johnleelabs

Defeating AI Anxiety | Practical Solutions for the AI Agent Era | Experiment. Iterate. Control.

[johnleelabs @johnleelabs]

拒绝 AI 焦虑 | 分享 AI Agent 时代实用解决之道 | 练习“出题”而非“做题” | 实验、迭代、掌控

[johnleelabs @johnleelabs]

Reddit 守着全球互联网最有价值的数据金矿（之一），没薅到 AI 的羊毛，有点儿不可思议

[Orange AI @oran_ge]

互联网已死

“AI 不会带来流量，既没有给我们，也没有给其他人。”
—— Reddit CEO

[johnleelabs @johnleelabs]

置身钉内，黄豆小羊，黄峰达，阿里的前同学们太喜欢指点江山了，众人皆知我独醒 🐸

[johnleelabs @johnleelabs]

@ruanyf 频繁429，根本没法用 🫥

[johnleelabs @johnleelabs]

@Stargridcompass 20 亿 token 能不能卡在 150 元全看缓存命中率：命中有 98% 就够，掉到 80% 或者高峰时段用得多就不够。

把重活挪到 off-peak（北京 12:00–14:00、18:00–次日 09:00、周末全天）。直接省 50%。

过去两周，我全天运行 WorkBuddy 加 DeepSeek，数据如图

[johnleelabs @johnleelabs]

⏺️ 目前 SOTA 模型 claude fable5.1 等，是人类的系统工程赋予了它们跨领域的生存能力，而不是模型本身具备脱离框架后、在开放的物理/数字世界里自主存活并重构自身架构的能力。

[johnleelabs @johnleelabs]

再度AGI相关领域的文章，笔记：

⏺️ AGI 需要具备自主设定目标并为之持续迭代的主体性。

⏺️ Claude 5.5等模型，是在已有的知识海洋里做最高维的插值和拟合，而不是像人类科学家那样，能够从零创造全新的底层范式。

[johnleelabs @johnleelabs]

@keking99 与AI斗，才有赛博流量 😄

[johnleelabs @johnleelabs]

七牛的「免费鸡蛋」限流了 🤡

[johnleelabs @johnleelabs]

七牛这个「免费鸡蛋」确实能吃：每天 1 亿 token，效果还可以。qiniu/conatus-free 后面接的模型感觉像 DeepSeek，应该不是最新的 DeepSeek 1017。

[johnleelabs @johnleelabs]

目前 IT 行业最舒服的就是传统行业的 CIO、CTO 们。AI 赋能互联网，那是革自己的命；AI 赋能传统行业，那是真正的产业革命、工业革命

这些 CIO、CTO 位置刚好，上通下达，人情世故都是老江湖，现在招人好招啊。以前望着互联网公司的薪资只能却步，现在应该能招到性价比很高的互联网公司高阶毕业生了

[johnleelabs @johnleelabs]

@AsiaWawrzyniak 用下来总体感觉跟 DeepSeek 官网 API 没差别 😀

[johnleelabs @johnleelabs]

为什么Dario的小伙伴们都跑了？这也是Dario当年从OpenAI跑路的原因：

“我们的策略一直是我所说的‘向顶尖赛跑’——通过展示如何负责任地构建这项技术，来拉高整个 AI 行业的安全标准。”

Anthropic 现在的商业化路线偏离了他们的初衷，小伙伴们只能 fork，再开一摊儿 🤡

[johnleelabs @johnleelabs]

香，zcode是真会给码农们找活啊😍 周末又得蹬两天🥰

[johnleelabs @johnleelabs]

我在使用 DeepSeek harness 桌面客户端和 Web 客户端时，总觉得这两个版本不在一个档次上，能力差别很大。

不管是文档能力还是编程能力，我后端接的都是同样的大模型，Key 都是一样的，但表现出来的却是代际差异。为什么呢？

DeepSeek 自己说的 🤡

[johnleelabs @johnleelabs]

Anthropic 禁止虐待AI，设置这条规则的原因是什么？是影响模型的数据吗？还是影响模型的能力呢？

[johnleelabs @johnleelabs]

传说中的中转站。。。

[Vonng @RonVonng]

TiDB Cloud 大脱裤事件，请 Gemini 搜集信息分析一下，完整的故事是怎么样的。

[johnleelabs @johnleelabs]

@realchendahuang 还有一个“Java之父余胜军”，都是代码界的翘楚啊 🤡

[johnleelabs @johnleelabs]

虽然天天骂它a➗，dario 还是有大格局啊

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透明度与公益：投入巨大精力编写详细风险报告；将公司 5% 股权划入公共利益基金。

生物安全：设置极高标准的生物拦截机制，宁可接受高误报率导致生物学用户流失，也绝不妥协生物武器风险。

[Dario Amodei @DarioAmodei]

Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic.

That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it.

I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits:

1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed.

2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it.

3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do.

4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this.

5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future.

6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent.

7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it.

8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company.

9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund.

All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking.

For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea.

Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option.

I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent.

But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole.

To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years.

I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better.

Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so.

Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way.

Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.

[johnleelabs @johnleelabs]

🎉

[Yifan Xu @yifanxu_ephai]

LayerFS v1.0.0 is out 🎉 Open-source (MIT)

Recursive filesystem for AI agents.

6 core ideas:

1. Ephemeral computing: agents spend ~80% thinking, so each Bash call mounts a Workspace, then commits
2. Decentralized agents: every agent commits to one Store, no DB server
3. Multiagent coordination: every tool call is a Commit; same-file clashes refused
4. Recursive filesystem: forks copy no files, at any depth
5. Ultra storage: dedup, then compression: 10 copies → 0.31
6. Instant bootstrap: ignored files stay in every Commit. ~5 min install vs 0.05 s mount

And 4 more:

7. Auto checkpoints: fork from any step
8. Append-only objects: content-addressed, never rewritten
9. .lww: lockfiles and caches take the newest edit
10. Big file, small edit: a write keeps only its bytes

Here are our links:

github: https://t.co/hwcUUBzhYw
website: https://t.co/8LInNxyN77

Watch the video below, and have Fun!!!

[johnleelabs @johnleelabs]

实践出真知👍，只是行业里半桶水太多，辩不过来

[Handy Wote @HandyWote1]

还是那句话 RAG 不等于你指的那种切片ebd rerank那种，RAG只是技术规范，不是指某种技术，他的全称是检索增强生成，所以你使用llm wiki本质还是rag。以及为什么不用llmwiki主要是生产情况还有就是并发能力，对于切片ebd rerank这种，可以用消息队列等等后端手段限流优化，可以解藕出来动态扩缩容，而且中间产物可追溯，出了问题方便排查