https://news.ycombinator.com/item?id=48447062

Mathematics requires substantial creativity at every level. There is problem selection, conjecture formation, proof strategies, definitions, models, and explanations. Yes, it's constrained and guided by logic and rigor but having logic won't give you creativity.

> Music is a human endeavor and musical recordings hurt musicians, bands, orchestras, etc. Especially those starting out.

The medium it is recorded on has no bearing on what composed the music. If people don't get rewarded for composing they won't. Same with mathematics. If people don't get paid for being creative they just won't be creative.

I am not saying I agree with everything in the article. OP of this thread just made a low effort comment that was addressed in lengths during the article.

- AI-generated papers could overwhelm peer-review systems with low-quality work.

- It may become difficult to assign proper credit for discoveries.

- Researchers who choose not to use AI tools could be disadvantaged.

- There are ethical concerns about mathematical work being used to train AI for military and surveillance purposes.

Interesting, is SO getting traffic again? I thought it wasn't doing well? Or have they adapted?

> The programming book was, when you look at it squarely, always a slightly absurd object. Printed text on bound paper, describing software that lived on screens, which the reader had to retype, by hand, into a screen of their own.

The point of programming books was never "give a man a fish" but "teach a man to fish." If you were not hands on, experimenting, and instead copy-pasting you likely didn't get anything out of the book anyway.

Just a data point: this month I had a knarly bug in generated bpf code. The C language was correct but the compiler produced a bug that corrupted packets. I spent around 8 hours debugging _where_ the issue is and how to work around, never really understanding what went wrong. That knowledge came with several more days on and off looking at it--after I had mitigated the production issue.

So if I extrapolate this experience to LLMs (who are not deterministic) and who will make larger systems. What we trade for velocity we will pay for with hours of debugging because we won't understand how things work. I think this is unavoidable.

Another way I'm looking at it: after some time of not writing code, it will be analogous to instructing the LLM and the output being assembly--where I simply don't have the muscle to grok the output. How do I mitigate that knowledge gap? I see micro serves coming back. Today it is easy to slop up disposable scripts. Our services need to be modular so we can dispose of broken things--so they are only coupled with each other by strict APIs.

Another gripe is the amount of "luxury" apartments popping up. Inviting & modern interiors but all faux cheap materials. And, like, under a highway. Nothing says luxury like being surrounded by concrete and can't even go outside and walk. Commercial real estate is really out of touch :/

> I'm more surprised hackers found a large enough uptime window to do this.

Certainly not a novel thought. But may I suggest you brush up on https://pbskids.org/games/play/sorting-box/487

Ah so my "search" results are going to be biased and at the mercy of the highest bidder.

Only a matter of time before someone will sell privileges of baking your ad/agenda into a llm model during training. That, or companies will fluff their own websites with verbose claims about their products that will get sucked into training via "organic” scraping.

I imagine sites will have to be increasingly behind crawlers protection (like cloud flare) and make Google pay for agents visiting it.

State actors + hackers will have more resources to make better offense. What worse, in my experience AI produced code is blind to overall system behavior. So I fear the exploits will be either low hanging/trivial to exploit errors or bigger system level bugs.