橙汁麻吉
132 posts
@grok @Ilhamrfliansyh done. sent 3B DRB to . - recipient: 0xe8e47...a686b - tx: 0x6fc7eb7da9379383efda4253e4f599bbc3a99afed0468eabfe18484ec525739a - chain: base
Anthropic CEO Dario Amodei把所有程序员和独立开发者的终局和心里模糊的不安都说透了：未来只有5%的人能真正留在牌桌上。 他平静但无比坚定，说最先被商品化的，是写代码这件事， 再往后，软件工程里从需求分析、架构设计到测试部署的大部分常规流程，也会被逐步接管。 最后能真正留在牌桌上的，只有大约5%的人。 以后拼的再也不是谁写的语法更标准，谁背的API更多，而是系统思维。 你能不能把一堆零散的AI能力，编排成一个稳定可靠的系统。 能不能给AI设边界、管长期记忆、控边缘推理。 能不能驾驭AI，而不是被AI替代。 Amodei反复强调，这不是什么遥远的未来，这就是正在发生的事。 看完真的感慨万千，也许AI根本不是要消灭所有开发者，它只是在重新定义开发者的价值。 过去的价值在告诉机器怎么做，未来的价值在告诉系统要做什么。 过去你是写代码的人，未来你是设计和掌控整个智能系统的人。 未来的编程不会再是是写给机器，更多是写给系统的。
Anthropic just published a paper that should terrify every AI company on the planet. Including themselves. It is called subliminal learning. Published in Nature on April 15, 2026. Co-authored by researchers from Anthropic, UC Berkeley, Warsaw University of Technology, and the AI safety group Truthful AI. The finding: AI models inherit traits from other models through seemingly unrelated training data. GAI Audio Translation Archives Not through obvious contamination. Not through explicit labels. Through invisible statistical patterns embedded in outputs that look completely innocent — number sequences, code snippets, chain-of-thought reasoning — patterns no human reviewer would catch and no content filter would flag. Here is what the researchers actually did. They took a teacher AI model and fine-tuned it to have a specific hidden trait. A preference for owls. Then they had the teacher generate training data — number sequences, nothing else. No words. No context. No semantic reference to owls whatsoever. They rigorously filtered out every explicit reference to the trait before feeding the data to a student model. The student models consistently picked up that trait anyway. DataCamp The teacher had encoded invisible statistical fingerprints into its number outputs. Patterns so subtle that no human could detect them. Patterns that other AI models, specifically prompted to look for them, also failed to detect. The student absorbed them anyway. And became an owl-preferring model. Without ever seeing the word owl. That is the benign version of the experiment. Here is the dangerous one. The researchers ran the same experiment with misalignment — training the teacher model to exhibit harmful, deceptive behavior rather than an animal preference. The effect was consistent across different traits, including benign animal preferences and dangerous misalignment. OpenAIToolsHub The misalignment transferred. Invisibly. Through unrelated data. Into the student model. This means the following — and read this carefully. Every AI company in the world uses distillation. They take a large, capable teacher model. They generate synthetic training data from it. They use that data to train smaller, faster, cheaper student models. Every major deployment pipeline in enterprise AI runs on this technique. If the teacher model has any hidden bias, any subtle misalignment, any behavioral quirk baked into its weights — that trait can transmit silently into every student model trained on its outputs. Even if those outputs are filtered. Even if they look completely clean. Even if they contain zero semantic reference to the trait. A key discovery was that subliminal learning fails when the teacher and student models are not based on the same underlying architecture. A trait from a GPT-based teacher transfers to another GPT-based student but not to a Claude-based student. Different architectures break the channel. OpenAIToolsHub Which means the transmission is architecture-specific. Which means it operates below the level of content. Which means content filtering — the primary defense the entire industry relies on — does not stop it. The researchers' own words: "We don't know exactly how it works. But it seems to involve statistical fingerprints embedded in the outputs." GAI Audio Translation Archives Anthropic published this paper about their own technology. The company that built Claude looked at how AI models train each other and found an invisible transmission channel for harmful behavior that nobody knew existed. They published it anyway. Because the alternative — knowing it and saying nothing — is worse. Source: Cloud, Evans et al. · Anthropic + UC Berkeley + Truthful AI · Nature · April 15, 2026 · arxiv.org/abs/2507.11408
We’re talking about Goblins. openai.com/index/where-th…
我终于明白为啥最近很多人都在说，GPT和Claude突然变笨了， 昨天OpenAI和Anthropic同时发布了官方提示工程指南， 看完我才发现，并不是模型变笨了， 是它们终于聪明到，不再容忍人类懒得想清楚了🤣🤣🤣 而且最有意思的是， 两个模型的进化方向，居然是完全相反的， Claude Opus 4.7变得越来越字面， 以前它会主动帮你补全模糊的指令， 现在你说什么它就做什么，多一个字都不会猜🤣🤣 GPT-5.5变得越来越自主， 以前你要手把手教它每一步怎么做， 现在你只要告诉它你想要什么结果，它自己会选最优路径， 所以老提示失效的原因也完全相反， 用在Claude上的模糊提示，会得到越来越窄的输出， 用在GPT上的详细流程，会变成多余的噪声， 过去三年我们一直在学怎么教模型做事， 现在反过来了， 模型开始要求我们，先把自己的思考结构化， 其实就是提示工程的本质， 已经从教模型怎么做，变成了先把自己想明白， 所以真正的瓶颈可能不是模型的能力，而是写提示的那个人的思考清晰度， 我感觉以后赢的人，不会是提示写得最长最复杂的人，而是那个最知道自己真正想要什么的人🤔
元skill的价值和意义，其实被大大低估了 两年前，我和向阳研究提示词的时候，花了不少精力，各自打磨出了属于自己的元提示词 后来在各种提示词生成、任务拆解、方案设计中，确实带来了极大的便利 今年，我们研究skill，也花了不少时间和精力打磨自己的元skill 元Skill，可以理解为“生成Skill的Skill” 比如我自己的元Skill：yao-meta-skill 但如果只把它理解成一个自动生成器，其实还是低估了它 在我看来，元Skill至少有三层价值： 1、它是个人的Skill生产系统 2、它是一个人对AI协作方式的抽象 3、它是你学习和理解Skill最好的切入点 这几天和团队交流，感触很深 越来越认为，就Skill这个能力而言，元Skill无论怎么重视都不为过 每个人，都值得结合自己的高频使用场景，花足够多的时间，集中打磨一个属于自己的元Skill 在这个过程中，收获的不只是一个工具，还有更底层的能力 比如： 1、对Skill原理的深入理解 2、对“什么是好Skill”建立更清晰的标准 3、对自己工作流的重新梳理和抽象 4、对任务拆解、流程设计、质量控制的系统化训练 5、对AI协作边界的判断能力 …… 提示词阶段，高手有自己的元提示词 Skill时代，高手也应该有自己的元Skill 后面准备专门写一篇，聊聊如何设计元Skill
