paulguo.eth @guokai
That which does not kill us makes us stronger. 👨🏻💻 Front-end engineer and architect, love LLM & Web3. Singapore Joined April 2009-
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血月，是月光坠入了地球的影子。我不喜血月，我把它当作警示：要永远绽放自己的光芒，不要被他人所遮蔽，更不要做他人的影子。把今天当作生命中的最后一天，去发光，去奋斗，去战斗，去赢得胜利。
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昨天已经下单第二批预售的DingTalk A1，预计月底之前发货，希望钉钉不只是价格优势，转录质量、服务体验但愿都能跟上，如果试用体验好的话，就可以替换掉手头价格不低、效果一般、服务较差的TicNote了。
昨天买了TicNote，今天刚好下午有方案讨论会，试用了下，发现体验还不错，生成的思维导图能够比较准确的把握关键要点，并且提供了AI洞察功能。智能硬件的优势是把特定场景的大模型工作流产品化和实体化了，用户只需要一键使用，比较适合高频或刚需场景。
小米SU7 Pro今天顺利提车了！
OpenAI released GPT-4.1 models with better coding, instruction following, and 1M-token context—faster, smarter, and cheaper than before.
技术的浪潮不断翻涌，但真正深刻的变革不在于系统如何更迭，而在于人与世界如何相处——从占有信息，到被理解；从机械操作，到唤醒创造。
耗时26分钟，利用不足50美元的云计算费用（16 NVIDIA H100 GPUs），在数学推理任务上超越了 OpenAI o1-preview！s1论文中提出的Budget Forcing本质上是基于Test-Time Scaling的高效激活预训练模型推理能力的SFT方法，核心思想是在尽可能小的高质量数据集上，通过最小化的成本，最大化模型的推理能力。
抽时间读了DeepSeek的五篇论文，也在RTX 3090上实际体验了本地推理效果，再次感叹开源精神和团队技术创新能力的重要性。DeepSeek通过MoE架构优化，降低了大规模训练成本；采用GRPO的强化学习（RL）减少了对 SFT数据和过程的依赖；结合MLA和模型蒸馏，降低了部署成本并提升了推理性能。
读了DeepSeek的Paper，感觉核心是它的训练方法，不需要标注数据，而且弱依赖SFT，并且基于计算成本更低的GRPO的（无监督）强化学习和少量冷启动数据让R1的推理能力能够接近o1，然后生成高质量数据蒸馏出来参数量更小的模型。
我刚在理发店被动听了他们员工现场开会，全程在讲门店能够按时发出工资的不易，在讲寒冬和降本增效，还提到了极越的危机，手机行业的残酷竞争现实，也提到了对明年经济的不乐观预期。
打疫苗好比是对免疫系统的Pre-Training，上周打完疫苗虽然没有严重的反应，但是嗓子疼、流鼻涕、咳嗽的症状都经历了一遍，有点像是轻微感冒的感觉，但总比中招后高烧难受还需要请假的情形好太多了。
很久没有对开源社区贡献了，发布一个基于近期想法验证后实现的标注加速器TurboLabel，融合了 YOLO、SAM和LLM对图像进行二阶段的自动化标注，也可以通过自然语言方式扩展对不同格式的支持，欢迎交流反馈。
Approximately 801 people in total.
Approximately 801 people in total.
如今的时代，我们很多人都像是被人狠狠踩进泥土里的种子，每个人都想破土而出，但却无力挣脱深陷的泥泞；有些种子选择了窒息，有些种子选择了顺其自然，但还有些种子依然不屈地在泥土中挣扎，每天努力地呼吸每一缕新鲜空气，用力地沐浴每一缕阳光，等待着能够萌芽的那一天。
技术高度的攀登是在不断的好奇、冲动、不服输、成就感中驱动向上，这是骨子里的驱动力，绝不是物质刺激的外部力量所能带动。对内在驱动力不敏感的技术人，绝无可能做到优秀的。
作为拨号上网时代早期接触互联网的人来讲，雷军22年的年度演讲依然非常撼动人心。无论时代浪潮如何翻滚，无论岁月如何变迁，无论我们的容颜如何改变，都不应该放弃曾经燃起的激情火花和伟大梦想。正如雷总所说，所有挫折、失败，都将成为最宝贵的财富，让我们保持信念，永远相信美好的事情即将发生。
今天和一位同事讨论到团队管理，关于提到的（中层）管理“不用管过程，只需要管结果”的观点我暂时不是很认同，尤其是对于研发团队，一定要综合直接下属的胜任度和整体团队的成熟度来看。因为结果是管不了的，只有关注过程才有可能管理结果，当然要注意的是过程管理不等于微观管理。
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