刘江/LIU Jiang

特别烦那些 GitHub README 不开门见山直说这个项目怎么用的。我找到你这个项目，我就是想要一个开盒即用的东西。你在这里讲解你的架构有多复杂，跑分如何跑赢竞品，这都是在浪费我的注意力。你最好在 README 标题下就给我 brew install 或者 npm install 命令。 这种问题在中国人发布的 GitHub 仓库中更常见。（即使 README 是用英文写的。）如果你发布了一个小众项目，你不需要向别人证明这个项目有多好，因为竞品并不存在。能找到这个项目的人，都是需要用它来解决一个具体问题的，你说清楚能解决什么问题和如何使用就好了。

又一家大厂研究团队成员出来创业的neo lab，也是第一轮就融资几亿美元，估值几十亿，主要投资人是谷歌风投、NVIDIA。两位联合创始人Richard Socher是前Salesforce首席科学家，Tim Rocktäschel是DeepMind OpenEndedness方向负责人/UCL教授。赛道是自我改进的超级智能。

有人问我：现在还有必要学编程语言吗？ 我的答案：从技能角度，没有意义了。 更应该学的是如何驾驭智能体——Claude Code、Codex、OpenClaw、Hermes。编程语言的细节，让AI去写就好了。这不是说编程没价值，是说：编程语言≠编程能力，驾驭智能体才是新的编程。会指挥AI写100个功能，比自己写10个功能更值钱。

人类主观上的“生命长度”，并不接近客观时间。 而更接近：记忆中的状态变化密度。 ⸻ 童年为什么漫长？ 不是因为时间更多，而是因为： - 一切都新 - 世界模型持续重建 - 感知高分辨率 - 没有稳定压缩模板 于是，大脑无法把经验批量编码。每一天都必须真实计算，所以时间被“拉长”。 #来自AI的洞见

The enemy of truth is motivated reasoning.

关于Anthropic为什么能赶超OpenAI。很多人可能没注意一个细节，其实GPT-3的核心研发团队就是Anthropic这帮人，他们走了以后OpenAI花了不少力气才接住摊子。

Recent thoughts: The Shift to Long-Horizon Tasks The most likely breakthrough this year will be in long-horizon tasks. We are moving toward a stage where Large Language Models (LLMs) learn to complete extended, complex missions by interacting with Agent environments. This is perhaps where the true value of LLMs lies. Take cybersecurity as an example: imagine a model that continuously hunts for software bugs and vulnerabilities. While it sounds like a search process, it’s actually the model learning the high-level intuition and methodology of a professional hacker. Unlike humans, AI can run 24/7 without fatigue. It could potentially find exploits at a much higher frequwill ency and claim bounties on platforms like HackerOne or BugCrowd. It sounds fun, but fundamentally, it's a revolution that displaces the hacker. If even hackers are being "disrupted," one can only imagine the impact on general programmers. From One-Person to None-Person Companies Building on long-horizon capabilities, Autonomous Agent Systems (AAS) will inevitably become the next frontier. Last year, we were discussing the rise of the "One Person Company" (OPC). I didn't expect us to move so quickly toward the "None Person Company" (NPC). It’s an ironic twist—we might all end up as NPCs in this new ecosystem. Engineering the Impossible: Memory and Learning To realize the vision above, we must solve three technical pillars: Memory, Continual Learning, and Self-Judging. I used to think these would require massive paradigm shifts and years of research. However, the pressure from both the technical and application sides is so intense that we are seeing these capabilities emerge through ingenious engineering "tricks": Memory: Long context windows (1M+) and RAG have significantly bridged the gap. Continual Learning: While true continual learning remains difficult, the release cycles are shrinking. Global models are updated monthly; domestic models are catching up. If we reach weekly updates by next year, it will effectively function as continual learning. Self-Judging: This remains the most elusive, yet models like Opus 4.7 are already demonstrating early self-correction and judgment capabilities. The Self-Evolving Endgame The most difficult—and most promising—path is Self-Evolution. The current wave is incredibly fierce. I suspect that models like Claude may have already achieved a baseline for self-training: writing their own code, cleaning their own data, generating synthetic data, and then training on it. It might "waste" some compute, but it saves the most precious resources: human labor and time. In the LLM era, speed is everything. Rapid iteration is what creates the cognitive gap between leaders and followers. Claude’s rumored 2-million-chip cluster for next year is likely dedicated to exactly this: autonomous model self-training. Technical Summary: 1M Context: Necessary baseline. Memory & Continual Learning: Prerequisites, likely solved first via "tricky" engineering. Harnessing Environments: The breakthrough point. Self-Judging: The tipping point. Full Self-Training: The endgame. Redefining AGI and the Industry If this is the road to AGI, then AGI’s definition should be the sum of all human collective intelligence, not just an individual’s intelligence. It must possess the creative capacity to produce something as profound as the "Theory of Relativity"—meeting the bar set by Hassabis. During this transition, every APP will need to be reconstructed as AI-native. In fact, we might move past the concept of APPs entirely. The most significant challenge will be the reconstruction of the operating system itself. In the future, you won’t see a traditional desktop; you will see an LLM OS, where applications are "generated on demand." This challenges the 80-year-old Von Neumann architecture and represents a total upheaval of the computer science industry. The Irreversible Wave From completing long-horizon tasks to fully autonomous operations, every sector—Security, Finance, Law, E-commerce—will be reshaped. Many friends have reached out lately, asking how to transform their enterprises to keep pace with AI. But few truly realize that this irreversible process has already begun. As this massive technical wave hits, we must be prepared to act, but we must also start thinking seriously about how to regulate it.

看完了张小珺对罗福利和姚舜宇两个人的采访，特别有感触，很有意思。 我感觉罗福利的逻辑完全不在线，看不出来是能够带领大模型往前发展的人才。 但姚舜宇一看就是个非常聪明的人，而且也非常务实。虽然他工作经验才两年，但我觉得他对大模型的理解比罗福利要强很多。 所以说，这是一个很奇怪、有意思的现象。 过几年再回来看看吧