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2026-08-25
日期、来源、摘要、可信度和合规说明均来自通过完整审核门禁与哈希校验的每日公开快照；周报发布后由同期完整发布包接管。
Exa MCP search metadata: Perplexity partners with Nvidia to launch Portable Computer, a fully local AI agent with zero token costs | VentureBeat. # Perplexity partners with Nvidia to launch Portable Computer, a fully local AI agent with zero token costs | VentureBeat Published: 2026-08-25T06:00:00-07:00 Perplexity partners with Nvidia to launch Portable Computer, a fully local AI agent with zero token costs | VentureBeat 6:00 am, PT, August 25, 2026 Perplexity is launching Portable Computer today, a version of its agentic"Computer" platform that runs entirely on hardware users already own — starting with Nvidia's DGX Spark desktop supercomputer and Linux machines equipped with Nvidia RTX GP... 来源：Exa MCP。Exa MCP public search metadata via the Agent Reach documented route; no cookies, login state, or full article body stored.
打开公开来源RSS 公开元数据：Funding better evaluations of AI’s impact on wellbeing。We’re launching a $5 million grant program to fund independent research into how AI impacts users’ wellbeing. The program will provide direct funding, access to our models, and technical support to grantees building open-source evaluations that help the AI industry measure how our models affect those who use them. Grantees will work fully independently, and will publish their work as open-source projects that any developer can make use of. AI systems have become central to how many people work, learn, and solve problems. They’ve also become conversational partners and can be sources of emotional support during difficult times.... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Wire It, Run It, Deploy It: AI Workflows in Gradio。Update on GitHub:last-child]:mb-0"> Most interesting AI apps are pipelines. You generate an image, then cut out its background if you want to, or edit it into something new. You write a script, then generate a voice for it, or swap the voice while keeping the script the same. We usually wire these steps together in Python, and the moment something looks off we go back to print-debugging to find which step produced the odd value. gr.Workflow, built right into Gradio, makes the pipeline the interface. You describe your steps as a graph of typed nodes, and Gradio serves a drag-and-drop canvas where every node is runnable and every in... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Jalapeño’s first results show industry-leading speed and efficiency in AI inference。How we measured Jalapeño’s performance How we measured Jalapeño’s performance Jalapeño widens the lead at previous-best TBT Jalapeño delivers more tokens per user Jalapeño delivers more throughput per kilowatt Architecting for speed and efficiency within a single chip We used AI to design the chip, and designed the chip so AI could program it The path ahead for efficient, ultra-fast inference Appendix Jalapeño is pareto frontier at GPT‑OSS 120B Jalapeño leads across GPT-OSS operating points Jalapeño is pareto frontier at DeepSeek R1 670B Jalapeño leads across DeepSeek R1 operating points Jalapeño i... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored.
打开公开来源RSS 公开元数据：Introducing the Admin plugin for ChatGPT Work and Codex。Helping admins manage growing workspaces Helping admins manage growing workspaces Automate recurring admin workflows Scale administration with existing controls How OpenAI’s IT team uses ChatGPT Work and Codex Install the Admin plugin for ChatGPT Work Helping admins manage growing workspaces Automate recurring admin workflows Scale administration with existing controls How OpenAI’s IT team uses ChatGPT Work and Codex Install the Admin plugin for ChatGPT Work We’re introducing the Admin plugin for ChatGPT Work (opens in a new window) and Codex (opens in a new window) —a faster, easier way to analyze workspace information, ... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：The full stack behind abundant intelligence。Jalapeño widens the lead at previous-best TBT Jalapeño widens the lead at previous-best TBT Build for breadth, own for leverage Turning efficiency into economic value A compounding advantage Jalapeño widens the lead at previous-best TBT Build for breadth, own for leverage Turning efficiency into economic value A compounding advantage Progress in AI compounds fastest when the entire system improves together. That is how I think about OpenAI’s compute strategy: one integrated system spanning data centers and chips, frontier models, our developer platform, consumer and enterprise products, and AI-native devices, with each layer strengthenin... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Quantization-Aware Healing: a compressed, 4-bit model that outperforms its full-precision original。:last-child]:mb-0"> Making a large language model smaller almost always comes with a cost. The now-standard recipe for efficient deployment is to compress the architecture first, cutting the parameter count by removing layers, heads, or neurons, and then quantize the remaining weights down to 4 bits to shrink memory and compute further. Both steps save a lot, but together they systematically degrade the capabilities people actually care about: reasoning, mathematical problem-solving, and code generation. Because of this, serious deployment pipelines add a recovery step, usually calle... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
打开公开来源RSS 公开元数据：Granite 4.2 LLMs: How They're Built。:last-child]:mb-0"> A technical walkthrough of how we built the Granite 4.2 reasoning model family. Authors: Granite Team, IBM TL;DR: Granite 4.2 is our first family of dense, decoder-only reasoning LLMs, released in three sizes: 3B, 8B, and 30B. Each model is pre-trained from scratch on roughly 15T tokens with a five-phase strategy that extends the context window to 512K tokens, supervised fine-tuned on chain-of-thought, reasoning, and agentic-trajectory data, then post-trained with a multi-stage reinforcement learning pipeline. That pipeline includes agentic RL, where the 8B and 30B models learn to act with tools inside real sandboxed environm... 来源：RSSHub。RSSHub public RSS metadata only; no full article body stored. Trafilatura public URL extraction; stored short extract only, no full article body, cookies, or headers.
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