精神紧张时，你会跑向哪个角落给自己"紧急扩容"？有人上班业余时间就冲向户外，用骑行、徒步、夜跑来释放 被代码困住的大脑内存；有人在工位上搭载一座手办展示塔，用二次元的"高维力量"对冲工作带来的损耗；还有 人抓紧一切碎片时间，组队开黑、撸猫遛狗、天台发呆，感受今天的风儿有点喧嚣……程序员们自有一套回血方 式，简直比编程语言还多样！正所谓"人生苦短，我用python"，要问你觉得世界上最好的"状态回血"语言是什 么，你会给出怎样的回答？ 1024程序员节前夕，我们想征集100+位程序员的【状态恢复小妙招】，你的方式或许正是另一个同行急需的"救 命代码"，欢迎分享你的回血姿势～让我们一起搭建这个属于程序员们的"解压仓库"，在每一个宕机的时刻，彼此 点亮："别慌，我们都有办法power off power on!" 点击参与 "能跑就行"，是程序员们一种通用的自嘲语言。别管代码底层逻辑怎样，程序能运行起来了，就不要管了。人生 也是如此，程序员的一天总是过得满满的：需求压垮界面，BUG叠满屏幕，老板夺命连环call……代码有负载爆 表的时刻，人也会压力过载，但不管是程序还是人，"能跑就行"！放过自己，未尝不是一种选择。 ...

10月9日，恒华科技盘中上涨2.06%，截至13:07，报6.44元/股，成交5419.49万元，换手率1.66%，总市 值38.63亿元。 资金流向方面，主力资金净流入387.15万元，大单买入1091.19万元，占比20.13%，卖出704.04万元，占 比12.99%。 恒华科技今年以来股价涨4.89%，近5个交易日涨2.22%，近20日涨1.74%，近60日跌3.74%。 资料显示，北京恒华伟业科技股份有限公司位于北京市东城区安定门外大街138号皇城国际中心A座12 层，成立日期2000年11月23日，上市日期2014年1月23日，公司主营业务涉及面向智能电网的信息化服 务供应商,致力于运用信息技术、物联网技术,并秉承信息技术与物联网技术相结合的理念,为智能电网提 供全生命周期的一体化、专业化信息服务。主营业务收入构成为：软件服务44.86%，系统集成 44.19%，技术服务7.41%，软件销售2.75%，硬件销售0.80%。 恒华科技所属申万行业为：计算机-软件开发-垂直应用软件。所属概念板块包括：SAAS、职业教育、 养老产业、数据要素、水利建设等。 截至6月30日，恒华科技股东户数2.99万，较 ...

10月9日，顶点软件盘中上涨2.03%，截至10:19，报43.73元/股，成交9617.09万元，换手率1.09%，总市 值89.82亿元。 资金流向方面，主力资金净流出337.24万元，特大单买入328.81万元，占比3.42%，卖出534.21万元，占 比5.55%；大单买入1384.53万元，占比14.40%，卖出1516.36万元，占比15.77%。 责任编辑：小浪快报 截至6月30日，顶点软件股东户数2.18万，较上期减少10.92%；人均流通股9373股，较上期增加 12.26%。2025年1月-6月，顶点软件实现营业收入2.46亿元，同比减少7.97%；归母净利润5287.79万元， 同比增长8.40%。 分红方面，顶点软件A股上市后累计派现8.14亿元。近三年，累计派现4.90亿元。 机构持仓方面，截止2025年6月30日，顶点软件十大流通股东中，香港中央结算有限公司位居第七大流 通股东，持股225.10万股，相比上期减少43.68万股。华商新趋势优选混合（166301）位居第十大流通股 东，持股120.00万股，为新进股东。华宝中证金融科技主题ETF（159851）退出十大流通股东之列。 ...

人民财讯10月9日电，企查查APP显示，近日，杭州光跃互娱科技有限公司成立，法定代表人为陆遥， 经营范围包含：软件开发；信息技术咨询服务；技术服务、技术开发、技术咨询等。企查查股权穿透显 示，该公司由腾讯科技(上海)有限公司全资持股。 ...

Core Insights - The article questions the effectiveness of AI in enhancing software development productivity, highlighting a discrepancy between developers' perceptions and actual performance metrics [1][6][21] - Despite widespread adoption of AI tools, there is no significant increase in the volume of new software releases, contradicting claims of enhanced productivity [8][20][21] Group 1: AI Productivity Claims - Developers believe AI tools improve their productivity by approximately 20%, but research indicates a decline in actual speed by 19% [1][6] - A personal experiment showed that using AI tools resulted in a median speed decrease of 21%, aligning with the findings of the METR report [4][21] - Prominent AI tools like GitHub Copilot and Claude Code claim to significantly boost productivity, yet these claims lack substantial evidence [6][7] Group 2: Software Release Trends - Data analysis reveals no significant increase in new software releases, suggesting that the anticipated "flood" of applications has not materialized [8][20] - The expected exponential growth in software production due to AI adoption is not reflected in the data, which shows flat trends instead [20][21] - The core premise that AI tools would lead to increased output is fundamentally flawed, as developers are not delivering more products than before [21][23] Group 3: Industry Implications - The pressure on developers to adopt AI tools is driven by a fear of missing out on competitive advantages, leading to organizational restructuring and layoffs [7][21] - Many developers feel overwhelmed by the need to master AI tools, which can lead to decreased job satisfaction and productivity [21][22] - The narrative surrounding AI's transformative impact on software development is challenged by the lack of tangible results and evidence of improved efficiency [22][23]

【导读】：一位拥有 17 年经验的高级全栈开发者，刚为初创公司做出盈利项目，就因暂助其他团队埋 下"祸根"。他发现团队存在 git force push、任务管理混乱等问题，还找出会泄露密钥的安全漏洞。 即便他给出修复方案，却遭后端开发指责，更被 CTO 以"给同事压力大"为由开除。明明句句属实，却被贴 上"外行"标签，这样遭遇，引发无数人共鸣。 他在 Reddit 的网名是 WorstDeveloperEver（"最烂开发没有之一"，下面就他"烂哥"吧），可见对其遭遇非常 生气的。他的奇特遭遇引发热议。 误入某创业公司 我在一家小型初创公司工作了两个多月。作为一名拥有 17 年工作经验的高级全栈开发，我负责的项目与团队 其他项目相互独立。 公司要求我从零开始搭建这个项目，且尽量减少依赖，最终整个项目的体积控制在了 300KB 以内。其中包含 200KB 经过优化的 webp 格式图片和 100KB 的打包文件，这是一款 SaaS 产品。 CTO 对这个项目非常满意，上线后很快就开始盈利，还让我接手下一个新项目的开发。我对项目进行了全面 优化，最终所有性能指标都达到了 100 分。 在此期间，由于新项目的设计 ...

"最近新接手了老项目升级维护，刚刚收到反馈说有个页面用户说选不了 2027 年以后的时间。然后我看到了下 面的东西……" 网友回复 - EOF - 推荐阅读 点击标题可跳转 1、 宇宙厂海外员工晒 7 点下班，国内同事表示才开始晚班，对方回怼"我们效率高"，这边瞬间气炸了… 2、 "同事被裁了，私人物品全带走，只留下一堆荣誉奖杯"。网友：随便丢，都是骗打工人卖命的 3、 "不想搞 IT 了！微软工程师凌晨猝死，警醒 33 岁手握 300 万美元的程序员：我能自由退休吗？" 网友爆料 l 4、 "辛辛苦苦卖命，替公司裁员约 200 人，给公司省了 150 多万，最后我也被裁了" ...

【导读】：一位拥有 17 年经验的高级全栈开发者，刚为初创公司做出盈利项目，就因暂助其他团队埋下"祸 根"。他发现团队存在 git force push、任务管理混乱等问题，还找出会泄露密钥的安全漏洞。 即便他给出修复方案，却遭后端开发指责，更被 CTO 以"给同事压力大"为由开除。明明句句属实，却被贴 上"外行"标签，这样遭遇，引发无数人共鸣。 他在 Reddit 的网名是 WorstDeveloperEver（"最烂开发没有之一"，下面就他"烂哥"吧），可见对其遭遇非常 生气的。他的奇特遭遇引发热议。 误入某创业公司 我在一家小型初创公司工作了两个多月。作为一名拥有 17 年工作经验的高级全栈开发，我负责的项目与团队 其他项目相互独立。 公司要求我从零开始搭建这个项目，且尽量减少依赖，最终整个项目的体积控制在了 300KB 以内。其中包含 200KB 经过优化的 webp 格式图片和 100KB 的打包文件，这是一款 SaaS 产品。 CTO 对这个项目非常满意，上线后很快就开始盈利，还让我接手下一个新项目的开发。我对项目进行了全面 优化，最终所有性能指标都达到了 100 分。 在此期间，由于新项目的设计 ...

Core Insights - The vibe coding movement has evolved through multiple waves, with the current phase focusing on mobile app development and seamless transitions between devices [2] - Supabase, an open-source application development platform, has seen significant growth, increasing its user base from 1 million to over 4 million developers in the past year, with AI builders making up about 30% of new signups [2][3] - Supabase recently raised $100 million in Series E funding, achieving a valuation of $5 billion, bringing total funding to $500 million since its inception in 2020 [3][4] Company Developments - Supabase's Series E funding round was primarily restricted to existing investors, with Figma being the only new institutional investor [4] - The company aims for a future valuation of $50 billion to $100 billion, emphasizing the importance of existing investors in achieving this goal [5] - Supabase's CEO believes that while coding may become easier, there will still be a strong interest in coding and development, leading to a potential increase in the number of people wanting to build applications [5]

Core Insights - The AI software development sector is currently facing three major technical challenges: low efficiency in multimodal algorithm integration leading to 15%-20% semantic loss in cross-platform content generation, insufficient frame rate stability when real-time rendering engines collaborate with AI models (with traditional solutions showing an average frame rate fluctuation of ±12.3% at 4K resolution), and the dilemma of balancing data privacy protection with model performance, where 78% of enterprise developers must compromise between encryption strength and inference speed [1] Group 1: Company Technology Breakthroughs - Anhui Jie Chuan Information Technology Co., Ltd. has developed a "multi-engine dynamic adaptation framework" that enables intelligent switching of engines for live streaming, digital humans, and short videos through modular design, achieving a video rendering efficiency 2.3 times higher than traditional solutions [2] - The company's hybrid precision inference engine dynamically adjusts the FP16/FP32 computation ratio, reducing inference latency from the industry average of 120ms to 68ms while maintaining a model accuracy of 99.2% [4] Group 2: Performance Data Validation - In AI live streaming scenarios, the solution supports processing 16 simultaneous 4K video streams with a CPU usage reduction of 42% compared to traditional methods, and the digital human generation system can complete high-precision modeling in 3 minutes with a facial detail restoration rate of 98.7%, surpassing the industry average of 92.4% [5] - The short video matrix generation system can produce 2,800 structured content pieces per day, improving efficiency by 140 times compared to manual production [5] Group 3: Application Effectiveness Evaluation - After adopting the AI live streaming system, a provincial media organization reduced its live streaming incident rate from 3.2 times per month to 0.7 times, with audience retention increasing by 27% [6] - In the e-commerce sector, the AI digital human customer service achieved a response accuracy of 93.5%, an increase of 41 percentage points over traditional rule engines, with a stable response time of 280ms under 100,000 concurrent requests, meeting 99.9% service availability requirements [6] Group 4: Solution Advantages Analysis - Compared to traditional development models, the solution shortens project delivery cycles by 65%, with a low-code platform enhancing development efficiency by 3.8 times [7] - The federated learning architecture reduces model training data retention by 82% while maintaining 95.3% model performance, leading to a decrease in data breach risk index from 4.7 to 1.2 after deployment in a financial institution [7] Group 5: User Value Feedback - A fast-moving consumer goods brand increased its average monthly content output from 120 to 3,500 pieces using the AI short video matrix system, with user interaction rates rising by 310% [11] - The CEO reported that the system's automated content strategy optimization improved ROI from 1:2.8 to 1:5.6, while operational costs decreased by 58% [11] - The company has established a complete solution in AI live streaming, digital humans, and short videos, with a customer NPS of 78, significantly higher than the industry average of 52 [11]