To be clear, I agree that the line you quoted is almost assuredly incorrect. If they changed it to "thousands of deepfake apps powered by open source technology" then I'd still be dubious, simply because it seems weird that there would be thousands of unique apps that all do the same thing, but that would at least be plausible. Most likely they misread something like https://techxplore.com/news/2025-05-downloadable-deepfake-image-generators.html and thought "model variant" (which in this context, explicitly generally means LoRA) and just jumped too hard on the "everything is an open source app" bandwagon.

I did some research - browsing https://github.com/topics/deepfakes (which has 153 total repos listed, many of which are focused on deepfake detection), searching DDG, clicking through to related apps from Github repos, etc..

In terms of actual open source deepfake apps, let's assume that "app" means, at minimum, a piece of software you can run locally, assuming you have access to arbitrary consumer-targeted hardware - generally at least an Nvidia desktop GPU - and including it regardless of whether you have to write custom code to use it (so long as the code is included), use the CLI, hit an API, use a GUI app, a web browser, or a phone app. Considering only apps that have as a primary use case, the capability to create deepfakes by face swapping videos, there are nonetheless several:

• Roop
• Roop Unleashed
• Rope
• Rope Live
• VisoMaster
• DeepFaceLab
• DeepFaceLive
• Reactor UI
• inswapper
• REFace
• Refacer
• Faceswap
• deepfakes_faceswap
• SimSwap

If you included forks of all those repos, then you'd definitely get into the thousands.

If you count video generation applications that can imitate people using, at minimum, Img2Img and 1 Lora OR 2 Loras, then these would be included as well:

• Wan2GP
• HunyuanVideoGP
• FramePack Studio
• FramePack eichi

And if you count the tools that integrate those, then these probably all count:

• ComfyUI
• Invoke AI
• SwarmUI
• SDNext
• Automatic1111 SD WebUI
• Fooocus
• SD WebUI Forge
• MetaStable
• EasyDiffusion
• StabilityMatrix
• MochiDiffusion

If the potential criminals use easier ready-made (commercial) web-services instead of buying a RTX 5090, learning ComfyUI, dealing with the steep learning curve etc, we’d know we have to primarily fight those apps and services, not necessarily the generative AI tools.

This is the part where, to be able to answer that, someone would need to go and actually test out the deepfake apps and compare their outputs. I know that they get used for deepfakes because I've seen the outputs, but as far as I know, every single major platform - e.g., Kling, Veo, Runway, Sora - has safeguards in place to prevent nudity and sexual content. I'd be very surprised if they were being used en masse for this.

In terms of the SaaS apps used by people seeking to create nonconsensual, sexually explicit deepfakes... my guess is those are actually not really part of the figure that's being referenced in this article. It really seems like they're talking about doing video gen with LoRAs rather than doing face swaps.

Without searching for them myself to confirm, it’s plausible, especially if you take it to mean “apps leveraging open source AI technology.”

There are a ton of open source AI repos, many of which provide video related capabilities. The number of true open source AI models is very slim, but “Open weight” AI models are commonly referred to as open source, and from the perspective of building your app, fine tuning the model, or creating Loras for it, open weight is good enough.

Some Loras come with details on the training data set, so even if the base model is only open weights, the Lora can still be open source.

Until recently, Civitai had Loras for famous people, e.g., Emma Watson, and apparently just regular people. There was a post here last week, I think (or maybe to some other community), to 404 Media, about those being taken down thanks to credit card processors drawing a line in the sand at deepfake imagery.

ComfyUI is a self hostable AI platform (and there are also many hosts that offer it) that lets you build a workflow from multiple nodes, each of which generally integrates some open source AI tech that was otherwise released. For example, there are nodes that add the capabilities to perform:

• image generation with Stable Diffusion, Flux, Hidream, etc
• TTS with KokoroTTS, Piper, F5 TTS, etc
• video generation with AnimateDiff, Cog, Wan2.1, Hunyuan, FramePack, FantasyTalking, Float
• video modification, i.e., LatentSync, which takes a video and lipsyncs it to a provided audio file
• image manipulation, i.e., controlnet, img2img, inpainting, outpainting, or even specific tasks like “remove the background” or “change the face to this other face”

If you think of a deepfake as just a video of a recognizable person doing a thing, you can create a deepfake by:

• taking an existing video and swapping the face in each frame
• faceswap video specific approaches, i.e., Roop.
• an image to video workflow, i.e., with Wan: “the person dances.” You can expand the options available with Wan by using Loras.
• a text to video workflow, where you use a Lora for that person
• an image+audio to video workflow, i.e., with FantasyTalking/Float, creating a lipsync to an audio file you provide
• a video+audio to video workflow with LatentSync to make it look like they said something different, particularly using a TTS (like F5 TTS) that does voice cloning to generate the new audio

My suspicion is that most of the AI apps that are available online are just repackaging these open source technologies, but are not open source themselves. There are certainly some, of course, though the ones I know of are more generic and not deepfake specific (ComfyUI, SwarmUI, Invoke AI, Automatic1111, Forge, Fooocus, n8n, FramePack Studio, FramePack Eichi, Wan2GP, etc.).

This isn’t a licensing issue, as many open source projects are licensed with MIT or Apache licenses, which don’t require you to open source derivative products. Even if they used the GPL, it wouldn’t be required for a SaaS web app. Only the AGPL would protect against that, and even then, only the changes to the AGPL library would need to be shared; the front end app could still be proprietary.

The other issue could be them not knowing what “app” means. If you think of a Lora as an app, then the sentence might be accurate. I don’t know for sure that there were thousands of Loras for people that published their training data, but I wouldn’t be surprised if that were the case.

Have you tried just setting the resolution to 1920x1080 or are you literally trying to run AAA games at 4K on a card that was targeting 1080p when it was released, 4 and a half years ago?

I think the best way to handle this would be to just encode everything and upload all files. If I wanted some amount of history, I'd use some file system with automatic snapshots, like ZFS.

If I wanted to do what you've outlined, I would probably use rclone with filtering for the extension types or something along those lines.

If I wanted to do this with Git specifically, though, this is what I would try first:

First, add lossless extensions (*.flac, *.wav) to my repo's .gitignore

Second, schedule a job on my local machine that:

1. Watches for changes to the local file system (e.g., with inotifywait or fswatch)
2. For any new lossless files, if there isn't already an accompanying lossy files (i.e., identified by being collocated, having the exact same filename, sans extension, with an accepted extension, e.g., .mp3, .ogg - possibly also with a confirmation that the codec is up to my standards with a call to ffprobe, avprobe, mediainfo, exiftool, or something similar), it encodes the file to your preferred lossy format.
3. Use git status --porcelain to if there have been any changes.
4. If so, run git add --all && git commit --message "Automatic commit" && git push
5. Optionally, automatically craft a better commit message by checking which files have been changed, generating text like Added album: "Satin Panthers - EP" by Hudson Mohawke or Removed album: "Brat" by Charli XCX; Added album "Brat and it's the same but there's three more songs so it's not" by Charli XCX

Third, schedule a job on my remote machine server that runs git pull at regular intervals.

One issue with this approach is that if you delete a file (as opposed to moving it), the space is not recovered on your local or your server. If space on your server is a concern, you could work around that by running something like the answer here (adjusting the depth to an appropriate amount for your use case):

git fetch --depth=1
git reflog expire --expire-unreachable=now --all
git gc --aggressive --prune=all

Another potential issue is that what I described above involves having an intermediary git to push to and pull from, e.g., running on a hosted Git forge, like GitHub, Codeberg, etc.. This could result in getting copyright complaints or something along those lines, though.

Alternatively, you could use your server as the git server (or check out forgejo if you want a Git forge as well), but then you can't use the above trick to prune file history and save space from deleted files (on the server, at least - you could on your local, I think). If you then check out your working copy in a way such that Git can use hard links, you should at least be able to avoid needing to store two copies on your server.

The other thing to check out, if you take this approach, is git lfs. EDIT: Actually, I take that back - you probably don't want to use Git LFS.

It’s the new hyped up version of “no-code” or low-code solutions, but with AI so you have more flexibility to footgun.

Not any lazier. Script kiddies didn’t write the code themselves, either.

Are you talking about a warning for a self signed cert or for not using HTTPS?

You should try watching the live action series next - I bet you’d love it.

From the Slashdot comments, by Rei:

Or, you can, you know, not fall for clickbait. This is one of those...

Ultimately, we found that the common understanding of AI’s energy consumption is full of holes.

"Everyone Else Is Wrong And I Am Right" articles, which starts out with....

The latest reports show that 4.4% of all the energy in the US now goes toward data centers.

without bothering to mention that AI is only a small percentage of data centre power consumption (Bitcoin alone is an order of magnitude higher), and....

In 2017, AI began to change everything. Data centers started getting built with energy-intensive hardware designed for AI, which led them to double their electricity consumption by 2023.

What a retcon. AI was *nothing* until the early 2020s. Yet datacentre power consumption did start skyrocketing in 2017 - having nothing whatsoever to do with AI. Bitcoin was the big driver.

At that point, AI alone could consume as much electricity annually as 22% of all US households.

Let's convert this from meaningless hype numbers to actual numbers. First off, notice the fast one they just pulled - global AI usage to just the US, and just households. US households use about 1500 TWh of the world's 24400 TWh/yr, or about 6%. 22% of 6% is ~1,3% of electricity (330 TWh/yr). Electricity is about 20% of global energy, so in this scenario AI would be 0,3% of global energy. We're just taking at face value their extreme numbers for now (predicting an order of magnitude growth from today's AI consumption), and ignoring that even a single AI application alone could entirely offset the emissions of all AI combined. Let's look first at the premises behind what they're arguing for this 0,3% of global energy usage (oh, I'm sorry, let's revert to scary numbers: "22% OF US HOUSEHOLDS!"):

• It's almost all inference, so that simplifies everything to usage growth
• But usage growth is offset by the fact that AI efficiency is simultaneously improving at faster than Moore's Law on three separate axes, which are multiplicative with each other (hardware, inference, and models). You can get what used to take insanely expensive, server-and-power-hungry GPT-4 performance (1,5T parameters) on a model small enough to run on a cell phone that, run on efficient modern servers, finishes its output in a flash. So you have to assume not just one order of magnitude of inference growth (due to more people using AI), but many orders of magnitude of inference growth.   * You can try to Jevon at least part of that away by assuming that people will always want the latest, greatest, most powerful models for their tasks, rather than putting the efficiency gains toward lower costs. But will they? I mean, to some extent, sure. LRMs deal with a lot more tokens than non-LRMs, AI video is just starting to take off, etc. But at the same time, for example, today LRMs work in token space, but in the future they'll probably just work in latent space, which is vastly more efficient. To be clear, I'm sure Jevon will eat a lot of the gains - but all of them? I'm not so sure about that.   * You need the hardware to actually consume this power. They're predicting by - three years from now - to have an order of magnitude more hardware out there than all the AI servers combined to this point. Is the production capacity for that huge level of increase in AI silicon actually in the works? I don't see it.

There’s a difference between a tool being available to you and a tool being misused by your students.

That said, I wouldn’t trust AI assessments of students to determine if they’re on track right now, either. Whatever means the AI would use needs to be better than grading quizzes, homework, etc., and while I’m not a teacher, I would be very surprised if it were better than any halfway competent teacher’s assessments (thinking in terms of high school and younger, at least - in university IME the expectation is that you self assess during the term and it’s up to you to seek out learning opportunities outside class if you need them, like going to office hours for your prof or TA).

AI isn’t useless, though! It’s just being used wrong. For example, AI can improve OCR, making it more feasible for students to hand in submissions that can be automatically graded, or to improve accessibility for graders. But for that to actually be helpful we need better options on the hardware front and for better integration of those options into grading systems, like affordable batch scanners that you can just drop a stack of 50 assignments into, each a variable number of pages, with software that will automatically sort out the results by assignment and submitter, and automatically organize them into the same place that you put all the digital submissions.

What would moving your account here from Reddit entail? What would transfer with you? Your posts? Comments? Followed and moderated subreddits? Upvotes and downvotes?

They also had much higher distribution costs and a much smaller audience back then, so even if prices have gone down since the late 70s, profits haven’t.

Pac-Man was the best selling Atari 2600 game and it sold 8 million copies. Mario Kart on the Switch, by contrast, has sold over 60 million copies. A mere 1% of PC game sales are physical and a mere 16% of console game sales are physical.

after the interview, the robot — and the company — then ghosted them with no future contact.

For fuck’s sake, if you’re going to use a GenAI powered interview process to filter through resumes, interview and eliminate candidates, spend the ten extra seconds it takes to set up an automated follow-up email to the candidates you ruled out.

I had a lot better luck finding a good PCP through Zocdoc than through my insurance company’s directory. If you have trouble I recommend checking it out.

You can run a NAS with any Linux distro - your limiting factor is having enough drive storage. You might want to consider something that’s great at using virtual machines (e.g., Proxmox) if you don’t like Docker, but I have almost everything I want running in Docker and haven’t needed to spin up a single virtual machine.

I think the better question than “Does the experience system sound like it has potential,” then, is “Does the overall concept / system have potential?”

My gut is probably, but it depends a lot more on what you’re willing to put into it and what you want out of it. What’s your metric for success? If it’s something you want to run yourself and to share online to have a few groups use it, then that’s a lot more achievable than being able to get a publishing deal, for example. And in-between, publishing on drivethrurpg or something similar, at a nominal cost (like $2-$5), would take more effort than the former and less than the latter; and the higher the cost and the higher the number of players you’d want, the higher the effort you need to put in (and a lot of that isn’t just in system building, but in art, community building, marketing, etc.).

From what you’ve shared, it sounds like an interesting system. I could especially see it working in an academy setting where grinding skills to be able to pass practical exams is one of the players’ goals. I also could see it working well by a loosely GMed play by post system, with the players self-enforcing (or possibly leveraging some tools built into the site to track resource pools, experience, rolling, etc.), though I haven’t played in a forum game myself, so I might be way off-base.

Did your system have classes or was it completely free-form in terms of gaining access to those skill trees?

I run a Monster of the Week game and my players get experience throughout sessions, as well as at the end. The mechanics are basically:

• It takes 5 experience points to level up.
• If you fail a roll, you get an experience point.
• If you level up, you get the benefit immediately.
• At the end of the session, everyone gets 0-2 experience points.

I think other PbtA (Powered by the Apocalypse - systems inspired by Apocalypse World) systems do something similar.

I grew increasingly frustrated with the system of only distributing advancement/experience points at the end of a session.

Isn’t the simple fix to this to just distribute experience points as soon as they’re earned?

At some point, I started to divise a play system that relied on a split experience atribution system, with players being able to automatically rack experience points from directly using their skills/habilties, while the DM would keep a tally of points from goals/missions achieved, distributable at session end.

Your system sounds like the way that skill-based video game RPGs (Elder Scrolls games and Arcanum come to mind) handle experience.

In a lot of games I’ve played, I’d rather get experience for in-game accomplishments immediately and to be able to train skills like this during downtime - generally between games.

To those with more experience in TTRPGs: would this be feaseable? Or enticing? Interesting?

I could see people being interested in it. You get instant gratification and a bit of extra crunchiness. A lot of players enjoy that.

With the right skill system I could see this being useful. My main concern is that if you put this on top of a system with relatively few skills, it could encourage people to game it by grinding. There are ways to mitigate that, though.

In a system with fewer skills, instead of just being experience points, the “currency” you earned this way could be used for temporary power ups related to the skill in question.

You could also limit it so you only rewarded players for story-related tasks.

Copied from the post:

You may have seen reports of leaks of older text messages that had previously been sent to Steam customers. We have examined the leak sample and have determined this was NOT a breach of Steam systems.​

​

We’re still digging into the source of the leak, which is compounded by the fact that any SMS messages are unencrypted in transit, and routed through multiple providers on the way to your phone.​

​

The leak consisted of older text messages that included one-time codes that were only valid for 15-minute time frames and the phone numbers they were sent to. The leaked data did not associate the phone numbers with a Steam account, password information, payment information or other personal data. Old text messages cannot be used to breach the security of your Steam account, and whenever a code is used to change your Steam email or password using SMS, you will receive a confirmation via email and/or Steam secure messages.​

​

You do not need to change your passwords or phone numbers as a result of this event. It is a good reminder to treat any account security messages that you have not explicitly requested as suspicious. We recommend regularly checking your Steam account security at any time at ​

https://store.steampowered.com/account/authorizeddevices​

​

We also recommend setting up the Steam Mobile Authenticator if you haven’t already, as it gives us the best way to send secure messages about your account and your account’s safety.

This is an interesting parallel, but I feel like I missed some key part of it.

In the US, at least, we historically killed off a lot of deer’s natural predators - mostly wolves - and as a result, the deer population can get out of control, causing serious problems to the ecosystem. Hunters help to remedy that. The relatively small violences that they perform on an individual basis add up to improving the overall ecosystem.

That isn’t the same as being a bigot, or a sexist, or a fascist… and I don’t know why anyone would assume that a person holds those views because they’re mean and petty. They hold those views for a variety of reasons - sometimes because they’re a child or barely an adult and that’s just what they learned, and they either don’t know any better or haven’t cared enough to think it through; sometimes because they’ve been conditioned to think that way; sometimes because they’re sociopaths who recognize that it’s easier to oppress that particular group.

It doesn’t really matter what their reason is. Either way, they’re a worse person because of it, and often they’re overall a bad person, regardless of the rest of their views, actions, and contributions.

Being a hunter, by contrast, is neutral leaning positive.

It makes sense that a rational person who loves being in nature, who loves animals, who wants their local ecosystem to be successful, would as a result want to help out in some small way, even if that means they have to kill an animal to do so. It doesn’t make sense that a rational person who loves all people, who wants their local communities to be successful, would as a result want to oppress and harm the people in already marginalized groups.

I don’t think equating being bigoted with holding unjustifiable opinions does it justice. The way we use the word opinion generally applies to things that are trivial or unimportant, that don’t ultimately matter, e.g., likes and dislikes. Being a bigot is a viewpoint; it shapes you. For many bigots, their entire perspective is warped and wrong. And there’s a common misunderstanding that you can’t argue with someone’s opinions; because it’s just how they “feel.” But being a bigot, whether you’re sexist, racist, transphobic, queerphobic, homophobic, biphobic, etc., is a belief, and it’s one that, in most cases, the bigot chooses (consciously or not) to keep believing.

If an adult with functioning cognitive abilities refuses to question their bigoted beliefs, then they’ve made a choice to be a bigot.

Assuming you’re using ollama (is there another reason to use ollama.com?), you can use compatible files from huggingface directly in ollama. The model page will give you the instructions for the command to run; I always change ollama run to ollama pull , though. Instructions: https://huggingface.co/docs/hub/ollama

You should be able to fit Qwen3 32B at Q4_K_M with an acceptable context, and it did very well on math benchmarks (with thinking enabled). You can disable thinking by including /no_think at the end of your prompt to speed up responses, but I’m not sure how well it handles math under those circumstances. I wouldn’t even consider disabling thinking unless you were grading one question per prompt.

The ollama Qwen3 page is https://ollama.com/library/qwen3:32b and the default 32B quant is Q4_K_M. I personally am using the Q6_K quant by unsloth, and their quants have been great (when supported by ollama), often being the first to fix bugs impacting other quantizations.

I’m not sure if Q4_K_M is the optimal quant style for Intel Arc, but the others that might be better are not supported by ollama, anyway, as far as I know.

Qwen3’s real world knowledge is bad, so if there are questions that rely on that you may need to include the relevant facts as part of the prompt or use an ollama frontend that supports web searches.

Other options: This does seem like something Gemma3 27B would be good at, so it’s too bad you can’t use it. Older Gemmas may be good, but I’m not sure. Llama3.3 70B is also out, unless you have a decent amount of system RAM and are okay with offloading less than half to GPU. I could see it outperforming my recommendation below but I would be very surprised for the 8B version to outperform it. Older Qwen2.5 is decent at math but unless you grab QwQ doesn’t include thinking.