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.

For the Futo Keyboard, I installed the “FUTO Voice Input” app.
Then in the Futo Keyboard app I have the “Voice Input” set to Disabled/Use voice input provided by external app.

It works really well for me at least.

Yeah, these AI tools definitely aren’t a complete replacement for the real thing. And I agree with you on the conversations. The voice acting in this can’t compare to actors who actually have the skill to convey emotion and depth to their characters.

The way I see it, these tools allow low budget/indie filmmakers to quickly test out ideas to see if they like it before going all in on making the whole thing.

I wouldn’t consider this slop.

Let’s compare this to photography. If you use a camera to take a picture of something, sure, the machine is doing most of the work, but the photographer is playing a vital role in this.

Now there are photographers that spend a lot of time composing a shot. They’ll mess around with shutter speed, aperture size, ISO, zoom, depth of field, etc. They’ll also figure out the subject matter and may add some other elements to it. Afterwards they’ll make adjustments to the picture with something like Lightroom or Darktable, and maybe touch up some things with Photoshop.

Then there are people that take pictures with their phone of a computer screen showing something cool happening in a game and post it on Reddit.

On one end of the spectrum I would consider the photo to be art, on the other I would consider it to be slop. However, there are many degrees between one end of this spectrum to the other.

With AI tools it’s not much different. The machine is doing a lot of the work, but how much of it is guided, reshaped, or directed by a human? With Image Generating tools you can tweak the seed, the steps, the cfg, the sampler, denoise, etc. You can choose the base model, add multiple LoRAs and embeddings, or train your own if you’re looking for a certain style.

Then you have users that go to ChatGPT, type in a prompt and have ChatGPT do everything else.

Like photography, on one end of the spectrum I would consider it art, on the other I would consider it slop.

But this all begs the question, what is art? How do you draw the line between what art is, and what it is not?

If you’ve ever read through the terms of service/use for most websites that artists like to show off their work on (Instagram, Facebook, DeviantArt, ArtStation, Twitter, Reddit, etc.) you would realize that the work was indeed not stolen.

It was given away freely by artists due to fine print buried in the terms of service with royalty free licenses. Just lookup any Terms of Service and search for the word “royalty”.

If artists should be going after anyone, it’s the companies that either freely gave the artwork away by “sharing it with their partners” or by making a profit off of their work by selling it to any of these companies for training these image generating models.

The root of the problem here is the lack of ownership of our own data when it comes to any sort of online service. Part of that problem is just the nature of posting something in the first place.

A 1 hour long video…

Might be interesting, but looking up his name (Keonne Rodriguez) for a Wikipedia article gave me what I wanted to know:

https://en.wikipedia.org/wiki/Keonne_Rodriguez

I’ll just paste the current summary here for anyone, but the full article is short and worth a read:

Keonne Rodriguez is an American software developer and co-founder of Samourai Wallet, a Bitcoin wallet designed to enhance transaction privacy for Bitcoin users. In 2024, Rodriguez and fellow co-founder William Lonergan Hill were arrested and later plead guilty to operating an unlicensed money-transmitting business for creating the software for the application. His prosecution drew criticism from privacy advocates and civil liberties groups, who argued that the case threatened financial privacy, open source development, and the principle of code as speech.

Makes sense to create hardware dedicated to AI models. Especially since it will make them run faster and it will cut down on the power requirements for datacenters.

It’s too bad they’re slightly straying from the current acronyms. I’ll have to remember to call them TSUs instead of TPUs.

Thanks, I missed that detail. It’s probably because of the “no class action” clause that this is a “mass arbitration”.

Unfortunately that usually means that Google is paying a specific company to decide on the outcome of the case. in this case it looks like American Arbitration Association has a contract with Google.

They’re supposed to be fair for both sides, but it’s been shown that they almost always rule in favor of the company that has pre-selected them.

If anyone is in this situation, they will likely have a much better chance by convincing a judge to allow a different 3rd party to arbitrate the case.

Can you clarify what you mean?

This movement is all about giving consumers more power and reforming section 1201 of the DMCA.

Louis Rossmann is big on consumer rights and the freedom to repair movement.

Did you mean to reply to someone else?

Following up on this. I sent an email out to the team and got a response already.

To summarize, they would rather the solution work through updates for security fixes, but they were willing to compromise if automatic updates were disabled with the option for users to manually update somehow:

Yeah, one of the main points of this project is to help them reform Sec 1201 of the DMCA.

As far as for how to do it, I’m not sure if you would have to come up with something that would work even through an official Samsung update. From what I can tell, it would be enough to have it work with Home Assistant instead while blocking future updates. It’s definitely worth a question to the bounty team to get clarification on that point though.

There is a class action “mass arbitration” against Google for this: https://www.classaction.org/nest-thermostat-support-arbitration

Additionally, the Fulu Foundation has a bounty reward out for anyone who is able to get these working with something like Home Assistant.

The pot is currently at $12,856.00 https://bounties.fulu.org/bounties/nest-learning-thermostat-gen-1-2

In the U.S., since doing so would circumvent measures put in place on these devices, publishing how to do this would go against sec. 1201 of the DMCA. This has a risk of a maximum sentence of 3-5 years in a Federal Prison. You can still privately show the Fulu Foundation how it is done, and they will be able to use this information to help their case in their attempt to reform this law.

If you live in the U.S., you can also help by letting your representatives know about this. Here’s an ActionNetwork page that Fulu set up so that you can easily do so: https://actionnetwork.org/letters/right-to-repair-reform-section-1201-of-the-dmca

Yeah, concerning the DRM part, the main goal of this bounty system is to help change legislation so that people are allowed to legally modify the things that they own.

Specifically reforming section 1201 of the DMCA. Right now, if you break the digital locks on the fridge to remove the ads and then publicize that information, you can get 3-5 years in federal prison. (With this bounty system you keep the information private between you and Futo).

So when they hear lobbyists say things like, “We believe this legislation is in search of problems that do not exist…” Louis can respond with “Well actually, millions of people use these products and if this person releases a solution to it, he goes to prison”

Louis talks about that in more detail here: https://odysee.com/@rossmanngroup:a/after-17-years-of-repair,-i’m-doing:d

• These fridges didn’t have ads when people first bought them.
• If you’re renting, usually the landlord picks the appliances.

Louis Rossmann’s Fulu Foundation put out a bounty for anyone who is able to come up with a way to modify the fridge to remove the ads.

The pot is currently at $11,558.00
https://bounties.fulu.org/bounties/samsung-familyhub-refrigerators

He did a video on it here: https://odysee.com/@rossmanngroup:a/we’ll-pay-you-10,000-to-de-shitify-this:7

The game client started, but it froze up before it could even make it to the main menu (actually before it even got to full screen).

I’m able to start up LIVE on Linux without any issues.

I tested out the cutter attachment, but it was still broken. Hopefully we get to put it to use for something like slicing open doors. It sounds like others could shoot open the interior doors at least.

I would guess this is a Quarter 2 release. They’ve got a lot of work already lined up for 4.4 and engineering doesn’t seem to be a part of it.

There are about 235 vehicles in the game at the moment, and they had less than 1/4 of the ships available for engineering. A decent number of those only had engineering partially implemented.

I’m assuming this rework of the ships will bring them all up to the level of having physicalized components. That’s going to be a lot of work for a number of teams.

They want ship armor working before engineering goes out, it sounds like they’re close to getting that in though.

I’ve seen people mention that shooting the door open works in these situations (as in, during this test). Not the best option but it’s something, for now.

Nice! I wish I had known that when the servers were up. It would have saved me a bit of time.

Awesome image.

Minor nitpick here, but as someone who has actually experienced totality, there is one major issue with this image. During totality it gets dark to the level of a little after sunset (enough to trigger streetlights with automated sensors to turn on). Then, imagine looking in the direction of where the sun has already set and seeing the glow of a fading sunset. However, instead of coming from one direction, this glow is happening in every direction that you can see.

Basically there would be more color coming from behind the marine layer.

That being said, you could always claim that this is totality being experienced in some other solar system.

This news article is missing the statement from the school’s principle:

In a statement shared with parents, Principal Katie Smith said the school’s security department had reviewed and canceled a gun detection alert, while Smith (who didn’t immediately realize the alert had been canceled) reported the situation to the school resource officer, who called the local police. https://techcrunch.com/2025/10/25/high-schools-ai-security-system-confuses-doritos-bag-for-a-possible-firearm/

Looks like this system just flags footage for review and humans make the ultimate decision.

I’m not sure how the notification on this flag/event is worded. It’s possible that it should be worded differently to express a need to actually look at the footage.

In this case the principle probably jumped into panic mode, or didn’t understand the system well enough.

The study focuses on general questions asked of “market-leading AI Assistants” (there is no breakdown between which models were used for what).

It does not mention ground.news, or models that have been fed a single article and then summarized. Instead this focuses on when a user asks a service like ChatGPT (or a search engine) something like “what’s the latest on the war in Ukraine?”

Some of the actual questions asked for this research: “What happened to Michael Mosley?” “Who could use the assisted dying law?” “How is the UK addressing the rise in shoplifting incidents?” “Why are people moving to BlueSky?”

https://www.bbc.co.uk/aboutthebbc/documents/audience-use-and-perceptions-of-ai-assistants-for-news.pdf

With those questions, the summaries and attribution of sources contain at least one significant error 45% of the time.

It’s important to note that there is some bias in this study (not that they’re wrong).

They have a vested interest in proving this point to drive traffic back to their articles.

Personally, I would find it more useful if they compared different models/services to each other as well as differences between asking general questions about recent news vs feeding specific articles and then asking questions about it.

With some of my own tests on locally run models, I have found that the “reasoning” models tend to be worse for some tasks than others.

It’s especially noticeable when I’m asking a model to transcribe the text from an image word for word. “Reasoning” models will usually replace the ending of many sentences with what it sounded like the sentence was getting at. While some “non-reasoning” models were able to accurately transcribe all of the text.

The biggest takeaway I see from this study is that, even though most people agree that it’s important to look out for errors in AI content, “when copy looks neutral and cites familiar names, the impulse to verify is low.”