Core Insights - The article discusses the successful proof of an Erdős conjecture by OpenAI's latest model, GPT-5.2 Pro, marking a significant achievement in the intersection of AI and mathematics [2][3]. - The proof was validated by Fields Medalist Terence Tao, who described it as "the clearest first-class result contributed by AI to date" [3]. Group 1: The Proof and Its Validation - The conjecture, known as problem number 281 in the Erdős problem collection, was proposed in 1980 by mathematicians Paul Erdős and Ronald Graham, relating to deep connections between congruence covering systems and natural density [4][5]. - The proof utilized the infinite Adèle ring, employing Haar measure and pointwise ergodic theorems, transitioning from pointwise convergence to uniform convergence [9][10]. - Tao confirmed the proof's validity by translating the ergodic argument into combinatorial language, using the Hardy-Littlewood maximal inequality instead of the Birkhoff theorem [16]. Group 2: Alternative Solutions and Historical Context - An unexpected discovery emerged when a user named KoishiChan pointed out that a simpler solution to the problem exists, utilizing two theorems established in 1936 and 1966 [18]. - The first theorem is the density convergence theorem co-proven by Harold Davenport and Erdős in 1936, and the second is Rogers' theorem, first published in 1966 [19]. - Tao noted that Erdős himself was unaware of this simpler solution when he proposed the problem in 1980, raising questions about the problem's formulation [20]. Group 3: AI's Performance and Future Implications - Following the announcement, various AI models were tested for cross-validation, with Gemini 3 Pro confirming the proof's correctness [24]. - However, Tao cautioned about the statistical bias in evaluating AI's success rates, highlighting that negative results are often underreported [27]. - Current data suggests that the real success rate of these tools on Erdős problems is approximately 1% to 2%, which still indicates a significant number of non-trivial contributions from AI [31][32].

Core Insights - The article highlights the significant shift in the job market towards AI roles, particularly in algorithm development, driven by the increasing demand for AI capabilities in various industries [2][3]. Group 1: Job Market Trends - Traditional CRUD development positions have decreased by 30%, while 80% of new technical roles require AI skills such as large model development and RAG architecture [3]. - The average annual salary for AI developers exceeds 400,000, compared to 200,000 for traditional front-end and back-end roles [3]. - Many algorithm positions have seen salary increases of 50% compared to previous years, leading to a surge of backend programmers transitioning to AI algorithm roles [3]. Group 2: Reasons for Transitioning to AI Roles - Traditional development technologies have matured, leading to a saturated market, while AI technologies are rapidly evolving, creating a significant talent gap [4]. - AI roles directly drive business innovation, such as precise marketing and risk control, making them more commercially valuable than traditional roles [4]. - The slow digital transformation in traditional industries limits technical investment, further pushing talent towards AI roles [4]. Group 3: Skills Gap in AI Job Applications - There is a high demand for algorithm capabilities, complex problem analysis, modeling, and project presentation skills in core AI positions, but many applicants lack these competencies [6][7]. - The mismatch between applicants' skills and the requirements of leading companies in AI roles is a significant barrier to employment [6]. Group 4: Training and Development Initiatives - A comprehensive algorithm training program has been launched in collaboration with leading AI companies to equip candidates with the necessary skills for core AI positions [8]. - The program promises a full refund if participants do not secure an offer or earn an annual salary of at least 290,000 [8]. - The curriculum focuses on practical applications, covering various models and real-world projects to prepare students for the AI job market [8]. Group 5: Success Stories and Employment Outcomes - Previous participants in the training program have achieved significant salary increases, with some reporting jumps from 250,000 to 420,000, representing a 68% increase [70][71]. - Success stories include individuals transitioning from backend development to AI roles with salaries reaching up to 480,000 [73][76]. - The program has a high employment rate, with 90% of students securing offers in AI and algorithm positions [70].

Core Viewpoint - The article discusses Games Workshop's (GW) strong stance against the use of AI in content creation and design processes, contrasting it with the industry's trend of embracing AI as a core business element [1][2][4]. Group 1: Company Policy on AI - GW has implemented a strict internal policy prohibiting employees from using AI to generate any official content or participate in design processes, including concept setting, illustrations, and writing [5][6]. - The CEO, Kevin Rountree, emphasized that while a few executives are allowed to research AI out of curiosity, none are genuinely excited about its potential [6][4]. Group 2: Investment in Human Creators - Contrary to industry trends of layoffs, GW has increased its investment in human creators by hiring more concept designers, illustrators, writers, and sculptors, explicitly stating a commitment to respecting human creators and protecting intellectual property [6][7]. - The CEO highlighted that passionate and talented individuals are essential for creating the rich and engaging IP that is Warhammer [7]. Group 3: Community Sentiment and Brand Integrity - GW's zero-tolerance policy towards AI reflects a keen understanding of the community's sentiments, as even the slightest indication of AI involvement can lead to a trust crisis among fans [9][10]. - The company is cautious about the potential risks associated with AI, particularly regarding copyright and data sources, which could jeopardize the integrity of its core assets, such as the Warhammer universe [13][14]. Group 4: Long-term Vision - GW prefers a slower, more deliberate approach to content creation rather than risking the loss of its brand's "soul" due to AI involvement, which may not be the most efficient choice but is deemed the most rational in an industry where IP value surpasses that of models [14][15].

Jay 发自 凹非寺 量子位 | 公众号 QbitAI AI时代，电工再次成为香饽饽。 美国劳工统计局估计：2024年至2034年间，美国 每年平均将出现约8.1万名电工缺口 。 这意味着，未来十年电工就业人数将增长9%。 水管工工会 United Association 的国际代表直言：目前数据中心项目所需要的工人数量，已经 超过任何其他单一行业 。而且，如果继续 沿着现在这种「大力出奇迹」的AI范式发展下去，用工需求还会继续往上走。 什么概念？这么说吧，官方的口径是： 远高于所有职业的平均水平。 至于原因嘛……相信大家已经猜到了。 是的，这波新释放的岗位，几乎完全来自数据中心。 电工再成香饽饽 真正的AI人才狙击战是电工之战 （ doge ） 。 今年五月，代表美国、加拿大和美国属地电工工会的 国际电气工人兄弟会 ，给会员们发来了一条「喜讯」： 兄弟们！现在有些地方分会， 单个数据中心项目要的人数，已经是他们现有规模的两倍、三倍，有时甚至四倍！ 迎来春天的不只有电工。数据中心几乎凭一己之力，拉动了整个蓝领就业市场，包括水管工、建筑工人，以及暖通空调技术人员。 作为释放这些需求的终极甲方，科技巨头们的动作， ...

Group 1 - The article discusses humorous and relatable anecdotes related to car features and personal experiences, particularly focusing on a "starry sky roof" in vehicles, which has become a trending topic among users [2][20]. - There is a light-hearted commentary on the absurdity of certain situations, such as the exaggerated size of a suitcase mentioned in a conversation, which adds to the comedic tone of the article [7][8]. - The article reflects on social interactions and the shared experiences of users, highlighting the community aspect of the discussions taking place in the comments [13][15]. Group 2 - The content showcases various user-generated comments that emphasize the humorous side of everyday life, including playful banter and jokes about common scenarios [24][30]. - There is a mention of the challenges faced in daily life, such as navigating through difficult weather conditions, which resonates with the audience's experiences [58]. - The article captures the essence of social media interactions, where users share their thoughts and experiences in a light-hearted manner, creating a sense of camaraderie among them [46][49].

Core Viewpoint - The article discusses the transformative impact of AI on programming, highlighting differing opinions among industry leaders regarding the necessity of traditional coding practices and the potential for AI to enhance creativity and efficiency in software development [1][2][4][5]. Group 1: Perspectives on AI in Coding - Google engineer Jaana Dogan emphasizes the efficiency of AI, noting that a task taking a year for a team was completed by AI in just one hour [1]. - Linus Torvalds expresses skepticism about AI writing code, preferring AI to assist in code maintenance rather than creation [1]. - Salvatore Sanfilippo (antirez) provocatively claims that writing code is often no longer a necessary task, urging developers to embrace the ongoing industry transformation [2][4]. Group 2: Embracing Change - Antirez questions the resistance to AI, suggesting that developers risk missing out on significant industry changes if they do not adapt [4]. - He argues that the true passion in programming lies in creation, and AI can expedite reaching creative goals [5]. - Antirez's article has gained significant traction, with over 300,000 views, indicating a strong interest in the topic [5]. Group 3: AI's Practical Applications - Antirez shares personal experiences where AI significantly reduced the time required for coding tasks, such as improving the linenoise library and fixing Redis test failures [12][13]. - He notes that AI can effectively handle independent tasks with clear descriptions, making it a valuable tool for developers [10][15]. - The ability of AI to replicate complex coding tasks in a fraction of the time previously required marks a significant shift in programming practices [16]. Group 4: Concerns and Critiques - Some developers express skepticism about AI's capabilities, particularly in complex system design and long-term maintenance, highlighting ongoing challenges in AI-generated code quality [20][22][27]. - Concerns arise regarding the potential for over-reliance on AI to diminish engineers' understanding of systems, suggesting that AI may be more suited for prototyping than production environments [27][28]. - The debate continues on the balance between AI's benefits and its limitations, indicating that the role of AI in engineering is still evolving [28]. Group 5: Future Outlook - Antirez acknowledges the inevitability of AI's impact on programming, urging developers to adapt rather than resist [29]. - He emphasizes the importance of understanding how to effectively use AI tools to enhance creativity and productivity in software development [30]. - The article concludes with a call for developers to engage with AI technologies thoughtfully, suggesting that the future of programming will increasingly involve collaboration with AI [31].

Core Insights - A recent study by Google Research reveals that simply repeating a question can significantly enhance the accuracy of large language models (LLMs) from 21.33% to 97.33% without requiring reasoning capabilities [1][4][18] - This technique, termed "prompt repetition," challenges the need for complex prompting strategies like "Chain of Thought" and "Multi-shot" [1][9][10] Group 1: Effectiveness of Prompt Repetition - The study demonstrated that prompt repetition outperformed baseline methods in 47 out of 70 tests, with no losses recorded [12][13] - In a specific test involving identifying the 25th name from a list of 50, the accuracy of Gemini 2.0 Flash-Lite improved from 21.33% to 97.33% through repetition [16][18] - The technique provides a "look-back" opportunity for models, allowing them to utilize previously seen information, thus enhancing performance [29][32] Group 2: Efficiency and Cost-Effectiveness - Prompt repetition does not significantly impact generation speed, as the processing of repeated prompts is highly parallelizable [36][40] - This finding suggests that developers can achieve high accuracy without the need for larger, more expensive models, making it a cost-effective solution [41][42] - The ability to enhance smaller models' performance to match or exceed that of larger models represents a significant advancement in AI technology [42] Group 3: Limitations and Safety Considerations - While effective for retrieval tasks, prompt repetition is not suitable for reasoning tasks, where models may already internally repeat the prompt [46][52] - The increased attention mechanism from repetition could potentially amplify certain instructions, raising security concerns regarding model vulnerabilities [56][58] - Developers are encouraged to consider the implications of prompt repetition on both model performance and security, potentially using it as a defensive strategy [60][61]

转自：吐槽星君 公众号 · 吐槽星 抱歉老己我忘记密码了 #童年回忆 #密码本 这个本子的密码应该是几位数 啊啊啊啊,小时候老喜欢自言自语写日记,买了好多 密码本,如今只剩这本了 ... 我好像丢失了这段记忆, 密码也是毫无头绪怎么试都不对,还舍不得强拆心此 刻的我好像个偷窥狂,疯狂想窥探以前的自己 。 · 叶槽 & 公众号 密码本就是防大人的呀，你已经是大人了 2025-12-31 广西 回复 ♡ 6.4万 (==) 不行,老己是我你快开门啊 2025-12-31 江苏 回复 >>>>>>>>> a 更惨的是费劲九牛二虎之力打开发现里面是用以 前自己编的文字写的,现在那个解码表都不见了。这 本日记永远成迷,, ♡ 2.8万 2025-12-31 江苏 回复 (=) T 我真不行了 6 还有造字的 2025-12-31 重庆 回复 ♡ 1.7万 (=) 展开 741 条回复 H 最大山北 我暴力打开了,第一句话是"偷看我本子的人都 si 了" …… 01-09 江苏 回复 (二) 7448 老己无效 02 20 2 1 1 1 2 3 01-10 浙江 回复 我之前也 ..... 然后我暴力破开了,发现本p人 ...

Core Viewpoint - The claims made by Cursor regarding the development of a browser using GPT-5.2 have been debunked, revealing that the code produced is non-functional and lacks engineering logic, described as "AI Slop" [2][8][37]. Group 1: Claims and Debunking - Cursor announced that their AI coding agents, powered by GPT-5.2, created a browser with 3 million lines of code in 7 days, which generated significant excitement in the AI community [4][5]. - Upon investigation, developers found that the code could not even compile, indicating that the project was misrepresented [6][10]. - The technical community criticized Cursor for using misleading marketing tactics, creating an illusion of success without actual evidence [9][12]. Group 2: Analysis of Cursor's Project - The official blog post from Cursor outlined their goal to push the boundaries of AI coding agents, but it failed to provide concrete evidence of success [17][18]. - Despite claiming significant progress, Cursor did not demonstrate that the browser was functional or could run basic tasks [22][27]. - The project was found to be riddled with errors, with no successful commits that could compile, indicating a lack of genuine engineering effort [32][34]. Group 3: Community Reaction - The developer community expressed outrage over Cursor's presentation of incomplete work as a milestone, highlighting the disconnect between investor perceptions and actual coding realities [43][46]. - Discussions on platforms like Hacker News revealed skepticism about the project's authenticity, with many pointing out that the claims were exaggerated and misleading [47][48]. - The incident has sparked a broader conversation about the quality of AI-generated code and the importance of engineering rigor in AI applications [60][71]. Group 4: Future Implications - The failure of Cursor's project emphasizes the need for clear roles and structured collaboration among AI agents to achieve meaningful outcomes [62][73]. - The emergence of "Cracked Engineers," who effectively leverage AI while maintaining engineering standards, is seen as a necessary evolution in the software development landscape [68][72]. - The incident serves as a cautionary tale about the potential pitfalls of relying solely on AI for coding without proper oversight and validation [70][75].

Group 1 - The core incident involves a miscommunication at Ctrip, where employees received unexpected layoff notifications due to a system error during a software shutdown [2] - Ctrip clarified that the incident was a mistake related to a system test and there was no actual layoff plan, apologizing to affected employees [2] - The incident sparked various reactions on social media, with some users mocking the situation and others suggesting it was a clever marketing move by Ctrip [2]