比诞生之初还冷清，Stack Overflow彻底凉透了！ 当初的程序员问答圣地，现在的提问数量甚至比18年前上线首月时的问题数量还要少。 （这个下降趋势好像来时路……） 全球开发者数量翻了好几倍，工具和语言层出不穷，但「提问」，却消失了。 当然了，Stack Overflow这么一凉，大家第一反应肯定是把锅扣在AI Coding头上。 那么，为啥它的衰落会引起这么多人惋惜呢？ 巅峰时期有180+子站 时间倒回2008年，Stack Overflow带着高质量、可复用答案的定位上线，很快成了程序员圈子里的救命稻草。 因为在2000年代，程序员主要靠论坛或者个人博客解决编程遇到的难题。 但这样的回答通常很零散，并且不可搜索，这种低效的方式也让很多高手不愿意分享解决方案。 于是，Fog Creek创始人、《Joel on Software》的作者Joel Spolsky和知名程序员、《Coding Horror》的作者Jeff Atwood创建了Stack Overflow，2008年正式 上线后持续走红。 这么一套下来，大多数卡壳的问题都能搞定。 2013年到2017年，Stack Overflow达到顶峰时期 ...

闻乐 发自 凹非寺 量子位 | 公众号 QbitAI 当初的程序员问答圣地，现在的提问数量甚至比18年前上线首月时的问题数量还要少。 比诞生之初还冷清， Stack Overflow 彻底凉透了！ （这个下降趋势好像来时路……） 全球开发者数量翻了好几倍，工具和语言层出不穷，但 「提问」 ，却消失了。 当然了，Stack Overflow这么一凉，大家第一反应肯定是把锅扣在AI Coding头上。 那么，为啥它的衰落会引起这么多人惋惜呢？ 巅峰时期有180+子站 时间倒回2008年，Stack Overflow带着 高质量、可复用答案 的定位上线，很快成了程序员圈子里的救命稻草。 因为在2000年代，程序员主要靠论坛或者个人博客解决编程遇到的难题。 但这样的回答通常很零散，并且不可搜索，这种低效的方式也让很多高手不愿意分享解决方案。 于是，Fog Creek创始人、《Joel on Software》的作者 Joel Spolsky 和知名程序员、《Coding Horror》的作者 Jeff Atwood 创建了 Stack Overflow，2008年正式上线后持续走红。 它面向具体问题，强调可验证、可复 ...

Core Viewpoint - Python continues to dominate as the most popular programming language, achieving a remarkable lead over its competitors, particularly Java, in the IEEE Spectrum 2025 programming language rankings [2][4][5]. Group 1: Python's Dominance - Python has secured its position as the top programming language for ten consecutive years, marking a significant achievement in the IEEE Spectrum rankings [6]. - This year, Python has not only topped the overall ranking but also led in growth rate and employment orientation, making it the first language to achieve this triple crown in the 12-year history of the IEEE rankings [7]. - The gap between Python and Java is substantial, indicating Python's strong growth trajectory [4][5]. Group 2: Python's Ecosystem and AI Influence - Python's rise can be attributed to its simplicity and the development of powerful libraries such as NumPy, SciPy, matplotlib, and pandas, which have made it a favorite in scientific, financial, and data analysis fields [10]. - The network effect has played a crucial role, with an increasing number of developers choosing Python and contributing to its ecosystem, creating a robust community around it [11]. - AI has further amplified Python's advantages, as it possesses richer training data compared to other languages, making it the preferred choice for AI applications [12][13]. Group 3: Other Languages' Challenges - JavaScript has experienced the most significant decline, dropping from the top three to sixth place in the rankings, indicating a shift in its relevance [15]. - SQL, traditionally a highly valued skill, has also faced challenges from Python, which has encroached on its territory, although SQL remains a critical skill for database access [18][21][23]. Group 4: Changes in Programming Culture - The community culture among programmers is declining, with a noticeable drop in activity on platforms like Stack Overflow, as many now prefer to consult AI for problem-solving [25][26]. - The way programmers work is evolving, with AI taking over many tedious tasks, allowing developers to focus less on programming details [30][31]. - The diversity of programming languages may decrease as AI supports only mainstream languages, leading to a stronger emphasis on a few dominant languages [37][39]. Group 5: Future of Programming - The programming landscape is undergoing a significant transformation, potentially leading to a future where traditional programming languages may become less relevant [41]. - While high-level languages like Python have simplified programming, the ultimate goal may shift towards direct interaction with compilers through natural language prompts [46]. - The role of programmers may evolve, focusing more on architecture design and algorithm selection rather than maintaining extensive source code [49][50].

Core Insights - The article discusses how entrepreneurs can leverage exponential forces and build network effects to create lasting value in the tech industry [3][4][5] Group 1: The Power of Networks and Network Effects - Many significant internet services are networks that become more valuable as more people use them, exemplified by email and social media platforms like Facebook and Instagram [5][6] - The tech industry benefits from powerful exponential forces, such as Moore's Law, which states that semiconductor performance doubles approximately every two years, leading to rapid advancements [6][7] - Entrepreneurs should focus on identifying these exponential forces, as they will dominate any tactical product work [6][10] Group 2: Strategies for Building Networks - Successful companies often start with a strong product that attracts users, then leverage existing networks to grow, as seen with Instagram and Substack [10][11] - The challenge lies in making networks useful from the beginning, as initial user bases can be small and unappealing [12] - The emergence of "narrow startups" that charge premium prices for specialized services indicates a shift towards more focused business models in the tech landscape [23] Group 3: The Role of Branding and Pricing - Brand power and consumer inertia are significant in the tech sector, as seen with ChatGPT's rapid rise to prominence despite lacking traditional network effects [15][21] - The increasing willingness of consumers to pay higher prices for software suggests a shift in spending priorities, with software potentially consuming a larger share of disposable income [14][21] Group 4: The Impact of AI and Open Source - The rise of AI tools has diminished the need for traditional web traffic, leading to a decline in SEO-driven traffic for many websites [20][21] - Open source software has played a crucial role in democratizing technology, allowing startups to thrive with minimal initial investment [35][36] - The future of open source AI remains uncertain, with potential for it to lag behind proprietary models, but it could provide affordable solutions for consumers [36][37]

Core Viewpoint - The evolution of programming has transitioned from manual coding to AI-assisted development, significantly changing the role of programmers and the tools they use [4][6][8]. Group 1: Stages of Programming Evolution - **First Stage: Manual Craftsmanship** Early programming involved basic languages like Basic, Pascal, and C, with no IDE support, leading to a high dependency on accuracy [4][5]. - **Second Stage: Copy and Paste Dominance** The rise of graphical IDEs and the internet allowed programmers to leverage search engines and online resources, shifting the focus from original coding to code assembly [6][7]. - **Third Stage: The Era of AI** The introduction of AI programming tools has transformed coding practices, allowing programmers to rely on AI for code generation and optimization, reducing the need for traditional coding skills [8][10]. Group 2: AI Programming Tools - **Cursor** An AI IDE optimized for VS Code, known for its strong code understanding and project-level analysis capabilities [13]. - **Windsurf** An AI tool with long-term memory, capable of understanding project context and suitable for complex tasks [14]. - **Trae** Developed by ByteDance, this AI IDE integrates deeply with AI to provide intelligent Q&A and code auto-completion features [15]. - **Lingma IDE** An Alibaba product that integrates cloud services, allowing AI to automatically call tools for end-to-end task completion [16]. - **VS Code + Copilot** This combination offers a rich plugin ecosystem, enhancing AI capabilities through the Copilot plugin [17].