在 Reddit 有用户发布了一个帖子，大概意思是 DeepSeek V3.1 模型 有严重 bug： 模型在生成内容时会 在输出中随机插 入"extreme""极"或"極" 这些字词。 其他博主在第三方 API 上碰到了同样的问题： 有用户猜测 "极"字对应的 token ID 为2577，与省略号（...）的 token ID 2576 相邻，推测该异常可能源于训练数据污染或模型在推理时误选了 相邻 t oken 路径。 | DeepSeek V3.1严重恶性bug: 输出中随机带有「极」字 小 开发调优 ♣ 人工智能 | | --- | | HCPTangHY Regular | | 最开始在第三方API上出现了这个问题 | | VolcEngine DeepSeek Reasoner (V3.1) 深度求索 08/25 17:37 | | 已深度思考(用时 5.9 秒) | | | | 1 var datal = [ | | 2 [0,0,0.5],[0,1,0.5],[0,2,0.5],[0极,3,0.5],[0,4,0.5],[0,5,0.5],[0,6,0.38],[0,7,0.44],[0, ...

2025 年最受欢迎的 10 款 API 客户端，切图仔们用过几个？ 写代码调 API 应该是很多人日常工作的一部分，特别是前端开发人员，Vue/React 等都用 API 来调用数据： 平时呢，调用数据都需要搭配一些 API 的客户端工具，这种工具有很多，像 Postman、Inso mnia、Hoppscotch、HTTPie、reqable 等。 工具其实 也没有好 跟坏，还是看个人使用，适合哪一款就用哪一款～ 今天给大家介绍一个号称永久免费的 API 客户端工具 -- Requestly 。 Requestly 并不仅仅是一个 API Client，它是 集 HTTP 拦截、请求篡改、接口测试与数据模拟于一身 的全能工具。 它具备开发者所需的全套能力： 集合管理（Collections） ：像管理代码一样管理 API，层次清晰，结构优雅。 多环境变量 ：开发 / 测试 / 生产环境一键切换，减少重复输入。 完整请求历史 ：调试过程全程留痕，方便回溯与对比。 可视化界面 ：简洁优雅，操作流畅，避免"功能太多反而找不到"的尴尬。 协作功能 ：支持团队同步，让多人调试与共享配置更轻松。 与市面上很多依赖云端 ...

DeepS eek 终于更新迭代了一个版本，距离上一次 DeepSeek-R1-0528 的发布已经过去快 3 个月了： 3 个月对于 AI 技术的发展相当于一个时代过去了，现在海内外各大厂家更新都非常快，国内都有媒体开始报道 DeepSeek 由于迭代太慢使用率都 下降到 3%，期待的 DeepSeek-R2 也迟迟没来。 | | | Web Visit Statistics | | | --- | --- | --- | --- | | Domain | Feb-25 | Mav-25 | Percentage | | | | | Change | | DeepSeek | 614 M | 436 M | -29.00% | | ChatGPT | 3,905 M | 5,492 M | 40.60% | | Claude | 73 M | | 100 M semianalysis 36.50% | | Gemini | 284 M | 528 M | 85.80% | | Grok | 51 M | 179 M | 247.10% | 不过就在昨天 DeepSeek 在自己的媒体上宣布正式发布 Deep ...

Core Viewpoint - The article discusses the rapid advancements in artificial intelligence (AI) technology and the legal challenges faced by Meta regarding copyright infringement related to adult films used for AI training [1][4]. Group 1: AI Development Stages - Early AI relied on limited data for simple tasks, such as chess strategies or recognizing handwritten digits [5]. - The internet provided vast amounts of data, allowing AI capabilities to grow significantly, exemplified by GPT-3's training on hundreds of gigabytes of text [5]. - Current AI can process diverse data types, including video and audio, enhancing its learning and application in various fields [5]. Group 2: Legal Issues Faced by Meta - Meta is being sued by two adult film companies for allegedly downloading and distributing 2,396 copyrighted films without permission for AI model training [4][7]. - The plaintiffs demand that Meta remove all infringing films from its AI training dataset and seek damages of up to $15,000 per film, totaling approximately $359 million [8][18]. - The lawsuit claims that Meta's actions are part of an industrialized toolchain designed for AI training, rather than isolated employee misconduct [12]. Group 3: Evidence and Allegations - The lawsuit alleges that Meta used virtual private clouds to disguise its activities, downloading and sharing films continuously [10][15]. - The plaintiffs assert that Meta's actions not only saved significant licensing fees but also circumvented mainstream platform protections against data scraping [15]. - The lawsuit calls for Meta to delete training weights, destroy model copies, and publicly audit its algorithms to prevent further copyright violations [16].

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Core Viewpoint - The article highlights the remarkable journey and technical prowess of Cai Jingxian, also known as "Duolong," a legendary figure in Alibaba's history, emphasizing his contributions to the development of Taobao and his status as a revered technical expert within the company [2][3][4]. Group 1: Technical Expertise - Duolong is regarded as a unique talent at Alibaba, with unparalleled problem-solving abilities, often resolving issues that stumped entire teams [4][5]. - His technical contributions include the development and maintenance of Taobao's search engine, file systems, and various middleware, showcasing his capability to handle complex projects single-handedly [18][19]. - Duolong's approach to problem-solving involves deep dives into source code, demonstrating his passion for technology and commitment to finding solutions [4][21]. Group 2: Personal Background - Born in a rural family in Zhejiang Province, Duolong's early education was marked by a strong interest in mathematics and a fascination with computers, which laid the foundation for his future career [8][9]. - Despite not being formally trained in computer science, his curiosity and self-driven learning led him to excel in technology [7][8]. Group 3: Career Development - Duolong was one of the early programmers at Taobao, joining a secretive team tasked with developing the platform to compete with eBay [10][11]. - His commitment to coding and problem-solving earned him a reputation as a go-to person for technical challenges within the company [12][14]. Group 4: Recognition and Impact - Duolong's achievements led to his selection as an Alibaba partner, a prestigious position that reflects his influence and contributions to the company [25]. - His estimated wealth reached 2.6 billion RMB, highlighting the financial success that accompanied his technical expertise [26]. Group 5: Insights on Becoming a Technical Expert - Duolong emphasizes that becoming a top technical expert requires dedication, continuous learning, and a willingness to tackle challenges head-on [27]. - He advocates for the importance of problem-solving and refining one's skills through practice and experience [27][28].

虽然 VS Code 是现在的主流编辑器，但用久了就发现安装了很多扩展，内存嗖嗖往上涨，写个脚本都能把风扇吹得像要起飞。 | C Microsoft Edge | 3,3 GB | | --- | --- | | WindowServer | 1,5 GB | | × Code | 1,1 GB | | OneDrive | 356 мв | | G Ighub_agent | 307 мв | | * Raycast | 267 мв | | & TickTick | 260 мв | 明明只是写个简单的脚本，电脑却像在跑大型应用，风扇呼呼转个不停。 别说了， Electron 的锅？ 今天给你安利一款 轻量级的 编辑器界—— Lapce ，一个用 Rust 写的、本地运行、超快启动、插件走 WASM 的开源代码编辑器 。 Lapce（发音为 /læps/） 是一款完全由 Rust 编写 的本地代码编辑器，界面构建使用的是自家开发的 GUI 框架 Floem 。 Lapce 继承了 Xi-Editor 的 Rope 科学文本结构 ，在性能上实现了极致优化，处理大文件和复杂操作依然非常流畅。 Lapce 使用 ...

Core Insights - The article discusses the rise of AI programming tools and how they have lowered the barrier to coding, making it easier to develop websites, apps, and small programs [1][2]. - It highlights the success of individual developers who identify small pain points in niche markets, demonstrating that even minor needs can lead to significant market opportunities [3]. - The case study of Stagetimer.io illustrates how a simple, user-focused tool can generate substantial income without heavy marketing or investment [4]. Group 1: Product Overview - Stagetimer.io is a browser-based countdown timer tool designed for various scenarios such as presentations and meetings, offering features like countdowns, color alerts, and message flashing [8]. - The tool also has desktop versions for Windows, Mac, and Linux, allowing offline use, which caters to users needing stability in no-internet environments [9]. Group 2: Development Journey - The developer, Lukas Hermann, identified a pain point during a friend's online summit rehearsal, leading to the rapid creation of Stagetimer.io over a weekend [13][14]. - The product was initially launched as a free tool for quick validation, with no login or payment required, allowing for immediate user feedback [15][20]. - Following positive feedback, a paid version was introduced in June 2021, which quickly gained subscribers, indicating a strong market demand [20][21]. Group 3: Technical Stack - The technology stack includes Vanilla JS and Tailwind for the front end, Firestore for real-time synchronization, and Firebase Auth for user authentication, ensuring a smooth user experience [25][26]. Group 4: Marketing Strategy - The developer relied on organic growth through user feedback and word-of-mouth rather than hiring marketing personnel, with significant traction gained from Reddit discussions [27][30]. - SEO strategies were employed, including a domain name that directly reflects the product's purpose, enhancing discoverability [30]. Group 5: Lessons Learned - The article emphasizes the importance of observing everyday needs to identify potential product opportunities, encouraging developers to adapt successful strategies to their unique contexts [35][36].

Core Viewpoint - The article emphasizes the importance of mastering AI large model capabilities for programmers to remain competitive in the job market, as companies are increasingly focusing on AI applications and those with relevant skills are seeing significant salary increases and job opportunities [2][3][20]. Group 1: AI Skills and Job Market - Programmers who understand AI large models are more valuable than those who only perform basic CRUD operations, with salary increases exceeding 50% for skilled individuals [3][20]. - Companies of all sizes are prioritizing the implementation of AI applications, making it essential for technical professionals to enhance their skills in this area [2][3]. - The article promotes an "AI Large Model - Employment Practical Camp" that offers training on technical principles, practical projects, and career planning to help individuals transition into high-paying roles [3][6][22]. Group 2: Course Offerings and Benefits - The course includes two live sessions focusing on technical principles, practical project replication, and career guidance, with a limited enrollment of 100 participants [6][16]. - Participants will receive a job-seeking package that includes internal referrals, interview materials, and knowledge graphs, aimed at enhancing their job prospects [8][18]. - The course will cover key steps in large model application development, including understanding core technologies, practical product development, and continuous learning [12][20]. Group 3: AI Technologies and Applications - The article discusses various AI technologies such as RAG (Retrieval-Augmented Generation) and Function Call, which enhance the capabilities of large language models [9][12]. - RAG is particularly useful in scenarios requiring constant knowledge updates, while Function Call allows for the execution of specific code blocks to improve task complexity [12][14]. - The article highlights the importance of practical experience in AI applications, encouraging participants to apply learned skills directly to their resumes [12][20].

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].