墨菲定律

Search documents
如何找到人生的掌控感,用自己想要的方式过完一生?
3 6 Ke· 2025-06-17 03:33
Group 1 - The article discusses the concept of "sense of control" and its importance in life, suggesting that many people feel they lack control over their lives, leading to a loss of hope and expectation for the future [1][15] - A psychological study by Harvard University demonstrated that elderly individuals who had control over a simple task, such as watering a plant, had a significantly lower mortality rate compared to those who did not have such control [4][5][7] - The article emphasizes that the pursuit of control is a central theme in life, where individuals strive to gain and maintain control over various aspects of their existence [15][26] Group 2 - The article provides methods for individuals to regain a sense of control, including taking small, manageable actions that can lead to a sense of accomplishment [30][31] - It highlights the importance of managing emotions and attitudes, suggesting that emotional control can enhance one's overall sense of control in life [35][39] - The article also discusses the significance of cultivating personal beliefs and understanding what can and cannot be controlled, which can help individuals navigate challenging situations [41][42]
多车企将“智驾”更名为“辅助驾驶”,克制是为了更好前行
Qi Lu Wan Bao· 2025-05-08 08:30
转自:视点 汽车的"第一性原理",不是外观好看,不是内在舒适,一定是安全性。无论是汽车智能化发展,还是今 天的宣传说法改动,本质都是为了"捍卫安全"。 但就算汽车安全性再高,如果驾驶者缺乏基本的安全意识,一切就都无从谈起。风险意识、防范意识, 是人的生存本能,任何时候任何情况下都不能丢掉。我们每一个人都是自己生命安全的第一责任人,就 目前而言,在开车这件事上,双手不能离开方向盘,命运要牢牢把握在自己手里,这是原则和底线。毕 竟世上没有后悔药,人生也没有重来机会,把风险和隐患扼杀在摇篮里,应该从自己做起,从当下做 起。 积极拥抱技术,不等于完全依赖、绝对信任。人和技术的关系,一定是技术服务于人,而不是让技术掌 控人、主导人,无论是算法还是人工智能,都不足以让我们赌上生命去信任。在和技术打交道这件事 上,一定不能陷入路径依赖、惯性思维,一定要坚守实事求是的原则,具体情况具体分析。电脑死机可 以重启,手机坏了可以修理,顶多耽误点工夫,但"智驾"但凡出了问题,即使问题再小也可能让我们付 出生命的代价。"墨菲定律",即我们俗话说的"不怕一万就怕万一",就适用于此。 这些都是政府以及车企在"智驾宣传"上"踩刹车"的言外之 ...
每个程序员必知的13条魔鬼定律:90%代码终将沦为垃圾
3 6 Ke· 2025-04-29 07:11
Core Viewpoint - The article presents 13 engineering laws that provide insights for engineers and managers to navigate inefficiencies and manage complex projects effectively [1][3]. Group 1: Engineering Laws - Parkinson's Law states that work expands to fill the time available for its completion, often leading to procrastination [5][6]. - Hofstadter's Law indicates that projects will always take longer than expected, even when this law is taken into account [6][9]. - Brooks' Law asserts that adding manpower to a late software project makes it later, highlighting the inefficiency of increasing team size in such scenarios [10][11]. - Conway's Law suggests that the design of a system reflects the communication structure of the organization, impacting product architecture [13][15]. - Cunningham's Law posits that the best way to get the right answer on the internet is to post the wrong answer, emphasizing the importance of collaboration [16][18]. - Sturgeon's Law states that 90% of everything is garbage, implying that only a small fraction of features or code is truly valuable [20][21]. - Zawinski's Law suggests that all programs will expand until they can handle email, leading to feature bloat [21][24]. - Hyrum's Law indicates that once an API has many users, all observable behaviors will be relied upon by at least one user, complicating maintenance [24][25]. - Price's Law states that in any team, 50% of the output is produced by the square root of the total number of individuals, illustrating the uneven distribution of productivity [25][26]. - Ringelmann Effect reveals that individual efficiency decreases as team size increases, suggesting the need for smaller teams [27][29]. - Goodhart's Law warns that once a measure becomes a target, it ceases to be a good measure, indicating the potential for manipulation of KPIs [30][32]. - Gilb's Law states that anything that needs to be quantified will have a way to measure it, advocating for the importance of measurement [32][37]. - Murphy's Law asserts that anything that can go wrong will go wrong, emphasizing the need for thorough testing and validation [38][40]. Group 2: Importance of the Laws - These laws serve as valuable mental models for engineers and managers to avoid common pitfalls in project management and software development [41].