I've played with bpf iterators and wrote a post about them [1]. The benefit of iterating over tasks instead of scanning procfs is a pretty astounding performance difference:
> I ran benchmarks on current code in the datadog-agent which reads the relevant data from procfs as described at the beginning of this post. I then implemented benchmarks for capturing the same data with bpf. The performance results were a major improvement.
> On a linux system with around 250 Procs it took the procfs implemention 5.45 ms vs 75.6 us for bpf (bpf is ~72x faster). On a linux system with around 10,000 Procs it took the procfs implemention ~296us vs 3ms for bpf (bpf is ~100x faster).
> it's the resulting content that matters, not how it's presented
What a wild thing to say. If you had a coworker who was brilliant and taught you many great things, but only screamed instead of talking, would you feel the same way?
> If a person anthropomorphizes an LLM in their mind (rather than just in their speech patterns), then they probably have pre-existing mental problems.
Correct, and that's why these tools should be built responsibly under the assumption that people with mental problems are going to use them. It's clear in the article I linked (and my wording linking to it) that it can exacerbate issues for people. Chatgpt told him that he's sane and his mom was trying to kill him. He didn't understand what an LLM actually was.
I'm not claiming the purpose of this prompt is to get better information. Yes, it's just a prompt.
You're asserting quite a lot of bias when you say "What most people want are useful results." Maybe in our circles of software engineers or lawyers, but many people are using AI for companionship. Even if they're not seeking companionship, unless you have a very clear understanding of how LLMs work, it's very easy to get caught up thinking that the chatbot you're talking to is "thinking" or "feeling". I feel companies that offer chatbots should be more responsible with this as it can be very dangerous.