Project ideas from Hacker News discussions.

A look at the math that OpenAI claims computes faster Fourier transforms

📝 Discussion Summary (Click to expand)

Theme 1 – Practical relevance of theory
Many commenters argue that theoretical results that lack immediate practical use are of little value.
- “If algorithmic theorists don’t care about practical performance then why should we care about what they’re doing? It’s just a strange game they’re playing, of no interest to anyone else.” — skybrian
- “No, I don’t care. Until there is a real, practical result, I don’t care.” — adastra22
- “Theory with no possible practical relevance is useless… a computational complexity result requiring arbitrary‑precision arithmetic that is impossible to implement using any physical system qualifies.” — modeless

Theme 2 – Critique of big‑O and unrealistic model assumptions
Several participants point out that big‑O notation can hide enormous constants or rely on operations (e.g., exact complex arithmetic) that cannot be performed in constant time on real hardware, making the claimed improvements “galactic.”
- “The argument… is about whether the n in any particular big O is relevant… the result holds only if you don’t include the cost to multiply arbitrarily large complex numbers.” — burpingtree
- “the constants inside a big‑O are not really constant: they depend on what you assume is a constant‑time operation.” — LPisGood
- “> the algorithm requires you to multiply complex numbers whose magnitudes grow superpolynomially with n. Err... doesn't that completely invalidate the big‑O analysis!?” — jiggawatts

Theme 3 – Long‑term value of seemingly impractical theory
Defenders note that many mathematical breakthroughs were initially useless but later enabled major practical advances (e.g., number theory → cryptography, relativity → GPS).
- “You don't need to care. But every so often breakthroughs in theory can be turned into breakthroughs in practice.” — eru
- “Doing theory is what gives you a chance to push the envelope to places you can’t even imagine when you’re knee deep in real world problems… proving something is possible (if impractical) gives an incentive to find a better algorithm.” — cpa (cites Fermat → modern cryptography)
- “Plenty of mathematics was practically useless for long periods of time, waiting for us to invent computers, cryptography, error correcting codes, etc.” — nullc


🚀 Project Ideas

Practical Complexity Analyzer

Summary

  • A web tool that takes an algorithm description (pseudocode or LaTeX) and estimates its realistic runtime using bit‑complexity, memory‑hierarchy, and finite‑precision cost models.
  • Flags unrealistic assumptions (e.g., O(1) arbitrary‑precision complex multiplication) and outputs a “practicality score” to highlight galactic gaps.

Details

Key Value
Target Audience Theoretical computer science researchers, algorithm designers, graduate students
Core Feature Input algorithm → automatic cost analysis under realistic models → report of assumptions and practicality score
Tech Stack Python (sympy, numba), Flask backend, React frontend, optional Z3/SMT for constraint checking
Difficulty Medium
Monetization Revenue-ready: SaaS subscription (free tier for individuals, team/enterprise plans)

Notes

  • HN users complained that “the algorithm requires you to multiply complex numbers whose magnitudes grow superpolynomially with n” and that big‑O lets theorists “cheat” with unrealistic assumptions; this tool would make those assumptions visible. ([yorwba], [jiggawatts])
  • By providing concrete cost estimates, theorists could see whether their results survive under realistic hardware models, encouraging more practically relevant work.

ModelCheck Plugin

Summary

  • A plugin for proof assistants (Lean, Coq, Isabelle) and LaTeX editors that scans proofs for uses of O(1) operations on unbounded domains (e.g., exact complex arithmetic, infinite‑precision integers) and warns the author.
  • Helps catch “galactic” loopholes before publication.

Details

Key Value
Target Audience Formal methods researchers, theorists using proof assistants, authors of algorithmic papers
Core Feature Static analysis of proof files → detection of unbounded‑precision O(1) calls → inline warnings and suggested refinements
Tech Stack Rust/Lua for Lean plugin, Python AST parser for LaTeX, integration via Language Server Protocol
Difficulty High
Monetization Hobby

Notes

  • Commenters noted that OpenAI‑style results “assume you can store and multiply complex numbers at arbitrary precision” and that this is a “severe caveat”; ModelCheck would flag such assumptions directly in the proof. ([SpicyLemonZest], [jiggawatts])
  • By embedding the check in the workflow theorists already use, it creates immediate feedback and discourages reliance on exploitative modeling assumptions.

AlgBenchmark Hub

Summary

  • A community‑driven repository where researchers can upload empirical benchmark results for algorithms alongside their theoretical bounds.
  • Includes automated comparison visualizations, versioning, and discussion threads to surface gaps between theory and practice.

Details

Key Value
Target Audience Algorithm engineers, practitioners, theorists interested in empirical validation
Core Feature Upload benchmark data (input size, runtime, environment) → automatic chart comparing empirical vs. O‑notation → comment thread for discussion
Tech Stack PostgreSQL, Node.js/Express backend, React/D3.js frontend, Docker for reproducible benchmark environments
Difficulty Medium
Monetization Revenue-ready: Transaction fee on premium feature access (e.g., private benchmark groups, advanced analytics)

Notes

  • Users expressed a desire for “a replacement for big-O that’s not so exploitable” and wanted to see whether “galactic algorithms” ever translate to real gains; AlgBenchmark Hub provides the data to test that claim. ([skybrian], [modeless])
  • The platform would foster discussion between theorists and practitioners, making it easier to identify which theoretical advances have practical promise.

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