[{"data":1,"prerenderedAt":368},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$fXLcQG22Nfpi0r-I3X6g7fk2KapO3L_3gz9SvghlLVo4":245,"article-18":367},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"popular":19,"latest":86,"banner":126,"list":151,"cache":244},[8,9,10],"Agent","OpenAI","LLM",[8,12,13,14,9,10,15,16,17,18],"Google","Nvidia","Claude","DeepSeek","OCR","Chat","Generator",[20,29,37,45,54,62,70,79],{"id":21,"publish_date":22,"is_original":23,"collection":5,"cover_url":24,"cover_url_1_1":25,"title":26,"summary":27,"author":28},411,"2023-09-10",1,"article_res/cover/451ef50c225a8dc61c4336506794d13b.jpeg","article_res/cover/3ba9dc7a72f87d40b20fc2d225289ee3.jpeg","Idealism","Reality is created by the mind, we can change our reality by changing our mind. - Plato","Renee's Entrepreneurial Journey",{"id":30,"publish_date":31,"is_original":23,"collection":32,"cover_url":33,"cover_url_1_1":34,"title":35,"summary":36,"author":28},108,"2024-12-07","#LLM #AGI #AI Agent","article_res/cover/0039044422e4ec9f61c18e8ee1693bb0.jpeg","article_res/cover/4220971b108a91d21407d87bb02fbaa6.jpeg","Freysa.ai: The World's First Adversarial AI Agent Game","说服 Freysa 把钱包里的钱都拿出来",{"id":38,"publish_date":39,"is_original":23,"collection":40,"cover_url":41,"cover_url_1_1":42,"title":43,"summary":44,"author":28},12,"2025-03-09","#Oxford #Reasoning #LLM #Tool Use","article_res/cover/d448e9b3617a0b5302e1bd10c438bca9.jpeg","article_res/cover/864a468f9cc4c9317efadb3811909888.jpeg","Agentic Reasoning Framework - Significantly enhance the reasoning ability of LLMs through the integration of external tools using agents","Agentic Reasoning: Reasoning LLMs with Tools for Deep Research",{"id":46,"publish_date":47,"is_original":4,"collection":48,"cover_url":49,"cover_url_1_1":50,"title":51,"summary":52,"author":53},480,"2023-04-14","#Stable Diffusion","article_res/cover/0bdbe7cb1de4a78e54536e5d9afa7ec9.jpeg","article_res/cover/b3d6ffec0608dcfaf18c5a69906d1490.jpeg","【AIGC Learning】Generate Prompts Using Word Graphs - Stable Diffusion Web UI Series 13","AI will become a powerful tool in education, transforming the way we learn and deliver instruction.  \n- Reid Hoffman","--",{"id":55,"publish_date":56,"is_original":4,"collection":57,"cover_url":58,"cover_url_1_1":59,"title":60,"summary":61,"author":28},413,"2023-09-08","#Neuroscience","article_res/cover/74f8302d78a23d9430f22171eae136b6.jpeg","article_res/cover/87ca08af81bb304746be5261160964c0.jpeg","Can machines be conscious?","Do we have an ethical obligation to not turn off conscious machines? Would turning them off be murder? No. I don't lose any sleep over unplugging a conscious machine.\n- Jeff Hawkins, \"A Thousand Brains\"",{"id":63,"publish_date":64,"is_original":23,"collection":65,"cover_url":66,"cover_url_1_1":67,"title":68,"summary":69,"author":28},178,"2024-09-09","#Entrepreneurship","article_res/cover/a7224f025b55d1820408085faef63079.jpeg","article_res/cover/11a9995b096cbf64465ef01b8673b154.jpeg","37signals company","This damn sense of relaxation",{"id":71,"publish_date":72,"is_original":4,"collection":73,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":78},460,"2023-05-12","#Google","article_res/cover/b970687b12faa52da976f91248c2aa7b.jpeg","article_res/cover/d1e71b52cfd2c63bc6e71f3e85ff135c.jpeg","Learn what BRC-20 and Ordinals are using Google Bard","Ordinals - a new protocol that allows users to store arbitrary data on the Bitcoin blockchain","Google Bard mainly writes",{"id":80,"publish_date":81,"is_original":23,"collection":5,"cover_url":82,"cover_url_1_1":83,"title":84,"summary":85,"author":28},309,"2024-03-26","article_res/cover/9877f95894ee88532d0e6012c23a2df3.jpeg","article_res/cover/20092164ddc109ce6ae56b1984246751.jpeg","Learning the Cancun Upgrade with lepton and perplexity","Building a quick conversation-based search demo with Lepton AI.",[87,95,103,111,119],{"id":88,"publish_date":89,"is_original":23,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":28},627,"2025-03-20","#AI Avatar #AI Video Generation","article_res/cover/d95481358f73924989f8c4ee9c75d1c8.jpeg","article_res/cover/b74bc0fab01f8b6a6aa87696c0c3ed8b.jpeg","DisPose: Generating Animated Videos by Driving Video with Reference Images","DisPose is a controllable human image animation method that enhances video generation.",{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},626,"2025-03-21","#Deep Dive into LLMs #LLM #RL #Andrej Karpathy #AlphaGo","article_res/cover/446553a5c8f8f2f07d97b20eaee84e56.jpeg","article_res/cover/e6c2823409c9b34624064b9acbaca6f1.jpeg","AlphaGo and the Power of Reinforcement Learning - Andrej Karpathy's Deep Dive on LLMs (Part 9)","Simply learning from humans will never surpass human capabilities.",{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},625,"2025-03-22","#Deep Dive into LLMs #LLM #RL #RLHF #Andrej Karpathy","article_res/cover/8da81d38b1e5cf558a164710fd8a5389.jpeg","article_res/cover/96f028d76c362a99a0dd56389e8f7a9b.jpeg","Reinforcement Learning from Human Feedback (RLHF) - Andrej Karpathy's Deep Dive on LLMs (Part 10)","Fine-Tuning Language Models from Human Preferences",{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},624,"2025-03-23","#Deep Dive into LLMs #LLM #Andrej Karpathy #AI Agent #MMM","article_res/cover/a5e7c3d48bb09109684d6513287c661d.jpeg","article_res/cover/d3f22b7c0ab8d82fd2da457a299e0773.jpeg","The Future of Large Language Models - Andrej Karpathy's In-Depth Explanation of LLM (Part 11)","preview of things to come",{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},623,"#Google #Voe #AI Video Generation","article_res/cover/c44062fea0f336c2b96b3928292392c2.jpeg","article_res/cover/a041041c69092ad3db191c5bf3ff981b.jpeg","Trial of Google's video generation model VOE2","Our state-of-the-art video generation model",[127,135,143],{"id":128,"publish_date":129,"is_original":23,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":28},300,"2024-04-16","#AI in Science #AGI","article_res/cover/6bf01e793e0f33e848572412eebdf9b0.jpeg","article_res/cover/91a5ee21dafecb914fabeb9430d46ec1.jpeg","Would Einstein lose his job - AI and Quantum Computing: A Glimpse into the Near Future","So Einstein's job is still safe.",{"id":136,"publish_date":137,"is_original":23,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":28},101,"2024-12-14","#Nvidia #AI 3D Generator","article_res/cover/693e07c85980c5c0c8fde3f037733f23.jpeg","article_res/cover/9ea8edff2d5d303ff3fffff3f6f9c3d9.jpeg","NVIDIA's open-source 3D project LLaMA-Mesh","LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models",{"id":144,"publish_date":145,"is_original":23,"collection":146,"cover_url":147,"cover_url_1_1":148,"title":149,"summary":150,"author":28},131,"2024-11-10","#OpenAI","article_res/cover/87f8ed353ce39f31960e7cdfaf075a35.jpeg","article_res/cover/f597a63935f5cd32e484b4aadd6019e8.jpeg","ChatGPT has launched the Search function","Get fast, timely answers with links to relevant web sources.",{"big":152,"small":214},[153,181],{"title":154,"list":155},"AGENT",[156,157,165,173],{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":158,"publish_date":159,"is_original":23,"collection":160,"cover_url":161,"cover_url_1_1":162,"title":163,"summary":164,"author":28},622,"2025-03-24","#OWL #AI Agent #MAS #MCP #CUA","article_res/cover/cb50ca7f2bf4d1ed50202d7406e1c19a.jpeg","article_res/cover/4aa7aa3badfacf3cc84121334f1050dd.jpeg","OWL: Multi-agent collaboration","OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation",{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},620,"2025-03-26","#LLM #Google #Gemini #AI Agent","article_res/cover/53751a6dbbe990b1eb0b63f3b062aed4.jpeg","article_res/cover/031344981f0a212ff82d1f3a64aa5756.jpeg","Gemini 2.5 Pro, claimed to be far ahead of the competition, has been released with great fanfare: comprehensively surpassing other LLMs and topping the global rankings","Gemini 2.5: Our most intelligent AI model",{"id":174,"publish_date":175,"is_original":23,"collection":176,"cover_url":177,"cover_url_1_1":178,"title":179,"summary":180,"author":28},616,"2025-03-29","#MAS #AI Agent #AI Coder #MetaGPT #MGX","article_res/cover/9dcd702ad2035902e5e77967c34a1f1e.jpeg","article_res/cover/0a97fc4a922753c8f46ff38792020df8.jpeg","MGX - An automated website-building platform composed of multiple AI Agents","Your 24/7 AI Team | Dream, Chat, Create.",{"title":182,"list":183},"OPENAI",[184,191,199,206],{"id":185,"publish_date":167,"is_original":23,"collection":186,"cover_url":187,"cover_url_1_1":188,"title":189,"summary":190,"author":28},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":192,"publish_date":193,"is_original":4,"collection":194,"cover_url":195,"cover_url_1_1":196,"title":197,"summary":198,"author":28},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - OpenAI",{"id":200,"publish_date":201,"is_original":4,"collection":146,"cover_url":202,"cover_url_1_1":203,"title":204,"summary":205,"author":28},417,"2023-08-24","article_res/cover/bccf897d50a88b18364e35f7466387e0.jpeg","article_res/cover/2f871085c1073717c1703ae86e18056f.jpeg","The GPT-3.5 Turbo fine-tuning (fine-tuning function) has been released～","Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.",{"id":207,"publish_date":208,"is_original":4,"collection":209,"cover_url":210,"cover_url_1_1":211,"title":212,"summary":213,"author":28},407,"2023-09-22","#OpenAI #AI Image Generator","article_res/cover/c59005e903d35cfc32346e2756e2728a.jpeg","article_res/cover/ba011d265e6d84b5c8cb6fd6b757b6cc.jpeg","Dall-E 3","DALL·E 3 understands significantly more nuance and detail, allowing you to easily translate your ideas into images.",[215,221,241],{"title":10,"list":216},[217,218,219,220],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":223},"GOOGLE",[224,225,226,234],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},615,"2025-03-30","#AI Researcher #AI Science #HKU #Google #AI Agent","article_res/cover/21fadf906067714bb0db31ae13a77c15.jpeg","article_res/cover/2697999a72bd26b22e85f0e92936d3ed.jpeg","AI-Researcher: LLM-driven全自动 scientific research assistant","AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents  \nOpen-Sourced Alternative to Google AI Co-Scientist",{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},463,"2023-05-09","article_res/cover/89800f207723acdb55fc53bf999ebdc9.jpeg","article_res/cover/5764f369b4accd8f83e94aa4c077a175.jpeg","The Smallville sandbox world - A town with 25 virtual residents","Believable proxies of human behavior can empower interactive apps: Immersive environment, Rehearsal space, Prototyping tool",{"title":242,"list":243},"NVIDIA",[],true,{"code":4,"msg":5,"data":246},{"id":247,"publish_date":248,"is_original":23,"collection":249,"articles_id":250,"cover_url":251,"cover_url_1_1":252,"title":253,"summary":254,"author":28,"content":255,"popular":256,"list":321,"category":365,"tag":366},18,"2025-03-05","#AI Stock #LLM #AI Agents","ObIh5zdF26Y2En4xP1F_Nw","article_res/cover/4ab06a737f424174b566860de6a8f152.jpeg","article_res/cover/616077e8acb0c8a79d93f4b08a9cbdd1.jpeg","FinSphere: AI Assistant for Financial Stock Analysis","FinSphere: A Conversational Stock Analysis Agent Equipped with Quantitative Tools Based on Real-Time Database","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Before AI was used for financial stock analysis, I've seen two papers:\u003C/span>\u003C/p>\u003Cul style=\"list-style-type: disc;\" class=\"list-paddingleft-1\">\u003Cli>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">\u003Ca style=\"\" href=\"https://mp.weixin.qq.com/s?__biz=MzkwOTMzMzk0MQ==&mid=2247492959&idx=1&sn=89d90dce1090c2964238df2c8e2f12d8&scene=21#wechat_redirect\" textvalue=\"年化50% 的 AI 炒股 TradeExpert\" data-itemshowtype=\"0\" target=\"_blank\" linktype=\"text\" data-linktype=\"2\">AI Stock Trading with 50% Annualized Returns - TradeExpert\u003C/a>\u003C/span>\u003C/p>\u003C/li>\u003Cli>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">\u003Ca style=\"\" href=\"https://mp.weixin.qq.com/s?__biz=MzkwOTMzMzk0MQ==&mid=2247493071&idx=1&sn=a15db6cf541ce85992ba9fb8db4daece&scene=21#wechat_redirect\" textvalue=\"股票交易AI Agent - TradingAgents - MIT 和 UCLA 合作的股票交易框架\" data-itemshowtype=\"0\" target=\"_blank\" linktype=\"text\" data-linktype=\"2\">Stock Trading AI Agent - TradingAgents - A stock trading framework collaborated by MIT and UCLA\u003C/a>\u003C/span>\u003C/p>\u003C/li>\u003C/ul>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">Today, I am looking at a new one: FinSphere. It is a conversational AI assistant for stock analysis, aiming to revolutionize the field of financial analysis.\u003C/span>\u003C/p>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">FinSphere has three major innovations\u003C/span>\u003C/span>\u003C/h2>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-ratio=\"1.1940850277264325\" data-s=\"300,640\" data-type=\"png\" data-w=\"541\" type=\"block\" data-imgfileid=\"100010643\" style=\"height: auto !important;\" src=\"./assets/17423769665280.17066670426881236.png\">\u003C/section>\u003Col style='box-sizing: border-box;margin: 8px 0px;cursor: pointer;list-style-type: decimal;padding: 0px 0px 0px 25px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;' class=\"list-paddingleft-1\">\u003Cli style=\"box-sizing: border-box;cursor: pointer;\">\u003Csection style=\"box-sizing: border-box;cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cp style=\"box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">Stocksis Dataset\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: Carefully curated by industry experts, specifically designed to enhance the stock analysis capabilities of LLMs.\u003C/span>\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;cursor: pointer;\">\u003Csection style=\"box-sizing: border-box;cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cp style=\"box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">AnalyScore Evaluation Framework\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: A systematic tool for evaluating the quality of stock analysis, making the objectivity and accuracy of the results quantifiable and comparable.\u003C/span>\u003C/p>\u003C/section>\u003C/li>\u003Cli style=\"box-sizing: border-box;cursor: pointer;\">\u003Csection style=\"box-sizing: border-box;cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;font-weight: normal;\">\u003Cp style=\"box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: 0em;text-align: left;text-indent: 0em;padding: 8px 0px;\">\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">FinSphere Intelligent Agent\u003C/span>\u003C/strong>\u003Cspan leaf=\"\">: Capable of generating high-quality stock analysis reports based on specific user needs.\u003C/span>\u003C/p>\u003C/section>\u003C/li>\u003C/ol>\u003Ch3 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 20px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">FinSphere Intelligent Agent\u003C/span>\u003C/span>\u003C/h3>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">4.1 Strong Quantitative Tools Based on Real-time Databases\u003C/span>\u003C/strong>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">One of FinSphere's core advantages lies in its seamless integration with the company’s mature quantitative analysis tools, which have been widely deployed and validated in production environments. These tools can access the company's real-time financial database that comprehensively covers all market stocks, including structured data (stock price trends, trading volumes, financial indicators) and unstructured data (company announcements, analyst reports, market news), etc.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">When FinSphere identifies specific quantitative analysis requirements, it automatically triggers the corresponding tools. The tools query the real-time database, extract the latest relevant data, and perform complex calculations to generate professional analyses such as technical indicators, fundamental valuations, or market sentiment assessments. Each tool is customized for the user's queries, fully utilizing the constantly updated database to ensure that the analysis results accurately reflect real-time market conditions. This architecture ensures that FinSphere's responses are always based on the latest market data while benefiting from proven quantitative methods.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">4.2 Specialized Instruction Tuning\u003C/span>\u003C/strong>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">We fine-tuned the Qwen2-72B model using the expert-curated Stocksis dataset, significantly enhancing the model's financial analysis capabilities. The Stocksis dataset contains 5,000 high-quality training pairs, each consisting of structured prompts, comprehensive outputs from quantitative tools, and corresponding expert analyses. The fine-tuning process employed LoRA (Low-Rank Adaptation) technology, which efficiently updates parameters while maintaining the model's general capabilities. Through this method, the model learned to understand various outputs from quantitative tools, integrate multidimensional analytical perspectives, and generate well-structured reports following professional analysis patterns.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cstrong style=\"box-sizing: border-box;font-weight: bold;cursor: pointer;color: rgb(0, 0, 0);background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;margin: 0px;padding: 0px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">\u003Cspan leaf=\"\">4.3 Overall Workflow of FinSphere\u003C/span>\u003C/strong>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">FinSphere completes comprehensive financial analysis through a systematic, multi-stage process. After the user submits a query request, FinSphere first uses the chain-of-thought (CoT) method to break down the analysis request into structured subtasks and determine the required quantitative tools for each task.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">After completing the task decomposition, the selected quantitative tools independently access the real-time financial database, retrieve the latest market data, and conduct professional analyses. These analyses cover multiple dimensions, from technical indicators to fundamental indicators, ensuring the comprehensiveness and real-time nature of the content.\u003C/span>\u003C/p>\u003Cp style='box-sizing: border-box;margin: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;text-indent: 0px;padding: 8px 0px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;font-weight: 400;orphans: 2;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">The final stage is completed by the model fine-tuned with the Stocksis dataset. The model integrates and deeply analyzes the professional analyses generated by each quantitative tool, ultimately outputting a high-quality comprehensive analysis report. Through instruction fine-tuning, the model demonstrates excellent capabilities in understanding quantitative outputs and generating professional-level financial analyses. This integrated workflow ensures that FinSphere's analysis results combine the precision of quantitative analysis with the deep insights of expert financial reasoning, while always maintaining real-time market relevance.\u003C/span>\u003C/p>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Comparison\u003C/span>\u003C/span>\u003C/h2>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010642\" data-ratio=\"0.4537037037037037\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" type=\"block\" style=\"height: auto !important;\" src=\"./assets/17423769668210.14137553161735683.png\">\u003C/section>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan style=\"box-sizing: border-box;cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;text-align: left;font-weight: bold;display: block;\">\u003Cspan leaf=\"\">Example\u003C/span>\u003C/span>\u003C/h2>\u003Csection style=\"text-align: center;\" nodeleaf=\"\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100010644\" data-ratio=\"0.7307692307692307\" data-s=\"300,640\" data-type=\"png\" data-w=\"780\" type=\"block\" style=\"height: auto !important;\" src=\"./assets/17423769664960.8554317594011811.png\">\u003C/section>\u003Ch2 style='box-sizing: border-box;margin: 30px 0px 15px;color: rgba(0, 0, 0, 0.85);font-weight: 500;cursor: pointer;padding: 0px;display: block;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-style: normal;font-variant-ligatures: normal;font-variant-caps: normal;letter-spacing: normal;orphans: 2;text-align: left;text-indent: 0px;text-transform: none;widows: 2;word-spacing: 0px;-webkit-text-stroke-width: 0px;white-space: normal;background-color: rgb(255, 255, 255);text-decoration-thickness: initial;text-decoration-style: initial;text-decoration-color: initial;'>\u003Cspan leaf=\"\">\u003Cbr>\u003C/span>\u003C/h2>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,265,274,282,290,298,306,313],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":4,"collection":5,"summary_md5":262,"cover_url":263,"cover_url_1_1":264},510,"6da82269745e9611d6b00ee90be439e6","2022-07-01","078425eaf316a180b0989442e53f920b","b34c567a96074f9d5595b793b0d8809d","article_res/cover/e0a46cf8dda97bb002086e13c00a1b98.jpeg","article_res/cover/cf9f1df4872bf5bef0fe7705c786febb.jpeg",{"id":266,"title_md5":267,"publish_date":268,"author_md5":269,"is_original":23,"collection":270,"summary_md5":271,"cover_url":272,"cover_url_1_1":273},84,"c1c7ee59ace8f8a90289685e4d5ee0a1","2024-12-31","bc27fa490c4d0d525bac812fc0793534","#Robotics #Embodied AI #Nvidia","a792c2c2621d23d2dbd779ecbaaaa612","article_res/cover/9674435f69d7a9096f49e354619b1b4b.jpeg","article_res/cover/792b21b2cfc9af32151ee3d0c4a85864.jpeg",{"id":275,"title_md5":276,"publish_date":277,"author_md5":278,"is_original":4,"collection":5,"summary_md5":279,"cover_url":280,"cover_url_1_1":281},534,"80e31f27ce48e2a3940fc59930bb7eef","2022-05-22","8b3607d0f4181a3cb6ffdccf7185f09b","8e7d5bfc6c8db879b1ab5ace9ffdc46d","article_res/cover/0ebfe9c490974ead7e3dfd58e9c6bb07.jpeg","article_res/cover/53265d43b8157200837007872bfbef0b.jpeg",{"id":283,"title_md5":284,"publish_date":285,"author_md5":269,"is_original":23,"collection":286,"summary_md5":287,"cover_url":288,"cover_url_1_1":289},236,"fb2902d49d48930fde5eb60bc4514657","2024-06-24","#AI Grant","2a78bd9e32d06e085d4f477fe978b9a4","article_res/cover/03a7cf88a6fad78e165f4e300b41a04c.jpeg","article_res/cover/0686015569acf3ce812cf206f4cf2906.jpeg",{"id":291,"title_md5":292,"publish_date":293,"author_md5":269,"is_original":4,"collection":294,"summary_md5":295,"cover_url":296,"cover_url_1_1":297},331,"ba36b00a94e7c9b866a7e67db56079a5","2024-02-04","#AI Game #AI Agent","f2b376fba0153a860d58d5c3f77788dd","article_res/cover/a06fb659ab32b0ddcf3ebb6c330fc35f.jpeg","article_res/cover/df5aa34d411e2e9d1a3115cb816fde82.jpeg",{"id":299,"title_md5":300,"publish_date":301,"author_md5":302,"is_original":4,"collection":5,"summary_md5":303,"cover_url":304,"cover_url_1_1":305},597,"6d8b6c0ffb5f922ac19bdc9d5aafe829","2022-03-20","f44d4b523ff110f3126ff57530ea5253","2b9ec63f25101bef92dd6e958867b62d","article_res/cover/f5c2cebc3f32d0077d60768fec35d308.jpeg","article_res/cover/02148d1febb25706e85c474273552723.jpeg",{"id":307,"title_md5":308,"publish_date":309,"author_md5":269,"is_original":4,"collection":5,"summary_md5":310,"cover_url":311,"cover_url_1_1":312},317,"bf569498f48ed05c7697d8c1efb2aeab","2024-03-05","366a8f6f3b4cc9223da9cb5a5f535388","article_res/cover/087869c806adedeb9a241f11b0ceaf2c.jpeg","article_res/cover/de4dffc825492c48a6bf2b61f292a8e5.jpeg",{"id":314,"title_md5":315,"publish_date":316,"author_md5":269,"is_original":23,"collection":317,"summary_md5":318,"cover_url":319,"cover_url_1_1":320},66,"e1cbbd2cfb7427ee271f64c58b568d32","2025-01-19","#Google #Imagen3 #AI Image Generator","ad19ef13bbd6671ff9f5d7bfd175416e","article_res/cover/56ae40decdd38deb3bc1b59a2efa02ed.jpeg","article_res/cover/8df243692026cd39a5b6ecbe2c6ec2ec.jpeg",{"related":322,"small":350},[323,324,325,333,342],{"id":38,"publish_date":39,"is_original":23,"collection":40,"cover_url":41,"cover_url_1_1":42,"title":43,"summary":44,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":326,"publish_date":327,"is_original":23,"collection":328,"cover_url":329,"cover_url_1_1":330,"title":331,"summary":332,"author":28},57,"2025-01-27","#DeepSeek #o1 #LLM #OpenAI","article_res/cover/a806a72fdf64da2a1da4fbde8ea37907.jpeg","article_res/cover/d47dfa9583819e321c50b8caa3dd9a0c.jpeg","Comparison of the reasoning processes between ChatGPT o1 pro and DeepSeek R1","DeepSeek R1 Vs ChatGPT 01 (My Experience)",{"id":334,"publish_date":335,"is_original":4,"collection":336,"cover_url":337,"cover_url_1_1":338,"title":339,"summary":340,"author":341},165,"2024-09-29","#History of Intelligence #Neuroscience","article_res/cover/8382e1be385e1572eea807d72618b064.jpeg","article_res/cover/8d840d1fb10cf0d9256b63524b17fb6f.jpeg","【A Brief History of Intelligence】4. Simulating (Mammals)","Thinking was not born within Prometheus’s clay creatures, but in the small underground tunnels of Jurassic Earth.","Notes on \"A Brief History of Intelligence\"",{"id":343,"publish_date":344,"is_original":23,"collection":345,"cover_url":346,"cover_url_1_1":347,"title":348,"summary":349,"author":28},95,"2024-12-20","#Devin #AI Code Generator","article_res/cover/6bf285124b727832a418553395e1e996.jpeg","article_res/cover/7bf890a8a3b18d73ebef13e2ddd893b5.jpeg","AI Engineer Devin is officially online","Devin is a collaborative AI teammate. Built to help ambitious engineering teams achieve more.",[351,357,363],{"title":10,"list":352},[353,354,355,356],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":358},[359,360,361,362],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},{"title":242,"list":364},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646417732]