伯乐在线 . 伯乐在线分享IT互联网职场和精选干货文章（原域名已不再维护）。组织维护10万+star的开源技术资源库， 包括：Python, Java, C/C++, Go, JS, CSS, Node.js, PHP, .NET 等 这事居然还上微博热搜了： 以下文章来源于伯乐在线 ，作者伯小乐 小麻哥今年 27 岁，河南郑州某央企的程序员，年薪大概 15 万元。 小麻哥 认为，跑外卖是一种解压的方式，因为在工作中经常要做很多逻辑思考，一下班就想放松。 跑外卖就只用看 着手机，前往某个地方取餐，还能吹吹风，跟女友一起溜达看看城市夜景。 小麻哥说表示， 总体而言还是挺好玩的，就像寻宝游戏，拿到餐，抵达目的地后获得金币。 小麻哥 也做自媒体，评论区里也有不友好的言论，说我一个有工作的人为什么要抢别人没工作的人饭碗。 对于恶评，他说看得还是比较开的，比较无所谓，有时候阐述一下自己的观点，但更多的是不予理会。 网友留言 "15 万的央企程序员，有个毛的压力" "主要不是跑外卖，更多可能是希望自媒体能火，那就主营业务变成网红了" "醉翁之意不在于下班跑外卖，而是把跑外卖的经历拍成短视频，本意还是想发展自媒体。至于跑外 ...

市场分析师的核心能力画像 如果把市场分析师比作商业世界的"数字翻译官"，能力树需要同时扎根硬技能土壤和软技能枝叶——既能用Excel把用户行为变成可 视化的流量地图，也能在会议室里把数据结论翻译成老板听得懂的生意经。 软技能金字塔 三段式成长路线图 1. 筑基期（0-1年） 通关任务：把Excel玩成数据透视的魔法水晶球 升级秘籍：从日常报表里挖掘3个反常识现象（比如周四的转化率总是飙升），用对比分析法写成月度洞察报告，你会突然发现总监 看你的眼神不一样了。 2. 突破期（2-3年） 觉醒时刻：当你能用Python自动生成竞品价格监测周报 实战演练：在用户画像项目中尝试关联规则挖掘，你会惊讶地发现购买猫粮的用户有27%同时在看健身环——这时候该建议市场部推 出"喵星人健身大礼包"吗？ 3. 飞跃期（5年+） 终极形态：用机器学习预测下季度市场渗透率 （职场进阶就像搭乐高，每块能力积木都要精准卡位） 硬技能四象限 职业升级方程式 知识输入 × 实践验证 + 认证背书 = 指数级成长建议每完成一个学习阶段，就用CDA认证作为能力检验的"期中考试"。当你的LinkedIn 简历上出现那个蓝色徽章时，猎头的电话会来得 ...

Core Viewpoint - The article highlights the challenges faced by students in programming courses, particularly those using outdated technology, and emphasizes the importance of adaptability and resourcefulness in learning programming skills. Group 1: Student Experience - A student from Xi'an University of Electronic Science and Technology faced difficulties with a second-hand computer that could not run code efficiently, leading her to write programs by hand in a notebook [1][4]. - The reliance on traditional methods, such as using paper and pen for coding, reflects a struggle against the limitations of technology in a digital age [2][6]. Group 2: Community Reactions - Online reactions to the article included humorous and relatable comments from users who resonated with the struggles of learning programming without adequate resources [8]. - Suggestions from the community included affordable alternatives for students, such as purchasing a Raspberry Pi for programming tasks, indicating a shared understanding of the challenges faced by students [8]. Group 3: Professional Insights - The article references a past incident involving a well-known case of a programmer who managed to write 300,000 lines of code in a month, illustrating the dedication and effort required in the programming field [10].

Core Viewpoint - The TIOBE Index for August 2025 highlights that AI programming assistants have significantly boosted Python's popularity, achieving its highest ranking ever in the index [1][3]. Group 1: Python's Dominance - Python has reached a historical peak in the TIOBE Index, with a rating of 26.14%, far ahead of its competitors [18][23]. - The gap between Python and the second-ranked C++ is nearly 17%, indicating a substantial lead [17]. - The rise of AI tools like Microsoft Copilot and Google Gemini Code Assist has improved coding efficiency by approximately 20% when using popular programming languages [5][9]. Group 2: Competitors and Trends - The top six programming languages in the TIOBE Index are dominated by Python, C++, C, Java, C, and JavaScript, showcasing a "stronger gets stronger" phenomenon [9][22]. - Other languages such as Go, Perl, Delphi, and Fortran are engaged in a competitive struggle for rankings, with Perl making significant progress this year [10][23]. - Legacy languages like Visual Basic, SQL, and Ada continue to maintain relevance in specific domains, despite their declining popularity [14][13]. Group 3: Historical Context and Future Outlook - Python's historical ranking has improved significantly over the years, moving from 7th place in 2015 to 1st in 2025 [29]. - The article speculates on the potential for Python to dominate the programming landscape further if it overcomes its performance limitations [20][19]. - The TIOBE Index serves as a reflection of current trends in programming languages, influenced by the number of engineers, courses, and third-party vendors [30][31].

Group 1: Core Insights - The article emphasizes the importance of foundational skills in programming and software engineering for entering the AI field, with Python being the preferred language due to its ease of use and comprehensive ecosystem [1][2][4] - It highlights that while many AI roles stem from machine learning, the most sought-after positions are closer to software engineering, necessitating knowledge of languages like Java, GO, or Rust [1][2] - Continuous practice and real-world application are deemed essential for mastering programming languages, rather than solely relying on courses or books [2] Group 2: Recommended Resources - A variety of resources are suggested for learning Python, including a beginner's course that can be completed in four hours and a highly regarded specialization course [5] - For mathematics and statistics, specific books and courses are recommended to understand the underlying principles of machine learning and AI [9][10] - The article lists essential resources for deep learning and large language models, emphasizing the significance of frameworks like PyTorch and TensorFlow in the industry [13][14] Group 3: AI Engineering and Productization - The article stresses the need for skills in productizing AI models, indicating that most AI roles resemble traditional software engineering rather than pure machine learning engineering [11] - It mentions the importance of learning MLOps for model deployment, covering aspects like containerization and cloud systems [11] - The article concludes with advice on becoming an expert in the field through project-based learning and self-reflection [14]

Core Insights - The TIOBE Index for July 2025 reveals that Python, C, C++, Java, C, JavaScript, and Go have maintained their positions in the top seven programming languages for three consecutive years, forming a strong "first camp" [1][4] - The competition for the 8th to 12th positions is fierce among older languages like Visual Basic, SQL, Fortran, Ada, Perl, and Delphi, showcasing a "twilight of the gods" in the programming world [1][3] - Despite the emergence of newer languages like Rust, Kotlin, Dart, and Julia, they have not yet broken into the top ten due to the resilience of established languages [4][5] Rankings and Market Share - Python's market share has surged to 26.98%, significantly widening the gap with the second-ranked C++ at 9.80%, marking a dramatic lead [7][10] - The top ten programming languages in July 2025 are: Python, C++, C, Java, C, JavaScript, Go, Visual Basic, Ada, and Delphi/Object Pascal [14][16] Language Characteristics and Trends - Python is recognized for its ease of use and active community, despite criticisms regarding its performance speed [11][12] - Older languages like Visual Basic and SQL maintain relevance due to their roles in enterprise legacy systems and database foundations, respectively [5][4] - Ada is experiencing a resurgence in high-security applications, while Fortran remains entrenched in scientific computing [5][4] Historical Context - The TIOBE Index is updated monthly and reflects trends in programming languages based on the number of engineers, courses, and third-party vendors, providing insights into the current landscape of programming languages [20]

Core Insights - The article outlines an eight-step roadmap for efficiently advancing in AI/ML by focusing on essential skills and avoiding common pitfalls [1]. Group 1: Step-by-Step Learning Path - **Step 1: Master Python Core Libraries** Proficiency in Python is essential for AI/ML, including data cleaning, model building, and result visualization [2]. Key content includes Python basics, advanced AI programming techniques, and libraries like scikit-learn, NumPy, Matplotlib, Seaborn, and Pandas [4]. Recommended resources include CS50 Python course and "Python Data Science Handbook" [4]. Suggested learning period is 3-4 weeks [4]. - **Step 2: Solidify Mathematical Foundations** A strong grasp of linear algebra, probability, and calculus is crucial for understanding models [5]. Key content includes matrix operations, Bayesian thinking, and optimization techniques [5]. Recommended resources include "Linear Algebra" by 3Blue1Brown and MIT's Probability Introduction [5]. Suggested learning period is 4-6 weeks [5]. - **Step 3: Understand Machine Learning Basics** This step is pivotal for transitioning from beginner to competent AI/ML engineer [6]. Key content includes supervised vs. unsupervised learning, reinforcement learning, and deep learning [6]. Recommended resources include Google's Machine Learning Crash Course and "Machine Learning" by Andrew Ng [8]. Suggested learning period is 6-8 weeks [8]. - **Step 4: Hands-On Project Experience** Practical experience through real AI/ML applications is essential for job readiness [9]. Key content includes practical guides and project development [9]. Suggested learning period is ongoing [9]. - **Step 5: Learn MLOps** Understanding MLOps is vital for deploying and maintaining models in real-world scenarios [10]. Key content includes foundational concepts and best practices for model deployment [10]. Suggested learning period is 3-4 weeks [10]. - **Step 6: Specialize in a Domain** After building a foundation, focusing on a specific area like NLP or computer vision enhances employability [11]. Suggested learning period is ongoing [11]. - **Step 7: Stay Updated** Continuous learning is necessary to keep skills relevant in the fast-evolving AI field [12]. Key resources include ArXiv for research papers and notable figures in the field [12]. Suggested learning period is ongoing [12]. - **Step 8: Prepare for Interviews** Comprehensive preparation for interviews is crucial, including explaining model principles and system design [13]. Recommended resources include machine learning interview guides [13]. Suggested learning period is 4-6 weeks [13]. Conclusion - The article emphasizes a structured approach to mastering AI/ML, enabling individuals to transition from novices to job-ready professionals efficiently [1].

Core Viewpoint - The article discusses the launch of Stanford University's CS336 course "Language Models from Scratch," which aims to provide a comprehensive understanding of language models through practical development and implementation [5][7]. Course Overview - The course focuses on the foundational aspects of language models, which are essential for modern natural language processing (NLP) applications. It emphasizes the importance of understanding language models for scientists and engineers in the fields of AI and ML [5][7]. - The course is structured into five major modules: Foundations, Systems, Extensions, Data, and Alignment & Reinforcement Learning [7]. Course Requirements - Students are expected to have proficiency in Python, as most assignments will require extensive coding. The course will provide minimal scaffolding, resulting in a higher volume of code written by students compared to other AI courses [7]. - A background in deep learning and system optimization is necessary, particularly familiarity with PyTorch and basic system concepts like memory hierarchy [7]. - Foundational knowledge in calculus, linear algebra, probability, and statistics is required, along with a basic understanding of machine learning principles [7]. Assignments - The course includes several assignments that cover various aspects of language model development, such as implementing a BPE tokenizer, training models on specific datasets, and optimizing performance on GPUs [8]. - Assignments are designed to simulate real-world challenges, including data processing and model alignment, with a focus on practical application and hands-on experience [8]. Course Schedule - The course is structured with a detailed schedule that outlines topics, materials, and deadlines for assignments, ensuring a systematic approach to learning [9].

Core Viewpoint - The TIOBE programming language ranking for June 2025 indicates a decline in SQL's popularity, dropping to 12th place, marking its historical low, while Python continues to dominate the rankings with a significant lead over other languages [2][20]. SQL's Historical Context - SQL reached its peak ranking at 6th place in October 2003 but was removed from the TIOBE index from 2004 to 2018 due to debates about its classification as a programming language [5]. - SQL was reintroduced to the rankings in 2018, benefiting from its stronghold in the database domain, and returned to the top ten [5]. SQL's Applications - SQL is integral to various sectors, including banking, e-commerce, healthcare, and education, where it manages vast amounts of structured data [7]. SQL Database Overview - A list of common SQL databases includes MySQL, PostgreSQL, SQLite, MariaDB, Oracle, SQL Server, and IBM Db2, with most being open-source [9]. Rise of NoSQL - The emergence of NoSQL databases poses a significant threat to SQL, as NoSQL is designed to handle unstructured data and offers greater flexibility, making it suitable for rapidly changing requirements [10][12]. - NoSQL databases, such as MongoDB and Redis, are increasingly favored for their ability to manage big data and high concurrency scenarios [13][16]. Programming Language Rankings - As of June 2025, the top programming languages are Python, C++, C, Java, C, JavaScript, Go, Visual Basic, Delphi/Object Pascal, and Fortran, with Python holding a commanding lead at 25.87% [20][22]. - SQL's ranking has dropped significantly, now positioned at 12th place, reflecting a shift in developer preferences towards more flexible programming languages [26]. Historical Trends - The TIOBE index tracks the popularity of programming languages based on various metrics, including search engine queries and community engagement, providing insights into industry trends [29].

Core Viewpoint - The article discusses the increasing adoption of TypeScript among AI Agent companies, with approximately 60-70% of YC X25 Agent companies using it for development, highlighting a shift from the traditional Python-centric approach to a more TypeScript-focused ecosystem [1][2][12]. Group 1: Reasons for TypeScript Adoption - The rise in popularity of TypeScript is attributed to its static typing and IDE integration, which significantly enhance productivity, especially in rapidly iterating complex logic and linking tools [3][14]. - TypeScript's adoption rate has surged from 12% in 2017 to an impressive 35% in 2024, as reported by JetBrains [6]. - The language's ability to provide immediate feedback during development, allowing developers to see changes in real-time, is a key advantage that makes it appealing for AI application development [9][21]. Group 2: TypeScript vs. Python in AI Development - While Python remains the dominant language for AI training and development, TypeScript is emerging as a strong contender for AI application development due to its unique advantages, such as asynchronous programming capabilities and a strict type system [12][14]. - TypeScript's compatibility with popular AI libraries like TensorFlow.js and Brain.js allows developers to leverage existing JavaScript tools while benefiting from TypeScript's type safety [18][19]. - The article notes that many developers are using both Python and TypeScript, with some preferring TypeScript for its package management and type system advantages [24]. Group 3: Industry Trends and Future Outlook - Major AI development tools, including OpenAI's Agents SDK, are increasingly incorporating TypeScript support, reflecting a broader trend towards accommodating a larger developer community [16][15]. - The emergence of TypeScript-focused AI development frameworks, such as TypeAI and Axilla.io, indicates a commitment within the community to establish TypeScript as a first-class citizen in the AI ecosystem [19][20]. - The article concludes that while Python will likely maintain its dominance in AI development, the growing interest in TypeScript presents an intriguing alternative for specific use cases, making the future of TypeScript in AI development worth monitoring [24].