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on the DeepSeek API platform. (财新/@deepseek ai); Moonshot’s Kimi K3, paired with a new Harness, is closing in on Opus 5‑class performance on autonomous research benchmarks, and an OpenAI‑backed legal‑tech startup switched its stack to the open‑weight model, a notable vote of confidence from a US‑based customer. (量子位/SCMP); Alibaba shipped a lightweight, laptop‑capable Qwen model aimed at Meta’s and OpenAI’s smaller open releases and a beta AI music generator called HappyShrimp, while a separate

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ses no one." How did that affect your relationship with Anthropic, and how much did you think about that before you hit publish? Dan Shipper: Obviously, we know a lot of people at OpenAI and Anthropic, and you never want to be totally mean to people you're friends with. But actually, even before we publish anything, they're asking, "What do you think?" Because they want to make the model better, and they know that if we don't like it, it means something — and they'd rather know beforehand, hones

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long may the gains continue, 4.7 will be interesting results 7:53 PM · Aug 12, 2026 · 5.21K Views 8 Replies · 1 Repost · 49 Likes SpaceX + Cursor is now the 3 frontier lab besides OpenAI and Anthropic (rip Gemini, unless their next model is actually good). Elon says Grok 4.7 will be ready in the next 3-4 weeks , and it’s currently being post-trained on SpaceX’s company data to make the model best at real-world engineering , if not everything. The model card for Grok 4.6 also covers many benchmar

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e ratio, because to select what actually gets sent to brute-force computation, they had to pay a higher cost in vector computation to pick it out. I believe Anthropic’s models, or OpenAI’s models, don’t need to do this—because the machines they buy already have 32x CUDA compute. So put another way: our chip happens to have a quarter of Blackwell’s specs—but when running DeepSeek, it seems to work just fine. This is co-design—this is co-design across two layers—the algorithm colleague realized wh

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be the most heated debate in the U.S. tech industry this year: Banning open models or not, particularly Chinese open-weight models. Silicon Valley has split into two camps. One is OpenAI and Anthropic, the world’s top two AI labs, which insist on a closed-source approach and reportedly lobby for policies that could constrain open models. The other is a coalition of tech companies led by Nvidia, Microsoft and Meta, which are strongly in favor of open-weight models. They signed a letter last week

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in its blog , which includes all the evaluation trajectories . This is a lot of transparency for an open model release! DeepSeek-V4-Flash-0731 by deepseek-ai : Just one day after OpenAI has dropped the prices of their smallest model by 80% , the whale dropped an update to their V4 Flash model, beating Luna at the pareto frontier. The bigger model is not updated yet, so it remains to be seen where it will land in terms of performance. For the initial V4 releases, the Flash version was the star o

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important when looking at Microsoft these days is the revenue backlog, which is comprised mostly of Azure contracts that stretch out into the future and which has AI model builder OpenAI as a major contributor. Here is what the backlog situation looks like as fiscal 2026 came to a close: As Q4 came to a close, Microsoft had a $678 billion – that is billion with a “b” – backlog. Microsoft did not provide a separate backlog for OpenAI this time around, but you can back into it because Microsoft di

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mparing model quality only means something if you compare at the same cost, because when you compare two cars you compare cars in the same price bracket; Anthropic is now ahead of OpenAI—is that durable? I don’t think it’s durable, it’s certainly a phase. OpenAI and Google will most likely keep trading places going forward; In the global division of labor in AI, the role Chinese companies are quite likely to play is still the largest producer. By ordinary logic, our production capacity is the la

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aten by Claude Fable and GPT-5.6 Sol Max while being cheaper ), 1 overall in Frontend Code Arena , and more impressive results . Moonshot AI is going toe to toe with Anthropic and OpenAI with far, far fewer resources. It is clearly the strongest open model ever released. It should be clear looking at this model that if adversarial distillation from the closed frontier models in the U.S. contributed, it is at most to a relatively small degree. AI observers who followed the distillation panic and

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global tech markets. Moonshot AI released Kimi K3, which it's calling the world's largest open-weight model, and framed it as closing the gap with—and in some benchmarks rivaling—OpenAI and Anthropic. The release landed hard: Nvidia and other US Big Tech names sold off on renewed competitive fears, and the shockwaves compounded a brutal week for chip stocks. (Bloomberg/CNBC) CXMT prices Asia’s biggest listing of the year. Beyond Kimi K3 (above), the agent race kept accelerating this week. Aliba

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h economy: net income last year was $53bn, less than half of Apple alone. There are plenty of other comparisons one could make here: in the last six months Sam Altman has compared OpenAI both to Windows, a high-margin capital-light monopoly based on network effects, and electricity utilities, which are natural monopolies but also low-margin regulated utilities selling a pure commodity. One could also point to cloud (three leading players with good margins and clearly distinguished propositions,

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everything, or to companies decomposing tasks across smaller, cheaper ones? Ted leans hard toward the former, and the argument is economic, not sentimental. He cited Noam Brown at OpenAI, who plots test-time-compute curves with cost on the x-axis: a bigger model that reaches an equally good answer in far fewer tokens can come out cheaper than a small one grinding away. “It could be more cost-effective to just ask the bigger model.” His blunter version: “people underrate the value of having a rea

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s agreed to buy $21 billion in capacity from king of the neoclouds, CoreWeave. That makes Meta Platforms CoreWeave’s biggest customer, where previously it was Microsoft offloading OpenAI AI training and inference workloads that was CoreWeave’s largest customer. Now, it looks like Meta Platforms wants to sell access to an API running its more recent Muse Spark AI model, which is not open source like the Llama 3 and Llama 4 models were – no more free lunch, people, and we’re raising the price on t

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it’s not. You can clearly say, “Yes, this is done.” Partly that’s to be able to do things like RLVR, but also partly just to know that you haven’t moved the goalpost in answering. OpenAI can have their headline on disproving the unit distance conjecture because it’s a clear, distinct thing. It did it. Whereas imagine trying to have a headline on GPT-5.4 coming up with a really good conjecture. “We promise, everyone thinks it’s a good conjecture.” It just doesn’t land the same way. But maybe that

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周楠赌的是「非共识」。 真正难的不是 idea，而是把 wafer-scale 做出来。良率、散热、电源、编译器，每一个都是硬骨头。2017-2019 年几乎是至暗时刻，流片一延再延。但 Benchmark、Foundation、Eclipse 等早期投资人一直陪，给了足够耐心。芯片真的要十年才能看到结果。 现在 Cerebras 的机会在推理，而不是训练。OpenAI 签下至少 200 亿刀大单，就是看中它低延迟、高吞吐。英伟达的 CUDA 生态太强，训练阶段迁移成本极高，但推理场景下，Cerebras 的架构优势开始显现。Sam Altman 其实 2016 年就个人投了 Cerebras，比百度还早。 周楠感慨，百度美研其实是硅谷的「黄埔军校」。后来出去创业的人太多：Inflection、Adept、Anthropic、Cohere……可惜因为地缘政治，百度后来想单独募一个专注 AI 的基金没成，OpenAI、Databricks 当时都在 list 上，最终都没投成。 十年前大家就担心不能只依赖英伟达，结果英伟达还是成了事实垄断。但现在推理需求爆发，反而给了异构芯片新窗口。Ce

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s over time, we get this: The thing that stands out is that Mythos Preview looks way above the linear trend we’ve seen since early 2025 — about 7 months ahead.2 It’s also ahead of OpenAI’s GPT-5.5, which was “only” 2–3 months ahead of schedule.3 But if that’s the case, why did some people argue that Mythos Preview didn’t seem notably better than GPT-5.5? One clue is in the graph: confusingly, there are two versions of Mythos Preview — an “early” version from an internal checkpoint and a much str

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ide, infosec's a good bet for a long, stable career FOSS NetBSD may have found a new role: The AI-free FOSS OS Increasing awareness and adoption, followed by increasing donations; OpenAI dots inspire open source imitators amid technical difficulties Persistent agents appeal to AI companies, but demand isn't obvious; Ubuntu 'Stonking Stingray' beta swims out: Deeper Rust 'oxidization' plus LLM speech-to-text GNOME 51 is here, and Ubuntu interim releases are getting interesting again; Revive Raski

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ality more broadly. We think Anthropic tries harder to ensure correct implementation of more-central pillars of their safety plan. I’ll also take this as an opportunity to applaud OpenAI for seemingly succeeding in preventing their CoT from being optimized against. OpenAI’s models seem quite willing to describe their intent to misbehave in their CoT. See, for example, snippets from o3 in the anti-scheming paper. We’ve also heard that some people at OpenAI have been putting in lots of effort in o

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anyone in the world. The next 12-18 months might be rough, as offensive capabilities arrive before patches. This was all predicted by the Situational Awareness paper, from former OpenAI employee Leopold Aschenbrenner. He said there is a scary time in between where attacker will have the upside, and it seems we are entering this period. Conclusion With all of this actually comes a great opportunity, which is that many of the core projects that run the Internet and computing systems are going thr

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their pricing, their availability, their release cadence, and their tradeoffs. You're also missing what's happening outside of them. We've already seen how fast the ground shifts. OpenAI closed the gap faster than anyone expected; Google entered with competitive performance and dramatically different pricing; Benchmarks have tightened to the point where the differences between top models are increasingly hard to separate Teams are also paying closer attention to headlines around supply chain ris

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