搜索“RAG”

and how it landed for the student. Each key moment is a decision point where the tutor had to weigh scaffolding (making a problem more accessible) against pushing for rigor (encouraging the student to do harder thinking). TutorMoments runs by pausing a transcript at one of those key moments and handing the session to a language model, which takes over as the tutor for five turns with a simulated student. We call each of these model-generated continuations a replay . An LLM-based scoring pipelin

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l refuses harmless requests too often. The harmful prompts are more closely associated with safety, while the benign prompts are more closely associated with general reasoning. Averaging them into a single benchmark score can obscure that difference. BenchMIRT helps researchers separate those signals and see what’s actually driving a benchmark’s score. It does this by analyzing how models perform on each question or task and estimating which underlying capabilities are most closely associated wi

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ots that match human physical abilities.⁶ It’s difficult to predict exactly how these efforts will affect jobs and productivity. A ranking of occupations by current robot task coverage suggests where the impacts will appear first. Robots should affect jobs that they can already do before jobs they could do only with new technology and, in some cases, accommodating regulations. Current capabilities are also concrete and measurable, while forecasts of future capabilities must bet on which technolo

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rich data to Claude in several ways: Reference files with @ instead of describing where code lives. Claude reads the file before responding. Paste images directly . Copy/paste or drag and drop images into the prompt. Give URLs for documentation and API references. Use /permissions to allowlist frequently-used domains. Pipe in data by running cat error.log | claude to send file contents directly. Let Claude fetch what it needs . Tell Claude to pull context itself using Bash commands, MCP tools, o

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and includes 100 browser hours and 1GB of proxy traffic. Additional usage costs $0.12/browser-hour and $12/GB. The Startup plan ($99/month) includes larger allowances and lower overage rates. Pros Live inspection, replays, logs, and network/console tools; Reusable contexts for cookies and application data; Supports Selenium, Puppeteer, and Playwright; Specialized API endpoints for web search and page fetching Cons Free plan browser sessions are limited to 15 minutes; Advanced stealth requires th

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han multiplying listed rates by estimated token counts; Rerun the comparison when a candidate model or its price changes What a leaderboard rank does not tell you A leaderboard averages a model’s results across tasks that have nothing to do with yours. The model that ranks first for coding is not necessarily first at your structured-data extraction task, and a mid-tier model that looks unremarkable on a reasoning benchmark may answer your FAQ traffic accurately enough for your bar. Agent tasks a

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ad of allowing each token to attend to the entire preceding sequence, SWA restricts attention to a fixed-size local window. Imagine, for intuition, that a sliding window simply averaged the representations within that window. Two adjacent tokens would then be averages over almost exactly the same set of preceding tokens. Move them slightly farther apart and their windows overlap less. Move them more than one window apart and they may not overlap at all. So the closer two positions are, the more

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1 Sol. Once the discount ends, the cost rises to $3.98, about 20 percent above GPT-6 Astra. The price advantage comes from lower token rates, not from efficiency. Argon uses an average of 62,000 output tokens per task, while GPT-6 Astra needs only 27,000. Argon also made significant gains on agentic tasks, which according to Artificial Analysis have been a weak spot for Gemini models. On AutomationBench-AA, the Artificial Analysis variant, it takes first place at 77.5 percent, six points ahead o

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that the read-only enforcement is a regex-based classifier in the LocalSqlExecutionAccessChecker class. For instance, the regex for blocking EXEC looked like this. Blocklists are fragile security controls. There are often many ways a given capability can be bypassed. Besides that regex, there was no separate low-privileged database connection or read-only permission enforcement. That seemed worth poking at, and with the help of AI bypasses were found quickly. For example, an invocation pattern s

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Generative recommender systems reformulate personalization as sequence modeling over user behavior, and NVIDIA recsys-examples now provides an end-to-end HSTU inference workflow with Dynamo-Triton. PyTorch AOTI compiles HSTU ranking models into native C++ artifacts, while FlexKV-backed KV caching stores reusable attention state

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and ability to not go rogue or hack things in unintended ways. Second, they should “empower” an internal team to do that work (and remediate when things go wrong). Third, it encourages them to partner with an external monitor who is “empowered” to do independent assessments to make sure that all of that work is happening. And finally, they should make sure their board of directors has set up a committee to receive reports about all of these efforts. The voluntary commitments are some indication

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the product, not an integration you maintain. Comfy API is available on paid Comfy plans, starting with Standard. Usage is billed separately. GPU time is billed by the second. Storage is prorated hourly. See full Comfy API pricing. What this means, and what’s next ComfyUI gave people control over every step of how an image or video gets made. Every node is visible. Every parameter is adjustable. But getting that workflow in front of a team or a customer still meant rebuilding it somewhere else

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curating petabytes of multimodal data, to training a model across multiple nodes and then deploying it in production whether it’s on VMs or a Kubernetes-managed cluster. That coverage across the full AI lifecycle is also why Ray has become the standard substrate for post training and reinforcement learning (RL) at labs like Cursor, Microsoft AI and NVIDIA. Whether you’re new to distributed PyTorch or already running Ray in production, here’s your guide to the sessions worth blocking off on your

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Healthcare is another real-world setting: fragmented symptoms, dense reports, ongoing questions. Feedback from AQ and https://t.co/M7OKhVawk0 (Good Doctor Online) scenarios informed training. Ling-3.1-flash scores 65.35 on HealthBench Professional—an evaluation result, not clinical certification.

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mestamps for the episode at the bottom. Brought to You by • turbopuffer – The turbopuffer engineering team is doing something really cool: they are completely redesigning their storage architecture from first principles to make search faster, cheaper, and more reliable at scale. And they’re documenting all of it! Follow along their rewrite at turbopuffer.com/v3 • Linear – most of us work with agents in a “single-player” setting. Linear’s take is agent work should be teamwork: your teammates can

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Technical Blog sessionStorage.removeItem('facets.search.text'); sessionStorage.removeItem('facets.search.text-copy'); sessionStorage.removeItem('facets.search.filters'); Related Resources Developer Tools & Techniques Learn how to use execution traces to understand agent behavior and determine whether harness changes improve task outcomes. AI-Generated Summary NVIDIA NeMo Relay provides a common observability layer that captures ordered lifecycle events and structured trajectories for agent runs.

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e anyone can reference it. What are the risks to publicly sharing my interview? While we won’t publish your Claude account information, such as your name or email address, we encourage participants to consider that anyone can read a public interview. This includes employers, friends and relatives, as well as governments and other companies. Depending on what a participant shares, others may be able to link the participant back to their interview. Details that seem harmless on their own, such as

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n adjuster might need every open auto claim over $10,000 from last month. Both tasks require finding and combining evidence quickly and accurately. Retrieval Augmented Generation (RAG) uses retrieved documents to ground model responses. Amazon Bedrock Knowledge Bases is the fully managed RAG capability for documents. Amazon Bedrock handles parsing, chunking, embeddings, and vector storage, so you can build a conversational interface that returns cited answers from claim files. This technical how

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t the studio’s own back catalog. If the screen objects, the agent calls back to the composition agent for a replacement and re-screens. Delivered as a zip file on Amazon Simple Storage Service (Amazon S3) The workflow: a producer starts a track. The composition agent writes a brief and renders real audio on the instance’s GPU. The delivery agent opens that file, measures it, applies a chain it derived from those measurements, and measures again to prove the result landed on target. The complianc

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periences. For immediate tasks, it can scan recent photos to pull details — like populating your calendar from an event flyer, or finding a repair person after spotting a broken garage door in your camera roll. Second, it mines your photo library to come up with personalized ideas for everything from holiday vacations to birthday gifts. Given that plenty of tools can parse pictures — like looking up where to buy a product, or searching information on an interesting art piece — I was far more cur

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