Core Viewpoint - The article emphasizes the transformative impact of AI technology on the investment research industry, highlighting the necessity for financial professionals to embrace AI and Python for enhanced efficiency and data analysis capabilities [1]. Group 1: AI and Python in Data Acquisition and Processing - AI and Python play a significant role in efficiently acquiring and processing financial data, utilizing tools like web scraping with Python libraries such as requests and Selenium to gather key information from financial reports and market data [1]. - The integration of AI tools allows for rapid extraction and analysis of financial data, facilitating comparisons across multiple companies and enhancing the accuracy of financial evaluations through models like DCF [2]. Group 2: Report Generation and Data Visualization - AI excels in generating high-quality financial reports quickly, using visualization tools to present complex financial data in an intuitive manner, thereby increasing the report's persuasiveness and appeal [3]. - Python libraries such as Matplotlib and Pyecharts are utilized for dynamic data visualization, enhancing the clarity and impact of financial reports [3]. Group 3: Automation of Financial Processes - The combination of AI and Python enables the automation of financial processes, such as batch file generation and automated auditing, significantly improving work efficiency [4]. - Developing personalized AI systems can provide tailored support for investment research, streamlining data management and analysis [4]. Group 4: Quantitative Investment Strategies - The application of AI and Python in quantitative investment is promising, offering robust support for developing and backtesting investment strategies, including K-line charting [5]. - A dedicated quantitative strategy backtesting platform developed in Python allows investors to test and optimize their investment strategies, potentially increasing returns [5]. Group 5: Course Offerings - The course titled "AI Large Model + Python Empowering Financial Full Process Practice" aims to explore advanced applications of AI and Python in investment research, covering complex strategy construction and intelligent research system development [5]. - The course includes 86 detailed lessons totaling 32.5 hours, providing comprehensive coverage of AI and Python in financial research, along with practical case studies [8].

Core Viewpoint - The article emphasizes that AI technology is reshaping the investment research industry, making the adoption of AI and Python essential for financial professionals to enhance efficiency and effectiveness in their work [1]. Data Acquisition and Processing - AI and Python play a significant role in acquiring and processing financial data, enabling efficient retrieval of key information such as financial reports and market data through web scraping techniques [1]. - Tools like Python's requests and Selenium libraries facilitate data extraction, while regular expressions and Beautiful Soup (BS) libraries assist in data parsing for subsequent analysis [1]. Financial Analysis and Valuation - In financial analysis, AI tools can quickly extract and analyze financial data, allowing for comparisons between single and multiple companies [2]. - The combination of AI and Python, particularly using the Pandas library, enables in-depth analysis of key corporate metrics and the construction of DCF valuation models for more accurate enterprise value assessments [2]. Report Writing and Data Visualization - AI excels in report writing and data visualization, generating high-quality financial reports rapidly [3]. - Tools like Huohua Shutu and mind mapping software help present complex financial data in intuitive graphical formats, while Python libraries such as Matplotlib and Pyecharts enable dynamic data visualization [3]. Automation of Financial Processes - The integration of AI and Python allows for the automation of financial processes, such as batch file generation and automated auditing, significantly improving work efficiency [4]. - Developing personalized AI systems can provide tailored investment research support, enhancing data processing capabilities [4]. Quantitative Investment Strategies - The application of AI and Python in quantitative investment is promising, supporting everything from K-line chart plotting to the development and backtesting of classic investment strategies [5]. - Python-based quantitative strategy backtesting platforms allow investors to easily test and optimize their investment strategies, potentially increasing returns [5]. Course Offerings - The course "AI Large Model + Python Empowering Financial Full Process Practice" aims to explore advanced applications of AI and Python in investment research, covering complex strategy construction and intelligent research system development [5]. - The course includes 86 detailed lessons totaling 32.5 hours, providing a comprehensive overview of AI and Python in financial research, along with practical case studies [7]. Course Structure - The first chapter focuses on the application of AI large models in financial research, teaching participants how to design prompts and extract information from real financial documents [8]. - The second chapter covers practical skills in Python for finance, including data processing, automated data scraping, and tool development [10].

Core Viewpoint - The article emphasizes the transformative impact of AI technology on the investment research industry, highlighting the necessity for financial professionals to embrace AI and Python for enhanced efficiency and data analysis capabilities [1]. Group 1: AI and Python in Data Acquisition and Processing - AI and Python play a significant role in efficiently acquiring and processing financial data, utilizing tools like web scraping with Python libraries such as requests and Selenium to gather key information from financial reports and market data [1]. - The integration of AI tools allows for rapid extraction and analysis of financial data, facilitating comparisons across multiple companies and enhancing the depth of financial analysis through Python's Pandas library [2]. Group 2: Report Generation and Data Visualization - AI excels in generating high-quality financial reports quickly, using tools like Huohua Data and mind mapping to present complex financial data in intuitive graphical formats [3]. - Python libraries such as Matplotlib and Pyecharts enable dynamic data visualization, making reports more persuasive and engaging [3]. Group 3: Automation of Financial Processes - The combination of AI and Python enables automation of financial processes, such as batch file generation and automated auditing, significantly improving work efficiency [4]. - Developing personalized AI systems can provide tailored investment research support, enhancing the overall analytical capabilities [4]. Group 4: Quantitative Investment Strategies - The application of AI and Python in quantitative investment is promising, offering robust technical support for developing and backtesting investment strategies, including K-line chart analysis [5]. - A dedicated quantitative strategy backtesting platform developed in Python allows investors to test and optimize their investment strategies, potentially increasing returns [5]. Group 5: Course Offerings and Practical Applications - The course titled "AI Large Model + Python Empowering Financial Full Process Practice" aims to explore advanced applications of AI and Python in investment research, covering complex strategy construction and intelligent research system development [5]. - The course includes 86 detailed lessons totaling 32.5 hours, providing comprehensive coverage of AI and Python applications in financial research, along with real-world case studies [7]. Group 6: Course Curriculum Highlights - The curriculum focuses on the application of AI large models in financial research, teaching participants how to extract information from real financial documents and optimize analysis reports [8]. - The course also covers practical skills in Python, including data processing, automated data scraping, and the development of tools for financial analysis [10].

General Information - The content is a recommendation to reshare insightful information with one's network [1] - It promotes tutorials and insights on Data Science (DS), Machine Learning (ML), Large Language Models (LLMs), and Retrieval Augmented Generation (RAGs) [1] - It highlights a clear explanation of uv in Python with code [1]

Core Concept - The document explains uv in Python with code examples [1]

Agent Adoption & Scalability - LinkedIn aims to scale agentic adoption within the organization to enable broader idea generation [2] - LinkedIn built the Hiring Assistant, its first production agent, to automate recruiter tasks and free up time for candidate interaction [3] - The Hiring Assistant follows an ambient agent pattern, operating in the background and notifying recruiters upon completion [4][5] - LinkedIn adopted a supervisor multi-agent architecture, with a supervisor agent coordinating sub-agents that interact with LinkedIn services [6] Technology Stack & Framework - LinkedIn standardized on Python for GenAI development, moving away from its traditional Java-centric approach [7][8] - The company built a service framework using Python, gRPC, Langchain, and Langraph to streamline the creation of production-ready Python services [9][19] - Over 20 teams have used this framework to create over 30 services supporting Generative AI product experiences [9][10] - Langchain and Langraph were chosen for their ease of use and sensible interfaces, enabling rapid development and integration with internal infrastructure [22][23] Infrastructure & Architecture - LinkedIn invested in a distributed architecture to support agentic communication modes [10] - The company modeled long-running asynchronous flows as a messaging problem, leveraging its existing messaging service for agent-to-agent and user-to-agent communication [26][27] - LinkedIn developed agentic memory with scoped and layered memory types (working, long-term, collective) [29][30] - LinkedIn implemented a centralized skill registry, allowing agents to discover and access skills developed by different teams [34][35]