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teorth
erdosproblems
Python2025–2026
teorth / erdosproblems

erdosproblems

A community database for the problems on the erdosproblems.com site

23Distinct people
29Mentions
0Explicit recommenders
883GitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

A community database for the problems on the erdosproblems.com site

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by month29 dated mentions

Observed mentions: 2025-12-28 15:01:54 UTC to 2026-09-04 03:27:56 UTC.

0369122025-12-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 2 mentions; 2 submissions, 0 commentsDec 20252026-01-01 00:00:00 UTC to 2026-02-01 00:00:00 UTC (exclusive): 12 mentions; 4 submissions, 8 comments2026-02-01 00:00:00 UTC to 2026-03-01 00:00:00 UTC (exclusive): 4 mentions; 0 submissions, 4 comments2026-03-01 00:00:00 UTC to 2026-04-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2026-04-01 00:00:00 UTC to 2026-05-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 commentsApr 20262026-05-01 00:00:00 UTC to 2026-06-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2026-06-01 00:00:00 UTC to 2026-07-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2026-07-01 00:00:00 UTC to 2026-08-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2026-08-01 00:00:00 UTC to 2026-09-01 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2026-09-01 00:00:00 UTC to 2026-10-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsSep 2026

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Dec 20252025-12-01 00:00:00 UTC2026-01-01 00:00:00 UTC220
Jan 20262026-01-01 00:00:00 UTC2026-02-01 00:00:00 UTC1248
Feb 20262026-02-01 00:00:00 UTC2026-03-01 00:00:00 UTC404
Mar 20262026-03-01 00:00:00 UTC2026-04-01 00:00:00 UTC303
Apr 20262026-04-01 00:00:00 UTC2026-05-01 00:00:00 UTC202
May 20262026-05-01 00:00:00 UTC2026-06-01 00:00:00 UTC303
Jun 20262026-06-01 00:00:00 UTC2026-07-01 00:00:00 UTC000
Jul 20262026-07-01 00:00:00 UTC2026-08-01 00:00:00 UTC101
Aug 20262026-08-01 00:00:00 UTC2026-09-01 00:00:00 UTC110
Sep 20262026-09-01 00:00:00 UTC2026-10-01 00:00:00 UTC101

What Hacker News says

29 sources
What does this mean? There are 1217 problems that Erdos proposed, 595 of which are open: https://github.com/teorth/erdosproblems Unlike traditional benchmarks, it's difficult to overfit your models to produce flattering results to unsolved problems. The open problems very likely do not have published solutions (the initial batches were merely models surfacing data that wasn't published in obvious places, but we're past that now). New models will exhibit something novel by adding solutions. And the matter of solution is interesting as well (contradiction versus a positive proof). I would venture to predict it'll take years to decades to get to 0 open problems. But I'd be very happy to have this comment look foolish in retrospect as models continue to improve
In OpenAI's GPT-6 Astra on ARC-AGI-3
I'm grateful for the reference as context, but it simply does not settle the issue of transparency and it did reinforce the question. That's not controversial, nor is the statement that OpenAI avoids transparency, wants good publicity, will go to great lengths on both of these things like all corporations. It's also completely normal to think progress on Erdos problems is fascinating and inspirational, but aim for clarity on what the scope of the achievement really is (invention, composition, literature search) per the limited information available. How does any of this offend a reasonable person? It doesn't, because here is the project wiki addressing BOTH of these relevant points. https://github.com/teorth/erdosproblems/wiki/How-have-AI-com... https://github.com/teorth/erdosproblems/wiki/AI-contribution... What I raised is a real que
Read the rest
stion: the erdos folks are not affiliated with AI companies.. but is the AI company affiliated with them? Actually knowing the user/org accounts involved is optional because they could just divert resources to anyone prompting near the problem. Anecdotally.. I've noticed what appears to be token-discounts based on topic, for example more generosity for AI-related research than random stuff, but it's hard to know for sure. Wouldn't you promote interactions you could profitably train on? So again, allocating resources to Erdos one way or another is just a clearly smart business decision for something people are talking about and which has become an unofficial competition among vendors, not a big scandalous accusation. Something like a reasoning-trace is the only way to settle it. This isn't conspiracy or nitpicking because this is the topic itself: the AI usage is more a matter of public interest than the actual problem solution. What's the argument against more transparency?
In A recent experience with ChatGPT 5.5 Pro
If you asked a three-year-old a question that they proceeded to completely flub, would you then assume that all humans are incapable of answering questions correctly? Nobody is arguing for the quality of the search overviews. The models that impress us are several orders of magnitude larger in scale, and are capable of doing things like assisting preeminent computer scientists (the topic of discussion) and mathematicians (https://github.com/teorth/erdosproblems/wiki/AI-contribution...).
In Claude's Cycles [pdf]
> On the wiki, I have decided to expand Section 2(d), "AI tools used to rewrite an existing argument" to also include cases such as the one here, in which the AI tool indirectly caused the argument to be rewritten in a substantive way (beyond mere typos etc.) by identifying a non-trivial mathematical issue in the previous version of the text, which was then fixed by a human author. Fwiw, wiki Section 2(d): https://github.com/teorth/erdosproblems/wiki/AI-contribution...
In ChatGPT finds an error in Terence Tao's math research
"It's interesting to me that whenever some new result in AI use comes up, there's always a flood of people who come in to gesticulate wildly that that the sky is falling and AGI is imminent. Like with the recent solutions GPT 5.2 has been able to find for Erdos problems, even though in almost all cases such solutions rely on poorly-known past publications, or significant expert user guidance and essential tools like Aristotle, which do non-AI formal verification - many users here (even in this very thread!) think they know more about this than Fields medalist Terence Tao, who maintains this list showing that, yes, though these are not interesting proofs to most modern mathematicians, LLMs are a major factor in a tiny minority of these mostly-not-very-interesting proofs: https://github.com/teorth/erdosproblems/wiki/AI-contribution..." The
Read the rest
thing about spin and AI hype (besides being trivially easy to write) is that is isn't even trying to be objective. It would help if a lot of these articles would more carefully lay out what is actually surprising, and what is not, given current tech and knowledge. Only a fool would think we aren't potentially on the verge of something truly revolutionary here. But only a fool would also be certain that the revolution has already happened, or that e.g. AGI is necessarily imminent. The reason HN has value is because you can actually see some specifics of the matter discussed, and, if you are lucky, an expert even might join in to qualify everything. But pointing out "how interesting that there are extremes to this" is just engagement bait.
In GPT-5.2 derives a new result in theoretical physics
It's interesting to me that whenever a new breakthrough in AI use comes up, there's always a flood of people who come in to handwave away why this isn't actually a win for LLMs. Like with the novel solutions GPT 5.2 has been able to find for erdos problems - many users here (even in this very thread!) think they know more about this than Fields medalist Terence Tao, who maintains this list showing that, yes, LLMs have driven these proofs: https://github.com/teorth/erdosproblems/wiki/AI-contribution...
In GPT-5.2 derives a new result in theoretical physics

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