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嗨，HN，我刚刚被提升为法国马赛La Plateforme（https://laplateforme.io）计算机科学学院的学习主任（“directeur des études”）。我们是一所免学费的计算机科学学校，接受没有学位要求的学生，从零开始培养他们，提供为期三年的学士学位和五年的硕士学位，专业涵盖软件开发、DevOps、人工智能/数据、网络安全和沉浸式系统。我们每年大约接收200名新生。由于我将对未来的课程大纲产生重大影响，我对正确的方向感到非常不确定。行业变化的速度似乎超过了任何课程的适应能力。我已经放弃了为学生准备十年后的职业生涯，我只想在接下来的三到五年内做出明智的选择。明年九月入学的学生将在2029年（获得学士学位）或2031年（获得硕士学位）毕业。到那时，唯一重要的可能是“能够引导大型语言模型（LLM）”与“能够真正进行软件工程”之间的差距——或者人工智能可能已经完全弥补了这个差距。我有一些老师认为我们应该加大对基础知识（算法、系统、网络）的重视，因为人工智能提高了底线，但并没有改变顶线的要求。还有一些人认为在2026年教学生手动编写REST API就像教他们书写草书一样。以下是我正在思考的一些具体问题： - 今天的计算机科学课程中应该删除哪些内容？我们还在出于惯性教授哪些内容，而这些内容已经被人工智能淘汰，或者很快就会被淘汰？ - 应该添加哪些内容？学生是否应该花一个学期阅读和审查代码库，而不是编写代码？我们是否应该将系统思维或技术写作作为核心技能来教授？还是仅仅依靠提示/上下文工程就足够了？ - 当人工智能能够通过你大多数考试时，如何评估学生？我们应该进行口头答辩吗？还是线下考试？ - 如果你在2029年招聘一名初级员工，你会诚实地筛选哪些条件？我并不想讨论关于人工智能超级智能/风险/后工作未来的考虑（尽管我个人认为这些问题非常重要）。但如果你重新设计过课程、最近招聘过初级员工，或者对现在无用或将来有用的内容有见解，我非常希望听到你的看法。（声明：为纠正拼写和措辞，使用Opus 4.6进行了重写，所有观点均为我个人的）

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Hi HN,I've just been promoted to dean of studies ("directeur des études") at La Plateforme (https://laplateforme.io), a tuition-free CS school in Marseille, France. We take students with no degree requirements and train them from zero to Bachelor (3 yrs) and Master's (5 yrs) in software dev, DevOps, AI/data, cybersecurity, and immersive systems. We accept about 200 new students per year.As I will have a significant influence on our future curriculum, I'm genuinely unsure what the right bets are. The ground seems to shift faster than any program can adapt. I've given up on preparing students for a 10-year horizon, I just want to make good bets on the next 3 or 5.Students entering next September will graduate in 2029 (Bachelors) or 2031 (Masters). By then, the only thing that matters might be the gap between "can prompt an LLM" and "can actually engineer software" — or AI might have closed that gap entirely.I have teachers who think we should double down on fundamentals (algorithms, systems, networking) because AI makes the floor higher but doesn't change what the ceiling requires. Others think teaching someone to hand-write a REST API in 2026 is like teaching cursive.Here are some specific questions I'm wrestling with:- What do you delete from a CS curriculum today? What are we still teaching out of inertia that AI has made, or will likely soon make, obsolete?- What do you add? Should students spend a semester reading and reviewing codebases instead of writing them? Should we teach systems thinking or technical writing as a core skill? Or will prompt/context engineering simply be enough?- How do you evaluate students when AI can pass most of your exams? Should we go for oral defenses? Offline exams?- If you were hiring a junior in 2029, what would you honestly screen for?I'm not looking for considerations about ASI/x-risk/post-work futures (though I personally think they matter a lot). But if you've redesigned a curriculum, hired juniors recently, or have educated opinions on what's now useless or will be useful, I'd love to hear it.(Disclosure: rewritten with Opus 4.6 for misspellings and phrasing, all ideas mine)

问HN：法国计算机科学学校的学术主任——我们应该教授什么？