搜索“RAG”

-based tool for connecting applications and data sources. It allows teams to build automated business processes and integrate APIs. Modern companies need these connections: The average enterprise has around one thousand apps but integrates only 27% of them. Engineering teams use iPaaS to: Connect cloud applications: Move and transform data between separate systems without the need for middleware; Drive process automation: Build workflows that trigger actions across multiple cloud and on-site sys

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ead of walls of text with this skill. Just passed 10K stars. Have some AI experiments that you gave up on? Don’t trash them; instead, extract what’s worth keeping . One dictated paragraph before a meeting, a full report site by the time it ended. a16z’s State of Markets II : about 30% of S&P 500 companies report some AI impact, but only 2% track a metric for it. Ranju @whatRanjuSaid whose kid is this 11:27 PM · Sep 29, 2026 · 274K Views 86 Replies · 392 Reposts · 9.61K Likes Matt Pocock @mattpoc

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e is only part of the picture. Our technical report also documents experiments that informed how we train MoEs and measure their performance. For example: A score intended to encourage balanced routing could improve even as the actual workload became less balanced. We call this failure token gerrymandering . Lowering experts’ learning rates – the size of their training updates – because they process fewer tokens did not improve results in the model family we tested. GPU calculations took differe

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an the hearing aids division. After their time at Bose, Trivedi joined EssilorLuxottica, where he led smart eyewear efforts and worked on the Meta Ray-Ban smart glasses, while Romine went on to serve as COO of hearing care company Audicus. The pair then joined forces to explore the intersection of hearing technology and smart eyewear. Legato Frames can be purchased on the startup’s website or through select eye care providers in the U.S., where they are eligible for coverage by vision insurance.

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r graph intelligence. When a network fails, finding the root cause can take hours. For complex multilayer failures, remediation in traditional network operations centers (NOCs) averages four to five hours and can extend to days. The bottleneck is not engineering expertise; it is cognitive overload. Human operators cannot correlate hundreds of alarms, configuration files, and telemetry data across thousands of nodes faster than customer impact accumulates. Traditional approaches rely on temporal

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an the Flash line. Argon’s numbers put Google back in the frontier talks, but we need to see how it runs in a broader rollout before declaring this a comeback story. The Rundown: Fragmented tools and slow handoffs cost more than most teams realize. A Forrester Consulting study quantified it, and for a composite enterprise, the result was 312% ROI over three years, $38.6M net present value, and payback in under 6 months. This ROI calculator lets you: Plug in your team size and tool stack to see p

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des in the fleet constantly poll for new work to do, and our scheduler checks its backlog for eligible work, assigning the work if it fits. There are heuristics around placement, fragmentation, and cost, but fundamentally, the scheduler makes the choice about whether a node should pick up an item on a per-node basis. For multi-node scheduling, a single-node view is not sufficient - the atomic unit for scheduling expands from a single node to N nodes. This meant we had to build an entirely new sc

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ing post and select one. Generate ideas from as many angles as possible and evaluate them from both factual and reader perspectives as well as other publications doing similar coverage," the AI spun up three agents. When I sketched three teams in a few sentences (brainstormers, researchers, and a panel of readers), I got thirteen. Notice how little organizing I had to do. Selecting Ultra mode tells the model it can delegate, and I provided a framework, but the rest was up to the AI. Agents at wo

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arn that a similar approach could be used to reveal people’s inner thoughts and mental imagery, potentially without their consent. “The results seem very impressive,” says Tommy Sprague, a neuroscientist at the University of California, Santa Barbara. “But if there’s a way to surreptitiously extract information about what you’re thinking about, then …150 years of sci-fi can come true anytime, and that’s worrisome in a lot of ways.” Neuroscientists have been working for years on ways to use funct

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RAM, the calculations are done, and then the data is put back. But the brain doesn’t have a RAM area where it can load stuff from, compute things, and then put it back. All its storage is distributed. So all the different connections between neurons are tuned to allow the network to store different bits of information – that’s how we memorize things. It’s also how we have memories at different timescales. For example, you have a working memory of this conversation here, but you can also remember

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id lock-in. OpenTelemetry has merged semantic conventions for LLM workloads, which gives OpenTelemetry agent tracing standard definitions to build on Here's the same idea as a coverage map. One tool having more logos isn't the interesting part. These signals belong on one timeline rather than stitched across four. Doing it with Pydantic Logfire Logfire is an AI-native observability platform from the Pydantic team, built as an opinionated wrapper around OpenTelemetry. The part that matters for ad

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At discounted pricing it costs $1.99 per task versus $3.26 for Astra; standard pricing would raise this to $3.98. Token use : The savings come from price, not efficiency. Argon averages 62K output tokens per task against Astra’s 27K. Agentic work : It ranks 1 on AutomationBench-AA at 77.5% and scores 57% on Terminal Bench 4, behind Sonnet 5.5, Opus 5.5 and Astra. Hallucination : Its 15% rate on AA-Omniscience compares with 51% for Astra. The tradeoff is lower accuracy: 50% versus Astra’s 63% ( @

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, bucket by duration, evaluate on independent target and regression sets. What changes per language is the corpus metadata, transcription conventions, normalization, tokenizer coverage, script handling, and capability metrics. For another language, start with a small audited manifest and test the base tokenizer on names, numerals, borrowed words, and mixed-script sentences. Replace the Arabic normalizer with one that preserves meaningful distinctions in the target script, and validate unicode no

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影响的型号和版本发布了紧急修复程序。 此外，根据加密货币专家和Coinkite公司的描述，攻击过程是：算法缺陷导致的“助记词Seed phrase”（用于访问钱包的一长串单词）帮助攻击者推导出了钱包的位置，再通过比对公开区块链找到资产存放地，拿到私钥并转走货币。 值得注意的是，此次受影响的固件版本Mk2/Mk3最初发布于2021年3月， 从4.0.1到4.1.9一直存在编译配置错误 ：硬件真随机数被静默旁路，改用可预测软件伪随机数生成助记词。 市场反应 此次黑客盗币事件在社交媒体上引发了一系列恐慌，同时也让许多人意识到“ 自主托管”是加密货币的创始原则之一 。 区块链初创公司Predicate CEO Nikhil Raghuveera表示，“自主托管是数字资产的核心特质，但Coldcard事件说明，单一环节故障便足以冲击整套模式的公信力，因为该生态本就建立在缺乏信任度的承诺上。” 竞争对手Ledger的网络安全专家Vincent Bouzon对此评论称，任何钱包的根密钥安全性最终都取决于高质量的随机数（熵）生成过程，同时熵的生成必须依托安全硬件，架构上不能静默降级到不可信的软件随机。

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-only menu. SeedRealtime identifies the dishes straight from the scene, recommends them in English, and even draws on cultural background to explain "why there is no fish in fish-fragrant pork (yuxiang rousi)" and "how century eggs are made." As the server sets down a dish and casually says it "goes great with rice," the model understands the line in the context of the dish on screen and translates it for the guest. Visual information does not need to be turned into text first; it is aligned wit

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erate the video, ensuring that the composition and blocking of complex shots closely match the creator's expectations. Additionally, Seedance 2.5 improves lighting control. By leveraging the spatial information from the clay render, it generates realistic lighting effects that follow physical laws, such as light source direction, color temperature, intensity, and shadow projection. This results in more natural light and shadow in the final output. R2V prompt: Refer to @Clay Render 1 for camera m

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s. Based on long-horizon interaction runs across these tasks, we find that the overall agent performance in "environment learning" closely follows a log-sigmoid curve , with an average goodness of fit of R² = 0.998. Across frontier models from different generations, agent learning speed also appears to double roughly every three months. EdgeBench has now open-sourced 51 tasks and the full evaluation framework, enabling the broader community to further study how agents learn from real-world envir

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tal decorations. The background areas previously obscured by the main subject are also seamlessly inpainted and restored. These layers retain their transparency and can be freely dragged or scaled. Creators can even replace the core subject with a new element (such as swapping the parrot for a peacock). Multi-image fusion: combining multi-source materials for complex scenes By simultaneously inputting multiple reference materials and a target base image, the model fuses the different elements in

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eed and responsiveness became essential to product adoption. Enterprise agents behave more like distributed systems than chatbots. A single workflow may involve multiple rounds of RAG, tool use, planning, verification, and summarization. Some workloads require deep, multi-hop reasoning over long documents. Others like front-line service chatbots and dashboard-driven insights, require near-instant responses. As StackAI expanded into more regulated sectors, two requirements stood out: To support t

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reasoning surface area. Running on Cerebras, they can process 100x more documents (filings, calls, reports, etc.) in half the time versus GPU systems.(7) That kind of speed-to-coverage conversion is exactly how faster inference translates to higher-accuracy answers, and in this case a 2x speedup too. Learn more Biathlon and inference are similar in many ways. In biathlon, speed determines not only the distance between you and your competitors, but can also be the difference between a clean range

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