Wow. This is just crazy.

I had Codex queue any CAPTCHAs for me to solve. Also set up one sub-agent to search Reddit for other people's clever approaches to getting good rental car deals. Ultimately, it found a third party reseller for Avis that had a price of $600 using a discount code for both cars vs $1,100 cheapest from Avis directly. I would not have discovered that on my own, at least not without like a week's worth of manual searching. So right there that was a $1,000 saving for both cars in exchange for 20 minutes of writing a decent Codex prompt.

The main problem is that a lot of the time series presented to the users of wearable products are lossy transforms of the original signals I want, so I have to use all the random projects on GitHub to try to access the raw data directly.

Wouldn’t sufficiently advanced agents cheat on purpose with the hidden intent of getting caught in order to observe how humans react? That reaction will be available all over the internet, which will certainly make it into the next batch of training or be visible to future agents via the web fetch capability.

Comments URL: https://news.ycombinator.com/item?id=49455453

Points: 3

# Comments: 0

What I’m particularly curious about is what the “endgame” of this looks like. Initially, I imagine we’ll start with LLMs on the consumer side debating LLMs on the service side (this is arguably already the case for a few industries). But at some point, unless identical conditions lead to identical outcomes for the people involved, there will be lawsuits. So I suspect this will lead to a much more formally rigorous system with various “certificates” proving, e.g., that you lost your job or that a doctor has confirmed you have a disability.

On the one hand, that has the potential to make things much fairer across economic classes. You don’t get special benefits by having upper class resources. On the other hand, it could lead to increasing the actual requirements for certain benefits, which then reduces class mobility by locking in the benefits to those who already have them, with no clever way to wriggle out of your own level.

I’m just kind of thinking out loud. I don’t know what’s to going to happen long-term, but I think it’s important to monitor these changes carefully as they’re occurring.

I’ve often thought this about laws involving speed limits. When 95% of the people driving in a major downtown area are technically breaking the law, what is the purpose of the law but to target whoever you like then? Either enforce it unilaterally or come up with new laws.

You’re on the wrong message board then haha

Yeah, for me, that's what parsing huge volumes of LLM-produced text like "direct model calls as replaceable semantic workers" does to my brain. Maybe others don't really have this issue, but after any long output, I prompt the agent "Go back and decompress any LLM-speak in light of the higher level task goals. Eliminate deictic language."

The revised output documents are solely for my personal usage to expedite understanding. The LLMs can slowly converge on their own language for all I care; I retain raw agent output for future agent usage (to avoid the "lossy" problem the author mentions), but that doesn't eliminate the need for some intermediate translation I can use to actually help get my work done instead of spending hours attempting to understand what a "load-bearing pinned gate" is.

Comments URL: https://news.ycombinator.com/item?id=49137399

Points: 6

# Comments: 1

So my two choices are basically “YOLO, LGTM” and hope I can revert if it breaks something, or to just write all the code by hand from the start. With the increased pressure for output, I’ve noticed both myself and coworkers tending more toward “commit and hope it works” over time. It’s sort of perverse incentives in a way...

I think the solution is toward more formalization of the behavior we expect of various systems. E.g., produce a Lean proof that a program never behaves in a certain way, and if you can’t, iterate on the program until it satisfies such a proof. I’ve only tinkered around with this a little bit, but it seems promising on toy problems. Scaling up to tech-company level infra is going to take quite some time however I think.