Nitter/XCancel Link:
https://xcancel.com/willo2_Poly/status/2061640812132516321

At first I thought it could have just been a malicious ad that got onto his site somehow. But, no, this looks intentional:

From the user that blew the whistle on this: https://xcancel.com/dm4uz3/status/2057502403151212681

From a quick search, it looks like the construction site is here: Meta Stanton Springs Data Center #2 https://cartes.app/?allez=Meta+Stanton+Springs+Data+Center+%25232|w1282590996|-83.67669|33.59840

It’s very close to a number of streams/creeks that feed into lakes and other water sources nearby.

With the amount of vegetation that has been removed, all it takes is a good rainy day to have a lot of that mud running off into the water supply.

The title is missing a significant keyword here: “construction”

A more accurate headline:

Meta data center construction allegedly muddies Georgia town’s drinking water, investigation underway — EPA promises immediate investigation after congresswoman brings dirty jars of water to hearing

This isn’t exclusive to datacenters, any sort of large construction projects should be under the microscope right now. If there are legal loopholes allowing water supplies to be contaminated, that should be addressed. Meta and any other companies responsible for this should be billed the costs for cleaning up or for funding additional water treatment solutions to make up for this.

Sometimes those responses can be helpful, but there’s usually a link to whatever page that it sourced some of the info from. I would strongly recommend that you get in the habit of clicking that link just to double check.

The doorbell in this case is one that was bought on Temu by Naxclow:

The unit ships under the name “Smart Doorbell X3” and pairs through a mobile app called “X Smart Home”

Looks like Naxclow responded to the pen tester’s feedback very positively and are happy that he disclosed this issue to them. They’ve already started an internal review on the issue.

This is an awesome collection of letters.

A decent portion of them are entirely human translated and are definitely more useful than the AI assisted translations since I’ve noticed a number of them including translator’s notes to help add context to the text.

For those concerned about AI assisted translations, anytime some text has been translated with AI assistance, the website makes it very obvious. Links to original manuscripts are available. 19th century translations are also available for a decent number of those letters.

Nothing in that comment should have been construed as trying to defend the company from trying to weasel their way out of paying their water bill.

Some important context here:

QTS told Politico the 29 million gallons were consumed during temporary construction activities, including concrete work, dust control, and site preparation. The company markets a “closed-loop” cooling system for its data centers, which recirculates the same water rather than drawing from the municipal supply.

Thanks, looks like I was wrong. I looked into the evaporative cooling. It looks like this kicks in if it’s hot outside, otherwise the cooling units operate in a “dry” mode.

Although, none of that is directly heating up a lake, it looks like the water just flows into something like an A/C unit. Are there datacenters that are piping heat directly into lakes?

THE KICKER that’s just to run the data center, think of the demand for the HVAC and ecological damage to using a lake’s water to cool equipment (water would be coming out over 100⁰F).

I keep seeing this on Lemmy, but why do so many people think that these datacenters are using water from reservoirs for “cooling”?

Datacenters use A/C for cooling, and if there’s any sort of “Liquid cooling” being used on these servers, it’s in a closed loop system.

Water isn’t being pumped out as steam or into the environment directly from datacenters, unless there’s some other method I’m missing here?

What I think is getting mixed up here is that, for many forms of generating electricity, water is needed to be heated up in some way to create steam. The steam then turns a turbine which moves some magnets to generate electricity.

Some of those powerplants are in closed loop systems with their water, some of them are not. Additionally, if the energy is coming from solar/wind/hydro then there shouldn’t be any concerns about water getting turned into steam anyway.

Technically, you can get the same answer twice from an LLM, but only when you control the full input. When a model is being run, a random seed/hash is applied to the input. If you run the model locally you could force the seed to always be the same so that you would always get the same answer for a given question.

Constitution by Nick Webb?

I’m failing to see why the creative writing machine is better than a simulation set to ‘rough’.

This doesn’t sound like they are using LLMs for processing their 3D models. The way it’s described in the article sounds a lot more like some machine learning model trained on physics simulations for aerodynamics.

Which series was this? The original commenter deleted their comment.

Misleading title. This isn’t an “AI Company”. As far as I can tell, it’s some scammer that used AI Tools to create similar music and then copyright strike the original artist to steal their revenue.

The major issue here is how YouTube handles these claims. From the article:

YouTube’s dispute process places enormous trust in whoever files the claim, with little built-in protection for independent artists who lack legal resources.

This isn’t something new and was already being done before AI tools were available.

I mean there might be a way, but it’s not easy.

The laziest and worst method is to use ChatGPT and have it “pretend to be some character” with a system prompt.

If you want something really good, you would need to train the model from scratch based only on knowledge that one particular character would learn from their world up until that point. However this is going to be a ton of work just for one character.

For a middle ground you could probably cheat a little and start with a model that’s close to the knowledge base you would want most characters to have. Then you would use something like a LoRA, or RAG on top of it for each individual character.

For instance, if you wanted to make a game in a Victorian Era setting, you could start with this model that’s only trained on text from the 1800’s: https://github.com/haykgrigo3/TimeCapsuleLLM

To make it better you would have multiple base models that are trained on various backgrounds that NPCs could have (Farmers vs Merchants vs Soldiers vs Nobility, etc).

Even then, this would not work well for certain games. For example, if you’re trying to tell a specific story, you don’t want a character that will go off script or give away some information that spoils an intended plot twist.