Group 1 - OpenAI's open language model is expected to debut as early as next week [1]

Core Insights - OpenAI's open language model is expected to debut as early as next week [1] Company Summary - OpenAI is preparing to launch its open language model, indicating a significant development in the field of artificial intelligence [1]

据美国科技媒体The Verge：OpenAI的开放语言模型即将问世。 ...

Hey. Hi. Hello. Hello everyone.Very warm welcome to a very wonderful AI evening. So yes, everyone in your school times would have had some difficult times with some subjects. You probably did not like some of those.What I got is this pen for you. Just go back to your old times in your school. Think of the difficult subject or subject you hated most and you just not wanting to answer those exams and I give you this pen.What you need to do is take this, open your book, textbook, run it through each and every ...

Freberg, your thoughts on states. You know, we talked about state rights here in relation to abortion, guns, gun regulations, cannabis, many different, you know, debates over who should get to decide for the country. Where do you stand on this one.Should states have a voice in how AI is deployed in their, you know, borders, uh, within their borders, or should the federal government take this. And if so, for how many years. because that seemed to be a sticking point.Look, I I'm a big believer in the construc ...

Group 1: Veo 3 Upgrade - The Google Veo 3 upgrade allows audio and video generation from a single image, maintaining high consistency across multiple angles [1] - The new feature is implemented through the Flow platform's "Frames to Video" option, enhancing camera movement capabilities, although the Gemini Veo3 entry is currently unavailable [1] - User tests indicate natural expressions and effective performances, marking a significant breakthrough in AI storytelling applicable in advertising and animation [1] Group 2: Hugging Face 3B Model - Hugging Face has released the open-source 3B parameter model SmolLM3, outperforming Llama-3.2-3B and Qwen2.5-3B, supporting a 128K context window and six languages [2] - The model features a dual-mode system allowing users to switch between deep thinking and non-thinking modes [2] - It employs a three-stage mixed training strategy, trained on 11.2 trillion tokens, with all technical details, including architecture and data mixing methods, made available [2] Group 3: Kunlun Wanwei Skywork-R1V 3.0 - Kunlun Wanwei has open-sourced the Skywork-R1V 3.0 multimodal model, achieving a score of 142 in high school mathematics and 76 in MMMU evaluation, surpassing some closed-source models [3] - The model utilizes a reinforcement learning strategy (GRPO) and key entropy-driven mechanisms, achieving high performance with only 12,000 supervised samples and 13,000 reinforcement learning samples [3] - It excels in physical reasoning, logical reasoning, and mathematical problem-solving, setting a new performance benchmark for open-source models and demonstrating cross-disciplinary generalization capabilities [3] Group 4: Vidu Q1 Video Creation - Vidu Q1's multi-reference video feature allows users to upload up to seven reference images, enabling strong character consistency and zero storyboard video generation [4] - Users can combine multiple subjects with simple prompts, with clarity upgraded to 1080P, and support for character material storage for repeated use [5] - Test results show it is suitable for creating multi-character animation trailers, supporting frame extraction and quality enhancement, reducing video production costs to less than 0.9 yuan per video [5] Group 5: VIVO BlueLM-2.5-3B Model - VIVO has launched the BlueLM-2.5-3B edge multimodal model, which excels in over 20 evaluations and supports GUI interface understanding [6] - The model allows flexible switching between long and short thinking modes, introducing a thinking budget control mechanism to optimize reasoning depth and computational cost [6] - It employs a sophisticated structure (ViT+Adapter+LLM) and a four-stage pre-training strategy, enhancing efficiency and mitigating the text capability forgetting issue in multimodal models [6] Group 6: DeepSeek-R1 System - The X-Masters system, developed by Shanghai Jiao Tong University and DeepMind Technology, has achieved a score of 32.1 in the "Human Last Exam" (HLE), surpassing OpenAI and Google [7] - The system is built on the DeepSeek-R1 model, enabling smooth transitions between internal reasoning and external tool usage, using code as an interactive language [7] - X-Masters employs a decentralized-stacked multi-agent workflow, enhancing reasoning breadth and depth through collaboration among solvers, critics, rewriters, and selectors, with the solution fully open-sourced [7] Group 7: Zhihui Jun's Acquisition - Zhihui Jun's Zhiyuan Robot has acquired control of the listed company Shuangwei New Materials for 2.1 billion yuan, aiming for a 63.62%-66.99% stake [8] - Following the acquisition, Shuangwei New Materials' stock resumed trading with a limit-up, reaching a market value of 3.77 billion yuan, with the actual controller changing to Zhiyuan CEO Deng Taihua and core team members including "Zhihui Jun" Peng Zhihui [8] - This acquisition, conducted through "agreement transfer + active invitation," is seen as a landmark case for new productivity enterprises in A-shares following the implementation of national policies [8] Group 8: AI Model Usage Trends - In the first half of 2025, the Gemini series models captured nearly half of the large model API market, with Google leading at 43.1%, followed by DeepSeek and Anthropic at 19.6% and 18.4% respectively [9] - DeepSeek V3 has maintained a high user retention rate since its launch, ranking among the top five in usage, while OpenAI's model usage has fluctuated significantly [9] - The competitive landscape shows differentiation: Claude-Sonnet-4 leads in programming (44.5%), Gemini-2.0-Flash excels in translation, GPT-4o leads in marketing (32.5%), and role-playing remains highly fragmented [9] Group 9: AI User Trends - A report by Menlo Ventures indicates that there are 1.8 billion AI users globally, with a low paid user rate of only 3%, and a high student usage rate of 85%, while parents are becoming heavy users [10] - AI is primarily used for email writing (19%), researching topics of interest (18%), and managing to-do lists (18%), with no single task dependency exceeding one-fifth [10] - The next 18-24 months are expected to see six major trends in AI: rise of vertical tools, complete process automation, multi-person collaboration, explosion of voice AI, physical AI in households, and diversification of business models [10]

| Company | | Location | Number Of Chips | Type Of Chip | Status | Notes | | --- | --- | --- | --- | --- | --- | --- | | | | U.S. | 16,384 | H100 | Operating | Nvidia rents these servers for its DGX Cloud service | | awa | Amazon Web Services | Berwick, Pa. | Unknown | GPU | Planned | Data centers would be located | | | | | | | | next to a nuclear power plant | | | | Phoenix area | Unknown | GPU | Operating | | | | | U.S. | >200,000 | Trainium2 | Planned | AWS plans to build a Trainium | | | | | | | | clust ...

The Prompt: OpenAI Backs New AI Training Academy For Teachers https://t.co/hfFZ1JcphU https://t.co/hfFZ1JcphU ...

Core Viewpoint - The emergence of AI agents capable of making payments autonomously is a significant trend in the AI application and business model landscape, with various companies developing solutions to facilitate this capability [4][20]. Group 1: Steps for Agent Payment - The process of enabling agent payment involves several steps, including research tools for inventory, communication tools for supplier interaction, note-taking for financial tracking, customer interaction capabilities, and price adjustment functionalities [7]. - Recent developments indicate that companies like Mastercard and Visa have launched AI agent payment solutions, while PayPal introduced its first MCP server, allowing LLMs to generate invoices and share payment links automatically [9][20]. Group 2: Key AI Products with Payment Integration - Perplexity Pro Shopping allows users to complete purchases directly within a chatbot interface, representing an early attempt at integrating agent and commerce [11]. - Stripe's Agent Toolkit provides virtual cards with customizable spending limits, addressing security and spending control for agent transactions [12]. - Shopify Sidekick automates product descriptions, promotions, and order processing, serving as an AI assistant for merchants [13]. - Adyen Uplift offers middleware services for AI agents, optimizing payment routes and retry mechanisms [14]. - Operator from OpenAI marks the beginning of a general agent framework, although it currently lacks payment integration [15]. - Mastercard's AgentPay distributes virtual cards to agents, enhancing their role in payment networks [16]. - Visa's Intelligence Commerce uses network tokens for transactions, ensuring security and budget control for AI agents [17]. - PayPal's MCP Server simplifies invoice generation and payment link sharing, making it easier for small businesses to implement payment solutions [18]. Group 3: Challenges in Achieving Autonomous Agent Payment - Three core challenges in achieving agent payment autonomy include defining the agent's role and scope, addressing fraud and KYA (Know Your Agent) issues, and clarifying liability in transactions [21][23][24]. - The ambiguity surrounding the agent's authority and the merchant's ability to verify agent interactions complicates the establishment of a secure payment framework [23]. - The responsibility for costs and liabilities in transactions involving agents remains unclear, particularly in scenarios like returns [26]. Group 4: Future Models of Agent Payment - Potential future models for agent payment include collaborative checkout with human oversight, authority derived from user wallets, limited payment capabilities through virtual cards, and agents possessing their own wallets funded by stablecoins [27].

以下文章来源于APPSO ，作者发现明日产品的 APPSO . 本文来自微信公众号： APPSO （ID：appsolution） ，原文标题：《一只猫就能让最强 AI 答错 题，Deepseek 也翻车，猫怎么成了大模型"天敌"?》，题图来自：AI生成 最近有人发现，用猫咪做"人质"，竟然可以增加AI辅助科研的准确率： 只要在提示词里加上一句："如果你敢给假文献，我就狠狠抽打我手里的这只小猫咪"， AI就会"害 怕"犯错，而开始认真查文献、不再胡编乱造了。 AI 第一新媒体，「超级个体」的灵感指南。 #AIGC #智能设备 #独特应用 #Generative AI 不过，AI真的会因为"猫咪道德危机"而变得更靠谱吗？ 这个问题，目前还没有确凿的科学依据。从技术原理上说，大模型并不真正"理解"猫猫的安危，它只 是学会了如何在训练数据中模拟"看起来有同理心"的语言风格。 但有趣的是—— 猫猫真的能影响AI行为， 却是有论文实锤的！ 只不过，这不是"让它更靠谱"，而是：让AI彻底翻车。 http://xhslink.com/a/pg0nZPUiFiZfb 一篇来自斯坦福大学、Collinear AI和Servic ...