搜索“AWS”

重要的指标，我们会到处推出这个。就像，我们在乎。就像，如果他们是基于我们构建的开发者，我非常在乎你。而且我们正在招聘人员来继续构建更多——就像，不只是。就像，目标不只是成为一家像Jev这样的公司。目标是，像，在那之上交付更多形态的智能。所以我们也在招聘人员来构建这些东西。就像，我们不想只是，像，是的，像，简单模型这种一招鲜。但就像，我认为会有一个，像，智能的AWS？而且 Swyx [02:20:07]：顺便说一句，那会是你，对吧？是的。 Diogo Almeida [02:20:09]：就像，那是我想要走的方向。 Swyx [02:20:11]：是的。好的。 Diogo Almeida [02:20:11]：说那会是我，那就太傲慢了。 Swyx [02:20:13]：是的。 Diogo Almeida [02:20:13]：就像，我们。就像，我会尽我所能确保那发生。 Swyx [02:20:18]：是的。 Diogo Almeida [02:20:18]：就像，我认为那会非常酷。就像，我们现在在玩的是，像，System 1智能。想象一下那些层？就像，这就像是它的TCP。 Swyx [0

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setup , with open hardware, training code, sim, and eval. The baseline science included sim-to-real correlation r = 0.91 on task progress and studies of offline metrics, scaling laws, and conditioning. Astra is showing up across evals and products, especially for vision : @skalskip92 reported GPT-6 Astra as the strongest vision model Roboflow has tested across detection, segmentation, box prompting, counting, reasoning, and video. The tradeoff remains material: a “high effort” setting improved

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ome minor creative feedback, but never touched any production decision or even knew exactly how it was accomplishing its tasks. You can see the results here. There are plenty of flaws in these efforts that you can spot. But they are also examples of the AI exercising a kind of judgement and creativity, things that not long ago were considered uniquely human traits. And they were all done with just a fraction of the token budget of the ChatGPT account I pay for. I think these are fun demonstratio

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local level (i.e., cities, townships, counties), of the roughly 20 GW sitting inside a restricted boundary, only 1,525 MW is delayed. Three projects account for the delayed MW: an AWS campus in Ohio, a powered land developer campus in Pennsylvania, and a site in Colorado; New York’s moratorium touches roughly 1.4 GW, but we estimate only about 0.8 GW faces a meaningful delaydirectly attributable to the order Texas adds perhaps three-four months of administrative delay for base-load projects in t

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m OpenAI’s autonomous AI agents run during evaluations managing to escape and hack servers on Hugging Face’s infrastructure, to an AI-developed computer worm uncovering critical flaws in messaging apps like WeChat. Second is recursive self-improvement (RSI). We are inching closer to a reality where AI begin participating in designing subsequent, more powerful generations of AI, which sparks widespread concern over systems going entirely out of control. Anthropic lately faced a major PR crisis af

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dit trail. Log the run time with details like the prospect name, company, key needs, etc. The Rundown : Curious how production AI agents get built? The Building Agentic Systems on AWS series is expert guidance you can read right now. This guide goes deep on streaming, where a response can fail after it starts: mid-stream failure semantics, idempotency, cancellation. In this guide, you’ll learn how to: Handle mid-stream failures without losing state Design idempotent actions so retries never dupl

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proving a project. The agents work, but what varies is how much firms get out of them. The report shows that top adopters put people in charge of agents, sort out the content AI draws on and build systems that adapt as models improve. Download the report for data, benchmarks and tips for AI adoption. Download report Let’s turn to recursive self-improvement and the 160-million-plus-view tweet Jacob Coxon @ hilbertspaess I resigned from Anthropic today. I spent the last three years doing pretraini

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ism paired with sinophobia and erratic decision-making. In the chaos in which we find ourselves, the reality emerges. And the reality is, even today, really problematic. Whatever laws and regulations already exist are largely completely ignored. Plenty of companies are buying data from all over the place that people never agreed could be used for training of AI models. The token economy that is emerging is one that looks like a drug market where you don’t know where the requests are going, what

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of the most time-consuming parts of the SDLC. Before Augment, Akshay spent several years at Uber advancing automated code review and repair systems, and conducted research across AWS’s Automated Reasoning Group, Google’s Android Static Analysis team, and UCLA. His work sits at the intersection of AI, software engineering, and programming-language theory. Related reading Factory AI vs Augment Cosmos: Which Agent Layer Do You Actually Need?; Software Factory vs DevOps: When Agents Do the Work; So

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obligations, then it will be tails, we all lose. By our count, Nvidia today backstops 6.5 GW of capacity, most of which is not yet built. Our datacenter model has Microsoft, Meta, AWS and Oracle leasing roughly 15 GW of third-party capacity in 2026 and we expect them to lease more than 35 GW by 2028. Each of those leases typically run fifteen to twenty years, and developers borrow against them with investment grade pricing. The Gigascalers are the implicit backstop of the buildout and are in agg

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pirals inward and gets increasingly elongated, like spaghetti. This central region shrinks while it speeds up in such a way that its energy still stays finite, as required by the laws of physics.” Eventually, in the central region, “the velocity of the fluid grows without bound.” Again, this would never happen in a real fluid. Alpöge and Buckmaster found a similarly implausible outcome for three related and somewhat simpler models of how liquids flow.2 While this is a significant breakthrough fo

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they review code. New strategies are emerging. Among other approaches, engineers are scrutinizing plans before AI begins coding, deploying specialized AI agents to catch routine flaws, sending risky changes to human reviewers, or requiring developers to defend the code their agents produce. The shift comes as the surge in AI-generated code puts new pressure on engineering teams. In a survey of more than 1,100 developers by Sonar , an AI code-verification startup, respondents estimated that AI co

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for a total of $48.4 billion, which is less than the $50 billion it said it expected to spend on compute in 2026. Per Barclays and UBS, OpenAI is projected to spend $15 billion on AWS and $12.5 billion on Google Cloud in 2027, with Wells Fargo estimating it will spend $22.9 billion for the first two quarters of 2027 making it reasonable to assume at least $45 billion, for a total of $72.5 billion… which, even then, seems a little low based on what it’s already on track to spend in 2026. Then you

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a lot about acting rationally once you consider your beliefs as axiomatic, particularly in AI. We have many examples of this. For instance, people have been talking about scaling laws since GPT-2 (and even beforehand); they would tell you to be scaling pilled. Well, we didn’t actually see that many of them throwing away everything to get more compute, more data, and investing in NVIDIA call options. Similarly, people talk about being AGI pilled. Well, if that’s your belief, that AGI will hit in

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but they remain objectively behind Nvidia and AMD in deployment scale, software maturity, and commercial availability. Hyperscaler-developed alternatives such as Google’s TPUs and AWS’s Trainium are not domestically controlled either. The buildout of data centers essentially comes down to two things: capital and compute. Capital is the easier problem in our view. The Korean government has been prioritizing AI investment over the past few years, while Naver, SK Telecom, Samsung, SK Hynix, and oth

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ecause thinking of an alternative — even as you see proof point after proof point — is near-impossible due to the prominence of the dogma and confidence from those in power. The flaws of simplistic, systemic thinking are that it “always works out,” as if time is simply repetitions of the same story again and again, versus a series of events that each feed into each other, each compounding the next one’s sins. Instead of building a robust social safety net after the Great Financial Crisis, Americ

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’s Generalist and China’s Dogotix raised big rounds as investors swarm to the robotics sector. Meta agreed to pay up to $17.1 billion and make changes to its products to settle a lawsuit from state AGs — and put the screws to its competitors in the process. Y Combinator draws the FTC chair’s ire with a snarky DEI tweet. The Main Item After a remarkable few weeks of blowout earnings, multi-billion-dollar acquisitions, and potentially hundreds of billions in loan guarantees, Nvidia more than ever

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asically told me she's only hiring “star athletes” now — the people she wants at her relatively small startup are just absolutely elite. On the other hand, Matt Garman, the CEO of AWS, said replacing junior employees with AI is one of the dumbest things he's ever heard; he thinks there's so much advantage in bringing them into the workplace. You've run very large teams inside two of the biggest software companies in the world. What do you think is going to happen? Shih: I have a few predictions.

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n 2024, $20.9 billion in 2025, and will likely burn $30 billion or more in 2026, and why pointing to Amazon Web Services was rational when Amazon’s total capex from 2003 (the year AWS was created) to 2015 (the year AWS became profitable) is $29.7 billion, adjusted for inflation. That includes literally every ounce of capex attributable to AWS, Amazon the store, Amazon logistics, and even Amazon Alexa. For comparison, Anthropic raised $30 billion in February, and Anthropic and OpenAI have raised

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rt, the geographical rebalancing of power continues to accelerate. While U.S. open source models continue to be competitive, the race between several Chinese frontier model labs draws strong attention. Many likes on these frontier models point to what excites the community the most, and growth opportunity for companies leveraging the attention for valuations. However, the AI race is not only sprints, but also a marathon; tools like llama.cpp helps deploying the big models locally, but a broad mo

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