搜索“RAG”

pay 5x more, which matters for long-context agent loops; These two effects likely explain why Cursor’s “10x cheaper on short requests” differs from Anthropic’s “75% cheaper on average.”; Factual recall is weaker than Gemini 3.8 Flash and Luna; The over-refusal bug currently depresses the automation score; Not yet benchmarked: Arena scores are pending, and independent cost-per-task figures await tiered-pricing support Context Haiku 4.5 has been Anthropic’s small model since October 2025. Since t

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ngs. Anything they identify will be handed over to the Slovenian authorities for further investigation. Even if they find nothing, that result is valuable in its own right. I encourage anyone with an idle AI subscription to use it for something beneficial. There are many publicly available datasets containing potential discoveries that no human would ever have time to examine manually.” See Gil’s full workflow Visit The Rundown University. How do you use AI? Tell us for a chance to be featured.

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is actually not the important thing. It’s its capability. I think the more money and power you have at one of these companies, the more get mentally farther away from what the average consumer wants. Let’s say the average consumer does want an AI agent, which many of them don’t, as we know. But let’s say they do. If they do, they’re going to want it to be free or cheap, and they are going to want it to be safe to use without creeping them out too much. They’re willing to deal with a little bit

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al intelligence, warning of a potential “AI takeover” if the software evades control. “We have to act right now before it’s too late,” he told NBC News last month. The center encouraged people to sign a major petition to pause AI development and helped organize a hearing in Congress about the dangers of chatbots. It also advised on lawsuits and investigations probing AI labs for their design choices, including an ongoing case filed against OpenAI by parents of a teenager who died by suicide afte

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agents. PivotOPD on-policy distillation trains an agent to avoid its most damaging early mistake, and to recover when it happens anyway. Against 13 baselines, it posts the best average on ALFWorld, WebShop and Search-based QA for Qwen3-1.7B and Qwen3-8B students. The takeaway: recovery is learnable, and standard OPD rarely teaches it. TL;DR Size: A training method, not a model. Tested on Qwen3-1.7B and Qwen3-8B students, plus a Nemotron-3.5-SFT student on SWE-Bench Verified; Runs on: Trained on

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ion can have consequences for safety, reliability, and critical infrastructure. That makes responsible deployment central to the next wave of industrial automation. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” asks Arti Garg, chief technologist at AVEVA. The challenge is particularly acute as newer AI systems become more capable but also harder to predict and exp

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center of the climate and energy tech world. Another is that everybody wants a piece of the data center pie. And then there’s the fact that the grid needs support, with energy storage a key piece moving forward. Here’s what this year’s list reveals about the state of climate tech . The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 Scientists in Austria and China have built the first nuclear clocks They could become the w

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ne. Start with review, and the context you build carries into everything else; It’s built for agentic volume. When our PR volume rose 5-10x, 80% of our previous review bill was overage The problem with current code review tools Many existing tools are useful at first. You install them, connect a repository, add your configuration, and immediately start receiving automated comments on pull requests. It feels good to start, but over time you realise that, in many cases, the actual review process b

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involves three kinds of boundary: Compiled context is a useful analogy for deriving deterministic checks from requirements. It does not imply automatic translation or complete coverage of the original meaning. A state model describes valid transitions, and a validator enforces them. Tests and workflow gates can check other requirements, while a linter catches some implementation patterns. The team must validate what each check covers, record its limits and review it when the source requirement c

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s are structured and how they operate. 1. Data stopped being the bottleneck. The central complaint of our 2020 piece was that the CFO’s raw material – the data – was out of date, fragmented, and painful to pull together. Finance teams were spending most of their energy at the bottom of the pyramid stitching together inputs: pulling exports and reconciling spreadsheets and chasing down the explanation for a variance buried in an email thread, or worse, in Slack DMs and channels. AI makes it faste

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se for your app's operational data. This enables your app to read live analytical data from Databricks and write its own data back to Lakebase. Lakebase acts as persistent data storage for applications. It's important to look at how Lakebase compares to an external database. In the past, teams building apps on Databricks data had to rely on external Postgres or MySQL instances for the data that the app needs to maintain. This data, for example user inputs or app execution states, would sit outsi

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ng device, allocator, stream, and compiler abstractions through torch-spyre to the Spyre runtime and firmware. PrivateUse1 gives Spyre a real device identity, PyTorch allocator storage keeps tensors resident on device="spyre", streams provide familiar ordering semantics, and runtime events connect dependent work across streams. FX graphs stay in the Inductor compiler path. This post focuses on the PyTorch-to-Spyre device/runtime integration: how device identity, allocator-backed storage, streams

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protection by complimenting existing controls rather than replacing them, users can keep advisory scans, pinned revisions, network limits and scoped credentials in place while leveraging checks. Taken apart each task serves a different purpose in security layering. 1. Approval follows the code, not the name Imagine approving a model’s custom Python code, then returning after the repository changed. Should the old approval still count? Unsloth Studio says no. The repository shows it fingerprints

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est Use Cases The best fit is visual document search over PDFs, slides and scanned reports; Low-latency search: index in the cloud with 9B, then query on-device with 0.6B; Agentic RAG over large PDF or web collections The 0.6B model fits edge devices; the 9B model is built for index-time quality; Best score: 92.4% on MADQA. Weakest: 61.2% on ViDoRe v3 Markdown; 0.6B queries over a 9B index beat 0.6B on both sides; Storage growth and self-reported scores are the main caveats [Sponsored] The web i

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pic Haiku 5.5はClaude PlatformとClaude Codeで利用できる。Amazon Web Services、Google Cloud、Microsoft Azureでも提供を開始した。 Haiku 5.5 is now available in the Claude Platform and Claude Code. On average, it costs around 75% less to run than Haiku 4.5. It pairs well with Opus 5.5 or Sonnet 5.5 as a subagent. Use it for high-volume, cost-sensitive tasks like summaries, compactions, or database queries. https://t.co/RB9xCWo4Db— ClaudeDevs (@ClaudeDevs) October 7, 2026 大量の反復処理やサブエージェントでの利用を想定 Haiku 5.5は、要約、分類、会話履歴の圧縮

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ng tradition of geometry,” she says. She also drew scientific inspiration from her parents. Her mother worked as a chemical engineer and her father as a pharmacist — and both encouraged their daughter’s innate curiosity. Her early affinity for math led Delimitrou to study computer engineering at the National Technical University of Athens, even though she didn’t know much about the field. She quickly gravitated toward courses that focused on the applied science of engineering. For her diploma th

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commodate current and future power and thermal demands and address the growing token throughput and PCIe needs of such AI workloads as inferencing, retrieval augmented generation (RAG), and agentic AI, according to Carter. “Most of our customers who are using a PCIe-type GPU, what they're really telling us is, ‘We’re running out of power long before we run out of space, so make it bigger,’” he says. “We want to keep air cooling, so that's really where this is positioned. This is more for the ent

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AA-Video-I2V v1.0 prompt: "Sea turtle crawls across the sand, reaches the water, waves wash over it, swims off into the ocean. Flippers dragging, sand shifting, wave rushing, swimming away." https://t.co/QJ545QMeZd

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AA-Video-I2V v1.0 prompt: "The modern Chicago skyline begins to shift. The Bean dissolves away. Skyscrapers shrink and reshape into early 1900s brick buildings. Streets change from pavement to cobblestone and dirt. Cars fade into horse-drawn carriages. People in modern clothes become figures in vintage suits and dresses. The bustling modern city becomes old Chicago. City noise fades to horse hooves, distant train whistles, and ragtime piano."

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rms and changes between languages. Arabic benchmark results The Open Universal Arabic ASR Leaderboard , maintained by the ELM Research Center, ranks systems by the equal-weight average WER across six test sets. It also reports character error rate (CER). Lower values are better for both metrics. Our Falcon-ASR evaluation follows this protocol. WER = Word Error Rate; CER = Character Error Rate. A lower value indicates better performance. We evaluated Falcon-ASR on the same six benchmarks using th

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