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Comment by john_strinlai | Hacker News Reader

>They [anthropic] appear to have just told it to get some results and then strapped its nose to the grindstone until it found some.

it is fun how well this works.

i cant find the link immediately (will look and edit with it), but somewhere in the "hello there the jacobian conjecture is false thanx" thread, someone brought up a different conjecture breakthrough where the prompts were basically just repeated "no, keep going" until a result was found.

edit: https://chatgpt.com/share/6a60b2eb-0b64-83ee-9c76-7931ca1de0...

i especially like "you should do a breakthrough". each prompt is less than ~20 words. makes me really question the whole "prompt engineering" stuff.

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> i especially like "you should do a breakthrough". each prompt is less than ~20 words. makes me really question the whole "prompt engineering" stuff.

This is well past prompt engineering and into process engineering like six sigma. Just like in an early industrial revolution factory, we’re all still figuring out what works in the process of making stuff except this is so early that even simple things like “make this screw standardized” (or “no, keep going” in this case) is really high impact.

The degrees of freedom an LLM has is so large that we're going to be exploring their capabilities for decades, especially if they continue to get better. This is why IMO experts are always going to be better at LLMs in their field because they can force them LLM into processes (think prompt engineering -> CC dynamic workflows) that follow their work processes and get much better results out of them than “keep going.”

In response to your edit, you should check out Terry tao's chat gpt logs about the recent Jacobian result. The models are smart enough to brute force some things, but can cut to the meat much faster with good prompting

i read his, too. his replies are indeed more directed, but also quite short, unstructured, and natural sounding. if i recall, maybe 1 or 2 of his prompts exceeded 50(ish) words.

in my head, the comparison is the multi-paragraph prompts (borderline essays) i would read in various communities on reddit and similar forums, that people (often self-proclaimed "prompt engineers") said were "required" to get good output. or some of the prompts ive read in various logs that are like a thousand words of setup.

even looking back at the first prompts i was sending when i started to use chatgpt were (in hindsight) crazy long and full of unnecessary guidance/caveats/"ignore xyz"/etc.

I didn't use generative AI much until a couple months ago. My last employer had a copilot license that I dabbled in in 2024, and wasn't impressed with, and then I spent the latter half of 2025 and early 2026 budget traveling/hiking a lot. I started at a new company in May, and I've been astonished at how lazy I can be at prompting and still get impressive results with 2026 Claude Code.

I can paste entire failure logs with the word "why" lowercase, no question mark, and get into a productive chat session where it significantly speeds up the bug trace. I will paste the text from a groomed ticket with no editing or additional instructions into the chat and then give it a bit of feedback on the plan for a couple iterations.

I feel so vindicated in never spending time learning prompting as a specific skill, it really just took a couple more years and the models are really easy to interact with with simple natural language.

> were (in hindsight) crazy long and full of unnecessary guidance

Not sure when you started. However: I would never judge the necessity of details provided 2 or 3 years ago based on results that the current models give.

Same with cybersec. They're finding bugs a human could've found if they looked hard but humans don't look hard at 100% of the code and the LLM can, at high speed.

That second person stated that for many years they tried that particular graph problem on various AI models, starting with o1 and o3.

Its quite likely they now found the counterexample with a more serious prompt, and then for virality re-tried a few times with meme-prompts like "you should do a breakthrough", knowing that the model is capable of solving this particular one. Worst case the meme-prompts don't work and they share the real one they initially used.

>Its quite likely they now found the counterexample with a more serious prompt, and then for virality re-tried a few times with meme-prompts

i am not sure why this is "quite likely". it'd be pretty silly to get a mathematical breakthrough and then hide it for an undisclosed amount of time to get a few more likes on a tweet, when the impressive part is the breakthrough.

not saying your theory is impossible, but i think the simple answer is that the model is just smarter than o1 and o3.

and, in any case, the model ended up getting the result with the meme prompt and "keep going", which was what i find fun. just like how the crypto results were from prompts of, more or less, "keep going", and that's pretty damn cool.

You can open 10 tabs and prompt 10 times. We are talking about a few hours delay.

I agree with you that obviously no prompt engineering was needed just "solve this problem", but imagine it was you doing this problem with every model, wouldn't you have tested a new model with the best prompt you had from previous iterations, maybe with some partial previous results in it, exactly to maximize your probability for a mathematical breakthrough?

Not everything is a conspiracy

Could send random characters along with 'keep going' and it would change nothing, the seed is whats causing deviation between these responses.

Oh boy. The human here is effectively assuming the role of a Magic 8 Ball…

What a weird species of halting problem…

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