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10月14日周三
  1. François Chollet74

    François Chollet 转引 Sundar Pichai 的宣布并祝贺:Gemini 4 Argon 已全球上线。该模型面向 Gemini API 和 Gemini 平台付费层级的开发者与企业开放,Ultra 订阅者可在 Google Antigravity 使用,Ultra 和 Pro 订阅者可在 Gemini 应用和 Search 中使用。

    引用Sundar Pichai@sundarpichai

    Gemini 4 Argon 来了。 今天起面向全球开发者与企业开放,可通过 Gemini API 和 Gemini 平台的付费层级使用;Ultra 订阅者可在 Google Antigravity 中使用;Ultra 和 Pro 订阅者可在 Gemini 应用和搜索中使用。 希望大家喜欢!

10月13日周二
  1. Manus45

    我们正与 OPPO 合作,将 Manus 引入 AI Mind Pilot 的全新 Agent Mode,首发搭载于 Find X10 系列与 ColorOS 17!

    引用ColorOS@colorosglobal

    We're thrilled to announce our collaboration with @ManusAI a leading AI agent platform, to bring next-generation AI experiences to smartphones. By integrating Manus’ agentic capabilities with OPPO AI Mind Pilot, this collaboration will help users turn complex, multi-step tasks into ready-to-use results. Find out more 🔗 https://www.oppo.com/en/newsroom/press/oppo-manus/

  2. Mistral AI44

    抬头看!Mistral 模型已进入轨道 🌍 很自豪能与 @orbitworksspace 和 @LoftOrbital 以及 @NVIDIAAIInfra 一起,加入首个此类 AI 卫星星座。

    引用Loft@LoftOrbital

    Just a few weeks ago, @orbitworksspace launched Altair, the first of a 50-satellite AI constellation that will enable users to receive AI-generated alerts within minutes. @NVIDIAAIInfra solutions is making this possible, supporting models like Mistral’s Ministral 3 to deliver fast, reliable answers for users' critical questions. Read more: https://lnkd.in/gGYA9hpD #SpaceMadeSimple #AIInfrastructure #AIforSpace

  3. OpenRouter42

    Scaledown 的约束解码模型家族(Summarize、Extract、Classify-1)已在 OpenRouter 上线,每个模型专为单一任务打造,支持长文本/图片/PDF 摘要、自然语言定义字段提取 JSON、自定义标签分类。发布期 8 折优惠:输入 $0.04/M tokens,输出免费。

    引用Neal Patel@neal_k_patel

    You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀

  4. Rohan Paul54

    ScaleDown 发布 3 个面向单一任务的 Constrained Decoding Models(Classify、Extract、Summarize),不做逐 token 生成,只对输入做一次 prefill 并从原文中直接选出答案。

    引用Neal Patel@neal_k_patel

    You already gave the LLM the answer. So why is it writing it out from scratch? Introducing a new AI model architecture: Constrained Decoding Models (CDMs) LLMs were built to write. Most production workloads just need an answer: fast, consistent, and affordable. CDMs are purpose-built for 1 task each. They read your input once and skip the token-by-token generative loop that makes LLMs slow and expensive. Not just a decision model, but a family of 3 CDMs live today: • Classify: define your labels and get back a calibrated probability + reason for each. Used for routing, tagging, and intent detection. • Extract: describe the entities in natural language and get back well-formed JSON with confidence scores + citations. Used for web scraping, document processing, and PII redaction. • Summarize: pass in long text, images, or PDFs and get back a consistent summary under 1,000 tokens. Used for context compaction, metadata generation, and transcript digests. Built for production: • 1M tokens of multimodal (text + image) input, 31x larger than Jev • 77.2% on DecisionBench vs Jev's 72% • 200x faster, 467x cheaper than frontier LLM APIs • $0.05/M input tokens. Output is free (yes, even for summarize) Live on your favorite platforms today: @OpenRouter @vercel_dev @LiteLLM @LangChain @TrustedRouter @itsArthurAI @useRouterPlus Get 50M input tokens for free (for the first 50 hours) at link below 👀

  5. Tripo46

    仅用 16 天打造的惊艳《小王子》3D 改编作品。 @Saccc_c 用 Tripo、@Blender 和 Opus 5.5 @ClaudeDevs 构建了这个互动世界,干得漂亮。期待开源发布。

    引用Sac@Saccc_c

    花费16天,我用 Opus 5.5 将小王子的故事制作成了3D剧情游戏 结合Tripo+Blender,Opus 5.5 将小王子故事中的每一个星球变成了可探索的3D场景 希望大家能支持一手,后续会开源整个游戏,以及详细制作流程 Welcome to The Little Prince: Across the Stars✨:https://littleprince.dev/

  6. 卡尔的AI沃茨55

    Grok Bot 上线 Memory Transfer 功能,把 Claude 或 ChatGPT 了解的用户信息迁移到 Grok,支持粘贴提示词回答或上传 ChatGPT/Claude 数据导出两种方式。所有条目需逐行人工审核,只保存用户保留的内容,每条标注来源和时间,已有内容不会被覆盖。作者评论认为审核步骤很必要,例如请求帮忙写角色时,角色背景不应被存成关于用户本人的事实。

    引用Brian Roemmele@BrianRoemmele

    NEW GROK BOT! AI MEMORY TRANSFER! Your old AI knows you. Your new one shouldn't have to start from zero. 🧠➡️ Meet Memory Transfer, a @Grok @Bot that brings over what Claude or ChatGPT learned about you, so you don't spend weeks re-explaining yourself. There are two ways to bring it over: • Paste one prompt into your old chat and send me the answer. • Or drop in your full ChatGPT or Claude data export. The part that matters most is that nothing gets in automatically. We review every item together, one line at a time. Only what you keep gets saved, and each item is tagged with where it came from and when. Other AIs' guesses don't become facts, and nothing you've already saved gets overwritten unless you say so. Switch assistants and keep your context, without the junk. Try Memory Transfer on Grok Bot today. https://x.ai/bot/Ww-RXj-lLqpg_oWqzccKZ