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Wan 2.2. Testing Composition, Camera Movement, Cohesion and Consistency. #AI #Wan22 #GayAI #Homoerotic #Erotica #BarnLove #1930s #Interracial #Queer

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Homopolitan_AI

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18,627 görüntüleme • 11 ay önce •via X (Twitter)

NSFW#AI#Wan22#GayAI#Homoerotic#Erotica#BarnLove#1930s#Interracial#Queer
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This is crazy! With LTX 2 in ComfyUI i was able to keep my original camera movement + it did a full lip-sync on the singing haha. All running locally. I made the original video back in 2021 when Metahumans first came out and we were working in Unreal Engine 4. This music video was some kind of result from my first experiments with Livelink and Control Rig at that time. Now the new AI improved version is not perfect and i didn’t train any lora for character consistency yet. I also didn’t have the original video file anymore so I downloaded the Youtube version as input haha. However the camera result + facial expressions are really promising! I wonder how it would handle more complex and fast shots though.

Jerome | InsaneUnreal

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is genspark ($50M ARR in 6mo) underrated next to chatgpt, claude, gemini, and manus? i spent 34 minutes testing its “super ai agent” and how it actually works. 1/ you write one prompt and it runs through gpt-5, claude sonnet, gemini 2.5, and mistral large then gives you the best response. 2/ you upload one image and it runs it through nano banana, flux, and bytedance then returns the most realistic version. 3/ you generate a video and genspark routes it through wan 2.2, pika labs, gen-2, and runway ml combining the best clips and motion. 4/ you paste an idea for slides and it uses gpt-5, claude 3 opus, gemini advanced, and genspark’s slide model to create a full deck with visuals and market data. 5/ AI slides and building a fundraising deck with a few prompts (wasn't perfect, but it was a good start for my personal workflow) what's cool about genspark is it's inexpensive and does it all. kinda like the costco of LLMs. TLDR; you hand it one prompt + input, and genspark routes sub-tasks across models and tools behind the scenes. it’s orchestration over one ideal model. perspective shift. instead of jumping from model to model, llm to llm, genspark shows what the next generation of ai will look like orchestration over obsession. and for the rest of us using it, the only thing that matters is getting the best outcome. what other AI app should I review next? i'll keep doing it if it's helpful for folks.
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is genspark ($50M ARR in 6mo) underrated next to chatgpt, claude, gemini, and manus? i spent 34 minutes testing its “super ai agent” and how it actually works. 1/ you write one prompt and it runs through gpt-5, claude sonnet, gemini 2.5, and mistral large then gives you the best response. 2/ you upload one image and it runs it through nano banana, flux, and bytedance then returns the most realistic version. 3/ you generate a video and genspark routes it through wan 2.2, pika labs, gen-2, and runway ml combining the best clips and motion. 4/ you paste an idea for slides and it uses gpt-5, claude 3 opus, gemini advanced, and genspark’s slide model to create a full deck with visuals and market data. 5/ AI slides and building a fundraising deck with a few prompts (wasn't perfect, but it was a good start for my personal workflow) what's cool about genspark is it's inexpensive and does it all. kinda like the costco of LLMs. TLDR; you hand it one prompt + input, and genspark routes sub-tasks across models and tools behind the scenes. it’s orchestration over one ideal model. perspective shift. instead of jumping from model to model, llm to llm, genspark shows what the next generation of ai will look like orchestration over obsession. and for the rest of us using it, the only thing that matters is getting the best outcome. what other AI app should I review next? i'll keep doing it if it's helpful for folks.

GREG ISENBERG

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This custom GPT writes Sora 2 prompts that actually work 🤯 Most people prompting Sora 2 are getting garbage output. The problem isn't Sora 2. It's your prompts. Here's what I figured out after generating 100+ UGC videos: Sora 2 needs hyper-specific instructions—shot composition, timing, camera movement, lighting, scene details. Most people prompt like this: "Make a UGC video of someone using my skincare product" And then wonder why the output looks like AI slop. What actually gets results: "9:16 vertical handheld shot, soft natural window lighting, female creator mid-20s in bathroom mirror holding [brand] serum at eye level, genuine testimonial energy, 0-3s hook showing application and subtle reaction, shallow depth of field, clean warm color grade" That's the gap between unusable content and scroll-stopping UGC. So I built a custom GPT that writes these prompts for you: → Tell it your product (supplement, skincare, SaaS, whatever) → It generates a detailed timeline-based Sora 2 prompt → Shot composition, camera angles, timing breakdowns—all included → Optimized for 9:16 TikTok/Reels format → Copy and paste directly into Sora 2 No guessing. No prompt engineering rabbit holes. No "why does this look terrible" frustration. Professional UGC prompts in 10 seconds. Want free access to the Sora 2 Prompt Generator GPT? > Comment "SORA" > Like this post And I'll send it over (must be following so I can DM)

Mike Futia

13,262 görüntüleme • 6 ay önce

We're giving away the prompt we used to make this AI UGC video. Getting Sora 2 to output hyper-realistic, consistent footage isn't just about the tool. The prompt has to be structured in a very specific way... character description, cinematography, camera motion, lighting, dialogue, audio, authenticity keywords. Get it wrong and the footage looks off. Get it right and everything downstream becomes easier. We’re sharing the exact Claude template we use as step one of our AI UGC workflow. It takes a basic brief (who the subject is, where they are, what they're saying, what device it should look like, their accent) and structures all of that into the exact format Sora 2 needs automatically. It sits at the start of a full workflow that runs through Sora 2 for the hook, ElevenLabs for voice cloning, ChatGPT for subject consistency, Nano Banana for B-roll generation and Kling for animation. The whole thing is broken down in the latest D2C Diaries episode. But this prompt is the foundation. Without it, the rest of the workflow is harder than it needs to be. If you're building in this space or testing AI UGC for your brand, this is worth having. Retweet this post and comment PROMPT and I’ll send it over.
0:41

We're giving away the prompt we used to make this AI UGC video. Getting Sora 2 to output hyper-realistic, consistent footage isn't just about the tool. The prompt has to be structured in a very specific way... character description, cinematography, camera motion, lighting, dialogue, audio, authenticity keywords. Get it wrong and the footage looks off. Get it right and everything downstream becomes easier. We’re sharing the exact Claude template we use as step one of our AI UGC workflow. It takes a basic brief (who the subject is, where they are, what they're saying, what device it should look like, their accent) and structures all of that into the exact format Sora 2 needs automatically. It sits at the start of a full workflow that runs through Sora 2 for the hook, ElevenLabs for voice cloning, ChatGPT for subject consistency, Nano Banana for B-roll generation and Kling for animation. The whole thing is broken down in the latest D2C Diaries episode. But this prompt is the foundation. Without it, the rest of the workflow is harder than it needs to be. If you're building in this space or testing AI UGC for your brand, this is worth having. Retweet this post and comment PROMPT and I’ll send it over.

Joe Marston | Ecom Growth

17,422 görüntüleme • 4 ay önce

This is the best scene in Hell Grind, an entirely AI-made movie, the flashback. Watch it and read this analysis on where we are with AI movies today: time, cost, quality. Overall: Phenomenal technical demo by Higgsfield. Mediocre movie. Good graphics, hints of emotion, but superhero movie level quality in certain scenes at best. Too many cuts. That said, 660x fewer man hours, 50x faster and 36x cheaper than the median US film. Time: The 95 min film took 15 people 14 days. The median US theatrical production takes ~200 people ~2yrs. That’s a 660x improvement in man-hours and 50x in calendar time. Economics: It took $500k, 80% of which was compute. The final footage was cut from ~100hrs of footage generated from text to video / image to video models like Bytedance’s Seedance: a 64:1 “curation” ratio. The median US movie takes ~$18M, with even indie films costing $1-5M. Thats 36x cheaper than median. Quality: Average watch *at best*. Way too many cuts between shots, several characters change accents and have “AI” synthetic voices and characters feel like it’s AI too. Movement, editing and blocking feel artificial too. On the plus side, we’ve more or less solved character consistency, camera angles and realism. The reason the movie wasn’t amazing was more about poor directorial choices than innately unusable video models. Hard to put a number on it but maybe we’re at ~90% on quality that is technically achievable. If Scorsese made an AI movie, I reckon it would be quite good. I know the visceral reaction to anything AI is real and well-studied. But I think it’s folly to fight the inevitability of AI film. It’s too cheap and quick to ignore and almost there on quality. Creators with distribution *will* make AI films and shows and just put them on YouTube. This is the worst quality, slowest and most expensive it will ever be. In the end, good content beats “real” content.
2:07

This is the best scene in Hell Grind, an entirely AI-made movie, the flashback. Watch it and read this analysis on where we are with AI movies today: time, cost, quality. Overall: Phenomenal technical demo by Higgsfield. Mediocre movie. Good graphics, hints of emotion, but superhero movie level quality in certain scenes at best. Too many cuts. That said, 660x fewer man hours, 50x faster and 36x cheaper than the median US film. Time: The 95 min film took 15 people 14 days. The median US theatrical production takes ~200 people ~2yrs. That’s a 660x improvement in man-hours and 50x in calendar time. Economics: It took $500k, 80% of which was compute. The final footage was cut from ~100hrs of footage generated from text to video / image to video models like Bytedance’s Seedance: a 64:1 “curation” ratio. The median US movie takes ~$18M, with even indie films costing $1-5M. Thats 36x cheaper than median. Quality: Average watch *at best*. Way too many cuts between shots, several characters change accents and have “AI” synthetic voices and characters feel like it’s AI too. Movement, editing and blocking feel artificial too. On the plus side, we’ve more or less solved character consistency, camera angles and realism. The reason the movie wasn’t amazing was more about poor directorial choices than innately unusable video models. Hard to put a number on it but maybe we’re at ~90% on quality that is technically achievable. If Scorsese made an AI movie, I reckon it would be quite good. I know the visceral reaction to anything AI is real and well-studied. But I think it’s folly to fight the inevitability of AI film. It’s too cheap and quick to ignore and almost there on quality. Creators with distribution *will* make AI films and shows and just put them on YouTube. This is the worst quality, slowest and most expensive it will ever be. In the end, good content beats “real” content.

Deedy

34,584 görüntüleme • 1 ay önce