kevin42

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.

Can you share the prompt you're using for each model?

“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.

Because you don't keep logs.

That depends on the purpose of the image.

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.

https://docs.cloud.google.com/billing/docs/how-to/control-us...

Google Cloud is easy to set up soft budget alerts via email though, something that I had to use third party service for with AWS.

I've been working on an open source, fully self-hosted network video recorder for about two months now. https://github.com/kevinbentley/ronin-nvr/

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"

I would love to see the US drone industry thrive, it's a major gap in both the consumer and military market.

At the same time, several businesses have and are trying to compete in this business. The amount of capital required is enormous if anyone is going to compete with DJI and the like. I personally know someone in this situation. They have a great product and some traction, but going from low quantity bespoke solutions to cost competitive large scale manufacturing costs hundreds of millions.

And the problem is, investors don't trust that the ban is going to last forever. The government could reverse the ban at any time, and that puts the US company back in a position where they can't compete with DJI, so the investors lose money. And they know that.

"And with no American-made drones comparable to the category leaders, it’ll be a while before any company steps up to offer one."

The problem is that it would be extremely risky for a US company to spin up a comparable US built drone. Even if they can match the price/quality point, at any given time the government could remove the ban, killing the entire business model.

Have you considered that maybe you aren't using it well? It's something that can and should be learned. It's a tool, and you can't expect to get the most out of a tool without really learning how to use it.

I've had this conversation with a few people so far, and I've offered to personally walk through a project of their choosing with them. Everyone who has done this has changed their perspective. You may not be convinced it will change the world, but if you approach it with an open mind and take the time to learn how to best use it, I'm 100% sure you will see that it has so much potential.

There are tons of youtube videos and online tutorials if you really want to learn.

Hi, Author here! I wrote this piece after some conversations with friends and realizing how we all had some levels of cognitive dissonance towards AI, IP, etc. I noticed I was moving my own goalposts both when criticizing AI or defending it.

I have to admit that I'm less of a skeptic than most, but there are some coherent skeptical arguments. I'm especially interested in what people think about their own skepticism on the technical side, as there seems to be a pivot towards the social lately.

You don't need to do much, the /agent command is the most useful, and it walks you through it. The main thing though is to give the agent something to work with before you create it. That's why I go through the steps of letting Claude analyze different components and document the design/architecture.

The major benefit of agents is that it keeps context clean for the main job. So the agent might have a huge context working through some specific code, but the main process can do something to the effect of "Hey UI library agent, where do I need to put code to change the color of widget xyz", then the agent does all the thinking and can reply with "that's in file 123.js, line 200". The cleaner you keep the main context, the better it works.

Not that I have seen, which is probably a big part of the disconnect. Mostly it's tribal knowledge. I learned through experimentation, but I've seen tips here and there. Here's my workflow (roughly)

Create a CLAUDE.md for a c++ application that uses libraries x/y/z

[Then I edit it, adding general information about the architecture]

Analyze the library in the xxx directory, and produce a xxx_architecture.md describing the major components and design

/agent [let claude make the agent, but when it asks what you want it to do, explain that you want it to specialize in subsystem xxx, and refer to xxx_architecture.md

Then repeat until you have the major components covered. Then:

Using the files named with architecture.md analyze the entire system and update CLAUDE.md to use refer to them and use the specialized agents.

Now, when you need to do something, put it in planning mode and say something like:

There's a bug in the xxx part of the application, where when I do yyy, it does zzz, but it should do aaa. Analyze the problem and come up with a plan to fix it, and automated tests you can perform if possible.

Then, iterate on the plan with it if you need to, or just approve it.

One of the most important things you can do when dealing with something complex is let it come up with a test case so it can fix or implement something and then iterate until it's done. I had an image processing problem and I gave it some sample data, then it iterated (looking at the output image) until it fixed it. It spent at least an hour, but I didn't have to touch it while it worked.

Trying to one-shot large codebases is a exercise in futility. You need to let Claude figure out and document the architecture first, then setup agents for each major part of the project. Doing this keeps the context clean for the main agent, since it doesn't have to go read the code each time. So one agent can fill it's entire context understanding part of the code and then the main agent asks it how to do something and gets a shorter response.

It takes more work than one-shot, but not a lot, and it pays dividends.

This isn't meant as a criticism, or to doubt your experience, but I've talked to a few people who had experiences like this. But, I helped them get Claude code setup, analyze the codebase and document the architecture into markdown (edit as needed after), create an agent for the architecture, and prompt it in an incremental way. Maybe 15-30 minutes of prep. Everyone I helped with this responded with things like "This is amazing", "Wow!", etc.

For some things you can fire up Claude and have it generate great code from scratch. But for bigger code bases and more complex architecture, you need to break it down ahead of time so it can just read about the architecture rather than analyze it every time.

I think everything you said was true 1-2 years ago. But the current LLMs are very good about citing work, and hallucinations are exceedingly rare. Gemini for example frequently directs you to a website or video that backs up it's answer.

Compared to what though? I have ended up with needlessly convoluted solutions when learning something the old-fashioned way before. Then over time, as I learn more, I improve my approach.

Not everyone has access to an expert that will guide them to the most efficient way to do something.

With either form of learning though, critical thinking is required.