans": "AI 在科研、金融、医疗、教育和软件开发中的应用。" }, "pages": { "en": "https://aimap.page/en/learn/ai-industry/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-industry/" }, "lessons": [ { "id": "ai-for-science", "status": "outline", "level": 3, "minutes": 20, "audiences": [ "beginner", "researcher" ], "title": { "en": "AI for Science: Maths, Biology, Materials", "zh-hans": "AI for Science：数学、生物与材料" }, "summary": { "en": "Protein structure, AI-assisted proofs and materials discovery.", "zh-hans": "蛋白质结构、AI 辅助证明与材料发现。" }, "outline": { "en": [ "AlphaFold and protein design", "AI and mathematical proof", "Materials and chemistry", "Autonomous labs" ], "zh-hans": [ "AlphaFold 与蛋白质设计", "AI 与数学证明", "材料与化学", "自动化实验室" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-industry/ai-for-science/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-industry/ai-for-science/" }, "record": null, "software": [], "newsTopics": [ "science" ], "sources": [] }, { "id": "ai-in-finance", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer" ], "title": { "en": "AI in Finance", "zh-hans": "AI + 金融" }, "summary": { "en": "Research assistants, risk, fraud detection and regulatory limits.", "zh-hans": "投研助手、风控、反欺诈与监管限制。" }, "outline": { "en": [ "Research and analysis", "Risk and fraud", "Trading and its limits", "Model risk management" ], "zh-hans": [ "投研与分析", "风控与反欺诈", "交易及其局限", "模型风险管理" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-industry/ai-in-finance/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-industry/ai-in-finance/" }, "record": null, "software": [], "newsTopics": [ "finance" ], "sources": [] }, { "id": "ai-in-healthcare", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "researcher" ], "title": { "en": "AI in Healthcare", "zh-hans": "AI + 医疗" }, "summary": { "en": "Medical imaging, clinical documentation, drug discovery and evidence standards.", "zh-hans": "医学影像、病历文书、药物研发与证据标准。" }, "outline": { "en": [ "Imaging", "Clinical documentation", "Drug discovery", "Regulation and evidence" ], "zh-hans": [ "医学影像", "病历与文书", "药物研发", "监管与证据" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-industry/ai-in-healthcare/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-industry/ai-in-healthcare/" }, "record": null, "software": [], "newsTopics": [ "science" ], "sources": [] }, { "id": "ai-in-education", "status": "outline", "level": 1, "minutes": 15, "audiences": [ "beginner" ], "title": { "en": "AI in Education", "zh-hans": "AI + 教育" }, "summary": { "en": "Tutors, feedback, academic integrity and teaching with AI.", "zh-hans": "AI 家教、作业反馈、学术诚信与用 AI 教学。" }, "outline": { "en": [ "AI tutors", "Feedback and grading", "Integrity and detection limits", "Learning with AI effectively" ], "zh-hans": [ "AI 家教", "反馈与批改", "学术诚信与检测的局限", "如何高效地用 AI 学习" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-industry/ai-in-education/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-industry/ai-in-education/" }, "record": null, "software": [ { "id": "obsidian", "name": "Obsidian", "record": "https://aimap.page/api/v1/software/obsidian.json" } ], "newsTopics": [], "sources": [] }, { "id": "ai-coding", "status": "outline", "level": 2, "minutes": 20, "audiences": [ "beginner", "developer" ], "title": { "en": "AI for Software Development", "zh-hans": "AI 编程" }, "summary": { "en": "From autocomplete to coding agents: workflows, review and limits.", "zh-hans": "从代码补全到编程 agent：工作流、审查与局限。" }, "outline": { "en": [ "Autocomplete vs agents", "Spec, test and review loops", "Repository context", "Security and licence issues" ], "zh-hans": [ "补全与 agent", "规格、测试与审查循环", "仓库上下文", "安全与许可证问题" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-industry/ai-coding/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-industry/ai-coding/" }, "record": null, "software": [ { "id": "claude-code", "name": "Claude Code", "record": "https://aimap.page/api/v1/software/claude-code.json" }, { "id": "codex", "name": "OpenAI Codex", "record": "https://aimap.page/api/v1/software/codex.json" }, { "id": "cursor", "name": "Cursor", "record": "https://aimap.page/api/v1/software/cursor.json" }, { "id": "github-copilot", "name": "GitHub Copilot", "record": "https://aimap.page/api/v1/software/github-copilot.json" }, { "id": "aider", "name": "aider", "record": "https://aimap.page/api/v1/software/aider.json" }, { "id": "zed", "name": "Zed", "record": "https://aimap.page/api/v1/software/zed.json" } ], "newsTopics": [ "coding" ], "sources": [] } ] }, { "id": "ai-business", "stage": "mastery", "title": { "en": "AI Products & Business", "zh-hans": "AI 产品与商业" }, "description": { "en": "Designing AI products, unit economics, open vs closed models and go-to-market.", "zh-hans": "AI 产品设计、成本结构、开源与闭源选型，以及商业化。" }, "pages": { "en": "https://aimap.page/en/learn/ai-business/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-business/" }, "lessons": [ { "id": "ai-product-design", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer" ], "title": { "en": "Designing AI Products", "zh-hans": "AI 产品设计" }, "summary": { "en": "Choosing problems, handling errors, trust and human-in-the-loop UX.", "zh-hans": "选题、错误处理、信任与人在回路的交互设计。" }, "outline": { "en": [ "Picking AI-shaped problems", "Designing for errors", "Trust, citations and control", "Human-in-the-loop" ], "zh-hans": [ "挑选适合 AI 的问题", "为出错而设计", "信任、引用与可控", "人在回路" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-business/ai-product-design/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-business/ai-product-design/" }, "record": null, "software": [], "newsTopics": [], "sources": [] }, { "id": "ai-business-models", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer" ], "title": { "en": "AI Business Models and Unit Economics", "zh-hans": "AI 商业模式与成本结构" }, "summary": { "en": "Token costs, pricing, margins and where value accrues in the AI stack.", "zh-hans": "Token 成本、定价、毛利，以及 AI 产业链中价值落在哪里。" }, "outline": { "en": [ "Cost per task", "Seat vs usage pricing", "Margins and model price drops", "Where value accrues" ], "zh-hans": [ "单任务成本", "按席位还是按用量定价", "毛利与模型降价", "价值落在哪一层" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-business/ai-business-models/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-business/ai-business-models/" }, "record": null, "software": [], "newsTopics": [ "business" ], "sources": [] }, { "id": "open-vs-closed", "status": "outline", "level": 2, "minutes": 15, "audiences": [ "beginner", "developer", "researcher" ], "title": { "en": "Open vs Closed Models: Licences and Choosing", "zh-hans": "开源与闭源模型：许可证与选型" }, "summary": { "en": "Open weights vs open source, licences and a practical selection checklist.", "zh-hans": "开放权重与开源的区别、许可证，以及实用的选型清单。" }, "outline": { "en": [ "Open weights vs open source", "Common model licences", "Self-hosting trade-offs", "Selection checklist" ], "zh-hans": [ "开放权重与开源", "常见模型许可证", "自托管的取舍", "选型清单" ] }, "pages": { "en": "https://aimap.page/en/learn/ai-business/open-vs-closed/", "zh-hans": "https://aimap.page/zh-hans/learn/ai-business/open-vs-closed/" }, "record": null, "software": [ { "id": "ollama", "name": "Ollama", "record": "https://aimap.page/api/v1/software/ollama.json" }, { "id": "lm-studio", "name": "LM Studio", "record": "https://aimap.page/api/v1/software/lm-studio.json" }, { "id": "jan", "name": "jan", "record": "https://aimap.page/api/v1/software/jan.json" } ], "newsTopics": [ "llm" ], "sources": [] } ] } ], "counts": { "lessons": 55, "published": 13, "modules": 15 } }