My guess is design (features/functionality, not code). When you don't have to write every line of code and you can quickly iterate on features, you have a lot of freedom to dial in what you really want out of an app.
I have enough experience writing code by hand and designing complex systems with a team working for me. In a sense, this is no different. When I had a half-dozen mid to senior level developers, I did not verify every line of code they wrote. I did not have any expectation that they would write perfect code, and they didn't.
But as the owner of the company, I was still liable for the product my employees made. That is the same thing here. It's not negligent to have Claude code write code for you at 10x the speed you can do it yourself. If I hadn't supervised my employees and put practices in place to control quality and safety, I would be liable. I'm actually far less worried about liability now, because I have my hands in every part of the system.
Calling it vibe coding is pejorative at this point, meant to imply that someone who has no skill or craft in software development just types a few prompts and gets something they didn't have a hand in the design or development. That's not what the professionals who are using AI coding are doing.
You'll have to take my word for it because I'm not going to disclose my private business or personal financial records.
But, I'm running a small, two person business and we are being paid by a large company to develop a robotics project. We're working at a very fast velocity and have fielded a prototype within a few months. I've been doing this kind of work for years, and we're more productive than larger teams (8-10 developers) I've employed before. Five years ago, I would have needed at least 4 senior engineers to do the work we're doing now, and we're moving faster than we could even then.
And I'm being compensated at a flat rate by a major company so we're making really good money. The customer is happy with the results. Claude easily writes 90% of the code we use.
“We experience it, therefore it exists” proves less than it seems. It proves there is an experience to explain. It does not prove that consciousness is what it appears to be from the inside. A mirage is still seen, but the thing it seems to show is not really there. Consciousness might be real in that same limited sense, while our folk model of it could be deeply wrong.
If it's used to create a false narrative (like a deep fake), sure, you should care. But if it's used as an alternative to a stock photo, or as an easy way to make an infographic then no, I don't think you should care.
At this point I wish it were against the rules to accuse people or complain about articles as written by LLMs. It's creating so much noise that useful commentary is hard to find.
I don't see any signs of the parent comment being written by an LLM other than it's detailed and well-written.
If they can't distinguish LLM text, then why should they care?
Anti-AI people like to bring up hallucination as if everything AI generates is false.
I can write pages of text, with my own content, and then use AI to improve my writing and clarity. Then I review and edit. It might have some LLM markers in there, which I remove sometimes because it's distracting. But the final, AI assisted writing is easier to read and better organized. But all the ideas are mine. Hallucinations are not remotely a problem in this case.
"But here's the thing that gets missed in the narrative:"
That's a pretty big clue that it is LLM assisted at least. That said, I don't mind. The article has substance and other than a few LLM markers like that, I think it's well-written.
Most things I use it for could be done without it, it's just more convenient and entertaining.
I had it make a daily aviation weather brief for a private airpark. It uses METAR, outdoor IP cameras I have including one that looks at a windsock and another that looks at the runway surface, and a local weatherstation. It sends me a text message with all of that information aggregated into "It's going to be really windy this afternoon, visibility is high, but there is ice on the runway surface", that sort of thing.
The thing is, all I had to do is point it to a few endpoints and it wrote the entire script and set up a cron for me. I just gave it a few paragraphs of instructions and it wrote, then deployed the rest.
The other day, there was a post here about a new TTS model. I wanted to try it out, so I gave my claw the github URL, and it pulled everything down and had it running without any effort on my part. Then it sent me a few audio messages on discord to try.
When I'm away from home, I can text it to say "what's going on at home" and it will turn on the lights around the house, grab a frame from each camera turn lights back off, and give me a quick report. I didn't have to do any work other than tell it I wanted that skill.
I also have a group chat with some friends on signal that's hilarious. It roasts us, gives us reminders, lets us know about books we might be interested in, that sort of thing. It's really fun.
That's an interesting take, but I'm not sure 'easy to write' is the only advantage.
There is also a really good ecosystem of libraries, especially for scientific computing. My experience has been that Claude can write good c++ code, but it's not great about optimization. So, curated Python code can often be faster than an AI's reimplementation of an algorithm in c++.
What I love about OpenClaw is that I was able to send it a message on Discord with just this github URL and it started sending me voice messages using it within a few minutes. It also gave me a bunch of different benchmarks and sample audio.
I'm impressed with the quality given the size. I don't love the voices, but it's not bad. Running on an intel 9700 CPU, it's about 1.5x realtime using the 80M model. It wasn't any faster running on a 3080 GPU though.
I'd love to use something other than ROS2, if for no other reason than to get rid of the dependency hell and the convoluted build system.
But there are a lot of nodes and drivers out there for ROS already. It's a chicken and egg thing because people aren't going to write drivers unless there are enough users, and it's hard to get users without drivers.
It looks like their business model is to give away the OS and make money with FoxGlove-like tools. It's not a bad idea, but adoption will be an uphill battle. And since they aren't open source yet, I certainly wouldn't start using it on a project until it us.
I recently filed a lawsuit in federal court, but because of the nature of the suit (adversarial proceeding on a bankruptcy case, wanting to cut my losses knowing collection is going to be the problem) I decided to do it Pro Se.
I've used a lot of AI to do this, with a lot of research of my own, reading documents from similar cases, verifying citations, etc. So far, things are going well, I've won on all the motions so far. But I'm using critical thinking and carefully reviewing everything.
The real failure with slop filings is procedural, not technological. A competent attorney should never submit a brief built on case law they hadn’t verified. Legal practice has always relied on reading the sources, confirming relevance, and taking responsibility for interpretation.
There is a way to trigger a script when a budget is hit, but they don't make it easy. You set up a billing notification that triggers a script, which can disable resources (like APIs) automatically.
It works with cheap, generic IP cameras over RTSP. It's pretty easy to get it working with a Raspberry Pi too.
I was using the synology surveillance app, but after their recent shenanigans, I wanted something I could self host and modify on my own.
I'm using it at my property with 14 cameras right now and I'm really happy with it. There's still some work to do, but it's integrated with ML object detection, and even integration with a VLLM to describe a scene when certain things are detected.
This was my first attempt at a large-scale application that is heavily AI assisted. I need to update the screenshots and feature list for the readme, but if you have any questions or want to get involved, let me know.
>Even if its not "artisticallly worthwhile", the process is rewarding to the participant at the very least
I think that's the point though. What op did was rewarding to themselves, and I found it more enjoyable than a lot of music I've heard that was made by humans. So don't be a gatekeeper on enjoyment.
I’m genuinely curious how you feel about LLMs being trained on pirated material. Not being snarky here.
Your comment reflects the old “information wants to be free” ideals that used to dominate places like HN, Slashdot, and Reddit. But since LLMs arrived, a lot of the loudest voices here argue the opposite position when it comes to training data.
I’ve been trying to understand whether people have actually changed their views, or whether it’s mostly a shift in who is speaking up now.
We recognize slop because it's slop. Just because a bunch of people are submitting slop to open source projects doesn't mean that AI can only generate slop.
His argument is basically a tautology "People who don't know how to code write bad code. Therefore, tools that help people who don't know how to code produce bad code"