1. 我强烈建议尽快建立你的Prompt Collection（提示词库）。Prompt Collection就是将你常用的Prompt梳理、整理起来。有点像游戏里面，你把卡牌/武器收 集起来，遇到不同敌人使用不同的卡牌/武器。建立Prompt Collection，一方面能提高你的效率，另一方面可以基于此不断优化你的提示词库。 2. Bonus：如果你发现你的提示词库已经很久没更新，要反思一下为什么。因为这往往意味着你很久没有探索新的领域了。 3. 定期优化ChatGPT的记忆。我之前的笔记说过，Context（上下文）是决定AI回答质量最重要的因素之一。ChatGPT的记忆（memory）就是默认的上下 文。就像我们应该定期体检一样，我们应该定期去调整、更正ChatGPT对我们的记忆。 4. 遇犹豫不决之事，找一段空闲的时间，从一个安静的地方，走到另一个安静的地方，保证路程超过30分钟，期间使用ChatGPT语音功能，和它讨论30分 钟，有奇效。 本文来自微信公众号：Luis Lens，作者：Luis（双币VC投资人），题图来自：AI生成 我应该算是ChatGPT使用时间最长的用户之一了。ChatGPT的 ...

[Music] What does it mean to stand out? Typically, I was supposed to show you a slide that shows an apple, right? And that apple has a particular color. What's the generic color for apple in Nigeria? Thank you very much. You guys are helping me because what I was supposed to say was, "What is this color. " And you say green. I've practiced. Now green every one of you understands that it's green that's the conventional color so if I put the apple here you'd have all said green same color but if you look at t ...

Storytelling & Context - Stories are more than just content; they provide context and emotional connection, unlike mere data or statistics [3][4] - Understanding the context of stories is crucial for empathy and deeper understanding, moving beyond surface-level information [4] - Sharing personal experiences and emotions can create connections and transform data into relatable human experiences [3] Pandemic Experiences - The pandemic highlighted the importance of family and personal struggles alongside broader health crises [2] - Lockdown and COVID-19 brought about mental health challenges, economic disruptions, and changes in daily life [3] - Many individuals experienced similar feelings of isolation, anxiety, and disruption during the pandemic [2][3] Personal Growth & Resilience - Ordinary individuals can become heroes within their families and communities by facing challenges and supporting others [2] - People realized the importance of understanding individual journeys and struggles, such as postpartum depression or personal exploration [4] - Everyone is the real hero of their own stories, emphasizing the value of personal narratives and experiences [4]

Graph RAG Overview - Graph RAG aims to enhance LLMs by incorporating knowledge graphs, addressing limitations like lack of domain knowledge, unverifiable answers, hallucinations, and biases [1][3][4][5][9][10] - Graph RAG leverages knowledge graphs (collections of nodes, relationships, and properties) to provide more relevant, contextual, and explainable results compared to basic RAG systems using vector databases [8][9][10][12][13][14] - Microsoft research indicates Graph RAG can achieve better results with lower token costs, supported by studies showing improvements in capabilities and analyst trends [15][16] Knowledge Graph Construction - Knowledge graph construction involves structuring unstructured information, extracting entities and relationships, and enriching the graph with algorithms [19][20][21][22] - Lexical graphs represent documents and elements (chunks, sections, paragraphs) with relationships based on document structure, temporal sequence, and similarity [25][26] - Entity extraction utilizes LLMs with graph schemas to identify entities and relationships from text, potentially integrating with existing knowledge graphs or structured data like CRM systems [27][28][29][30] - Graph algorithms (clustering, link prediction, page rank) enrich the knowledge graph, enabling cross-document topic identification and summarization [20][30][34] Graph RAG Retrieval and Applications - Graph RAG retrieval involves initial index search (vector, full text, hybrid) followed by traversing relationships to fetch additional context, considering user context for tailored results [32][33] - Modern LLMs are increasingly trained on graph processing, allowing them to effectively utilize node-relationship-node patterns provided as context [34] - Tools and libraries are available for knowledge graph construction from various sources (PDFs, YouTube transcripts, web articles), with open-source options for implementation [35][36][39][43][45] - Agentic approaches in Graph RAG break down user questions into tasks, using domain-specific retrievers and tools in sequence or loops to generate comprehensive answers and visualizations [42][44] - Industry leaders are adopting Graph RAG for production applications, such as LinkedIn's customer support, which saw a 286% reduction in median per-issue resolution time [17][18]

Core Problem in AI Coding Space - Generating code is not difficult, but generating code that fits into existing codebases, adheres to organizational policies, personal preferences, and is future-proof is challenging [13][14][15] - The magic of AI coding tools like Windsurf lies in context, specifically "what context" and "how much" [16] Windsurf's Context Philosophy - "What context" is divided into two buckets: heuristics (user behavior) and hard evidence (environment/codebase) [17][18][19] - Relevant output is determined by the prompt, the state of the codebase, and the user state [20] - Windsurf prioritizes optimizing the relevance of context over simply increasing the size of the context window to address latency [21][22] Windsurf's Capabilities - Windsurf excels at finding relevant context quickly due to its background in GPU optimization [23] - Windsurf provides connectors for users to perform context retrieval at their level, including embedding search, memories, rules, and custom workspaces [24] Data Privacy - Windsurf processes information only within the user's editor and does not access the user's operating machine [31] - Windsurf's servers are stateless, and the company does not store or train on user data [31][32]

23 年 4 月以 AutoGPT 为代表的那一波里，Agent 更像是一个玩具，demo 都很炫，但实际应用价值很有限。 经过两年的发展，这波 Agent 确实能够在实际的工作和生活场景中解决问题，为大家带来价值了。 曲凯： Agent 是当下绝对的风口。关于 Agent 这个话题，我自己有一些核心在思考的问题，相信也是很多人同样会有疑问的地方。所以今天我们请来了长时间对 Agent 有研究和实操的文锋，想就这些问题展开一些讨论。 首先我想问，到底怎么定义 Agent？ 文锋： 我认为最好的就是 Anthropic 的定义：Agent 是让模型基于环境反馈去使用工具的一个程序。 曲凯： 那你怎么看最近这波 Agent 热？ 文锋： 这波 Agent 跟过去非常不一样。 之所以会有这种跃迁，一是因为底层模型能力有了很大的进步，尤其是在结合了 RL 之后，以 o1 为代表的模型还赋予了 Agent 长思维能力。 二是因为 Agent 的工程侧和产品侧也有很大的突破，主要表现就是大家更知道该怎么给 Agent 构建一个合适的 Context，从而更好地解决问题了。 曲凯： 怎么理解这个 Context？ 文锋： ...