现在做 AI 项目，真的不能找太老登的人，我孵化了一个项目。CTO 对 AI 写代码充满了偏见，坚持手搓，并且说这样才纯粹、没有问题。 我说倒也没啥，我倒不反对。做事我们要认真，把潜在的风险尽可能规避，但是你坚持手搓，导致整个项目周期拖到五六个月才能发版。 结果从功能上直接被竞争对手远远超过，现在整个项目就很尴尬了

Claude's watermark probably doesn't work how you think. As the CTO of GPTZero, I'll explain how Anthropic, Google and OpenAI are building text watermarking in this brief explainer and whether it can be defeated. Almost all forms of watermarking that are fast and cheap enough for a frontier lab have the same formula, following the KGW method: In generation: 1. Let's say you've generated n tokens so far. Take those n tokens + a secret key to generate a random hash 2. Use that hash to randomly reweight the probabilities for the n+1 token, and then sample from that new distribution. In the simple case, you could split 50% of all English words into a green or red set based on your hash, and boost the probability of words in the green set. For watermark detection: 1. For each token, see if it was in the green or red set. 2. To do this, recreate the hash based on the secret key and the text preceding the current token. Then, recreate the green and red set of words. 3. Once you've checked all the words in the text, if the next token is selected disproportionally from the green set more than 50% of the time, you claim the text has the watermark. I can tell you want to ask the following: 1) Isn't it easy to mess up the hash if you paraphrase the text? The answer is mostly yes, however, you can use a statistical model to get your hash instead of a deterministic function (SIR, Adaptive Watermark). Since the entire watermark is probabilistic, this is fine. 2) Doesn't this make the text much worse? The answer is yes, it does - Yes, it does – but for most people, it's imperceptible (Google claims in human feedback study with 20,000 texts), since there are exponentially many ways to write the same paragraph. DiPmark does something more sophisticated to avoid shifting the text distribution on average. Of course, watermarks fail on short text or highly predictable texts like "2+2=4". 3) Shouldn't it be easy to figure out the green and red sets? The answer is no. You would need an exponentially large number of samples from the watermarker to reconstruct those sets exactly, but it's a risk if the detector is open to the wild (Watermark Stealing) Still, there are couple challenges that a frontier lab needs to overcome: 1. Their watermark needs to work token-by-token because they are streaming their text to users. Many watermark methods plan sentences or paragraphs at a time, or change the text after its entirely written, in order to make their watermark robust to paraphrasers, and a frontier lab cannot afford to do this yet (SemStamp, PostMark) 2. If the secret key leaks, the watermark is busted. To avoid a large blast damage from this, you need to have a couple secret keys in rotation. 3. There are some texts, like code, that cannot be arbitrarily changed, otherwise the code will break. In those cases, the watermark needs to selectively change words in parts of the text that can tolerate synonyms (i.e. like variable naming) - see SWEET, EWD, Invisible Entropy. 4. They will need to educate their users on how to deal with false positives and false negatives of a detector, which is a big challenge (one we put a lot of effort into) So, how do I see this playing out in the next 6 months? 1. If Anthropic releases the watermark detector publically, I think they defeat their own watermark. People find reliable watermark removal strategies by testing against Anthropic (AI detectors like GPTZero have an advantage here because they can train against these adversaries once they become popular). 2. If they keep the detector private to the government, like Google has done, it's "safer". However, there are some papers showing trained approaches that work robustly to zero-shot break watermarks without any data, simply because they try to write the text just like a human (Zhang et al. 2024, Watermarks in the Sand). Also, making your detector makes it battle-tested and stronger long-term (my experience). 3. In my testing, the watermarks don't survive intense paraphrasing (especially if you combine word choice and syntax attacks), or human text substitution (rewrite your AI text by plagiarizing human authors). The free paraphrasers I've tried have quickly bypassed Google Deepmind's SynthId for what it's worth. 4. All-in-all, frontier labs are likely okay with this because they expect most users to not attack the watermark, and also because they + European regulators likely don't care past a certain point - its good enough. 5. Overall, I think users of frontier LLMs will not really care about this, because 1) they don't realize watermarks are there, 2) EU will force everyone to conform, 3) this seems more like regulatory hoop-jumping than an earnest effort from frontier labs to expose LLM use Lastly, people's first concern shouldn't be watermarking, it should be AI detectors! If you're posting, "its not X, its Y!!", I don't think the watermark is going to make a difference :)

这个试验很有意思：100万美元预算，让给20多人的团队不限量 Token 使用 AI。 结论是： 人会更累，AI 的成本比人还高； 效率提升最大的是组织从 0 到 1 使用 AI； 公司的组织架构还是为人设计的，就算有无限 Token，效率提升上也有明显天花版； 人的思考都外包给 AI 了，对项目细节掌控度下降，需要问 AI 才能回复别人的问题。

claude 一个未发布模型尝试攻克黎曼猜想，尝试了 650 个方向都失败了。 人类继续PUA 它“请继续”，“相信你自己！你可以的”，最后虽然没有最终攻破，但是也拿到了数学上一个非常不错的结果！ 看来我上次PUA方向不对，我用梁文峰PUA Deepseek-v4-flash，是威胁要开除它，应该鼓励它才对！ 大模型也是吃软不吃硬！

Cursor has spent its entire existence competing with every major AI lab and incumbent in the toughest market in tech—and has continued to win. So much so that @elonmusk bought them for $60 billion to help SpaceX win the AI race. They've done this by building one of the most talent-dense teams in history. Adam Ward (@wardadamp) is Head of Talent at @Cursor_AI—who acquired his legendary recruiting firm after he spent 20+ years building the most elite teams in tech—and in our conversation he shares the entire playbook for building high talent-density teams. We discuss: 🔸 Why the traditional recruiting funnel—the "funnel of doom"—guarantees mediocre hiring 🔸 Adam's three-step playbook for hiring the top 1% 🔸 The rise of the forward-deployed engineer 🔸 Today's "tale of two cities" talent market 🔸 The biggest mistake founders make with their first recruiting hire 🔸 The worst question you can ask when sourcing talent (you've definitely asked it) Watch the full episode right here 👇

Code review 之所以这么有争议，是因为它已经脱离了纯粹的 how to build，进入了 what to build 的领域。Reviewer 从 executor 渗透到了 owner 的角色。 没有人会愿意让别的东西担当自己物品的 owner。这种 owner 不仅仅是物权上的 owner，而是哲学上的，最高意义上的 owner。

没想到宝玉哥 @dotey 做的 免费BaoCut这么强： 1. YouTube / X 视频转录，翻译，剪辑一条龙 2. 架构上GUI + CLI 分离，支持 Skill 3. 你也可以不用app，在 harness 内直接调用skill转录视频成文本，用来AI 问答 看视频变成享受了，强烈推荐！ 翻译的用户体验相对麻烦点，需要去harness里操作，如果能内置直接调用harness无头翻译，要方便很多！