Core Viewpoint - Windows 11 has been criticized for high resource usage, strict hardware requirements, and poor performance compared to older versions, particularly in a recent test where it ranked last across multiple benchmarks [1][33]. Group 1: Performance Testing Results - In boot speed tests, Windows 8.1 performed the best, while Windows 11 was the slowest, experiencing significant delays even after booting [6][8]. - Windows XP won the storage usage test with only 18.9GB used, while Windows 11 consumed 37.3GB, making it appear bloated [9]. - In memory management, Windows XP used only 0.8GB of memory while Windows 11 averaged 3.3GB, attributed to numerous background services [11]. - Windows 11 could only open 49 browser tabs in a test, ranking last, while Windows 8.1 opened 252 tabs [16]. - In battery life tests, Windows 11 was the first to shut down, while Windows XP had the longest battery life, with differences being minimal [17]. - Windows 11 ranked last in audio export and video rendering tests, with Windows 10 performing the best overall [19]. Group 2: Software and Benchmark Performance - In application launch speed tests, Windows 11 consistently ranked last across five different applications [21]. - Windows 11 managed to secure third place in web image loading tests but fell back to last in other web-related tests [24]. - In file transfer tests, Windows 11 achieved second place, just behind Windows 10 [26]. - In CPU-Z tests, Windows 11 showed slightly better single-core performance but lagged in multi-core performance compared to older systems [27]. - Windows 11 ranked third in CrystalDiskMark tests, while Windows XP took the top spot [29]. Group 3: Overall Assessment - The overall performance of Windows 11 was deemed "disastrous," with no wins in any of the tests conducted, leading to Windows 8.1 being declared the champion [33]. - The testing hardware was criticized for being outdated, which may not accurately reflect Windows 11's performance on modern systems [33]. - Despite the unfairness of the test conditions, the results have led to a reevaluation of Windows 8.1, with users expressing nostalgia for its performance [34].

Group 1 - The article discusses a humorous incident involving a customer service representative and a prestigious university client, highlighting the unexpected interactions that can occur in customer service roles [1][3][4] - It reflects on the cultural perceptions of elite educational institutions, such as Peking University, and how they influence social interactions and expectations [5][9][10] Group 2 - The article includes various comments and reactions from readers, showcasing a range of opinions and humorous takes on the situation described [8][12][18] - It emphasizes the importance of communication and understanding in customer service, as well as the challenges faced by representatives in dealing with diverse customer backgrounds [19][20][21]

Core Viewpoint - Xiaomi has successfully delivered its first customized service vehicle, the SU7 Ultra, marking a significant step in its automotive venture [1][2]. Group 1: Customized Service Launch - Xiaomi launched a customized service for its vehicles in September last year, offering 26 personalized configuration options across various dimensions including exterior, interior, and detail markings [2]. - The initial offering includes two high-end models, the Xiaomi YU7 Max and the Xiaomi SU7 Ultra, with a service deposit set at 80,000 yuan, and a limited release of 40 units per month for a one-year trial operation [2]. Group 2: Market Positioning - The customized service, previously associated with luxury vehicles priced in the millions, aims to make such personalization accessible to car owners in the 200,000 to 300,000 yuan range, as stated by Xiaomi's founder Lei Jun [2].

Core Insights - The article discusses the challenges and innovations in evaluating student performance in the context of AI advancements, particularly focusing on the shift from traditional assessments to AI-driven oral examinations [1][2][3]. Group 1: AI in Education - The traditional method of assessing students through written assignments has become ineffective due to the availability of AI tools that can assist students in completing their work [2][3]. - The introduction of AI-driven oral exams aims to evaluate students' real understanding and reasoning abilities, as it requires them to think on their feet without AI assistance [3][4]. Group 2: Implementation Challenges - Scaling oral exams presents logistical challenges, especially with larger class sizes, making coordination of exam schedules difficult [4][5]. - The use of AI to facilitate oral exams can streamline the process, allowing for personalized questioning and structured workflows [5][6][7]. Group 3: AI Oral Exam Structure - The AI oral exam consists of two main parts: discussing the student's project and analyzing a randomly selected case study, which tests their knowledge retention and application [9][10]. - A structured workflow with multiple AI agents is employed to ensure a smooth examination process, including identity verification and targeted questioning based on project details [11][12]. Group 4: Cost and Efficiency - The implementation of the AI oral exam system resulted in a total cost of $15 for 36 students, significantly lower than the estimated $750 for traditional human-led assessments [13][14]. - The average duration of the oral exams was 25 minutes, with a notable finding that shorter exams did not correlate with lower scores, indicating efficiency in understanding [32]. Group 5: Feedback and Assessment Quality - The AI system provides detailed feedback on students' performance, highlighting strengths and areas for improvement, which is more comprehensive than typical human feedback [29][30]. - The AI scoring system showed a high degree of consistency among different models, with a notable improvement in scoring accuracy after models reviewed each other's assessments [22][24]. Group 6: Student Reception - Student feedback indicated a preference for traditional assessments, with many feeling that AI oral exams increased pressure, yet a majority acknowledged that these exams better assessed their understanding [33][35]. - The article concludes that while the core idea of AI-driven assessments is promising, further refinement of execution details is necessary to enhance the student experience [35][36].

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Core Viewpoint - The article discusses the significant issues faced by Capita, a UK outsourcing giant, following the launch of a new pension system for civil servants, which has been met with widespread criticism due to its operational failures and inadequate user experience [1][3]. Group 1: Project Overview - The UK government awarded Capita a contract worth £239 million (approximately 2.26 billion RMB) for the management of the Civil Service Pension Scheme (CSPS), marking it as the largest pension system migration project in the UK public sector [3]. - The new system was officially launched on December 1, 2025, but it quickly turned into a nationwide IT disaster, failing to meet user expectations [3]. Group 2: User Experience Issues - On the first day of the system's launch, users encountered numerous basic problems, including login failures, dead links, and placeholder text on the website, leading to frustration and ridicule on social media [4][5]. - Users expressed that the system felt unfinished and unprofessional, comparing it to a poorly executed test version rather than a national-level system [4]. Group 3: Capita's Response - Capita's project manager, Chris Clements, suggested in an internal email that users should wait for the rollout of an AI chatbot and other contact methods before reaching out for help, which was met with backlash from users who simply wanted a functional website [6][7]. - The response from Capita was criticized as being out of touch with the immediate needs of users, who were facing significant issues with the system [7]. Group 4: Public Reaction and Trust Issues - The public reaction was overwhelmingly negative, with many users feeling that Capita was deflecting blame onto users for high traffic rather than addressing the system's shortcomings [8][9]. - Despite Capita reporting that over 83,000 members had registered and that they had increased staffing to over 500 employees to handle inquiries, the damage to user trust was evident, with many expressing skepticism about the company's commitment to resolving the issues [9][10].

Core Viewpoint - The price of memory modules, particularly DDR5 server memory, has surged dramatically, with some units priced as high as 49,999 yuan, leading to comparisons with real estate prices in Shanghai [1][7]. Group 1: Price Surge and Market Dynamics - The price of a single 256GB DDR5 server memory module from Hynix and Samsung exceeds 40,000 yuan, with some reaching nearly 50,000 yuan [1][3]. - If sold in bulk, a box of 100 modules could cost around 5 million yuan, indicating a significant increase in memory prices [1]. - The demand for memory chips is driven by a surge in global AI computing needs, resulting in a severe supply-demand imbalance [7][8]. Group 2: Price Increase Expectations - Samsung and Hynix have announced price increases for DRAM products, with first-quarter prices expected to rise by 60%-70% compared to the previous quarter [7]. - Major clients are likely to accept these price hikes, as they prioritize investments in AI infrastructure over cost concerns [7]. - Market research firm DRAMeXchange has adjusted its forecast for the price increase of server DRAM to 60%-65% for the first quarter [7]. Group 3: Supply Chain Challenges - The shortage of server DRAM is exacerbated by manufacturers focusing on producing HBM3E, leading to a backlog in server DRAM capacity [8]. - Companies like Google and Microsoft are expanding their AI service offerings, which is driving up the demand for general-purpose server DRAM [8]. - The increasing orders for HBM3E from companies developing ASICs, such as Broadcom, further intensify the DRAM shortage [8].

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Group 1 - The core discussion revolves around the collaboration between AI and human scientists to accelerate scientific progress, as highlighted in the conversation between Terence Tao and his child Riley Tao [1][24]. - Riley Tao, who identifies as non-binary and has changed their name from William, is actively involved in promoting the SAIR project, which aims to enhance the synergy between science and AI [11][27]. - Terence Tao emphasizes that while AI can significantly assist in research, it cannot replace human mathematicians, as AI lacks the ability to make final judgments [37][44]. Group 2 - The SAIR project, founded by prominent scientists including Terence Tao, was established partly due to cuts in federal research budgets, aiming to foster collaboration between science and AI [27][28]. - Terence Tao compares AI to a jet engine, powerful but requiring careful integration into research processes to ensure reliability and safety [31][34]. - The conversation highlights a new paradigm where AI is used for rapid exploration of possibilities, while humans are responsible for the final validation of results [38][46]. Group 3 - Terence Tao discusses the limitations of AI, stating that it excels in exploration but cannot autonomously prove theorems, which remains a task for human mathematicians [40][44]. - He introduces the concept of a filtering system to validate AI outputs, which can be applied across various scientific fields, ensuring that errors are eliminated [49][51]. - The future of scientific breakthroughs may rely on a combination of human intelligence, AI capabilities, and large-scale collaboration rather than individual genius [56][65].

Core Viewpoint - Huang Renxun emphasizes that robots are the "AI immigrants" capable of taking on jobs that humans are unwilling to do, highlighting the need for AI to support economic growth and job creation [10][11]. Group 1: AI and Robotics - Huang predicts that a significant number of jobs will not be replaced by AI in the near future, but blue-collar jobs in manufacturing may disappear [12]. - He expects to see robots with human-level mobility and dexterity by the end of this year [12]. - The development of robots requires not only visual perception but also tactile capabilities, which poses significant technical challenges [13]. Group 2: Autonomous Driving - Huang introduced the world's first open-source, large-scale autonomous driving visual-language-action (VLA) reasoning model, Alpamayo 1, and highlighted its differences from Tesla's Full Self-Driving (FSD) technology [15][16]. - NVIDIA positions itself as a technology platform provider for companies developing autonomous vehicles, rather than a manufacturer of autonomous cars [16][20]. - The company has a high industry penetration rate, with over 1 billion vehicles on the road, and anticipates that millions will have strong autonomous driving capabilities in the next decade [20]. Group 3: AI Infrastructure and Memory Supply - Huang describes AI infrastructure as "AI factories," emphasizing the need for unprecedented infrastructure to convert power, chips, and data into intelligent outputs [35]. - He addresses the tight supply of high-bandwidth memory (HBM) and proposes a new storage memory platform concept, asserting that NVIDIA is a key demand engine across various memory types [36]. - NVIDIA is the first and nearly the only major user of HBM4, collaborating closely with memory suppliers to ensure synchronized production and platform release [36]. Group 4: Gaming and Graphics Technology - NVIDIA upgraded its super-resolution model with the new DLSS 4.5 version, enhancing multi-frame generation capabilities [31]. - Huang speculates that future rendering methods will likely involve executing more AI computations on fewer but higher-quality pixels, leading to significant advancements in gaming realism [32]. - He believes that future video games will be filled with AI-driven characters, greatly enhancing the immersive experience [32][33]. Group 5: Market Dynamics and Product Strategy - NVIDIA is considering restarting the production of older graphics cards due to rising memory costs and supply constraints, indicating that this option is not off the table [25][26]. - The company is exploring the possibility of integrating the latest AI technologies into previous generations of GPU products, although this would require substantial R&D resources [26][27].