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.

hm, I think I’ve been doing it wrong then …

someone should make an alternate history tv show where the ship made it. bonus if it’s of a parody kind.

fascinating. I wonder where the line is between the cold preserving the body and the cold causing hypothermia that could lead to death.

how so?

Isn’t Zipf law supposed to be log-log plot?

the whole premise of OP is that this monitors people, and many organizations use TOTP, which one could also use without internet connections or phones AFAIK.

I’m in academia and I wish this is implemented more. Data breaches are getting quite common, and Github is so entwined in software engineering that it is critical to increase security measures.

or maybe most of them in a folder? and one file that defines their locations for environment variables

what are the other alternatives to ENV that are more preferred in terms of security?

yeah I guess maybe the formatting and the verbosity seems a bit annoying? Wonder what the alternatives solution could be to better engage people from mastodon, which is what this bot is trying to address.

edit: just to be clear, I’m not affiliated with the bot or its creator. This is just my observation from multiple posts I see this bot comments on.

I’m curious, why is this bot currently being downvoted for almost every comment it makes?

thanks for the suggestion! will check it out!

gotcha! I’ve just started to use opensnitch (for linux) but I don’t usually inspect the detailed connections that often. thanks for the tip on firefox, I’ll be on the lookout for those.

How do you view these and how would you block them by the way? Via uBlock?

Thanks for the suggestions! I’m actually also looking into llamaindex for more conceptual comparison, though didn’t get to building an app yet.

Any general suggestions for locally hosted LLM with llamaindex by the way? I’m also running into some issues with hallucination. I’m using Ollama with llama2-13b and bge-large-en-v1.5 embedding model.

Anyway, aside from conceptual comparison, I’m also looking for more literal comparison, AFAIK, the choice of embedding model will affect how the similarity will be defined. Most of the current LLM embedding models are usually abstract and the similarity will be conceptual, like “I have 3 large dogs” and “There are three canine that I own” will probably be very similar. Do you know which choice of embedding model I should choose to have it more literal comparison?

That aside, like you indicated, there are some issues. One of it involves length. I hope to find something that can build up to find similar paragraphs iteratively from similar sentences. I can take a stab at coding it up but was just wondering if there are some similar frameworks out there already that I can model after.

got into coding cuz I found out that’s how I can automate analysis and play with research questions more easily.

Interesting data but in terms of viz, this is an odd choice of color scale - the max and background being very similarly dark-toned. It may look better with flipped color scale, or maybe background should be lighter. Plus, I’m mot sure Greenland being very dark means there’s no data or it’s the max.

this is an interesting story but for those who prefer to read, here the article linked in the video description:

https://thefourth.media/apartments/

I also ran this through smmry to summarize. Below is the result:

The Apartments With No Entrance A shady land sale has left the residents of Sea Park Apartments locked in a decades-long land dispute, with no control over their own homes.

These apartments are “Enclosed” in more ways than one: The original developer of the apartments sold the apartment’s carpark and common areas - which surround the apartment blocks - to an individual, leaving residents in the unusual position of having their homes completely encircled by someone else’s private land.

Built in the 70s and completed in the early 80s, Sea Park Apartments is one of the earliest apartments in Petaling Jaya, if not the earliest, constructed at a time when most residential developments in the area still involved landed properties.

This meant residents had no way to access their homes without first trespassing on private property, and no control over the common facilities sited on that private land.

The individual who purchased the disputed lands is Yap Say Tee, who once managed a hotel owned by the developer, and was earlier approached by the developer to manage the car park at Sea Park Apartments.

With the developer’s sale of these lands to Yap, the rules of the game changed: The developer is no longer the registered owner of the disputed lands nor responsible for addressing the remonstrations of the residents, which reached a peak in 2013.

With the facilities on private land, access road on private land, the property value will go down, and residents will have no agency.

i’m leaning towards “skull” tho