Analysis and impact
导读摘要
正文
该文章于 2026 年 8 月 13 日发布在 gruhn.me 博客上，由用户 theanonymousone 提交至 Hacker News 后迅速登上首页。截至统计时，帖子已获得 170 分和 108 条评论，讨论热度在当日技术社区中相当突出。文章标题「NP-overrated」直指计算复杂性理论中的核心概念——NP 类问题，暗示作者对这一问题类别在实践中的重要性持保留或批判态度。原文未提供文章正文的具体论点细节，但标题本身已足以在 HN 社区引发关于理论计算机科学与工程实践之间张力的讨论。
从同日 HN 社区的热门内容来看，技术讨论呈现出多元并行的态势。与本文同一时段上榜的还包括 Bluesky 的 AT Protocol 服务介绍（93 分、19 条评论）以及 SparrowMap 这一监控政府车辆的摄像头项目（110 分、25 条评论）。这些帖子共同构成了当日 HN 首页的技术讨论光谱：从抽象的理论议题到具体的协议实现，再到带有社会监督色彩的开源硬件项目。NP 复杂性理论属于计算机科学的基石性内容，通常出现在算法课程与理论研究语境中，而 HN 社区以工程实践者为主，标题中的「overrated」恰好切中了理论价值与工程实用性之间长期存在的认知落差。
值得注意的是，同一时间段内 HN 上还有两条涉及美国政府行为的热门讨论：一条关于美国对左翼及反 ICE 抗议者的大规模监控行动（104 分），另一条关于特朗普宣布对多类无人机及全部飞机零部件征收 100% 关税（来自 The Verge）。这些政治与社会议题与 NP 理论讨论并行出现在同一社区首页，反映出 HN 读者群在纯技术话题之外同样密切关注技术政策与社会影响。不过，这些新闻与《NP-overrated》本身并无直接关联，仅作为当日社区讨论生态的背景呈现。
由于原文博客正文内容未被抓取，文章的具体论证路径、作者身份及其论据支撑均无法在此进一步展开。但从 HN 评论区 108 条回复的规模来看，该话题确实触发了实质性的观点交锋——无论是对 NP 完全性在现实约束求解、密码学与优化问题中角色的辩护，还是对「理论模型过度简化现实计算」这一立场的呼应，都可能在讨论中有所体现。原文未提供评论区的具体内容，以上仅为基于话题性质的合理推测。
References
Original source text
NP-overrated
Aug 13, 2026
If you learned about NP-hard problems in university, your takeaway was probably this:
NP-hard problems are solvable in theory but it's hopelessly expensive in practice. It's basically proven that no good algorithms exist.
At least that's what I took away. And almost everyone I've talked to. And many people online. I keep seeing "No you can't do it. It's NP-hard. Blah blah" discussions. The myth is pervasive but these problems are not intractable.
At the time, my professor closed the final lecture with dramatic words (I'm paraphrasing slightly):
And now you've learned that almost all interesting problems are undecidable and of the remaining ones, almost all are NP-hard. For the project of computer science, that puts the final nail in the coffin.
Sheesh. Not sure if everyone got such a dire framing but that would explain.
The theory is not wrong, but in practice it's often irrelevant. Sure, any algorithm you can come up with will blow up on some inputs. But you might get a fast solution on 99.9% of inputs. Or 100% of the remotely relevant inputs. The theory does not rule that out.
In theory, there is no difference between theory and practice. But in practice, there is.
-- Benjamin Brewster
A few prominent NP-hard problems:
Dependency resolution (in package managers)
Type checking (not all type systems)
Scheduling
Traveling Salesman
Boolean Satisfiability (SAT)
For (1) and (2), the worst-case just doesn't occur. I mean, installing packages and type checking can surely be slow. But, at least in my career, I've never seen a galactic blow-up.
(3) and (4) are technically optimization problems. Everyone knows you can tackle those with heuristics, but you don't have to sacrifice optimality. We absolutely have tools that can find provably optimal solutions in reasonable time. There's no magic. No quantum computers. Just thinking harder and coming up with better algorithms. And that's what people have done. In fact, algorithmic speedup has outpaced hardware gains in the last decades. Taken together, this paper cites a 450-billion-fold speedup between 1991 and 2015.
Last but not least: even (5), the archetype of NP-hard problems, is routinely solved at scale. Amazon is solving a billion SMT problems a day. SMT is an even harder version of SAT. The SAT algorithms have gotten so good, it's now considered the easy part.
But what if you run into the worst-case? You don't have to wait for the heat-death of the universe. An HTTP request also doesn't come back sometimes. Add a timeout, show an error message, ... you know the drill.