Trading 🌐 Macro Hedge 🎯 A mix of all Show me the answer → 🏆 All 8 AIs gave the exact same answer — independently Claude · ChatGPT · Gemini · Grok · DeepSeek · Kimi · Qianwen · Doubao — without exception * Every AI was queried independently, without seeing each other's answers. Zero disagreement across 8 different models. This level of consensus is extremely rare. 📖 Experiment 1 Data — Read full experiment → 8 AIs were each asked independently: "Which investing philosophy should an ordinary person choose?" Every single one said Value Investing. None chose growth, index, or trend as their top answer. Their reasoning converged on one insight: value investing is the only system that turns a retail investor's biggest "weakness" into a strength — retail investors can't compete on speed or data, but they have one edge institutions don't: unlimited time horizon. Patience is the core weapon. "DeepSeek: AI is best used as an information pickaxe, not a crystal ball. Use it for 80% of information processing and cross-referencing — not predicting the 20% that requires reading people." "Claude: The only resource retail investors have that institutions don't is time horizon — value investing is the only system that turns this into an advantage." "ChatGPT: A system built on patience, research discipline, and emotional control aligns perfectly with the tools AI can actually provide — systematic information, not market timing." "Doubao: The retail investor's greatest advantage isn't speed, it's freedom — no quarterly earnings pressure, no redemption risk, no career risk. Value investing is the only school that exploits this fully." * Your turn Which path do you want to explore? All 8 AIs recommended value investing — but the final choice is yours. Pick the path that genuinely interests you. We'll generate a personalized Prompt Library tailored to your exact answers. 🦅 Value Investing Warren Buffett e.g. KO · AAPL · BRK.B 🌱 Growth Investing Philip Fisher e.g. NVDA · AMZN · SHOP 📊 Index Investing John Bogle e.g. SPY · VOO · VTI 📈 Trend Trading Jesse Livermore e.g. BTC · momentum plays 🌐 Macro Hedge George Soros · Ray Dalio e.g. GLD · TLT · oil 🎯 Core-Satellite Bogle + Buffett hybrid 80% index · 20% active bets ⚙️ I Have My Own System Self-defined framework Your own rules, edge, and decision-making process — we'll help you document and sharpen it Continue to my questionnaire → ← Back Question 3 of 4 — Your profile Tell me about yourself Answer honestly — the more accurate, the more useful your AI prompt will be. ← Back Generate My Investment OS → AI Analysis Engine — Running 0% 🏛️ Your AI Investment Committee 8 leading AIs independently reviewed your profile. Here are their verdicts. View Full Committee Report → 🦅 📋 Your Answers — Fit Analysis * 🚀 Your Personalized Prompt Library Choose a use case, copy the prompt (pre-filled with your answers), paste into any AI and start immediately. 💡 Tip: The Core-Satellite prompt is long — paste the whole thing into a new conversation so the AI reads it completely before you start. 📋 Copy Prompt↗ Copy + Open Claude↗ Copy + Open ChatGPT 🔒 Privacy tip: The copied prompt includes our most complete financial context template — delete any lines you'd rather not share, nothing is required. Fill it in directly in your AI chat, not here. We never see or store your conversation. Prompts are personalized based on your answers + the AI Investing Experiment Series — 7 articles · 8 AIs · 56 experiment answers. 📖 Read the story
behind this tool → Browse all AI investing tools → 🧪 Decision Lab — 6 Calculation Tools Each tool is a full decision engine: calculate → AI explain → visualize → decide. All 5 schools + 8 AI committee inside every tool. Tool #1 📊 DCA Strategy Lab Smile Curve · Compound sim · 5-school verdict #2 🔴 Recovery Planner Hold / Add / Cut · Opportunity cost · Sunk cost reset #3 🎯 Kelly Lab Win rate × Odds → Optimal size · Risk of Ruin #4 💚 Dividend Engine Freedom Date · DRIP · YoC · FCF safety check #5 🔺 Pyramid Builder Buy-down / buy-up · Risk budget · Stress test #6 🩺 Portfolio Doctor N-effective · Fake diversification · Black swan test Also available in Chinese — 中文版入口 → 🔗 Share Your Result 👆 Tap to select, then copy manually ✏️ Edit my answers🔄 Start Over ⚠️ For educational purposes only. Not financial advice. Investing involves risk — always do your own research.
华为云在泰国发布Agentic基础设施,启动代码智能体公测
华为云在泰国举办的技术峰会上,正式发布了其面向AI原生时代的Agentic基础设施方案,并同步启动了代码智能体(Code Agent)的公测。该基础设施旨在为企业和开发者提供更高效、智能的算力与开发环境,支持大模型应用的快速构建与部署。代码智能体作为核心组件,能够通过自然语言交互辅助编程、代码审查及故障排查,显著提升开发效率。此次在泰国的发布标志着华为云加速拓展东南亚市场,推动区域数字化转型。华为云表示,Agentic基础设施将结合本地需求,为泰国及周边国家的企业提供定制化AI解决方案,助力行业智能化升级。 #华为云 #Agentic基础设施 #代码智能体 #泰国 #AI #云计算 #数字化转型 #科技新闻
华为云在泰国举办的技术峰会上,正式发布了其面向AI原生时代的Agentic基础设施方案,并同步启动了代码智能体(Code Agent)的公测。该基础设施旨在为企业和开发者提供更高效、智能的算力与开发环境,支持大模型应用的快速构建与部署。代码智能体作为核心组件,能够通过自然语言交互辅助编程、代码审查及故障排查,显著提升开发效率。此次在泰国的发布标志着华为云加速拓展东南亚市场,推动区域数字化转型。华为云表示,Agentic基础设施将结合本地需求,为泰国及周边国家的企业提供定制化AI解决方案,助力行业智能化升级。 #华为云 #Agentic基础设施 #代码智能体 #泰国 #AI #云计算 #数字化转型 #科技新闻
Synth historian Oli Freke will spend big on a good bicycle
Oli Freke is a musician and journalist whose works have appeared in Sound on Sound , The Quietus , and Mixmag . This has included using math to explore the melodic potential of the Western 12-tone scale and deep dives on effects plug-ins . He’s even written a book tracing the evolution of the synthesizer from 1963 through 1995, charting the change from analog to digital and back.
Oli Freke is a musician and journalist whose works have appeared in Sound on Sound , The Quietus , and Mixmag . This has included using math to explore the melodic potential of the Western 12-tone scale and deep dives on effects plug-ins . He’s even written a book tracing the evolution of the synthesizer from 1963 through 1995, charting the change from analog to digital and back.
内存昂贵时如何优化向量搜索
随着RAG、语义搜索和智能体系统的发展,向量搜索成为AI基础设施的关键。当索引规模从百万级增长到亿级甚至十亿级时,完全将索引存储在RAM中每月成本高达数千美元,HNSW算法成为可扩展性的瓶颈。本文深入探讨了近似最近邻(ANN)算法的原理、不同实现及其权衡。向量数据库主要由嵌入、存储和搜索算法三部分组成。精确搜索(kNN)遍历所有条目,适合小规模数据,但无法应对生产规模。而ANN算法通过图结构(如HNSW和DiskANN)提供捷径来加速搜索,但需根据存储介质选择优化方向:HNSW将索引全存于内存,速度极快但成本高;DiskANN则针对磁盘存储设计,在大规模场景下大幅降低成本,并利用量化、剪枝等优化技术保持相近的搜索精度和召回率。 #向量搜索 #ANN #HNSW #DiskANN #AI基础设施 #数据库优化
随着RAG、语义搜索和智能体系统的发展,向量搜索成为AI基础设施的关键。当索引规模从百万级增长到亿级甚至十亿级时,完全将索引存储在RAM中每月成本高达数千美元,HNSW算法成为可扩展性的瓶颈。本文深入探讨了近似最近邻(ANN)算法的原理、不同实现及其权衡。向量数据库主要由嵌入、存储和搜索算法三部分组成。精确搜索(kNN)遍历所有条目,适合小规模数据,但无法应对生产规模。而ANN算法通过图结构(如HNSW和DiskANN)提供捷径来加速搜索,但需根据存储介质选择优化方向:HNSW将索引全存于内存,速度极快但成本高;DiskANN则针对磁盘存储设计,在大规模场景下大幅降低成本,并利用量化、剪枝等优化技术保持相近的搜索精度和召回率。 #向量搜索 #ANN #HNSW #DiskANN #AI基础设施 #数据库优化