Are the ad firms who buy this data immune to prosecution? Is legal to buy or own data if it's illegal to sell?
I'd hope that it's equally dangerous for both the buyers and sellers of this kind of data.
It's broadly illegal to hold personal data without an consented purpose under GDPR and DPA.
The only excuse the ad firm could have is if they have sought and received consent in the past and the user has ignored the wall of text and clicked agree. Which is entirely possible.
That said, the law revolves around the idea of legitimate use of data. DPA 2018 also has restrictions and extra hurdles for the processing and storing of medical data.
I don't think Grindr will have a legitimate cause to pass on that data to advertisers, even if they do need to store it and pass it onto prospective matches. I don't think the advertiser will have cause to collect the data (even if they asked) but I don't think any of this is as clear cut as it first seems.
Do you mind my asking, if you're working on private codebases, how you go about using GPT/Claude as a code assistant?
I'm just removing IP and pasting into their website's chat interface. I feel like there's got to be something better out there but I don't really know anyone else that's using AI code assistance at all.
Personally I don't paste anything. I ask for code examples that demonstrate what I want, and then I adapt it to my needs. It's definitely less powerful than directly sharing code, but it is what it is.
I also run a personal language model server, but that is far less capable than the models available as services. It can still be better than nothing for code O can't share with APIs.
I also use gpt.el a but for editor integration, but I honestly haven't workeded that into my workflow very much yet.
I'm building Plandex (https://github.com/plandex-ai/plandex), a terminal-based AI coding tool which currently uses the OpenAI api--I'm working on support for Anthropic and OSS models right now and hoping I can ship it later today.
You can self-host it so that data is only going to the model provider (i.e. OpenAI) and nowhere else, and it gives you fine-grained control of context, so you can pick and choose exactly which files you want to load in. It's not going to pull in anything in the background that you don't want uploaded.
There's a contributor working on integration with local models and making some progress, so that will likely be an option in the future as well, but for now it should at least be a pretty big improvement for you compared to the copy-paste heavy ChatGPT workflow.
I built a desktop tool to help reduce the amount of copy-pasting and improve the output quality for coding using ChatGPT or Claude: https://prompt.16x.engineer/
Vscode with GitHub copilot is great, been using it for about a year and a half, no complaints. The business tier allegedly doesn’t save/train on your data
Unless you have the privilege of being an enterprise customer with an SLA guaranteeing privacy, there's not much you can do other than using local models. I believe OpenAI says they don't train based on API requests but that's more of a "trust me bro" than any kind of guarantee.
Team and Enterprise come with the non-training guarantee, free and premium do not. Pretty much anyone can sign up for Team (I have, and I'm not a company) but you need to buy at least 2 seats for a total of $50/m. The rate limits are much better with that as well though.
I think you're reading it right.
They have the color key correct but the key for which side is seen vs not seen is incorrect.
It should be
<--Seen someone shot ... Not seen someone shot-->
Agreed. Spent a couple minutes trying to figure out how I was reading it wrong for several of the categories - sometimes it is correct, but often it is not.
I have the same issue. Not with every song but often enough.
The worst for me was an issue where occasionally my entire computer would slow down with no evidence from Activity Monitor that something was eating cycles or taking memory.
I finally traced it down to Apple Music while in fullscreen and using multiple monitors, which was my preferred setup. Filed a ticket with Apple feedback and it's still open with no response.
I think all the issues that you've listed are serious. They're the things that keep me up at night. But I don't think the content is really problematic (except maybe deep fakes), it's the untraceable nature of their sources. To me, it's anonymity that gives them their power.
If we knew that the content was posted by a specific REAL person/company/government, we could judge it's authenticity and hold them liable if it's criminal in some way.
Not saying there isn't a place for anonymity on the internet but to me, that's the problem that needs to be solved more so than AI.
Thanks. I was having such a hard time parsing this that I was beginning to have my own experience of subjective cognitive decline. Glad to know I'm not alone.
So those that get stoned are more confident in their mind's ability than those that don't?
I've tended to create a Config struct for each package and then a configs.Config struct that's just made up of each package's Config. It might not be a Go best practice but I like that I can setup the entire system's configuration on startup as one entity but then I only pass in the minimally required dependencies for each package.
It also makes testing a little easier because I don't have to fake out the entire configuration for testing one package.
If you don't mind me chiming in, mostly looking for advice if you've got any. I tend to run into a lot of issues when trying to play with projects that use TensorFlow. I have an Apple Silicon laptop and I seem to always get stuck resolving circular, conflicting dependencies.
The worst offender is https://github.com/magenta/magenta. It's such a cool project and I got it running once a few years ago but recently lost a few solid weekends trying to get it up and running again.
I will say that the most issues I’ve encountered with Python happened on macOS machines. The default installation was always old, and success using a modern version depended highly on the techniques used to install it.
For anything that involves dependencies, I rely on venv or pyenv to create a clean environment. When on macOS, I tended to use docker/containers as well, but primarily because 99% of my Python work has been in a Linux environment and I wanted like-for-like as much as possible. But a good version manager directly on macOS should help quite a bit.
> I will say that the most issues I’ve encountered with Python happened on macOS machines.
Same here. It's probably somewhat to do with my only partial MacOS/Homebrew knowledge, but every time I'd ressurect a Python project on MacOS, Homebrew would end up screwing up / confusing / munging the system Python and it's own ones.
Never ran into problems like that on Linux (except for that one time I tried Linux homebrew). Just something about the way homebrew does stuff seems incompatible with me understanding it. Seems to be a "just me" thing though.
I just want to add that using the tool "asdf" for managing python versions on your Mac is very handy. And "direnv" is also great for auto-loading your environment when going into a project directory.
I imagine it's also not truly see-through so if there's any kind of technical issue where the screen turns off or goes black, or it decides to put windows up where your eyes are focused, you are driving with a blindfold on.
What a huge bummer and loss.