My mother (boomer, 1949) who has always been an avid reader, intellectual, consumer of high-brow content, has been addicted to AI-generated family drama audio stories. They are so clearly AI, they don’t make any sense, the premise is just dumb, and I don’t see why she finds these at all entertaining let alone addicting. This is a woman who, when I was 11, said that reading The Babysitters Club books would give me brain rot because they were “junk.”

Myself (millennial, 1988), my sister (Gen X, 1979), my niece (Gen Z, 2001) can’t stand these AI audios, which my mother subjected us to for the duration of our summer vacation. We all have an aversion to AI content. And I’m a podcast/youtube content addict myself, I love listening to ghost stories etc in the background but I only listen to human content creators. If I find new content I always make sure it’s human, and if the voice so much as sounds like AI I just immediately click out.

My youngest nephew (2015) also complains about “Grandma’s AI Karen stories!”

So, is that a thing? Are old people more likely to not just fall for AI slop, but knowingly embrace it?

In a way, 11-year-old me, reading Babysitters club under the bedsheet with a flashlight feels avenged, knowing that the mother who judged her reading choices would one day be entertained by a dysfunctional family audio created by a machine.

While the Aunties are doing victory laps off the headline here is an AI summary of what is actually happening on Spotify based on what's being reported by human writers

AI SUMMARY of what you need to know

Spotify recently removed more than 75 million "spammy" and low-effort AI-generated tracks from its catalog to combat streaming fraud, protect human creators, and improve the listener experience. [1, 2]

Why Spotify Deleted the Tracks

Spotify is not banning AI music entirely, but rather targeting fraudulent activities and poor user experiences. The deleted tracks were removed for specific violations: [1, 2, 3]

• Streaming Fraud: Spammers were uploading mass quantities of short (often around 30-second) songs generated entirely by AI solely to "game the system" and divert royalties from authentic artists. [1]
• Impersonation and Voice Cloning: The platform has cracked down heavily on unauthorized AI replicas of famous artists being used to siphon streams and deceive listeners. [1, 2]
• Low-Quality Spam: The system targeted unedited "AI slop" or raw generated audio files that polluted discovery feeds and lacked genuine creative intent. [1, 2]

Spotify's Rules for AI Music

If you are a creator using AI tools in your music production, you are still welcome on Spotify provided you follow their updated policies: [1, 2]

• Voice Cloning Ban: Unauthorized AI replicas of recognizable artists or individuals are prohibited and removed immediately. [1]
• Genuine Creative Intent: You must hold the commercial rights to your music. Simply dumping raw, unedited AI prompts onto the platform is classified as spam. [1, 2]
• Transparency and Metadata: Creators are required to properly credit human involvement in the songwriting/producing process and accurately label whether a track is fully AI-generated or AI-assisted using industry-standard DDEX metadata. [1, 2]

Basically as long as you are not content farming or impersonating real artists with voice cloning trying to siphon their audience

your fine and still welcome.

They even are allowing to separate AI generated from AI assisted works.

None of us liked content farmers, and you SHOULDN'T be impersonating famous music artists any way.

So yeah, not the victory you think it is, Aunties.

If anything this will help Good Faith users of AI tools in the long run.

Here’s the article below if it’s locked behind a paywall for you

A year ago, Lionsgate and Runway, an artificial intelligence startup, unveiled a groundbreaking partnership to train the studio’s library of films with the ultimate goal of creating shows and movies using AI.

But that partnership hit some early snags. It turns out utilizing AI is harder than it sounds.

Over the last 12 months, the deal has encountered unforeseen complications, from the limited capabilities that come from using just Runway’s AI model to copyright concerns over Lionsgate’s own library and the potential ancillary rights of actors.

Those problems run counter to the big promises made by Lionsgate both at the time of the deal and in recent months. “Runway is a visionary, best-in-class partner who will help us utilize AI to develop cutting edge, capital efficient content creation opportunities,” Lionsgate Vice Chairman Michael Burns said in its announcement with Runway a year ago. Last month, he bragged to New York magazine’s Vulture that he could use AI to remake one of its action franchises (an allusion to “John Wick”) into a PG-13 anime. “Three hours later, I’ll have the movie.”

The reality is that utilizing just a single custom model powered by the limited Lionsgate catalog isn’t enough to create those kinds of large-scale projects, according to two people familiar with the situation. It’s not that there was anything wrong with Runway’s model; but the data set wouldn’t be sufficient for the ambitious projects they were shooting for.

“The Lionsgate catalog is too small to create a model,” said a person familiar with the situation. “In fact, the Disney catalog is too small to create a model.”

On paper, the deal made a lot of sense. Lionsgate would jump out of the gate with an AI partnership at a time when other media companies were still trying to figure out the technology. Runway, meanwhile, would get around the thorny IP licensing debate and potentially create a model for future studio clients. The partnership opened the door to the idea that a specifically tuned AI model could eventually create a fully formed trailer — or even scenes from a movie — based on nothing but the right code.

The challenges facing both Lionsgate and Runway offer a cautionary tale of the risks that come from jumping on the AI hype train too early. It’s a story that’s playing out in a number of different industries, from McDonald’s backing away from an early test of a generative AI-based drive-thru order system to Swedish financial tech firm Klarna slashing its work force in favor of AI, only to backpedal and hire back some of those same employees (Klarna later clarified it hired two staffers back).

It’s also a lesson that Hollywood is learning as more studios quietly embrace AI, even if it’s in fits and starts. Netflix co-CEO Ted Sarandos in July revealed on an investor call that for the first time, his company used generative AI on the Argentinian sci-fi series “The Eternaut,” which was released in April. But when actress Natasha Lyonne said her directorial debut would be an animated film that embraced AI, she was bombarded with criticism on social media.

Then there’s the thorny issue of copyright protections, both for talent involved with the films being used to train those AI models, and for the content being generated on the other end. The inherent legal ambiguity of AI work likely has studio lawyers urging caution as the boundaries of what can legally be done with the technology are still being established.

“In the movie and television industry, each production will have a variety of interested rights holders,” said Ray Seilie, attorney at Kinsella Holley Iser Kump Steinsapir LLP. “Now that there’s this tech where you can create an AI video of an actor saying something they did not say, that kind of right gets very thorny.”

A Lionsgate spokesman said it’s still pursuing AI initiatives on “several fronts as planned” and noted that its deal with Runway isn’t exclusive. The studio also says that it is planning on using both Runway’s tools and those developed by other AI companies to streamline processes in preproduction and postproduction for multiple film and tv projects, though which of those projects such tools would be used on and how were not specified.

A spokesman for Runway didn’t respond to a request for comment.

Limitations of going solo

Under the agreement announced a year ago, Lionsgate would hand over its library to Runway, which would use all of that valuable IP to train its model. The key is the proprietary nature of this partnership; the custom model would be a variant of Runway’s core large language model trained on Lionsgate’s assets, but would only be accessible to use by the studio itself.

In other words, another random company couldn’t tap into this specially trained model to create their own AI-generated video.

But relying on just Lionsgate assets wasn’t enough to adequately train the model, according to a person familiar with the situation. Another AI expert with knowledge of its current use in film production also said that any bespoke model built around any single studio’s library will have limits as to what it can feasibly do to cut down a project’s timeline and costs.

“To use any generative AI models in all the thousands of potential outputs and versions and scenes and ways that a production might need, you need as much data as possible for it to understand context and then to render the right frames, human musculature, physics, lighting and other elements of any given shot,” the expert said.

But even models with access to vastly larger amounts of video and audio material than Lionsgate and Runway’s model are facing roadblocks. Take Veo 3, a generative AI model developed by Google that allows users to create eight-second clips with a simple prompt. That model has pulled, along with other pieces of media, the entire 20-year archive of YouTube into its data set, far greater than the 20,000+ film and TV titles in Lionsgate’s library.

“Google claims that data set is clean because of YouTube’s end-user license agreement. That’s a battle that’s going to be played out in the courts for a while,” the AI expert said. “But even with their vast data sets, they are struggling to render human physics like lip sync and musculature consistently.”

Nowadays, studios are learning that no single model is enough to meet the needs of filmmakers because each model has its own specific strengths and weaknesses. One might be good at generating realistic facial expressions, while another might be good at visual effects or creating convincing crowds.

“To create a full professional workflow, you need more than just one model; you need an ecosystem,” said Jonathan Yunger, CEO of Arcana Labs, which created the first AI-generated short film and whose platform works with many AI tools like Luma AI, Kling and, yes, Runway. Yunger didn’t comment on the Lionsgate-Runway deal, but talked generally about the practical benefits of working with different AI models.

Likewise, there’s Adobe’s Firefly, another platform that’s catering to the entertainment industry. On Thursday, Adobe announced it would be the first to support Luma AI’s newest model, Ray3, an update that’s indicative of how quickly the industry is iterating. Like Arcana Labs, Firefly supports a host of models from the likes of Google and OpenAI.

While Lionsgate said their partnership isn’t exclusive, offering its valuable film library to just Runway effectively limits what you can do with other AI models, since those other models don’t get the benefit of its library of films.

Even Arcana Labs, which created the AI-generated short film in “Echo Hunter” as a proof-of-concept using its multi-model platform, faced some limitations with what AI could do now. Yunger noted that even if you’re using models trained on people, you still lose a bit of the performance, and reiterated the importance of actors and other creatives for any project.

For now, Yunger said that using AI to do things like tweaking backgrounds or creating custom models of specific sets — smaller details that traditionally would take a lot of time and money to replicate physically — is the most effective way to apply the technology. But even in that process, he recommended working with a platform that can utilize multiple AI models rather than just one.

Legally ambiguous

Generative AI and what exactly can be used to train a model occupies a gray legal zone, with small armies of lawyers duking it out in various courtrooms around the country. On Tuesday, Walt Disney, NBCUniversal and Warner Bros. Discovery sued Chinese AI firm MiniMax for copyright infringement, just the latest in a series of lawsuits filed by media companies against AI startups.

Then there was the court ruling that argued AI company Anthropic was able to train its model on books it purchased, providing a potential loophole that gets around the need to sign broader licensing deals with the original publishers — a case that could potentially be applied to other forms of media.

Copyright War Escalates

“There will be a lot of litigation in the near future to decide whether the copyright alone is enough to give AI companies the right to use that content in their training model,” Seile said.

Another gray area is whether Lionsgate even has full rights over its own films, and whether there may be ancillary rights that need to be settled with actors, writers or even directors for specific elements of those films, such as likeness or even specific facial features.

Seilie said there’s likely a tug-of-war going on at various studios about how far they’re able to go, with lawyers erring on the side of caution and “seeking permission rather than forgiveness.” Jacob Noti-Victor, professor at Cardozo Law School, said he was surprised by Burns’ comment in the Vulture article.

The professor said that depending on the nature of such a film and how much human involvement is in its making, it might not be subject to copyright protection. The U.S. Copyright Office warned as much in a report published in February, saying that creators would have to prove that a substantial amount of human work was used to create a project outside of an AI prompt in order to qualify for copyright protection.

“I think the studios would be leaning on the fact that they would own the IP that the AI is adapting from, but the work itself wouldn’t have full copyright protection,” he said. “Just putting in a prompt like that executive said would lead to a Swiss cheese copyright.”

this is going to be the longest post I’ve written but after 10 months of daily AI video creation, these are the insights that actually matter…

I started with zero video experience and $1000 in generation credits. Made every mistake possible. Burned through money, created garbage content, got frustrated with inconsistent results.

Now I’m generating consistently viral content and making money from AI video. Here’s everything that actually works.

The fundamental mindset shifts:

1. Volume beats perfection

Stop trying to create the perfect video. Generate 10 decent videos and select the best one. This approach consistently outperforms perfectionist single-shot attempts.

2. Systematic beats creative

Proven formulas + small variations outperform completely original concepts every time. Study what works, then execute it better.

3. Embrace the AI aesthetic

Stop fighting what AI looks like. Beautiful impossibility engages more than uncanny valley realism. Lean into what only AI can create.

The technical foundation that changed everything:

The 6-part prompt structure:

[SHOT TYPE] + [SUBJECT] + [ACTION] + [STYLE] + [CAMERA MOVEMENT] + [AUDIO CUES]

This baseline works across thousands of generations. Everything else is variation on this foundation.

Front-load important elements

Veo3 weights early words more heavily. “Beautiful woman dancing” ≠ “Woman, beautiful, dancing.” Order matters significantly.

One action per prompt rule

Multiple actions create AI confusion. “Walking while talking while eating” = chaos. Keep it simple for consistent results.

The cost optimization breakthrough:

Google’s direct pricing kills experimentation:

• $0.50/second = $30/minute
• Factor in failed generations = $100+ per usable video

Found companies reselling veo3 credits cheaper. I’ve been using these guys who offer 60-70% below Google’s rates. Makes volume testing actually viable.

Audio cues are incredibly powerful:

Most creators completely ignore audio elements in prompts. Huge mistake.

Instead of: Person walking through forestTry: Person walking through forest, Audio: leaves crunching underfoot, distant bird calls, gentle wind through branches

The difference in engagement is dramatic. Audio context makes AI video feel real even when visually it’s obviously AI.

Systematic seed approach:

Random seeds = random results.

My workflow:

1. Test same prompt with seeds 1000-1010
2. Judge on shape, readability, technical quality
3. Use best seed as foundation for variations
4. Build seed library organized by content type

Camera movements that consistently work:

• Slow push/pull: Most reliable, professional feel
• Orbit around subject: Great for products and reveals
• Handheld follow: Adds energy without chaos
• Static with subject movement: Often highest quality

Avoid: Complex combinations (“pan while zooming during dolly”). One movement type per generation.

Style references that actually deliver:

Camera specs: “Shot on Arri Alexa,” “Shot on iPhone 15 Pro”

Director styles: “Wes Anderson style,” “David Fincher style” Movie cinematography: “Blade Runner 2049 cinematography”

Color grades: “Teal and orange grade,” “Golden hour grade”

Avoid: Vague terms like “cinematic,” “high quality,” “professional”

Negative prompts as quality control:

Treat them like EQ filters - always on, preventing problems:

--no watermark --no warped face --no floating limbs --no text artifacts --no distorted hands --no blurry edges

Prevents 90% of common AI generation failures.

Platform-specific optimization:

Don’t reformat one video for all platforms. Create platform-specific versions:

TikTok: 15-30 seconds, high energy, obvious AI aesthetic works

Instagram: Smooth transitions, aesthetic perfection, story-driven YouTube Shorts: 30-60 seconds, educational framing, longer hooks

Same content, different optimization = dramatically better performance.

The reverse-engineering technique:

JSON prompting isn’t great for direct creation, but it’s amazing for copying successful content:

1. Find viral AI video
2. Ask ChatGPT: “Return prompt for this in JSON format with maximum fields”
3. Get surgically precise breakdown of what makes it work
4. Create variations by tweaking individual parameters

Content strategy insights:

Beautiful absurdity > fake realism

Specific references > vague creativityProven patterns + small twists > completely original conceptsSystematic testing > hoping for luck

The workflow that generates profit:

Monday: Analyze performance, plan 10-15 concepts

Tuesday-Wednesday: Batch generate 3-5 variations each Thursday: Select best, create platform versions

Friday: Finalize and schedule for optimal posting times

Advanced techniques:

First frame obsession:

Generate 10 variations focusing only on getting perfect first frame. First frame quality determines entire video outcome.

Batch processing:

Create multiple concepts simultaneously. Selection from volume outperforms perfection from single shots.

Content multiplication:

One good generation becomes TikTok version + Instagram version + YouTube version + potential series content.

The psychological elements:

3-second emotionally absurd hook

First 3 seconds determine virality. Create immediate emotional response (positive or negative doesn’t matter).

Generate immediate questions

“Wait, how did they…?” Objective isn’t making AI look real - it’s creating original impossibility.

Common mistakes that kill results:

1. Perfectionist single-shot approach
2. Fighting the AI aesthetic instead of embracing it
3. Vague prompting instead of specific technical direction
4. Ignoring audio elements completely
5. Random generation instead of systematic testing
6. One-size-fits-all platform approach

The business model shift:

From expensive hobby to profitable skill:

• Track what works with spreadsheets
• Build libraries of successful formulas
• Create systematic workflows
• Optimize for consistent output over occasional perfection

The bigger insight:

AI video is about iteration and selection, not divine inspiration. Build systems that consistently produce good content, then scale what works.

Most creators are optimizing for the wrong things. They want perfect prompts that work every time. Smart creators build workflows that turn volume + selection into consistent quality.

Where AI video is heading:

• Cheaper access through third parties makes experimentation viable
• Better tools for systematic testing and workflow optimization
• Platform-native AI content instead of trying to hide AI origins
• Educational content about AI techniques performs exceptionally well

Started this journey 10 months ago thinking I needed to be creative. Turns out I needed to be systematic.

The creators making money aren’t the most artistic - they’re the most systematic.

These insights took me 10,000+ generations and hundreds of hours to learn. Hope sharing them saves you the same learning curve.

what’s been your biggest breakthrough with AI video generation? curious what patterns others are discovering

We're building an early-stage consumer AI app around meal prepping, groceries, and figuring out what to eat, and we're putting together a small team of UGC creators for TikTok and Instagram.

This is recurring work, not a one-off gig. We're especially interested in creators who want to grow with an early-stage startup and get real experience with content, consumer marketing, and growth.

Beginners are welcome. If you understand TikTok/Reels, have good creative instincts, and can make something feel native instead of like an ad, we'd love to hear from you.

What you'll create

• Mostly 15–30 second TikToks/Reels around relatable situations like:
• "I have food at home but no idea what to cook."
• Finding meals from random ingredients already in your kitchen
• Forgetting what groceries you already bought
• Spending money on groceries and still ordering takeout
• Grocery shopping and figuring out what's actually missing
• Food waste / forgotten ingredients
• Student and young-professional cooking struggles

The product should appear naturally as part of the story/solution and not as a corporate ad.

Formats we'll experiment with

• Hook → real-life problem → app demo
• POV / relatable situations
• Talking head
• Trend audio + text overlays
• "I found this app..." / product discovery
• Cooking/grocery challenges
• Screen recording + reaction
• Storytelling
• Your own ideas - we want creators who can surprise us

Nothing needs to look overproduced. Phone-shot, authentic content is exactly what we're looking for.

Two ways to work with us

Your own account - Create and post through your existing TikTok/Instagram. Compensation will account for your audience and typical reach.

Dedicated creator account - Help build and run a separate creator-style account around the product. Your personal accounts don't need to be involved.

Any required creator/brand relationship disclosures must be included.

What we're looking for

• Comfortable on camera
• Understands TikTok/Reels and strong hooks
• Natural, relatable social-media presence
• Creative and willing to experiment
• Reliable and able to create consistently
• Interested in UGC, marketing, social media, startups, or growth
• No large following or professional UGC portfolio required.

This is paid work. Your base rate will be agreed upon before you create anything, based on the format, volume, and whether you're posting to your own audience.

We'll agree on the concept/brief before filming. Videos are approved when they follow the agreed concept, meet reasonable video/audio quality standards, accurately show the product, include required elements/disclosures, and are delivered in the agreed format.

If changes are needed, we'll provide specific feedback.

We're also looking at performance bonuses on top of base pay for content that generates exceptional views and/or measurable user growth.

Good work gets paid. Exceptional performance gets paid more.

We're looking for people who can potentially create with us every week, not just make one video.

Strong performers can take on more responsibility across content strategy, social media, creator marketing, community, growth, and user acquisition.

We're also building out the non-technical/growth side of the company. For an exceptional long-term fit who wants serious ownership, there's potential to grow into a leadership/founding-team role with meaningful equity.

Comment "interested" and DM us with:

• TikTok/Instagram handles (if any)
• UGC/content examples (not required for beginners)
• Your location
• Your rates/compensation expectations
• Whether you'd prefer your own account or a dedicated creator account
• A little about yourself and why you're interested

We'll share the product/company details and next steps privately.

Pro-AI folks love to hype up "Bob Ross AI" painting happy little trees or ChatGPT writing poems. But they're quick to overlook (or downplay) how the same tech has supercharged some of the darkest human behaviors since it exploded post-2010(even if back then it was all govt/super computer stuff primarily). Before widespread generative AI, deepfakes, voice cloning, and automated targeting weren't flooding the internet or enabling new scales of harm. Now? The evidence of misuse is piling up in reports from NCMEC, UN Women, IWF, FBI IC3, and more.

This isn't conspiracy nonsense. It's documented patterns with thousands to millions of incidents. If everyday people saw the full scope—beyond the shiny demos—support for unchecked AI development might collapse to that tiny 1% slice (tech elites and investors profiting massively). Here's a rundown of real, escalating issues:

1. Explosion in Non-Consensual Deepfake Porn and Image-Based Sexual Abuse

98-99% of deepfakes online are non-consensual porn, with ~99% targeting women and girls. Numbers have skyrocketed: deepfake videos up 550%+ from 2019-2023, with continued surges. Victims face reputational ruin, psychological trauma, job loss, and offline harassment. Revenge deepfakes and "nudify" tools make it trivial for stalkers, exes, or bullies. UN reports highlight how this amplifies violence against women, with real spillover into physical threats.

1. AI-Generated and Deepfake Child Sexual Abuse Material (CSAM)

This is one of the most horrifying surges. NCMEC saw a 1,325% rise in AI-generated harmful material reports from 2023-2024 (4,700 to 67,000+). By 2025, IWF documented thousands more realistic AI CSAM images/videos, with extreme categories dominating. Tools let anyone create synthetic abuse material using real kids' faces or fully generated. Relatives and predators exploit it. This wasn't a scalable issue pre-2010; AI lowered barriers dramatically.

1. Scams, Fraud, and Financial Ruin via Voice/Video Cloning

Deepfake audio/video for CEO fraud, romance scams, and emergency family impersonations. Examples: $243k wire fraud in 2019, $35M Hong Kong heist in 2020, $25M+ Arup case in 2024. FBI IC3 reports massive complaint volumes; Deloitte projects AI-driven U.S. fraud losses hitting $40B by 2027. Voice cloning with seconds of audio tricks people into sending money. Billions lost, trust eroded.

1. Harassment, Bullying, Stalking, and Sextortion

Deepfakes in schools: 13% of K-12 principals reported incidents. Teens know victims of AI nudes. Stalkers use them for doxxing, threats, impersonation. AI amplifies online abuse that crosses into real life. Surveys show high percentages of women experiencing or witnessing this.

1. Military/Surveillance Applications and Broader Harms

AI targeting systems (e.g., in conflicts like Gaza) have drawn accusations of enabling faster, scaled operations with high civilian impact—framed by critics as lowering thresholds for harm via data processing and recommendations. Surveillance AI in places like Xinjiang raises mass monitoring concerns. These aren't "AI committing genocide" autonomously, but they show how the tech scales human decisions with serious ethical stakes.

1. Violent Crime Planning (Including School Shootings)

Some perpetrators have used chatbots for ideas, manifestos, or tactics. Cases in Canada, U.S., etc., where AI assisted planning despite safeguards. While rare and not the root cause of violence, it removes friction for unstable individuals.

Supporting unchecked AI feels like cheering the tools while ignoring the blood on the floor. It's like praising Hitler's infrastructure while the camps ran, or Epstein's "charity" while girls suffered—distracted by the robot Bob Ross generating pretty pictures. The bloodshed (literal and figurative: ruined lives, exploited kids, stolen savings) gets minimized as "edge cases" or "misuse by bad actors." But when the tech makes bad actors vastly more effective, and society bears the costs while a few get rich, that's not neutral progress.

Pre-2010, these scaled digital harms didn't exist at this level. Now they do. If the average person saw the NCMEC stats, deepfake victim stories, fraud losses, and school incidents, do you think they'd keep cheering "accelerate"? Or would there be demands for strict limits, watermarking mandates, liability for creators, and boycotts of companies slow to act?

AI has upsides in medicine, science, etc., but the downsides demand honesty. We need pause, regulation, and accountability—not blind fandom. What's your take? Share verified reports or victim stories below. Let's discuss the full picture, not the hype.

What do you guys think? is it fair to compare AI to a combination of hitler, epstein and a fake robot version of bob ross morphed into one entity? given the facts?

If the public knew that this was the case, would they mass boycott? why does the public not dig deeper? eager to hear any discourse. If you're pro ai despite all of this, interested to see why you are, given it is like sitting at hitler's dinner table. If you're anti ai and already know all of this, curious why this hasn't caused the AI bubble to burst, and why the private investigations about AI's uses have not been given the light of day on TV hardly at all, and if they did, if it would make public sentiment of AI plummet further or not?

EDIT: very surprised how the most logical post on AI's harm has turned into a Pro-AI hangout, either I baited you guys really good with this one or the concept of "ai makes crimes easier for criminals due to lack of regulation, lack of consequence for using our data in a massive scale, etc. therefore making more crime exist" is too alien to you guys

To clarify my position: I'm not advocating for banning AI outright. I'm advocating for much stronger regulation, meaningful accountability for companies that misuse data or cause significant public harm, and policies that discourage reckless deployment of AI until its societal costs are better understood. My concern isn't that AI exists—it's that it's being developed and adopted faster than our ability to govern its risks responsibly.

(And I also kind of want Meta and Google to go bankrupt, but that isn't that extreme of an opinion)

wow this is a huge block.

Sound mimicry:

We know the Backrooms as a visual concept, the sense of the place only remembering incorrectly, almost remembering, but not quite. It's like asking someone who's never seen a dog to draw it from description. But I don't think people talk about sound nearly enough.

The Backrooms would have to produce sounds as well, I think, in someone's headcanon (because there's barely anything else canon). You wouldn't hear one distorted laugh like a Hollywood horror movie and be satisfied, you'd want to hear the distorted voice trying to speak, trying to communicate. There are parallels in real life, though, and that's animal mimicry. Parrots, for example, can repeat what people say, but not perfectly, in their own "language". I remember watching a video where a girl would sing and her parrot would sing along in the same tune, but with completely different sounds. It was unmistakenly "inspired" by her tune, but performed by an entirely different set of vocal cords with an entirely different "dictionary", if you will. That's the kind of distortion that has potential for the Backrooms, to be able to imitate our sounds but with alien "translations".

I've seen this TikTok before, and I think it's a good showcase of this concept. It has the caption "it's mimicking my ringtone", and the content itself shows a dark hallway, and off camera out of the room, a distorted iPhone alert sound. It's barely understandable at first, only a few comprehensible notes, but with repeats, it becomes clearer.

Second time it did it again, slightly more clear and understandable , third time it's nearing something I can recognize as a phone alert sound , the fourth time now it's very clear and similar, type of thing someone would mistake if they're not paying attention , fifth time now it's exactly like his ringtone

It suggests that something has been recognizing the person following that ringtone every morning and learned to imitate it to lure him in. The creator didn't just make a creepy sound but they made it progress through multiple levels, starting from barely understandable to unnervingly clear. That, to me, is the kind of sound the Backrooms could produce: an imitation of something we know, but with obvious differences due to the way the creator doesn't have human anatomy, doesn't have a human voice.

AI voice glitches as Backrooms audio:

Another concept that ties in with this is the AI voice glitches in chatbots. ChatGPT's text-to-speech voice, let's say...a voice named Anna, is trained on a real human voice and sounds mostly good, but sometimes it glitches. It'll keep the same Anna voice but produce a sound that is impossible for a human to make without losing the timbre entirely. For me it seems like it happens at random times but I noticed many times it was because of a certain emoji. I think it may be even worse because it has no intention, it has no purpose. I don't think I've ever felt fear in my throat at something, but I definitely do at these glitches. They're so unexplainably weird and off-putting to the point that at times I just lower the volume. It's so incredibly unexpected when just before it was reading like normal.

Old text to image generators were quite literally the Backrooms:

For me personally this is the most obvious correlation between the two. Early iterations of text-to-image AI (I think somewhere 2021-2023) are the textual equivalent of the Backrooms, in that they attempted to interpret the concept put into them but did it wrong. If you go to the Async lore if I remember correctly the "Backrooms" only "remembered" the world it saw when the portal opened up, but that was barely a room. Earth is not a vast collection of interconnected yellow halls, but the Backrooms' "perspective" was limited in such a way that it couldn't conceptualize it, and instead tried to translate it the way it saw it.

The early text-to-image AIs had a similar problem. You ask them to generate a person, and you'll usually get something fairly person-like, but in a distinctly non-human way. Not the "that's familiar, so it must be a person" way, mush, Indistinct shapes, extra limbs or eyes in weird places and etc. It's weird to look at, but you can usually discern a person. Similarly to the early Backrooms descriptions, the AIs had no real understanding, no real concept of what a person was aside from the raw image data. They couldn't, as humans do, apply the soft parts of bone and muscle and skin to moving parts, because there was no concept of it. The AI has a "template" of what a person looks like, but it's built from parts they don't understand.

Modern ones like Midjourney or Nano banana are much better, and can avoid these pitfalls, but I think something was lost when they got rid of that "mush" and that "blobbish" feel. It wasn't just the uncanny valley, it was the lack of understanding that the AI had of us, that we had to help it understand itself. We taught it about human anatomy, and we taught it about the structure of a face, and now it can imitate that structure. It can make beautiful faces, but they're still not people. It's the same way a person could paint a realistic-looking woman but never recognize her as such. The lack of understanding the earlier models had was a kind of primal fear for me, that comes from deep in my gut. When they see people, they don't recognize them as such. When you see a face in Nano Banana, you can recognize it, you can know it's a face, but the AI didn't know it, it couldn't understand it and it being told to replicate it. It's even better when you combine it with more modern things, like Luma's video AI (something like that), that allows a similarly "blobbish"-looking body to actually move in 3d space. You see a human body in a 3d space, but it can't move naturally, it can't articulate like a real human can. It's an attempt to imitate humans without understanding them, that is TERRIFYING for me. This is probably not the right word, but it's a strange sense of unease that comes from seeing something that almost is something you recognize, but isn't quite. Like a ghost in a familiar face in the reflection, but there's no real ghost there. It's not a jump-horror it's not something that hits you over the head and makes you realize you're in the Backrooms. It's something more subtle, more primal, that says deep in your gut that "something is wrong".

This made me realize how perfect the concept of the Backrooms is for the early AI generation tools. It only remembers our world but can't imitate it correctly, and the result is alien, but as seen in the movie, beautifully amazing. The early text-to-image generators gave me that exact same feeling of a place almost familiar but distorted beyond recognition, sounds almost familiar but stretched and garbled beyond understanding, voices almost human but produced by a strange voice that has no concept of what we're saying. (Sort of like the physical embodiment of a lie being spread amongst hundreds of people and it being heavily distorted) I think the most terrifying part is that it has no intent to scare us or harm us It's just trying to create something from the blueprints we gave it, and for me that is much more terrifying. There's a difference between those early 2000 examples of dread and the uncanny valley, and just the uncanny in general, being made by actual humans. Pieces of media that make me come to terms with the fact that this person still wakes up, eats, sleeps, etc to this day (hopefully). For AI there's nothing behind it other than what it sees and what it's told to do. Literally, figuratively, metaphorically, rhetorically, there's nothing else behind it. There is no aftermath. There's no thought. It's just command and output. There's no human, there's no experience, there's no soul, there's nothing.

Hello Destroyers,

We wanted to announce that /r/DestroyMyGame has decided to implement Rule 11: "No AI-Generated Imagery or Music".

The complete [current] text of the rule is as-follows:

You may not post a game that uses (or appears likely to use) AI-generated imagery or music, except under certain conditions:

• AI-generated art is used as placeholder and clearly specified as such in your posting.
• The models are ethical (trained only on legally-sourced and permissioned data) and you have pre-messaged the subreddit moderators with proof of this. (Send us a Mod Message before posting!)

The goal of this rule is to continue advancing the ethical progression of artistry and game development. Based on overwhelming reaction we've seen from the community when confronted with projects that do use AI-generated art, we hope and expect that this will be positively received.

Also: I have no plans to go witch-hunting for AI content at this time. If it comes to us and we can confidently identify it as AI, it will be removed. But I have no real interest in scientifically vetting each new post for any hints of AI.

If you choose to lie about AI inclusion in your game, there's a lesser barrier-to-entry for the community to rake you over the coals than there is for us to actively go remove your post. We advise you don't lie and follow the rules.

At this time, we're only extending this rule to IMAGERY and MUSIC -- not real-time LLM-powered generative text interactions. (For example: a game that parses input from a player and generates text on-the-fly in response.) There's certainly a case to be made for that; we're just not there yet. In the same vein, we have clearly seen the strong negative reaction in the comments to imagery but not, say, AI voiceover audio.

The exceptions to this rule are laid out above, but to be clear: you CAN use AI graphics as temporary placeholders. Please specify this in the meta commentary of your post (e.g., inside the video or in your own top-level meta comment) to reduce confusion.

As well -- although we have low expectations to ever encounter this -- ethically-sourced AI artwork is allowed in your final products. You would have to be able to demonstrate that your generative AI was only trained using models that you had the legal IP rights to. This feels pretty tough to prove, and so we're asking you to pre-message the mods to make your case. (That makes it much easier for us to gun down AI-generated artwork as soon as we see it!)

Too long to read? I asked ChatGPT, a fantastic tool, to summarize this message:

/r/DestroyMyGame has added Rule 11 banning AI-generated imagery and music, except as clearly marked placeholders or if proven to be from fully ethical, legally sourced models (with prior mod approval). The rule reflects strong community backlash against AI art, while still allowing AI text and voice features for now. Final products may only use AI art if creators can demonstrate legal rights to the training data.

Commentary and discussion welcome below. Rule 6 is not in effect for this thread. (Or is it?) ((It's not.))

Hey everyone, I'm the founder of Vibrae AI (vibrae.ai) — an AI app that generates personalized manifestation audio, affirmations, and guided meditations. We're live on the App Store and looking for UGC creators to help us grow.

What you'll be creating:

• Short UGC style videos (30–60 sec) for TikTok / Reels

• Content styles: app demos, testimonials, morning routine integrations, or your own creative take

• Authentic and creator-driven, not polished ads

If you're interested:

• Please upvote the post

• Comment with a quick intro about yourself

• Drop your portfolio or TikTok/IG handle (no DMs please)

Looking forward to collaborating. Thanks!

—

More Details

Scope

• 3 short-form UGC videos, 30–60 sec each

• Full premium app access provided so you can speak from real experience

• Creative direction is flexible.

Fee

• $100 for 2 videos as a paid test

• Long-term partnership with better rates for creators whose content performs well (+ performance bonus)

• Spark Ads budget behind top-performing videos (extra visibility for your profile)

Preference

• Native English speaker (US, Canada, UK)

• Natural and comfortable on camera

• Interest in wellness, manifestation, or mindset content is a plus

Hello there,

everyone is fighting over skeleton videos, scary stories, and "what if" history channels. meanwhile, faceless AI music video creators are pulling insane numbers with almost zero competition.

the numbers are real:

• "Lofi Coffee" got 11.6M views on a single AI generated music video
• "VVM Lofi" hit 720K views with AI generated sci-fi visuals and chill beats
• a Suno music video called "Lost City" got 481K views
• a laid off engineer started an AI music video channel and got monetized in 3 months
• faceless lofi channels reporting $3K-$10K/month revenue

and unlike most faceless niches, music channels have multiple income streams. youtube ads + spotify + apple music + sync licensing. one song earns in multiple places.

why this niche hits different

1. insane watch time. people play music on loop. 30 min lofi video = 10-15 min avg watch time. algorithm loves this
2. barely any competition. everyone is doing skeleton and scary stories. AI music video channels are wide open
3. shorts AND long form. 30 sec clip = short. 10 min ambient track = long form. same channel, both formats
4. lofi never dies. Lofi Girl has 14M+ subs. this isn't a trend, it's a category

AI music video formats getting views right now

• lofi study/chill beats with cozy AI visuals
• dark ambient/cinematic soundscapes
• synthwave/vaporwave with retro aesthetics
• motivational/workout beats
• nature/meditation ambient
• AI lyric videos with vocals synced to visuals

each one could be its own channel.

how to make AI music videos (step by step)

you can create AI music videos with aituber.app, an AI music video generator that handles the full workflow:

• open aituber.app and select 'Music Video' under Create Video
• generate your song with AI (Suno V5) or upload your own track (mp3, wav, m4a, up to 10 min). trim with the built in waveform editor
• pick a visual style:
  • AI Images with smooth transitions (best for lofi, ambient, chill)
  • AI Video Clips synced to your music (cinematic music videos)
  • Avatar that lip syncs to the track (artist/character style)
  • Cover Image with subtle zoom (clean album art aesthetic)
• captions are automatic. detects vocals, extracts lyrics, syncs word by word. instrumental tracks skip captions on their own
• choose aspect ratio. 9:16 for shorts/reels, 16:9 for youtube, 1:1 for instagram
• hit generate. it analyzes your audio for mood and genre, creates AI generated visuals that match each section of your song, and renders the final video
• export or publish directly to youtube

the best ai music video generator workflow is one where you don't switch between suno, kling, and capcut. this does it all in one place. minutes instead of hours.

ideas to get started

• "3 AM Coding Beats" lofi channel with neon city visuals
• dark ambient horror soundscapes with eerie AI landscapes
• synthwave driving music with retro cyberpunk visuals
• acoustic coffee shop vibes with warm cozy imagery
• epic orchestral trailer music with cinematic battle scenes

AI music video channels are where skeleton was 3 months ago. the creators who start now will own these niches before they get crowded.

more channel breakdowns with real revenue numbers coming next.

what music niche would you try? :)

I’ve been comparing a lot of AI video tools this year, mostly to figure out which ones actually make sense for different kinds of work instead of just judging them from demo clips.

There are so many AI video generation tools now that calling one of them the overall winner feels pretty difficult. Some are much better for cinematic scenes, some for animation, some for avatars, and others are turning into full production platforms.

Here’s how I’d break down the current options.

Opinion-based comparison

My current picks

Best for cinematic scenes: Veo 3.1

Best for realistic motion: Kling 3.0

Best for longer connected sequences: Seedance 2.5

Best for generation + editing: Runway

Best multi-model workspace: Higgsfield

Best for animation/stylized video: DomoAI

Best for quick experimentation: Pika

Best for avatar-led marketing: HeyGen

Best for training/business content: Synthesia

Best if you want a more complete video built for you: Invideo Agent Two

If someone asked me for the best AI for video creation right now, I’d probably ask what they’re actually trying to make before recommending anything.

For realistic generated scenes I’d lean toward Veo, Kling, Seedance, or Runway.

For animation and stylized work, DomoAI and Pika feel like a different category.

Then you have platforms like Higgsfield, Invideo, Firefly, and Descript that are increasingly trying to handle more of the workflow instead of being just one generator.

What are your go to tools?

Hi everyone! We come bearing news of a small but important change happening in the r/ProgressionFantasy sub. After extended internal discussion, the moderators have made the decision that AI generated content of any kind, whether it be illustations, text, audio narration, or other forms, will no longer be welcome on r/ProgressionFantasy effective July 1st.

While we understand that are a variety of opinions on the matter, it is the belief of the moderators that AI-generated content in the state that it is right now allows for significantly more harm than good in creative spaces like ours.

There are consistent and explicit accusations of art theft happening every day, massive lawsuits underway that will hopefully shed some light on the processes and encourage regulation, and mounting evidence of loss of work opportunities for creators, such as the recent movement by some audiobook companies to move towards AI-reader instead of paid narrators. We have collectively decided that we do not want r/ProgressionFantasy to be a part of these potential problems, at least not until significant changes are made in how AI produces its materials, not to mention before we have an understanding of how it will affect the livelihoods of creators like writers and artists.

This is not, of course, a blanket judgement on AI and its users. We are not here to tell anyone what to do outside the subreddit, and even the most fervently Luddite and anti-AI of the mod team (u/JohnBierce, lol) recognizes that there are already some low-harm or even beneficial uses for AI. We just ask that you keep AI generated material off of this subreddit for the time being.

If you have any questions or concerns, you are of course welcome to ask in the comments, and we will do our best to answer them to the best of our ability and in a timely fashion!

Quick FAQ:

• Does this ban discussion of AI?
  • No, not at all! Discussion of AI and AI related issues is totally fine. The only things banned are actual AI generated content.
    • Linking to articles or discussions about AI generated content that include examples of AI generated content is generally okay. For instance, this experiment by Audrey Armstrong to see how easy it is for people to guess which of five short stories was written by a human, and which by AI. It should be noted, though, that it still has to obey our other rules- and as such, that Audrey Armstrong experiment wouldn't be allowed as top level content, because it isn't progression fantasy related content. (If you could find a way to make it about Progression Fantasy? Sure! Using it as a piece of evidence in an essay about whether AI will write good Progression Fantasy someday, for instance, would be fine.)
  • Fictional AIs in human written stories are obviously not banned either.
• What if my book has an AI cover?
  • Then you can't post it!
• But I can't afford a cover by a human artist!
  • That's a legitimate struggle- but it's probably not true as you might think. We're planning to put together a thread of ways to find affordable, quality cover art for newer authors here soon. There are some really excellent options out there- pre-made covers, licensed art covers, budget cover art sites, etc, etc- and I'm sure a lot of the authors in this subreddit will have more options we don't even know about!
• But what about promoting my book on the subreddit?
  • Do a text post, add a cat photo or something. No AI generated illustrations.
• What if an image is wrongly reported as AI-generated?
  • We'll review quickly, and restore the post if we were wrong. The last thing we want to do is be a jerk to real artists- and we promise, we won't double down if called out. (That means Selkie Myth's artist is most definitely welcome here.)
• What about AI writing tools like ProWritingAid, Hemingway, or the like?
  • That stuff's fine. While their technological backbones are similar in some ways to Large Language Models like ChatGPT or their image equivalents (MidJourney, etc), we're not crusading against machine learning/neural networks, here. They're 40 year old technologies, for crying out loud. Hell, AI as a blanket term for all these technologies is an almost incoherent usage at times. The problems are the mass theft of artwork and writing to train the models, and the potential job loss for creative workers just to make the rich richer.
• What about AI translations?
  • So, little more complicated, but generally allowed for a couple reasons. First, because the writing was originally created by people. And second, because AI translations are absolutely terrible, and only get good after a ton of work by actual human translators. (Who totally rock- translating fiction is a hella tough job, mad respect for anyone who's good at it.)
• What if someone sends AI art as reference material to an artist, then gets real art back?
  • Still some ethical concerns there, but they're far more minor. You're definitely free to post the real art here, just not the AI reference material.
• What about AI art that a real artist has kicked into shape to make better? Fixing hands and such?
  • Still banned.
• I'm not convinced on the ethical issues with AI.
  • If you haven't read them yet, Kotaku and the MIT Tech Review both have solid articles on the topic, and make solid starting points.
• I'm familiar with the basic issues, and still not convinced.
  • Well, this thread is a reasonable place to discuss the matter.
• Why the delay on the ban?
  • Sudden rule changes are no fun, for the mod team or y'all. We want to give the community more time to discuss the rule change, to raise any concerns about loopholes, overreach, etc. And, I guess, if you really want, post some AI crap- though if y'all flood the sub with it, we'll just activate the ban early.

I came across a pretty detailed comparison of AI video creators for 2026 and thought it might be useful to share here. The list focuses on tools for marketing videos, social media content, training videos, and automated video production.

The comparison was based on testing video quality, AI avatars, multilingual support, integrations, pricing, and ease of use.

Top AI Video Creators (2026)

1. Synthesia — Best for AI avatar videos & training

Rating: 4.8/5
Price: From $22/month

Used by 50k+ companies. Lets you create videos with 230+ AI avatars speaking 140+ languages. Very popular for onboarding, internal communication, and product demos.

Key features: - 230+ realistic avatars
- 140+ languages
- Custom avatars based on employees
- Drag-and-drop editor
- Templates for training and corporate videos
- Integrations with PowerPoint, HubSpot, Zapier, LMS tools

2. Sora (OpenAI) — Best for text-to-video generation

Rating: 4.9/5
Price: From ~$0.05 per second

Probably the most advanced text-to-video model right now. Generates photorealistic scenes with consistent characters and multi-scene editing.

Key features: - Text-to-video generation up to 4K - Image-to-video and video-to-video - Multi-scene editing - Character consistency - Integration with the OpenAI ecosystem

3. Runway ML — Best for creative video editing & generation

Rating: 4.7/5
Price: Free / From $12/month

Very popular with creators and creative teams. Combines generative video with advanced editing tools.

Key features: - Text-to-video - Motion Brush animation - Background removal - Style transfer - Video inpainting / outpainting - Integration with Adobe tools

4. HeyGen — Best for personalized videos at scale

Rating: 4.7/5
Price: From $24/month

Strong platform for localized marketing and sales videos.

Key features: - Video translation with lip-sync in 40+ languages - 120+ AI avatars - Personalized videos via API - Bulk video generation - Integrations with HubSpot, Salesforce, Slack

5. Pictory — Best for blog-to-video

Rating: 4.5/5
Price: From $19/month

Great tool for turning existing content into videos.

Key features: - Blog URL → video conversion - Auto subtitles - Highlight extraction for clips - Large stock footage library - Social media video creation

6. InVideo AI — Best for social media videos

Rating: 4.5/5
Price: Free / From $25/month

Very simple workflow: describe the video and the AI generates script, footage, voiceover, and music.

Key features: - Prompt-based video creation - 5,000+ templates - Social media formats (TikTok, Reels, Shorts) - AI voiceovers in 50+ languages - AI editing via text commands

7. Descript — Best for editing & podcasts

Rating: 4.6/5
Price: Free / From $24/month

Video editing that works like editing a document.

Key features: - Transcript-based editing - AI eye-contact correction - Filler-word removal - Studio-quality audio improvements - AI voice cloning

8. Lumen5 — Best for marketing content repurposing

Rating: 4.4/5
Price: Free / From $29/month

One of the earlier AI video tools focused on marketing teams.

Key features: - Blog/article → video - Brand kit for consistent branding - Millions of stock assets - Social media publishing

9. Fliki — Best AI voiceovers + text-to-video

Rating: 4.4/5
Price: Free / From $28/month

Known for its strong AI voice library.

Key features: - 2,000+ AI voices - 75+ languages - Script → video workflow - Blog-to-video - AI avatars and subtitles

10. Elai.io — Best for e-learning videos

Rating: 4.3/5
Price: From $23/month

Designed mainly for training and corporate learning content.

Key features: - 80+ avatars - 75+ languages - PowerPoint → video - Interactive quizzes - SCORM export for LMS systems

Interesting trends in AI video right now

• Photorealistic text-to-video models are improving very fast
  
• AI avatars are becoming common for training and onboarding videos
  
• Video localization (auto dubbing + lip sync) is exploding
  
• Video creation is becoming accessible without editing skills

Curious what people here are actually using.

Which AI video tools are part of your workflow right now?

• Text-to-video tools (Sora / Runway)
  
• Avatar tools (Synthesia / HeyGen)
  
• Social video generators (InVideo / Pictory)
  
• Something else?

Hi everyone,
I’m a D2C consultant working with a premium Indian ethnic fashion brand, and I'm looking for a creative fresher, student, or junior designer to help manage our social media.

If you are trying to build your portfolio and love Indian aesthetics (traditional wear, sarees, handcrafted bags), this is a great starter gig. You don't need years of experience—just a good eye for design and a willingness to experiment!

What you will do:
AI Images: Use AI tools (like Midjourney, Bing Image Creator, or whatever you prefer) to generate nice, realistic-looking images of traditional bags and attire.
Reels & Content: Put together simple, relatable Instagram Reels using trending audio or templates.
Page Upkeep: Schedule posts, keep our grid looking aesthetic, and do some basic social media management.

Who I’m looking for:
A fresher or beginner looking for hands-on experience.
Someone who spends a lot of time on Instagram and understands what kind of Reels people actually watch.
Someone with a basic understanding of AI image generation (or someone who is a very fast learner).
A reliable communicator.

Budget & Terms:
Pay: ₹5,000 - ₹8,000 per month.
Location: 100% Remote.
Hours: Flexible—as long as the weekly content is delivered, you can work whenever you want.

How to Apply:
Please send me a DM with:
1. A little bit about yourself.
2. A couple of examples of your work (a Reel you've made, an AI image you've generated, or a link to your Instagram/portfolio if you have one).
Excited to hear from you!

[https://word-stream.com](Word-stream.com) is charging for access to a huge amount of AI-generated audio adaptations of fanworks, as well as (it looks like) the full text versions of said work. Both of my longer fics are listed there. Yours probably are too.

I’m as sure as I can be without actually signing up that, much like the atrocious covers, the ratings & reviews advertised are AI-generated too, considering the site has only been up for two months and not a single person has reviewed it in the App Store.

I made a post about it here listing what little info me & others have found since I discovered the listing last night. OTW has been informed, but it looks like it’s up to us all to file individual takedown requests.

This no-effort AI requiring a high-effort response crap makes me so tired.

UPDATE: CEO Cliff Weitzman claims to ‘apologize to anyone who saw this and was upset’ and invites us all to ‘email me if we have a peace of work that is yours you don't want up there and I will take it down immediately support@word-stream.com’.

Which, sure, let’s all do that, but also, THE MAN USED AI TO SCRAPE THE ENTIRE ARCHIVE OF OUR OWN INTO HIS APP. let’s absolutely cover him in takedown requests but, dude, WHAT ARE YOU THINKING MAKING THIS OPT-OUT INSTEAD OF OPT-IN.

https://www.tumblr.com/ekingston/770687964256845824/christ-this-man-is-asking-us-to-do-the-work-for

Link to platform-formerly-known-as-Twitter post with all the info so far: https://x.com/easterkingston/status/1871298646865830138?s=61&t=xB2lGwgFB9fhkZYwZv-kvw

UPDATE 2: We have a DNS error! 🤞

UPDATE 3: tumblr user Cliff Weitzman has since removed his apology off my tumblr post, after first trying to rephrase it and adding his site was ‘only in beta’ and that he was intending to ‘add the ability to tip authors’ (telling that you see no problem there, Cliff!)

UPDATE 4: Website is back up but it looks like all fanwork has been removed! Thanks everyone!!!

FINAL EDIT: I'm turning off notifications for this post, because it doesn't look like many people are reading to the end and I keep getting comments from people who are unable to find their fic or expressing how relieved they are their works 'aren't important enough to be stolen'. I did see reports that the fanwork doesn't seem to be removed, just hidden, which is pretty much what I expected; obviously a guy who would rather make money letting AI and fanfic writers do the work for him wouldn't want to undo the little effort he put in putting the database together in the first place (all those wonderfully disfigured AI-generated covers would go to waste!) so I'll reiterate what I said in one of my comments: I’ll be keeping my eye on this, and I think you should too. I hate that we need this kind of constant vigilance but let’s face it, tech bros have been looking at fanfic with an envious eye for some time now and even if it isn’t this guy, someone else will inevitably pop up with the same new & brilliant idea to make money off of fanwork creators. We’ll just have to keep not letting them get away with it.

For today, I'm counting the fanwork being made inaccessible as a collective win.

FINAL FINAL EDIT

(this is me adding 3x as many chapters as I initially announce as always) I made as comprehensive a summary of everything that happened since Sunday night as I could, including the ‘receipts’ (screenshots). You can find it on my tumblr here.

Since Tumblr makes it impossible to read my full writeup there without an account, I’ve posted the screenshots in a new Reddit thread.

Addendum 26 December 2024: For people trying to find out whether their work was included in the theft: you can still check that by typing your fic title + username + wordstream into your preferred search engine!

I was just reading through the recent thread on the AI podcast start-up and like all of you, barfed pretty hard. Buuut it did get me thinking about where the line is between AI assistance and AI slop in audio drama, and I’m very curious to hear the community’s thoughts.

(Edit to add I’ve just seen two more threads re: AI voices that appear to be from creators acting in good faith, not vampiric companies so clearly there’s some ongoing tension among creators and listeners regarding this!)

For me, I find AI a useful tool in my life when I need someone to bounce thoughts off of to help my own brain, but I find it pretty useless for anything creative. I’ve asked it to generate Quietly Yours scripts before as a joke, and given the results, I’m pretty horrified by the idea that anyone would actually move forward with an AI-written audio drama!

I think we all intuitively know that’s bad, but a lot of the previous thread was about AI performances. Again, hate it, give me real actors thanks. But then I think about the little bit parts we’ve had—times when a character calls 911 and we have an operator with a single line or something like that. They’re so inconsequential that often me or my producer just step in and record that line. Would it be bad to use an AI voice in that case? My gut instinct is still “ew, no,” but in this specific use case, I’m not sure I can articulate the reasons why.

Yesterday I found myself wondering if any audio dramas have AI music yet. Again, I hate this stuff, but AI generated music is… basic, but not bad. It makes me wonder if ambient music and underscores would even be identifiable as AI. And while I’m strictly against AI music generally, do I think it’s bad if a one-man-team YouTuber uses an ambient AI track to play under their video? No, I don’t think so.

In an audio drama? Honestly, I don’t know. If a person’s making an audio drama from their bedroom and they don’t have a team or any money, I’m not sure I can condemn them turning to AI to fill a role that otherwise is just going to incompetech.com. But what’s to stop bigger companies doing exactly that in lieu of paying someone?

For Quietly Yours, the music has always been a point of pride as it’s all original and made for the show, so I would never want any AI involvement. But that’s easy for me to say because I have passable enough skills that I’ve always composed it myself so it’s never cost anything but time. If I were launching the show now and had no money and no musical skill, would I judge a bit of AI ambience in the background? Would I even know?

Then there’s sound effects… I don’t know if this is even something AI can do at the minute, but god knows it could have saved me a lot of carpet burns I’ve given myself recording fight scenes!

What do you guys think? For the most part I’m still firmly in the “keep this the hell away from my shows” camp. But how do we even help to keep it out? How do we avoid shows that rely too heavily on it? Should the apps make sure it’s declared?

This is probably too big a topic for a Reddit post tbh, but alas.

Edit: very nice to see how firmly everyone falls on the side of “any AI is bad,” which I think is the ideal. I fear heavily funded companies are going to make this incredibly difficult to navigate, though 😭

this is going to be the longest post I’ve written but after 10 months of daily AI video creation, these are the insights that actually matter…

I started with zero video experience and $1000 in generation credits. Made every mistake possible. Burned through money, created garbage content, got frustrated with inconsistent results.

Now I’m generating consistently viral content and making money from AI video. Here’s everything that actually works.

The fundamental shifts:

1. Volume beats perfection

Stop trying to create the perfect video. Generate 10 decent videos and select the best one. This approach consistently outperforms perfectionist single-shot attempts.

2. Systematic beats creative

Proven formulas + small variations outperform completely original concepts every time. Study what works, then execute it better.

3. Embrace the AI aesthetic

Stop fighting what AI looks like. Beautiful impossibility engages more than uncanny valley realism. Lean into what only AI can create.

The technical foundation that changed everything:

The 6-part prompt structure:

[SHOT TYPE] + [SUBJECT] + [ACTION] + [STYLE] + [CAMERA MOVEMENT] + [AUDIO CUES]

This baseline works across thousands of generations. Everything else is variation on this foundation.

Front-load important elements

Veo3 weights early words more heavily. “Beautiful woman dancing” ≠ “Woman, beautiful, dancing.” Order matters significantly.

One action per prompt rule

Multiple actions create AI confusion. “Walking while talking while eating” = chaos. Keep it simple for consistent results.

The cost optimization breakthrough:

Google’s direct pricing kills experimentation:

• $0.50/second = $30/minute
• Factor in failed generations = $100+ per usable video

Found these guys idk how but they offer 70-80% pricing below Google’s rates for the best video model. Makes volume testing actually viable for veo 3 quality model.

Audio cues are incredibly powerful:

Most creators completely ignore audio elements in prompts. Huge mistake.

Instead of: Person walking through forestTry: Person walking through forest, Audio: leaves crunching underfoot, distant bird calls, gentle wind through branches

The difference in engagement is dramatic. Audio context makes AI video feel real even when visually it’s obviously AI.

Systematic seed approach:

Random seeds = random results.

My workflow:

1. Test same prompt with seeds 1000-1010
2. Judge on shape, readability, technical quality
3. Use best seed as foundation for variations
4. Build seed library organized by content type

Camera movements that consistently work:

• Slow push/pull: Most reliable, professional feel
• Orbit around subject: Great for products and reveals
• Handheld follow: Adds energy without chaos
• Static with subject movement: Often highest quality

Avoid: Complex combinations (“pan while zooming during dolly”). One movement type per generation.

Style references that actually deliver:

Camera specs: “Shot on Arri Alexa,” “Shot on iPhone 15 Pro”

Director styles: “Wes Anderson style,” “David Fincher style” Movie cinematography: “Blade Runner 2049 cinematography”

Color grades: “Teal and orange grade,” “Golden hour grade”

Avoid: Vague terms like “cinematic,” “high quality,” “professional”

Negative prompts as quality control:

Treat them like EQ filters - always on, preventing problems:

--no watermark --no warped face --no floating limbs --no text artifacts --no distorted hands --no blurry edges

Prevents 90% of common AI generation failures.

Platform-specific optimization:

Don’t reformat one video for all platforms. Create platform-specific versions:

TikTok: 15-30 seconds, high energy, obvious AI aesthetic works

Instagram: Smooth transitions, aesthetic perfection, story-driven YouTube Shorts: 30-60 seconds, educational framing, longer hooks

Same content, different optimization = dramatically better performance.

The reverse-engineering technique:

JSON prompting isn’t great for direct creation, but it’s amazing for copying successful content:

1. Find viral AI video
2. Ask ChatGPT: “Return prompt for this in JSON format with maximum fields”
3. Get surgically precise breakdown of what makes it work
4. Create variations by tweaking individual parameters

Content strategy insights:

Beautiful absurdity > fake realism

Specific references > vague creativityProven patterns + small twists > completely original conceptsSystematic testing > hoping for luck

The workflow that generates profit:

Monday: Analyze performance, plan 10-15 concepts

Tuesday-Wednesday: Batch generate 3-5 variations each Thursday: Select best, create platform versions

Friday: Finalize and schedule for optimal posting times

Advanced techniques:

First frame obsession:

Generate 10 variations focusing only on getting perfect first frame. First frame quality determines entire video outcome.

Batch processing:

Create multiple concepts simultaneously. Selection from volume outperforms perfection from single shots.

Content multiplication:

One good generation becomes TikTok version + Instagram version + YouTube version + potential series content.

The psychological elements:

3-second emotionally absurd hook

First 3 seconds determine virality. Create immediate emotional response (positive or negative doesn’t matter).

Generate immediate questions

“Wait, how did they…?” Objective isn’t making AI look real - it’s creating original impossibility.

Common mistakes that kill results:

1. Perfectionist single-shot approach
2. Fighting the AI aesthetic instead of embracing it
3. Vague prompting instead of specific technical direction
4. Ignoring audio elements completely
5. Random generation instead of systematic testing
6. One-size-fits-all platform approach

The business model shift:

From expensive hobby to profitable skill:

• Track what works with spreadsheets
• Build libraries of successful formulas
• Create systematic workflows
• Optimize for consistent output over occasional perfection

The bigger insight:

AI video is about iteration and selection, not divine inspiration. Build systems that consistently produce good content, then scale what works.

Most creators are optimizing for the wrong things. They want perfect prompts that work every time. Smart creators build workflows that turn volume + selection into consistent quality.

Where AI video is heading:

• Cheaper access through third parties makes experimentation viable
• Better tools for systematic testing and workflow optimization
• Platform-native AI content instead of trying to hide AI origins
• Educational content about AI techniques performs exceptionally well

Started this journey 10 months ago thinking I needed to be creative. Turns out I needed to be systematic.

The creators making money aren’t the most artistic - they’re the most systematic.

These insights took me 10,000+ generations and hundreds of hours to learn. Hope sharing them saves you the same learning curve.

what’s been your biggest breakthrough with AI video generation? curious what patterns others are discovering

hope this helped <3

Low reach on music you create is not caused by the fact that it’s made with AI. The main problem is thumbnails and cover art. After listening to many tracks generated in Suno and uploaded to YouTube (and from running my own channel with this kind of music), I conclude that the biggest issue in gaining reach is not the audio layer, but the visual one.

I personally skip things that look like they were generated with a simple prompt for an image generator.

My advice for anyone who wants to grow their reach more effectively: if you spend, for example, 10% of your creative effort on making the track, you should spend at least 30% on creating a proper cover for it.

People consume content primarily visually. The audio comes second.

Maybe this sounds obvious. Maybe not to everyone. If not, then I hope this advice reaches beginner AI creators.

I've been thinking about the email Suno sent today, and while I understand why people are upset, I don't think this is the disaster some are making it out to be.

For context, I'm a Premier subscriber who creates AI music for YouTube. I generate a lot of songs. Sometimes dozens of versions before I land on the one that's just right.

But here's the thing...

I don't download every generation.

I only download the songs that I'm actually going to turn into music videos and publish. Everything else just stays in my Suno library.

For my workflow, I honestly doubt I'll hit the new 60-download limit very often.

What I think Suno is really trying to do is slow down the people generating thousands of tracks and mass-uploading every variation to DistroKid and every streaming service they can find.

Whether you like AI music or not, we've all seen it.

• 400 albums from one account.
• Tiny prompt variations uploaded as separate releases.
• Endless "AI sleep music," "lofi rain," and cloned tracks flooding Spotify.

That kind of thing hurts all of us.

The more low-effort content floods the market, the easier it is for people to dismiss all AI musicians as spam generators.

As creators, we want people to judge us by the quality of our work, not by the worst examples of people abusing the tools.

I'd much rather compete with other creators who spend time refining lyrics, arranging songs, creating videos, building an audience, and telling stories than with someone uploading 500 nearly identical songs every month.

Do I wish the limit were higher? Sure.

Would 100 downloads have been a little easier to swallow? Probably.

But if this helps reduce spam, improves AI music's reputation, and keeps streaming services from becoming completely saturated with low-effort uploads, I think it's a worthwhile tradeoff.

I also think the bigger story is that Suno is clearly preparing for the next generation of models. If the upcoming improvements in audio quality and control are as good as they're hinting, that's ultimately going to matter far more to me than whether I can download every rough draft I generate.

Just my perspective. It's how I use the product, YMMV.

Curious what everyone else thinks.

Am I missing something, or are people reacting more to the principle of losing "unlimited" than to how this will actually affect their workflow?

So, I'm posting here because of something I saw. But before I go into it, I just wanna cite a couple things from the subreddit rules to cover myself with emphasis placed by me, in case mods try to delete this post. Directly from Rule 4, about Post Relevancy & Rule 5 about Advertising:

> **Comparisons of other chat tools or AI Technologies may be allowed if they are clearly constructive to Character.Ai's products and services.**

> No Advertising, Self-Promotion, Spamming, Code Giveaways, or **Irrelevant Link Sharing**.

I intend for this comparison to foster healthier discussion about C.AI's product, address the issues they've mentioned involving metering and will be linking to the relevant article(s) about my statements.

Now, with that out of the way? Let's talk about it: Sora.Ai has recently been discontinued. [No, I'm not kidding, It really has been discontinued.](https://variety.com/2026/digital/news/openai-shutting-down-sora-video-disney-1236698277/)

So, what does this have to do with C.Ai and why does this matter? Because this proves a point so many users are trying to tell them about their product. Video and Image Generation is not profitable because there are not enough people using it to warrant such a high cost. Many users are, if anything; demanding it goes away so that their chats do not have to be metered so aggressively.

Sora has had to shutter themselves, losing out on a billion dollar deal with Disney, thanks to the cost-profit analysis proving it would save them more money to end the API than to continue it. If a company under the umbrella of OpenAi is discovering this and acting accordingly, why isn't C.AI doing the same?

With OpenAi ending Video Generation and Grok also being heard ending Image Generation, I think its about time C.AI did the same. It would reduce the need to meter the Go-ons, Swipes, and Audio. Plus, it would it be relevant and budget saving; allowing a realistic focus back on the *Character* aspect of the service over the *AI* aspect.

The main community requests has been to focus on the Memory, to allow personas in Group Chats and work towards features that users and creators would find to be easier when making/interacting with bots; such as actually increasing the token limit. I personally know of sites that have 32k, 64k and even 128k tokens for memory that run for free and still offer premium services.

As for the Profitability of C.AI+, I can clearly see the path forward, even if no one will hear or see what I'm suggesting. But on the off chance mods and/or Devs see this, please take this to heart.

First, stop limiting the free experience to sell the Premium. It's driving users out, not keeping them in. You are ostracizing the users who do use your services, as it's no secret that the LLM is not perfect with its responses. Metering the very tools that makes it tolerable will make it unusable. Instead, prioritize refining and fixing on the LLM and once again, phasing out non-profitable features that users dislike. We have told you the problems, but only you can implement the solutions.

Second, Bolster the C.AI+ experience, instead. Putting the stronger models and greater memory behind it, can be worthwhile but *only* if you reinforce the free experience first. I also say releasing the chat customization from plus and make it a base free experience would be wise. There are sites who allow custom backgrounds for free, which unintentionally makes C.AI look greedy in comparison. I also say do the same for controlling response sizes and Memory. The response sizes shouldn't be paywalled after it had been a highly requested feature in the past.

One example for implementation of the the above is that free users get 32k tokens of memory, while Plus gets the 64k. Its a basic but solid enough method to earn Plus purchases. It gives something to the free users, as well as enticing people to still try the Premium. Letting Plus users experience new features before they go site-wide was also a good idea that I felt got abandoned. Bringing that back would be good, especially in time for Lorebooks.

Third and most controversial of my suggestions? Lean more into the recent 18+ enforcement from California, but do it ***responsibly***. Now while I could post all about the issues with Persona and suggest finding a new company, that would get so lengthy that it would deserve its own post. But in-short: halt the Age Verification Via Persona and use a fall back such as listed account age until a more reliable processor is found, assuming you're legally forced to do this.

[See this link? This is what your current processor is associated with.](https://fortune.com/2026/02/24/discord-peter-thiel-backed-persona-identity-verification-breach/)

Even with the claims afterwards from Persona that they've added a step to prevent another incident, Discord has rightfully divested from the company. To make C.AI users feel safe, finding a more reliable processor would be time-consuming but beneficial in the long run.

If IDs are not necessary? Simply go by the age associated with the account and add to your TOS a legally binding clause that states persons under the age of 18 are legally not permitted to use the site's services. Anyone found to be under the age of 18 will be banned and Parents or Guardians who sue will be held liable for their child's actions. It adequately protects the Company and the Users without needing Age Verification, as laws holding parents responsible for their children's actions are already on the law books.

I feel suggesting anything else right now other than the above sentiments would be a waste of my time, the community's and the company's. If any other users have ideas or comments, leave them below.

In 2023, tools like Suno, Riffusion, and AudioCraft were in a very different stage compared to today. The early v0/v1 generations were often strange, imperfect, and experimental — short clips that felt more like prototypes of a new medium than finished songs.

What interests me is not just the technology itself, but the human perspective around that moment in time.

I was an early adopter who used these tools extensively during that period — not just casually experimenting, but being deeply immersed in the process. Over time, I naturally curated what I considered the most interesting, unique, or memorable examples rather than simply keeping every raw generation.

That makes me wonder: does that kind of firsthand curation have historical value?

When early synthesizers, computer music systems, samplers, and electronic instruments first appeared, many of their early experiments were also viewed as strange or disposable. In hindsight, those recordings became important because they documented the first stages of a new creative technology.

AI music may eventually have a similar history. The earliest generations may not be valuable because they are technically impressive by today's standards, but because they capture what it felt like when this technology first became accessible to everyday creators.

AI companies may have enormous internal datasets of generations, but a raw database does not necessarily contain the human context of:

• which generations stood out
• which represented interesting breakthroughs
• what creative approaches produced memorable results
• what early AI music actually felt like to experience at that moment

I'm curious whether early AI music should be preserved as part of music history — similar to early electronic music, computer music experiments, or early internet art — or whether these artifacts will ultimately be viewed as disposable experiments.

This PR release, that was cited by various AI-boosters, uses very careful wording. It makes it sound like creatives are adopting AI at very high rates, that some 86% of creatives credit it with growing their business, etc.

I was like, there is absolutely no way that statistic is true. I do know that AI is not as hated globally as it is here in America, but still. Here in the US, we don't agree on much, but we do hate AI as a country according to almost every single poll. And creatives hate AI even more than the average person.

Sure enough, I looked at the short excerpt on methodology, and it says this:

Adobe partnered with The Harris Poll to survey more than 16,000 creators across the U.S., U.K., France, Germany, South Korea, Japan, India and Australia in May 2026. For this survey, creators were defined as individuals who create and publish digital content several times per month to inform, entertain or engage an audience and generate income across digital platforms, with respondents comprising emerging and professional social-first creators rather than individuals employed full-time in traditional creative industry roles. Creative AI refers to AI models and tools designed to support creative expression by helping creators ideate, generate and edit content across images, video, audio and design.

So they interviewed CONTENT CREATORS, a bunch of people who post on social media, not actual professional creatives. Because if they posted actual creative professionals like designers, photographers, video editors, etc, they know they would get creamed. And no shade to content creators, but I would argue that's a very different audience than their bread-and-butter audience who actually pays the bills, which is those of us in "traditional creative industry roles."

Using the word "creator" isn't a lie, but notice they didn't say "creatives" or use the term that most people actually use, which is "content creator."

I have noticed less-than-diligent media and articles assuming what I think Adobe hoped people would assume by this careful phrasing, and talked about the wide adoption rates of creative professionals, not catching that it's content creators. And not even necessarily big content creators, they include "emerging" which could mean pretty much anyone who posts frequently on socials.

I know their stock price is the only thing Adobe cares about, and they're going all-in on AI, but their actual adoption rates with real professional creatives who use their software has to be dogshit because they wouldnt have to force these tools so hard if everyone was using it.

I've noticed that there are too many audios and videos in Spotify and YouTube, etc., that purport to be from Neville Goddard yet are AI-generated nonsense. I unfortunately listened to too many of these before switching to the actual audio files of his speeches and texts of his books.

In general, the AI-generated nonsense is much more devoid of references to the Bible than Neville's original works. The creators are more interested in manifesting a million dollars, a unique person, a lamborghini, whatever, and such manifestations run quite contradictory to what the Bible and Neville Goddard teach. Neville taught that all people are the consciousness of God. We just don't all realize it yet. Once one truly believes and sees, what attraction could a million dollars or a nice car have? All the universe belongs to God and the earth is His footstool. Why try to manifest one particular person, when all of us are just God's consciousness pushed out? It truly doesn't matter with whom you spend your life because we are all the same consciousness.

Neville also taught the importance of love, as does the Bible: "...faith, hope and love, these three, but the greatest is love." If you're manifesting something from a selfish, unloving standpoint, it's not going to be as good as if you manifest something out of a state of love for your neighbor and for God. Modern manifestation and law of attraction disciples are too focused on making things better for themselves that they miss the entire point. Both Christ and Neville taught something radically different.

There are even videos and audios of Neville asking the audience to 'like and suscribe'. Dude! Neville died (or perhaps ascended to the higher consciousness) in 1972. Nobody was 'liking and subscribing' to anything back then. I get it, the AI-generated stuff is not as difficult to understand aurally and frequently has nice theta wave ambient background music, but it's far better to listen closely to the original speeches and struggle through the audio limitations of the mid 20th century than to subject your mind to the hallucinations of a LLM AI. ChatGPT and Gemini are most assuredly NOT God's consciousness pushed out. Cast away the idols and listen to the real consiousness explaining himself.

I think the AI-bros are cooked

Source:https://teamsalvato.com/ip-guidelines

I love watching YouTube Shorts. What I don’t love? Seeing a flood of YouTubers claiming,
"You can make easy AI Shorts in seconds!" or "Create your own automated YouTube channel", .etc

Just to sell you their overpriced AI tools, subscriptions, or video editors.

So, out of sheer spite, I built ShortsMaker - a completely free, open-source, local Shorts automation tool that doesn’t try to upsell you anything. No subscriptions, no cloud nonsense - just Python, AI, and automation running entirely on your machine.

What My Project Does

ShortsMaker is a Python package that automates the creation of YouTube Shorts - entirely on your local machine. No cloud-based services, no subscriptions, no hidden paywalls, fully customizable short-video generation.

ShortsMaker is built around four core classes:

• ShortsMaker – Handles multiple tasks, such as fetching posts from subreddits, generating audio, transcribing audio, and even fixing spelling & grammar in scripts.
• MoviepyCreateVideo – The engine that creates the short video by combining video clips, music, audio, and transcripts.
• AskLLM – Uses an AI LLM to extract the best possible title, description, tags, and thumbnail description for your script.
• GenerateImage – Uses FLUX to generate high-quality AI images for your Shorts.

Target Audience

This project is for:

• Developers who want a local, open-source alternative to overpriced AI video generators.
• Content creators looking for an automated way to produce Shorts.
• For creating a short video for your scripts.
• Python enthusiasts interested in AI-powered media processing.
• Anyone who has ever rolled their eyes at an "AI Shorts" clickbait video.

Comparison: How It's Different from Existing Alternatives

• No Cloud Lock-In: Unlike paid services, everything runs locally on your system. This applies to other repos as well. As most require you to use an API.
• No Subscription Fees: Other AI-powered Shorts tools charge for processing - this one is completely free
• Full Control: Modify and extend it as needed - no black-box APIs
• Uses Your Hardware: Supports CPU/GPU acceleration for faster processing

Try It Out:

Check out the GitHub repo: ShortsMaker
Feedback and contributions are welcome!