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现在可能是最不值得读 CS PhD 的时代之一
CS 的重要性和 CS PhD 的价值完全是两回事
如今大量学术研究被 funding 和 publication incentives 绑架。很多教授为了拿经费，不得不向 AI 靠拢，学生也被迫围绕热门方向制造 research stories。大量论文看似有完整的实验和漂亮的 benchmark，本质上却缺乏真正的科学贡献
更讽刺的是，AI 正在大幅降低独立研究和技术创新的门槛，但 PhD 仍然要求年轻人花 4–6 年去适应一套越来越重视包装、发表和经费的学术评价体系
我不否认真正有价值的基础研究，但我质疑的是：在今天，这套 PhD 体系究竟是在培养真正的科学探索者，还是在训练更擅长制造 publishable stories 的人？
We are entering the golden age of Computer Science or AI or something, not necessarily the golden age of CS PhDs
我反而觉得，计算机科学可能是今天最值得读 PhD 的学科之一 （虽然我觉得这个时代读计算机PhD是极其浪费时间，而且是傻逼的行为，这么说有点自相矛盾了）
今天的 Computer Science，早就不只是一门研究计算机的学科了。
如果回头看过去几百年的科学史，数学为物理学提供了描述世界的语言，实验方法让人类能够系统地验证关于自然的假设。
计算正在逐渐成为科学研究的第三种基础范式。
我们开始用神经网络研究智能，用计算模型理解大脑，用模拟研究物理世界，用机器学习探索生物系统。
很多最前沿的问题，本质上已经很难被简单地归类为计算机科学、物理学、生物学或者认知科学。
它们研究的对象是世界本身，只不过研究世界的工具、语言，甚至思考方式，越来越建立在计算之上。
这也是为什么我认为今天的 CS PhD 有一个非常特殊的位置。
它训练的不仅仅是如何编写程序、设计系统，而是如何用计算的方式去抽象、建模和理解复杂现象。
未来很多重要的科学突破，可能并不是来自某个传统学科内部，而是发生在计算科学与其他学科的交界处。
当然，这不意味着每个人都应该读 CS PhD。如果目标只是成为软件工程师，博士未必是最有效率的路径。
但如果你想研究智能是什么，机器如何理解物理世界，复杂系统如何从简单规则中涌现，或者我们能否用计算发现新的科学规律，我很难想到有什么理由认为这些问题不值得投入博士阶段去研究。
我们正在经历一个转变：从研究如何让计算机完成任务，到借助计算机重新理解世界。
而计算机科学恰好站在这个转变的中心。
Meet you at a very TrueNorth time
Week 2 of the TrueNorth × Lighter trading competition is complete.
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Exbyte retweeted
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Imagine a world where retail traders operate at the same efficiency as institutional desks
where you don’t need 50 analysts to have the same edge
Introducing TrueNorth, the world’s first agentic brokerage
built on @HyperliquidX 🧵
Exbyte retweeted
Imagine a world where retail traders operate at the same efficiency as institutional desks
where you don’t need 50 analysts to have the same edge
Introducing TrueNorth, the world’s first agentic brokerage
built on @HyperliquidX 🧵
Exbyte retweeted
On April 6, I ran an AI-assisted security scan on Kelp DAO and flagged their LayerZero DVN bridge config as an unresolved risk.
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What protocols are you exposed to that haven't disclosed their DVN config?
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