It's a spectrum. I don't think that they will be able to sell t-shirts using a drawing you've uploaded. But it will probably allow them to defend themselves a bit better if they get sued for selling the data for LLM training.
The console one seems the only relevant one. I'm a casual gamer, but the joycon running out of battery doesn't feel that annoying to me. That 16% has a much smaller impact on the JoyCon than on the main device.
LLMs can also be really good in fields where you are not an expert. You just need to be very aware of your limitations, and start parallel conversation so one agent fact checks the other.
>I wonder what will happen to the drivers if a large representation of the 1 million+ daily trips are displaced by automation?
If it happens gradually enough, they will just find other jobs. After the transition, society will be producing more with the same labor force, and thus the aggregate utility will increase.
I don't understand how this is the top comment. LLMs have unlocked a lot of value for me personally, and arguably for the society as a whole. They are also one of the coolest technologies I've tried in years. As a technologist, I'm really glad that money is pouring in and allowing us to find its limits.
I think you misunderstood, it's not about solving the problem, is about finding the most efficient solution. Give it a shot, and see if you can get to the top 10 on any task.
Thank you. Yeah, I'm doing all those things, which do get you close to the top. The rest of things I'm doing are mostly micro-optimizations such as finding a way to avoid AVX→SSE transition penalty (1-2% improvement).
But I don't want to spoil the fun. The agents are really good at searching the web now, so posting the tricks here is basically breaking the challenge.
> The above is a software engineering problem. Reimplementing a JSON parser using Opus is not fun nor useful, so that should not be used as a metric.
I've also built a bitorrent implementation from the specs in rust where I'm keeping the binary under 1MB. It supports all active and accepted BEPs: https://www.bittorrent.org/beps/bep_0000.html
Again, I literally don't know how to write a hello world in rust.
I also vibe coded a trading system that is connected to 6 trading venues. This was a fun weekend project but it ended up making +20k of pure arbitrage with just 10k of working capital. I'm not sure this proves my point, because while I don't consider myself a programmer, I did use Python, a language that I'm somewhat familiar with.
So yeah, I get what you are saying, but I don't agree. I used highload as an example, because it is an objective way of showing that a combination of LLM/agents with some guidance (from someone with no prior experience in this type of high performing architecture) was able to beat all human software developers that have taken these challenges.
You are looking at this wrong. Creating a json parser is trivial. The thing is that my one-shot attempt was 10x slower than my final solution.
Creating a parser for this challenge that is 10x more efficient than a simple approach does require deep understanding of what you are doing. It requires optimizing the hot loop (among other things) that 90-95% of software developers wouldn't know how to do. It requires deep understanding of the AVX2 architecture.
I know what's like running a business, and building complex systems. That's not the point.
I used highload as an example because it seems like an objective rebuttal to the claim that "but it can't tackle those complex problems by itself."
And regarding this:
"Claude is very useful but it's not yet anywhere near as good as a human software developer. Like an excitable puppy it needs to be kept on a short leash"
Again, a combination of LLM/agents with some guidance (from someone with no prior experience in this type of high performing architecture) was able to beat all human software developers that have taken these challenges.