January 30, 2026
P vs NP vs PR disaster
P vs. NP and the Difficulty of Computation: A ruliological approach
Big idea meets bigger eye-rolls: commenters call it “incoherent” and slam missing proofs
TLDR: An essay uses a try-it-and-see approach—running many tiny programs—to explore why some problems can’t be sped up, without solving P vs NP. Commenters torched it as incoherent and half-baked, citing ChatGPT vibes and missing pieces in its formal proof, with only a cheeky “good bot” as applause.
A bold essay tries a fresh angle on the legendary P vs NP puzzle—think: can problems that are easy to check also be easy to solve? Instead of pure theory, the author goes hands-on, enumerating tiny programs on “Turing machines” to see what’s fast, what’s not, and where shortcuts simply don’t exist. No grand claim to victory here—just small, concrete results and more evidence for computational irreducibility, the idea that some problems resist shortcuts.
The comments, though, showed zero chill. One top reply blasted, “You do not win. This is incoherent,” while another raged that a serious math question shouldn’t be mixing physics units like “km/s” or “Spectral Gap Magnitude,” comparing the vibe to an AI-written mashup. The biggest clapback? Accusations that the formal proof in Lean (a tool for machine-checked math) is loaded with placeholder “sorry” gaps. “Packed full of missing parts,” one critic sniped, as others chimed in with a curt “lmao” and the meme-ish “good bot.”
Drama highlight: a lone “good bot” wink versus a pile-on of skeptics calling it ChatGPT-core. Debate simmered over whether this empirical “try everything and see” approach is useful or just noise for P vs NP. Verdict from the crowd? Curiosity: 1, Credibility: under review.
Key Points
- •The article proposes an empirical approach to complexity questions by enumerating small programs and measuring their performance.
- •It uses Turing machines as the model of computation, focusing on machines that compute integer functions with a specific halting criterion.
- •The “ruliad” framework and ruliology are introduced to systematically explore the space of programs.
- •The work aims for restricted, concrete results related to P vs. NP, rather than a full resolution of the problem.
- •Evidence and explicit examples are presented for computational irreducibility, showing cases where no faster method exists within the studied class.