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.

I don't see any signs of the parent comment being written by an LLM other than it's detailed and well-written.

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.

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.

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.

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++.

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.

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'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.

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.

For about 3 years, I self-hosted my security camera system on the Synology surveillance station. It fit a lot of my needs, but I hated the hardware lock in. I was getting more and more frustrated with Synology's behavior, but I didn't find any alternatives I liked better.

So on Christmas 2025 I decided to put Claude Code to the test. I fully transitioned to this about two weeks later. I've been running 14 IP cameras doing 24/7 recording with live streaming and ML-powered detection.

The stack is FastAPI + React + PostgreSQL, orchestrated with Docker. Video is handled by FFmpeg with a single process per camera that does both live HLS streaming and MP4 recording simultaneously, so you don't burn multiple RTSP connections per camera.

It runs YOLO11 via ONNX Runtime on live HLS segments (no extra RTSP connections needed), uses background subtraction to skip static frames, and has a finite state machine that tracks object arrivals, departures, and state changes rather than just firing "person detected" over and over. Each event gets a snapshot with bounding boxes and an extracted video clip. There's also a Vision LLM integration that can describe what's happening in a scene.

Storage uses a tiered system (hot/warm/cold) with automatic migration, so recordings age off from fast local storage to larger drives and optionally to S3 (that part is still in-progress).

It supports GPU acceleration but also runs fine in CPU-only mode.

I know nothing about React, I've never been a front-end developer, so that's 99.99% Claude. I have edited some CSS though. I've personally done more work on the backend, but it's still mostly Claude. This is currently running behind my home VPN only, so the user management/authentication is kind of weak at the moment. I'd love feedback!

Comments URL: https://news.ycombinator.com/item?id=47006981

Points: 1

# Comments: 0

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.

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.

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.

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"

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.

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.

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.

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.