Rather just see them get Flocked honestly. Seems like the type of tech a child would dream up only to realize when it's too late that it's dystopian, creepy, and a detriment to society.
236 karma · joined August 13, 2024
Regarding remedy, we really need laws on this stuff yesterday. The problem is that we have to gut first amendment freedoms for some of this stuff, which wont go anywhere because there will always be too much overreach with today's representatives.
Anyone that cares about their perspective has missed the point.
Edit: I have no concept of what camera sensors are doing these days.
Personally, if I cared enough to obfuscate my plate info from these devices, I would just taint their data by wrapping my car in a wrap with various different "plates" themed art. I like cars and the exterior has traditionally been treated like art. Tainting data is just as effective at making the core dataset useless as omitting data in the first place.
At first I just blocked the people doing it, but today I just stay off the platforms where I'm seeing these people. If things stay the same, in the future I can only imagine that small gated communities are where real humans are communicating (think places like lobste.rs, but for normies). Smaller Discord communities are still working.
It's just wild to me how some platforms are fine with bots fluffing content creation. There comes a point where people will realize all the messaging on platforms like Facebook are AI generated and they just leave for greener pastures.
I believe people really want to connect with real people. It just can't be done if people aren't being themselves. I think there are a lot of creative people that are up in arms against the right things, but for the wrong reasons. I don't think the legal IP implications are as damning as the societal implications of everyone filtering their speech through AI.
Sorry, this came off way more ranty than I would like, but it's been bubbling in my head for a while now. So much so I'm actively doing something about it.
Personally the only place that I filter my own speech through an AI is when I couldn't care less about the person I'm communicating to but I need them to get the message (typically because I have too many 4 letter words to say about the subject matter).
Even if AI output isn't copyrightable, could you really argue a solid case if I taint random parts of the source code with my own isms? Which this is something I do, I don't care if the AI generated portions of my code base are copyrightable, perhaps my licensing is not valid for those portions, but throughout my code is going to be parts I hand crafted or patterned out because the AI just can't get it right. Those snippets are just proverbial land mines in waiting for a copyright infringer in waiting.
Right now I'm more interested in getting ACP working for gemini-cli and claude-code to serve it their models. My first goal is just to make the manually operated tool that just gets out of the way or whatever. Sane permissions out of the box.
If anyone wants to just come along and add an optional feature today, I'm happy to merge under the same license. Otherewise, I will eventually add this feature, I'm just not sure if it will be sooner or later.
The free sandwich I'm referring to with Go is the ability to just do `go funcnamehere()` and that's running concurrently and in parallel. If I need coordination of those goroutines, I can still do that with any number of locking patterns. It's extremely convenient, making the trade off of having a runtime baked in worth it imo.
I use AI daily and frankly I love it while thinking of it from the context of "I write some rough instructions and it can autocomplete an idea for me to an extremely great degree". AI literally types faster than me and is my new typewriter.
However, if I had to use it for every little thing, I'd do it. The problem though is when it reaches a point where I have to use it to replace critical thinking for something I really don't know yet.
The problem here is that these LLMs can and will churn out absolute trash. If this was done under mandate, the only thing I'd be able to respond with when that trash is being questioned is "the AI did it" and "idk, I was using AI like I was told".
It literally falls into the "above my pay-grade" category when it comes down as a mandate.
I really hope there's more nuance to articles like these though. I really hope these companies mandating AI use are doing so in a way that considers the limitations.
This article does not really clue me the reader in to if that is the case or not though.
Is just strange to me that my experience seems to be a polar opposite of yours.
If you're not running it on your own hardware and completely offline it's not private. I can prove that.
What these LLMs continue to prove those is they are no substitute for real domain knowledge. To date, I've yet to have a model implement RAFT consensus correctly in testing to see if they can build a database.
The way I interact with these models is almost adversarial in nature. I prompt them with the bare minimum that a developer might get in a feature request. I may even have a planning session to populate the context before I set it off on a task.
The bias in these LLMs really shines through an proves their autocomplete properties when they have a strong bias towards changing the one snippet of code I wrote because it doesn't fit in how it's training data would suggest the shape of it's code should be. Most models will course correct with instructions that they are wrong and I am right though.
One thing I've noted is that if you let it generate choices for you from the start of a project, it will make poor choices in nearly every language. You can be using uv to manage a python project and it will continue to try using pip or python commands. You can start an electron app and it will continuously botch if it's using commonjs or some other standard. It persistently wants to download go modules before coding instead of just writing the code and doing `go mod tidy` after (it literally doesn't need the module in advance, it doesn't even have tools to probe the module before writing the code anyway).
RAFT consensus is my go-to test because there is no 1 size fits all way for you to implement it. It might get an in-memory key store system right, but what if you want it to organize etcd/raft/v3 in a way that you can do multi-group RAFT? What if you need RAFT to coordinate some other form of data replication? None of these LLMs can really do it without a lot of prep work.
This is across all the models available from OpenAI, Claude, and Google.
Which model were you using? In my experience Gemini 2.5 Pro is just as good as Claude Sonnet 4 and 4.5. It's literally what I use as a fallback to wrap something up if I hit the 5 hour limit on Claude and want to just push past some incomplete work.
I'm just going to throw this out there. I get good results from a truly trash model like gpt-oss-20b (quantized at 4bits). The reason I can literally use this model is because I know my shit and have spent time learning how much instruction each model I use needs.
Would be curious what you're actually having issues with if you're willing to share.