I think that's where the future is going. It presents interesting challenges, but also some opportunities to make life better for everyone.

[1*] https://foundrs.com/have_your_ai_talk_to_my_ai.html

(*) I dislike the HN trend of starting citations at 0, I find it snobbish.

I don't know anything about the Linux scheduler, but if one core out of 96 is busy chasing after those ghost allocations, why would a network thread not use one of the other 95 cores, and instead be stuck for seconds? Unless that one core is blocking the entire kernel, but then that would be a lot more visible instantly.

Professionally, assembly is the last thing I would let an LLM generate for me. That's because if you need to write something in assembly, it's because it's critical: operating system context switch, interrupt handler, that kind of thing. You don't want to be 99% correct, you need that code to be 100% good.

I can imagine some other professional scenarios that I'm less familiar with, where you want to write a tight numeric loop of NEON64 assembly and let the LLM do it, because it's frankly a pain to do by hand, and you can test it to some degree of confidence.

But there’s nothing wrong with a car made by a robot factory. The car is useful, regardless of how it was made. AI lets some people create new products, those may be useful.

If you think a page is too vague, use a famous known writer's work as a reference.

Comments URL: https://news.ycombinator.com/item?id=48690101

Points: 1184

# Comments: 1240

High-risk – AI applications that are expected to pose significant threats to health, safety, or the fundamental rights of persons. Notably, AI systems used in health, education, recruitment, critical infrastructure management, law enforcement or justice. They are subject to quality, transparency, human oversight and safety obligations

That's a pretty common sense legislation to me.

That is not my experience mentoring 100+ startup founders. Building a prototype, the gateway to serious customer engagement, used to take months and many startups would die before finishing their first one.

There is a way to make the campfire approach a little bit less utopian: California could pass a law that makes it legal to have a second temporary job. So that FAANG engineers for instance, would be allowed to campfire/interview at another company with legal protections. Put a bit of grease in the interview system is good for candidates.

Did you do the math? Your estimate feels way off. First, I doubt an intern would process one PR in 30s. Maybe 2-3 minutes, to read 10 lines carefully looking for typos and indentation mistakes. We pay interns close to $100K these days (in a company like CloudFlare), so that's ~80c/minute. My estimate is therefore closer to $1.6 per PR. About 10X.

You are correct that there is a residual value with the intern, over time they would start learning (a little bit) about the code base.

Well... Now I can be that client. And let AI deal with my incomplete, always changing requirements. And get it done anyway.

Like you, we have seen numerous reports that more and more firms are capping their total headcount in favor of leaning on more AI tools, leading to downsizing their intern and new-graduate hiring. [...] But we think this misreads the moment completely, so we’re heading in the opposite direction.

Now ARM for the first time (this century) is making its own chip [design], which like most of its customers, is manufactured by a fab like TSMC.

The title is clear.

Having implemented my shared of highly complex high-performance algorithms in the past, the key was always to figure out how to massage the raw data into structures that allow the algorithm to fly. It requires both a decent knowledge of the various algorithm options you have, as well as being flexible to see that the data could be presented a different way to get to the same result orders of magnitude faster.