Wonder how the survey was sent out and whether that affected sampling.

Regardless, with -3-4k responses, that’s disappointing, if not concerning.

I only have a more personal sense for Lemmy. Do you have a source for Lemmy gender diversity?

Anyway, what do you think are the underlying issues? And what would be some suggestions to the community to address them?

I use gitlab ci mainly and dabble in github actions. Can you clarify how “Not even Github managed to pull that off”? IIRC, actions is quite featureful and it’s open-source, so I assume that can be run with self-hosted runners as well.

I’m also curious. A quick search came up with these. Not sure which one is most reliable/updated

• https://www.comparitech.com/blog/information-security/global-ransomware-attacks/
• https://www.comparitech.com/ransomware-attack-map/
• https://cissm.liquifiedapps.com/
• https://cybermap.kaspersky.com/special/ransomware

Many things are called “AI models” nowadays (unfortunately due to the hype). I wouldn’t dismiss the tools and methodology yet.

That said, the article (or the researchers) did a disservice to the analysis by not including a link to the report (and code) that outlines the methodology and how the distribution of similarities look. I couldn’t find a link in the article and a quick search didn’t turn up anything.

you should try to ask the same question using xAI / Grok if possible. May also ask ChatGPT about Altman as well

Based on this reddit comment, that website is not affiliated with the magic-wormhole CLI tool

I believe experiments like these should move slower and with more scrutiny. As in more animal testing before moving on to humans, esp. due to the controversies surrounding Neuralink’s last animal experiments.

re: your last point, AFAIK, the TLDR bot is also not AI or LLM; it uses more classical NLP methods for summarization.

Is there a database tracking companies that start out with good intentions and then eventually gets bought out or sells out their initial values? I’m wondering what the deciding factors are, and how long it takes for them to turn.

re 1: out of curiosity, do you encounter dnsleaks when using wireguard?

re 4: you can also check out https://starship.rs/, which helps configure shell prompt very intuitively with a toml file.

Hold up, are you sure you can’t view Discussions or Wiki? Which sites can you not view them?

I’m fine viewing them for public repos that I usually visit.

Asking to make sure that Github is not slowly rolling out this lockdown.

Reminds me of this article https://www.alexmurrell.co.uk/articles/the-age-of-average where the author pulls in different examples of designs and aesthetics converging to some “average”.

I’m feeling conflicted with these trends, on one hand it seems like things are becoming more accessible, while on another, feels like a loss.

This especially may be relevant with generative AI - at least for the very few generative arts I look at, at some point they start to feel the same, impersonal.

Something like this, unless they know the root cause (I didn’t read the paper so not sure if they do), or something close to it, may still be exploitable.

These are most, if not all, positive. I’d be curious if you can somehow make it list a mixture of positive and non-positive things before 2021 and then see where it goes from there.

yeah agreed with your sentiment. I think it’s good to have an intuition about something, but it’s much better when there’s data to back it up.

Cuz then, they can do the same with others, say Youtube or other streaming services, and start to compare the numbers, like % of ads, what types of ads, how long are the ads relative to content, how many of these ads are political, how many of these ads may be harmful, …

Having these numbers can be quite handy for other researchers and regulators to look into these issues more concretely, rather than just say, “as your brothers and sisters already know, tiktok serves ads”

This is straight out of the movie “The Congress”

I wonder whether emails, slack/discord notifications, chatrooms for work/study/play, … are the equivalent to social media notifications for adults.

As much as I despise snap, this instance bring some questions into how other popular cross-linux platform app stores like flathub and nix-channels/packages provide guardrails against malwares.

I’m aware flathub has a “verified” checks for packages from the same maintainers/developers, but I’m unsure about nix-channels. Even then, flathub packages are not reviewed by anyone, are they?

i’m leaning towards “skull” tho

I remember reading that this may be already happening to some extent, eg people sharing tips on creating it on the deep web, maybe through prompt engineer, fine tuning or pretraining.

I don’t know how those models are made, but I do wonder the ones that need retraining/finetuning by using real csam can be classified as breaking the law.