搜索“Anthropic”

ls is complexity that belongs in the system, not in the UI. If you have to choose, the tool isn't doing its job. That was true when one model was clearly the best. We started with Anthropic's Sonnet. It was the best thing available for real coding work, and it wasn't close. You could build around it, tune your prompts for it, and feel confident you weren't missing anything. Then GPT-5 showed up and was good enough that people wanted a choice. We shipped the picker. Then Opus 4.5 pulled ahead of

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at least fend off, co-opt and absorb the thousands of other people who are trying to do that. Third, while much of this applies to everyone else in the field as well, OpenAI, like Anthropic, has to ‘cross the chasm’ across the ‘messy middle’ (insert your favourite startup book title here) without existing products that can act as distribution and make all of this a feature, and to compete in one of the most capital-intensive industries in history without cashflows from existing businesses to lea

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ling to keep rising. These are very different pressures, and time favours the latter. The obvious counter-argument: if specialisation is where the value lives, why can’t OpenAI or Anthropic or Google just offer fine-tuning? They already have fine-tuning APIs. What stops them from capturing the specialisation layer too? The answer is that capturing the specialisation layer requires a fundamentally different organisational shape. Training a single frontier model is a concentrated effort: one massi

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fighting them. I hate every other LLM framework Seriously. The way they implement LLM objects is painful. So I wrote my own. Super easy way to do calling. That's all they do - for Anthropic, OpenAI, and Google. Based on our telemetry, these account for 95% of use cases. Same interface. Full control over caching, serialization, provider quirks. No magic. No surprises. It's so much easier to do caching and implement messages yourself. Completely model-agnostic. You're not locked into one provider.

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Claude's Constitution Anthropic now has a Constitution for Claude.[1] Their description says: “Claude’s constitution is a detailed description of Anthropic’s intentions for Claude’s values and behavior. It plays a crucial role in our training process, and its content directly shapes Claude’s behavior. It’s also the final authority on our vision for Claude, and our aim is for all our other guidance and training to be consistent with it.” This implies that Claude is trained on its Constitution. Bu

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让 AI 通过按需切换知识来覆盖整个流程。例如，Jesse Vincent 发布了一份有趣的免费技能清单，让 Claude Code 能够处理完整的软件开发流程，按需拾取各项技能，从头脑风暴和规划开始，一路推进到代码测试。从技术上讲，创建技能非常容易，用平实的语言即可完成，而且 AI 实际上还能帮你创建它们（稍后会详细说明）。 一个技能文本的示例，这里是由 Anthropic 发布的 Design Skill。注意它是如何用平实的语言写成的，并信任 AI 自行做出决策。除了 Skills 之外，Claude Code 还有其他招数来管理其有限的上下文窗口并解决难题。它还可以创建子智能体——实际上是启动其他专门的 AI 来解决特定问题。这在许多方面都很有用。由于 Opus 是一个庞大而昂贵的模型，它可以把较容易的任务交给更便宜、更快的模型。它还允许 Claude 同时运行许多不同的进程，使其像团队而非个人一样工作。而且这些模型可以非常专门化，拥有自己的上下文窗口。例如，我为研究和图像创建分别构建了独立的子智能体。主 AI 模型在需要时会“雇用”这些智能体来完成专门的工作。 而且你甚至不需

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Information article on this topic.) In my opinion, it makes total sense. I think that selling valuable and proprietary data, which can give a company an edge one day, to OpenAI or Anthropic could be a bit short-sighted. Right now, LLM development is prohibitively expensive and challenging at scale, which is why only a few major companies develop state-of-the-art LLMs. However, I think LLM development is becoming increasingly commoditized, as LLM developers frequently rotate between employers and

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r (setting apart Google, master of multi-datacenter-training). However, Colossus 1’s 300 MW looks modest next to the Gigawatt-scale clusters under construction by OpenAI, Meta and Anthropic. Their hyperscaler partners are happy to leverage their balance sheet and win the market by throwing dollars at it. Was xAI’s prowess a one-time wonder? Today we will publicize some data from our industry leading datacenter model over the last year that is accessible to clients. This is the our same proprieta

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ill only chip for use in conjunction with the Trainium3 Teton-3 Max NL72 (featuring a 72-logical GPU with all to all switched scale-up size just like the VR200 NVL144) can lean on Anthropic’s demand to codesign and gain adoption for their inference workloads. There will be major time to market challenges for AWS for its VR 144 CPX due to the fact that their 1U compute tray is already tightly integrated and packed with 4 large Rubin GPU packages and 8 CPX GPU packages leaving no space to add AWS’

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uce a large multimodal model, primarily focusing on text only due to compute limitations. They will release multimodal models this year, but they are still squarely behind OpenAI, Anthropic, and Google in many capabilties. If Blackwell GPU shipments come too early, that will accelerate China's pace. The second major implication of this is that China will now have more compute to serve and inference models to its population. Compute constraints affect user experience, with DeepSeek deliberately s

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t when this was implemented, and now she just joined OpenAI as head of product. The wheels are already in motion, this will be the future of free usage at ChatGPT. AI labs such as Anthropic and OpenAI are even paying startups hundreds of thousands of dollars to spin up replicas of popular sites like DoorDash and Amazon to RL agents on successfully completing end-to-end transactions. It is not a question of if, but when this capability happens. You can see the future. Imagine a world where you as

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emphasis SemiAnalysis GenAI acts as an extension to these tech giants’ businesses. They don’t have the same existential need to dominate new use cases like OpenAI in chatbots and Anthropic in coding APIs. This is readily apparent in the compute vs human capital allocation of a leading AI foundation lab like OpenAI compared to Meta. Zuck's impact on competing offers and inflation of salary will hurt badly. As a result, when measuring GenAI consumer app traction, Meta and Google meaningfully lag

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n a press release from a California State Senator , a speech (and tweets ) from UK Secretary of State for Science, Innovation and Technology Michelle Donelan; and a blog post from Anthropic . ↩ “Scaling” is meant broadly here, as in “scaling up AI systems’ capabilities beyond what’s been seen before.” “Responsible scaling” is meant to include things like improvements in plugins and tools, prompting and elicitation, fine-tuning methods and datasets, etc. ↩ This paragraph was edited on 10/26/2023

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