2025年就要结束了，原来真正的高手，隐藏在「民间」！ 不是谷歌、不是OpenAI，是Anthropic王者编程模型Claude Opus 4.5。 在METR最新公布报告称，Claude Opus 4.5已能够持续自主编码「长达5小时不崩」。 就连OpenAI最强编程模型——GPT-5.1-Codex-Max也甘拜下风。 现如今，全网都在为Claude Opus 4.5编码实力震撼。 AI编码智能体能处理的任务时长不仅在指数级增长——其增速还在持续提升！ 2019-2024年：任务时长每7个月翻一倍 2024-2025年：任务时长每4个月翻一倍 很多人第一次看到这条曲线，会本能地摇头。 网友认为这是关于AI最重要的图表： 有人不理解。有人不愿意接受。 但一个事实越来越清晰：AI编码智能体能连续完成的任务，正在从「分钟级」冲向「小时级」，并且加速度还在上升。 这张图为什么被称为「最重要的图表」？ 因为它在回应一个关键的问题： AI是否撞墙了？AGI是不是另一个乌托邦？2025年，AI到底进步了多少？ 普通用户感知不强，很正常。对大多数人来说，模型早就能应付日常提问： 「推荐部电影」「解释这个概念」「写段文案」 ...

Core Insights - The rapid advancement of AI programming tools is transforming the coding landscape, with models like GPT-5 and Gemini 2.5 enabling a degree of automation in development tasks [1][2] - The adoption of AI coding agents has become a norm not only for programmers but also for professionals in product and design roles, leading to an increasing proportion of AI-generated code [3] - Despite the benefits, challenges remain regarding code quality and analysis efficiency, prompting developers to explore the use of multiple AI agents in parallel [3][5] Summary by Sections - **Parallel Coding Agent Lifestyle**: Simon Willison initially had reservations about using multiple AI agents due to concerns over code review bottlenecks. However, he has since embraced this approach, finding it manageable to run multiple small tasks without overwhelming cognitive load [5][6] - **Task Categories for Parallel Agents**: - **Research Tasks**: AI agents can assist in answering questions or providing suggestions without modifying core project code, facilitating rapid prototyping and validation of concepts [7][9] - **System Mechanism Recall**: Modern AI models can quickly provide detailed, actionable answers about system functionalities, aiding in understanding complex codebases [10][11] - **Small Maintenance Tasks**: Low-risk code modifications, such as addressing deprecation warnings, can be delegated to AI agents, allowing developers to focus on primary tasks [13][14] - **Precisely Specified Work**: Reviewing code generated from detailed specifications is less burdensome, as the focus shifts to verifying compliance with established requirements [15] - **Current Usage Patterns**: Willison's primary tools include Claude Code, Codex CLI, and Codex Cloud, among others. He often runs multiple instances in different terminal windows, executing tasks in a YOLO (You Only Live Once) manner for manageable risks [16][19] - **Developer Community Response**: The blog post has garnered significant attention, resonating with current pain points in coding workflows. Many developers are experimenting with parallel AI agents, with some reporting that a substantial portion of their coding work is AI-assisted [21][22] - **Concerns and Discussions**: While some developers express apprehension about the unpredictability of AI-generated code, others, including Willison, advocate for the benefits of parallel agent usage, particularly for non-code-committing research tasks [26][29]