Open AAI has delivered on their promise to release a state-of-the-art open-source model. This is GPTOSS. Now, I think the mystery model Horizon Alpha that was on Open Router is actually this open source model from OpenAI, although they have not confirmed that to me, but we do have an incredible model.Let me tell you about all the details. So, first it comes in two sizes, a 120 billion parameter version and a 20 billion parameter version. These are state-of-the-art openweight language models.Open weight. So ...

Core Insights - The rapid development of AI Agents has ignited a trend of "everything can be an Agent," particularly evident in the competitive landscape of model development and application [1][2][10] - Major companies like OpenAI, Google, and Alibaba are heavily investing in the Agent space, with new products emerging that enhance user interaction and decision-making capabilities [2][7][8] - The evolution of AI applications is categorized into three phases: prompt-based interactions, workflow-based systems, and the current phase of AI Agents, which emphasize autonomous decision-making and tool usage [17][19] Group 1: Model Development - The AI sector has entered a "arms race" for model development, with significant advancements marked by the release of models like DeepSeek, o3 Pro, and Gemini 2.5 Pro [5][6][14] - The introduction of DeepSeek has demonstrated that there is no significant gap between domestic and international model technologies, prompting major players to accelerate their model strategies [6][10] - The focus has shifted from "pre-training" to "post-training" methods, utilizing reinforcement learning to enhance model performance even with limited labeled data [11][13] Group 2: Application Development - The launch of OpenAI's Operator and Deep Research has marked 2025 as the "Year of AI Agents," with a surge in applications that leverage these capabilities [7][8] - Companies are exploring various applications of AI Agents, with notable examples including Cursor and Windsurf, which have validated product-market fit in the programming domain [9][21] - The ability of Agents to use tools effectively has been a significant breakthrough, allowing for enhanced information retrieval and interaction with external systems [20][21] Group 3: Challenges and Opportunities - Despite advancements, AI Agents face challenges such as context management, memory mechanisms, and interaction with complex software systems [39][40] - The future of Agent applications may involve evolving business models, potentially shifting from subscription-based to usage-based or outcome-based payment structures [40][41] - The industry is witnessing a competitive landscape where vertical-specific Agents may offer more value due to their specialized knowledge and closer user relationships [42][46]

以下文章来源于海外独角兽 ，作者拾象 Founder Park 正在搭建开发者社群，邀请积极尝试、测试新模型、新技术的开发者、创业者们加入，请扫码详细填写你的产品/项目信息，通过 审核后工作人员会拉你入群～ 海外独角兽 . 研究科技大航海时代的伟大公司。 Deep Research 产品可被理解为 一个以大模型能力为基础、集合了检索与报告生成的端到端系统，对信息进行迭代搜索和分析，并生成详细报告作为输 出。 参考 Han Lee 的 2x2 分析框架，目前 Deep Research 类产品在 输出深度、训练程度 两大维度呈现分异。 输出深度 即产品在先前研究成果的基础上进行了 多少次迭代循环以收集更多信息，可进一步被理解为 Agentic 能力的必要基础。 低训练程度 指代经过人工干预和调整的系统，比如使用人工调整的 prompt，高训练程度则是指利用机器学习对系统进行训练。 和传统 LLM Search 产品相比，Deep Research 是迈向 Agent 产品雏形的一次跃迁，可能也将成为具有阶段代表性的经典产品形态。 Deep Research 产品通过系列推理模型嵌入，已生长出了 Agent 产品 ...