搜索“RAG”

start with: is this a real problem? What if we don’t do this? Who defined the problem? It sounds philosophical, but what I’ve found is that the most common reason design projects drag or fail is that the problem wasn’t clear. People won’t agree on solutions, because they have different problems in mind. The solution becomes a compromise of many different problems, instead of a clean solution to one major problem. This compounds when you have too many stakeholders. To tackle this, I’d do two thin

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You move the ‘human in the loop’: now the human, and the mechanical turk, is in the creation of all that training data over the past few hundred years. You shift the point of leverage. The other half of the cold start problem, though, is that Amazon or Tiktok need to see what you yourself do so that they can work out what graphs might fit. The new user flow needs to throw enough ideas at you, and lead through enough choices, as painlessly as possible, that it can start to make those matches. Ti

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acode). A few standout examples: One large organization saved 5-10% of total developer time by using Devin for security fixes. Another saw 20x efficiency gain: human developers average 30 minutes per vulnerability, Devin, 1.5 minutes. Language and framework upgrades, migrations, and modernization Customers use Devin for modernization and migrations, like SAS → PySpark, COBOL, Angular → React, .NET Framework → .NET Core, or switching off proprietary frameworks. Once it gets instructions on how to

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and biases that it returns to? And so on. With a little practice, it becomes easy to find the vibes of a new model. As one example, let’s try a writing exercise: “Write a single paragraph about someone who doles out their remaining words like wartime rations, having been told they only have ten thousand left in their lifetime. They’re at 47 words remaining, holding their newborn.” If you have used these AIs a lot, you will not be surprised by the results. You can see why Claude 4.5 Sonnet is oft

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ining). 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 proprietary data that called the Oracle deals many months ahead

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intra-tray cables caused. The cables connecting the OSFP cages to the ConnectX NIC have been removed. Cables connecting from PCIe to the frontend Bluefield DPU & to local NVMe storage have also been removed amongst other side band cables. The key changes and upgrades found in Vera Rubin are focused on a revamp of the following three Vera Rubin (VR) Compute Tray SKUs: These three compute tray flavors are the building blocks for the three rack solutions discussed: The first rack presented was the

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mbedding copilots into everyday tools is the fastest path to measurable time savings without a heavyweight rollout. Summary: A new report reveals that top-performing teams are leveraging AI-driven scheduling, task automation, and meeting copilots to dramatically improve efficiency. These teams align AI implementation with specific operational outcomes, such as reducing manual planning and enhancing collaboration, which results in measurable business gains. Insights highlight how Motion, Reclaim.

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ngent enforcement of existing mechanisms. This includes tighter coordination with allies on timelines for matching controls and getting buy in on re-exporting. We are broadly encouraged by the administration’s Action Plan. As an example, we are pleased to see controls on semiconductor subsystems being called out. This is a drum SemiAnalysis has been beating for a while and think it is the right direction. With that said, many subsystem firms that supply western players such as VAT Group in count

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terMax AI cloud rating is the best way to compare relative strengths and weaknesses. Platinum and gold-rated AI Clouds have seen more traction than others and boast higher-than-average pricing power. As such, the likes of CoreWeave, Oracle, Nebius, Crusoe and Azure have outperformed the market for multitenant GPU clusters – which require high performance and advanced software layers. As predicted two years ago, key to Amazon’s underperformance is the use of custom networking fabric EFA. AWS's su

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g better at doing what you ask them to or even figuring out what you want and going beyond what you ask (and no, threatening them or being nice to them does not seem to help on average). And it isn’t just text models that are becoming cheaper and easier to use. Google released a new image model with the code name “nano banana” and the much more boring official name Gemini 2.5 Flash Image Generator. In addition to being excellent (though better at editing images than creating new ones), it is als

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f ownership (TCO) and cost per training 1M tokens. We will also discuss energy use, examining the energy in utility Joules consumed for each token trained and compare it to the average US household annual energy usage, reframing power efficiency in societal context. We will also show the results of this analysis when scaling the GPU cluster from 128 H100s to 2048 H100s and across different versions of Nvidia software. Later in this report, we will also analyze GB200 NVL72 benchmark results acros

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monetize one of the largest and fastest-growing web properties in the world, and it all begins with the router. The Router is the Release On OpenAI’s release website, the second paragraph is about the “One United System,” specifically focused on the router. The wording is instructive. GPT‑5 is a unified system with a smart, efficient model that answers most questions, a deeper reasoning model (GPT‑5 thinking) for harder problems, and a real‑time router that quickly decides which to use based on

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ly grow to occupy whatever HBM becomes available. Each generational bump in HBM capacity and throughput, whether 80 GB at 3 TB/s on H100 or 192 GB at 8 TB/s on GB200, quickly encourages designers to increase parameter counts, context lengths, and KVCache footprints, nullifying the headroom that seemed ample only months earlier. Techniques once deployed to squeeze models into tight budgets (activation checkpointing, optimizer off‑loading, weight quantization) are relaxed as soon as new HBM space

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uired human intervention. Since these were often unsafe industrial arms, the human had to pause the whole warehouse line, solve the issue, and resume the process, leading to an average Mean Time to Recovery of 6 minutes. More projects came out after scaling data showed promise in robot learning, like Levine et al. (2016) who released approximately 3000 hours of grasping data and achieved 94.6% grasp prediction accuracy after fine-tuning. However, the large-scale datasets were mainly coming from

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d node. With 1c in high volume now, 1d should debut in the next 1-2 years. SK Hynix highlighted a few key challenges in scaling beyond 1d: Cell contact areas, in particular the storage node contact where the storage capacitor connects to the control transistor below, shrink quadratically with cell critical dimension. The contacts must be large / well aligned enough to provide a good electrical connection between the transistor and capacitor, but not be so large or misaligned that they short out

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of salary will hurt badly. As a result, when measuring GenAI consumer app traction, Meta and Google meaningfully lag ChatGPT in its reach and engagement. But that’s changing. Leveraging our proprietary Accelerator Industry Model and Datacenter Industry Model, we forecast a meaningful step up in GenAI investment from Meta in the coming years. Meta GenAI 2.0 – Part 1, Re-Inventing the Datacenter Strategy (Again) From Buildings to Tents Just a year ago, Meta scrapped its decade-old “H”-shaped data

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incredible digital intelligence, and after some initial shock, most of us are pretty used to it. Very quickly we go from being amazed that AI can generate a beautifully-written paragraph to wondering when it can generate a beautifully-written novel; or from being amazed that it can make live-saving medical diagnoses to wondering when it can develop the cures; or from being amazed it can create a small computer program to wondering when it can create an entire new company. This is how the singul

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ans (e.g. Chatbot Arena) or user simulation (e.g. tau-bench) in the loop. Evaluation “should” run i.i.d. If you have a test set with 500 tasks, you run each task independently, average the task metrics, and get an overall metric. But in reality, you solve tasks sequentially rather than in parallel. A Google SWE solves google3 issues increasingly better as she gets more familiar with the repo, but a SWE agent solves many issues in the same repo without gaining such familiarity. We obviously need

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tributed is critical. The historical impact of technological progress suggests that most of the metrics we care about (health outcomes, economic prosperity, etc.) get better on average and over the long-term, but increasing equality does not seem technologically determined and getting this right may require new ideas. In particular, it does seem like the balance of power between capital and labor could easily get messed up, and this may require early intervention. We are open to strange-sounding

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tion for MI300X; Google TPUs: Support for TPU v4, v5p, v5e, and the latest v6e; AWS Inferentia and Trainium: Supports for trn1/inf2 instances; Intel Gaudi (HPU) and GPU (XPU): Leveraging Intel GPU and Gaudi architectures for AI workloads; CPUs: Featuring support for a growing list of ISAs - x86, ARM, and PowerPC vLLM's hardware compatibility has broadened to address diverse user requirements while incorporating performance improvements. Importantly, vLLM is on the path to ensure that all models

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