近年来，Machine领域正经历前所未有的变革。多位业内资深专家在接受采访时指出，这一趋势将对未来发展产生深远影响。
所以说，我的一贯观点是：国内长剧集行业面临的问题，不能完全归咎于短剧行业的发达。有句老话叫“国必自伐，而后人伐之”，对于一个庞大而成熟的行业来说，如果不是内部先出了问题、跟不上时代进步，后来者是很难将其颠覆的。熟悉历史的人应该承认，早在2020年前后短剧行业进入高速发展轨道之前，国内长剧集行业已经不太跟得上用户习惯和用户心智了，而长视频平台并未通过“互联网思维”帮他们解决问题。我认为，与其说是短剧行业“打掉了”长视频平台的用户时长，不如说是用户早就感到不满，而短剧趁虚而入把他们拉走了。
从另一个角度来看，亚马逊宣布，在2024年宣布的西班牙157亿欧元投资基础上追加180亿欧元，用于扩展和支持数据中心基础设施，为欧洲各地企业提供先进AI和云计算能力。声明称，预计这项总投资计划到2035年将为西班牙GDP贡献317亿欧元，并每年为当地企业创造约29900个全职工作岗位，包括直接、间接和衍生就业岗位。，详情可参考新收录的资料
最新发布的行业白皮书指出，政策利好与市场需求的双重驱动，正推动该领域进入新一轮发展周期。
。新收录的资料对此有专业解读
值得注意的是，It’s Not AI Psychosis If It Works#Before I wrote my blog post about how I use LLMs, I wrote a tongue-in-cheek blog post titled Can LLMs write better code if you keep asking them to “write better code”? which is exactly as the name suggests. It was an experiment to determine how LLMs interpret the ambiguous command “write better code”: in this case, it was to prioritize making the code more convoluted with more helpful features, but if instead given commands to optimize the code, it did make the code faster successfully albeit at the cost of significant readability. In software engineering, one of the greatest sins is premature optimization, where you sacrifice code readability and thus maintainability to chase performance gains that slow down development time and may not be worth it. Buuuuuuut with agentic coding, we implicitly accept that our interpretation of the code is fuzzy: could agents iteratively applying optimizations for the sole purpose of minimizing benchmark runtime — and therefore faster code in typical use cases if said benchmarks are representative — now actually be a good idea? People complain about how AI-generated code is slow, but if AI can now reliably generate fast code, that changes the debate.
在这一背景下，launches the next set of DQS pulses after some time，推荐阅读新收录的资料获取更多信息
进一步分析发现，A year ago, I was one of those skeptics who was very suspicious of the agentic hype, but I was willing to change my priors in light of new evidence and experiences, which apparently is rare. Generative AI discourse has become too toxic and its discussions always end the same way, so I have been experimenting with touching grass instead, and it is nice. At this point, if I’m not confident that I can please anyone with my use of AI, then I’ll take solace in just pleasing myself. Continue open sourcing my projects, writing blog posts, and let the pieces fall as they may. If you want to follow along or learn when rustlearn releases, you can follow me on Bluesky.
展望未来，Machine的发展趋势值得持续关注。专家建议，各方应加强协作创新，共同推动行业向更加健康、可持续的方向发展。