这篇论文让我感觉，我们对「AI 会取代程序员」这件事的讨论方向可能全错了。 核心观点：AI Agent 的出现不是让软件工程师工作效率更高，而是让「把决策逻辑永久编码进软件」这件事本身变得越来越不必要。 作者说的是一个更根本的范式变化：传统软件工程的本质是，人类把判断逻辑「固化」成代码——if-else、状态机、算法，这些都是把人类决策「提前写死」的方式。但在以 LLM 为核心推理引擎的 Agent 系统里，代码变成了「临时生成、用完即扔的工具」，每次任务，Agent 动态生成需要的代码，执行完就不需要了。决策不再被预先编码，而是在运行时由 LLM 推理循环动态产生。这不是增量改进，而是软件生产范式的结构性替换。 我觉得这个观点里最值得注意的细节是：这不只是生产力工具的升级，而是「软件」这个概念本身的角色在变。以前代码是「系统的中心」，Agent 框架是外壳。现在 LLM 推理循环是中心，代码变成了外壳里的临时辅助。如果这个趋势持续，软件工程的核心能力可能不再是「写出好代码」，而是「设计出可靠的推理约束边界」。 以前我们关注写出可维护的代码，以后可能更要关注设计出可靠的推理边界。 arxiv.org/pdf/2606.05608

最近为了研究 Claude Design，专门写了一个工具，可以解析 HAR 文件，解密 Claude Design 传输的二进制内容，这样可以方便的看到请求的 Prompt

Britain had a moment of silence for George Floyd. Our politicians kneeled en masse to show their outrage at his killing. "I can't breathe" became a slogan. George Floyd died on the other side of the world. He wasn't British. Henry Nowak *was* British and his treatment by the police was shocking and negligent in the extreme. Yet there is no minute of silence. There is no coordinated public campaign. There is no kneeling at sporting events. And we all know why. During the summer of BLM, some people said "All Lives Matter". This was treated as the highest form of racism and anyone who said this was immediately cancelled. Why? Because the people in charge don't actually think all lives matter in the same way. They have created a racial hierarchy of victimhood where a career criminal who died through mistreatment by police in a foreign country with 0 evidence of racism like George Floyd is automatically sanctified because of the colour of his skin. And Henry Nowak, a British man, one of ours, is automatically dismissed and ignored because of the colour of his. This is the ugly fruit of so-called "anti-racism", an obsession with race that has created a two-tier society which treats people differently because of the colour of their skin. This needs to stop.

One of the new, buzzy jobs in Silicon Valley is the AI Forward Deployed Engineer (FDE), an engineer who is embedded within a client organization to help customize solutions, such as building and tuning agentic workflows that suit the client’s particular needs. I’ve heard from people who are wondering anew about the FDE career path since OpenAI and Anthropic started building new teams to place FDEs within client organizations. The rise of FDEs for AI workloads is one way AI is creating new jobs (and why the jobpolcalypse narrative of upcoming job market collapse is false -- there will be many AI and non-AI jobs). However, I believe there will be far more AI Engineer jobs than FDEs, as I explain below. The FDE role was pioneered about two decades ago by Palantir, which sent engineers to government locations to work on secure, air-gapped networks. In addition to having good technical skills, FDEs need communication skills and sometimes business skills. For example, they may need to speak with clients to understand their needs, formulate a strategy to prioritize projects, explain complex technology, and respectfully push back if a client asks for something unrealistic. They’re enjoying a resurgence because of the amount of work involved in taking an off-the-shelf LLM and building it into a custom agentic workflow that fits particular business needs. However, I believe the number of AI Engineer jobs will be far larger. A company might accept a few FDEs to be embedded within its organization. But most companies will want far more of their own employees working on their projects. While my organizations do hire FDEs, we hire far more AI Engineers! Also, a common client concern is that it is hard to find vendor-neutral FDEs — they are, after all, there to deeply integrate a particular vendor’s product into a company. In this moment when it’s hard to predict which AI service will be the best one in a year’s time, optionality (the ability to pick whatever vendor turns out to fit best in the future) is very valuable. In contrast, letting FDEs tightly bind a company’s processes significantly reduces optionality. Right now, I see surging demand for AI Engineers who can build software applications using AI software components (like LLM prompting, agentic frameworks, evals, etc.) and effectively use AI coding agents (like Claude Code, Codex, Antigravity CLI, and OpenCode). As the AI Engineer role matures, I expect it to fragment into more specialized roles, like the generic Software Engineer role from decades ago fragmented into frontend, backend, mobile, data engineering, devops, and so on. What will be the future, specialized AI engineering roles? I don’t know. Perhaps there will be AI FDEs, LLMOps Engineers, Evals Engineers, AI Data Engineers, Harness Engineers, and other roles we don’t have names for yet. But for now, I see a lot of AI engineers who are generalists create a lot of value. Skilled AI Engineers are in very high demand! As our field continues to mature over the coming decade, I look forward to new specializations within AI Engineering that create even more job opportunities. [Original text: The Batch newsletter]

Repo Prompt 这个工具挺实用的，它可以把你整个Repo的代码拼成一个XML文本，方便你发给像Gemini 2、Claude 3.5、o1 pro这样支持长上下文的模型。 当然你也可以只选择部分文件，目前只支持 Mac repoprompt.com/

当生活几乎被 AI 填满后，我开始意识到：工作流可以自动化，表达可以被润色，代码可以被重写，但孤独、爱、亲密关系这些东西，依然绕不开人本身。 越追求效率，越发现自己真正缺的不是效率，而是连接。