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Dreamina's masterfully built next-gen AI video model serves to essentially change the game. Stepping dynamically from "clip stitching" to "long narrative × multi-asset × powerful editing" — let's review what's actually new: Seedance 2.5 is completely live worldwide on Dreamina — today — while a broader US rollout begins...

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Jennifer Thompson

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12,459 просмотров • 1 день назад •via X (Twitter)

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🎬 Global Launch: Dreamina Seedance 2.5 is now live! Our most cinematic video model yet has arrived, and Dreamina AI is your official platform. Step into a new era of precision control and long-take storytelling: · Native 30s videos in a single generation · A new interactive editing experience: reshape any part of the frame · Long video mode: up to 3 minutes, with consistency that holds throughout · Dreamina AI plugins for Maya and Blender, built for film-grade production pipelines · Up to 50 multimodal references, now with support for 3D white models and green-screen footage — bringing AI generation into real production workflows · More true-to-life lighting and shadows, richer motion detail, and precise timestamp control down to 1 second Dreamina Seedance 2.5 is more than a model. It's an upgrade to what you can create. Now open to Dreamina AI subscribers. 🕐 Available now across Southeast Asia, the Middle East, Africa, Europe, and South America. More countries and regions coming soon. ✨ Watch Aura Design in action with the new model ⏳ Limited time: save up to 32% on credits per generation 🎁 Repost and comment – first 300 users get 1,000 credits in 12 hours #DreaminaSeedance25 #DreaminaAI
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🎬 Global Launch: Dreamina Seedance 2.5 is now live! Our most cinematic video model yet has arrived, and Dreamina AI is your official platform. Step into a new era of precision control and long-take storytelling: · Native 30s videos in a single generation · A new interactive editing experience: reshape any part of the frame · Long video mode: up to 3 minutes, with consistency that holds throughout · Dreamina AI plugins for Maya and Blender, built for film-grade production pipelines · Up to 50 multimodal references, now with support for 3D white models and green-screen footage — bringing AI generation into real production workflows · More true-to-life lighting and shadows, richer motion detail, and precise timestamp control down to 1 second Dreamina Seedance 2.5 is more than a model. It's an upgrade to what you can create. Now open to Dreamina AI subscribers. 🕐 Available now across Southeast Asia, the Middle East, Africa, Europe, and South America. More countries and regions coming soon. ✨ Watch Aura Design in action with the new model ⏳ Limited time: save up to 32% on credits per generation 🎁 Repost and comment – first 300 users get 1,000 credits in 12 hours #DreaminaSeedance25 #DreaminaAI

Dreamina AI

495,250 просмотров • 2 дней назад

Bytedance is dropping the best video gen model in the world in early July: Seedance 2.5! The video below (audio on) is the launch video from their Volcano Engine conference this week. It cements China’s absolute dominance in video. — 2x’d generation length of all previous models to 30s, with audio + 4k video — >5x’d reference images / audio / video to 50 — Allows localized editing (specific characters, closing, detail), will come with copyright filter Seedance 2 is already the #1 video model and does a whopping $2B in ARR, in a mere 4.5mos! At the current pricing of $2.5/15s, that implies >3.3M hours of video (!) have been generated. That’s 3x every feature film ever made and dozens of Netflixes. Only 3 US AI startups make more revenue. We are 2x’ing realistic video gen length every 6mos. — May 2025: Veo 3 does audio + video for the first time, 15s — Jan 2026: Kling 3 does 15s — Feb 2026: Seedance 2 does 15s, big quality bump — July 2026: 2.5 will do 30s In 18mos, entire music videos will be oneshotted by AI. China continues to extend its lead on video models vs America.
2:12

Bytedance is dropping the best video gen model in the world in early July: Seedance 2.5! The video below (audio on) is the launch video from their Volcano Engine conference this week. It cements China’s absolute dominance in video. — 2x’d generation length of all previous models to 30s, with audio + 4k video — >5x’d reference images / audio / video to 50 — Allows localized editing (specific characters, closing, detail), will come with copyright filter Seedance 2 is already the #1 video model and does a whopping $2B in ARR, in a mere 4.5mos! At the current pricing of $2.5/15s, that implies >3.3M hours of video (!) have been generated. That’s 3x every feature film ever made and dozens of Netflixes. Only 3 US AI startups make more revenue. We are 2x’ing realistic video gen length every 6mos. — May 2025: Veo 3 does audio + video for the first time, 15s — Jan 2026: Kling 3 does 15s — Feb 2026: Seedance 2 does 15s, big quality bump — July 2026: 2.5 will do 30s In 18mos, entire music videos will be oneshotted by AI. China continues to extend its lead on video models vs America.

Deedy

69,653 просмотров • 1 месяц назад

CapCut is bringing Dreamina Seedance 2.5 to creators, taking video creation to the next level. Now available across Asia, the Middle East, Africa, Europe, and South America. ✨ Create from scratch Turn your ideas into videos with CapCut, giving you unprecedented creative controls with 30 second-long generation, interactive editing, and up to 50 reference materials. Available in: • App: AI Lab, AI Generator, AI Video • Web: Video Studio • Desktop: AI Video 🎬 Transform your videos Reimagine your videos with AI-powered generation through infinite scene extension, frame-precise editing, and visual transformation. Available in: • App and Desktop: AI Extend, AI Edit, Edit Pilot Create more for less. Get up to 60% off CapCut subscriptions for a limited time. RT+comment in 5hr to get 500 free credits in your DM.
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CapCut is bringing Dreamina Seedance 2.5 to creators, taking video creation to the next level. Now available across Asia, the Middle East, Africa, Europe, and South America. ✨ Create from scratch Turn your ideas into videos with CapCut, giving you unprecedented creative controls with 30 second-long generation, interactive editing, and up to 50 reference materials. Available in: • App: AI Lab, AI Generator, AI Video • Web: Video Studio • Desktop: AI Video 🎬 Transform your videos Reimagine your videos with AI-powered generation through infinite scene extension, frame-precise editing, and visual transformation. Available in: • App and Desktop: AI Extend, AI Edit, Edit Pilot Create more for less. Get up to 60% off CapCut subscriptions for a limited time. RT+comment in 5hr to get 500 free credits in your DM.

CapCut

463,396 просмотров • 2 дней назад

Seedance 2.0 is about to change the entire internet. A “Chinese Sora 2”, but far better — and with almost no censorship. That’s why Chinese models are ahead. I’m not just talking. I’m proving it. This is a compilation of the best videos made with Seedance 2.0. It does everything: anime, gameplay, 3D animation, fight choreography, cinematic scenes — all with surreal mastery. Don’t take my word for it. Watch the full video. I guarantee you’ll be impressed. Now the question is: Between Hailuo 3.0, Veo 4, and PixVerse 6, which one actually has a chance to surpass Seedance 2.0 — which, based on what it already delivers, is the best in the world? And let’s be clear: Design is about to become obsolete. This isn’t an opinion. It’s a fact. The most challenging task humans have ever faced — video production — has truly been conquered by AI. Seedance 2.0 is about to change everything. Production models, business structures, and industry formats will be completely transformed. Leave your thoughts 👇
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Seedance 2.0 is about to change the entire internet. A “Chinese Sora 2”, but far better — and with almost no censorship. That’s why Chinese models are ahead. I’m not just talking. I’m proving it. This is a compilation of the best videos made with Seedance 2.0. It does everything: anime, gameplay, 3D animation, fight choreography, cinematic scenes — all with surreal mastery. Don’t take my word for it. Watch the full video. I guarantee you’ll be impressed. Now the question is: Between Hailuo 3.0, Veo 4, and PixVerse 6, which one actually has a chance to surpass Seedance 2.0 — which, based on what it already delivers, is the best in the world? And let’s be clear: Design is about to become obsolete. This isn’t an opinion. It’s a fact. The most challenging task humans have ever faced — video production — has truly been conquered by AI. Seedance 2.0 is about to change everything. Production models, business structures, and industry formats will be completely transformed. Leave your thoughts 👇

nachos2d

69,121 просмотров • 5 месяцев назад

The wait is finally over! A lot of you have been asking what platform I’ve been using to create those amazing Seedance 2.0 videos I’ve been sharing lately. Here’s the answer: TopviewAI And it’s not just about getting access to the best AI video model out there. Seedance 2.0 is now live on Topview... with 365 days of unlimited use. That alone is a big shift. Because while most AI video tools still keep you stuck in 5–15s clips and fragmented workflows… This is built for actual filmmaking. With Topview Agent V2 you can: – Create long-form video with no duration limits – Generate multi-scene sequences in a single workflow – Edit everything on a unified timeline – Go from idea➡️storyboard➡️final video – Generate videos fast (really fast) – Use Image to Video, Text to Video, and Omnireference No patchwork. No jumping between tools. This is what happens when AI video stops being a toy. Smart automation + pinpoint control. In the next post of the thread, I’ll give you more information 👇
0:45

The wait is finally over! A lot of you have been asking what platform I’ve been using to create those amazing Seedance 2.0 videos I’ve been sharing lately. Here’s the answer: TopviewAI And it’s not just about getting access to the best AI video model out there. Seedance 2.0 is now live on Topview... with 365 days of unlimited use. That alone is a big shift. Because while most AI video tools still keep you stuck in 5–15s clips and fragmented workflows… This is built for actual filmmaking. With Topview Agent V2 you can: – Create long-form video with no duration limits – Generate multi-scene sequences in a single workflow – Edit everything on a unified timeline – Go from idea➡️storyboard➡️final video – Generate videos fast (really fast) – Use Image to Video, Text to Video, and Omnireference No patchwork. No jumping between tools. This is what happens when AI video stops being a toy. Smart automation + pinpoint control. In the next post of the thread, I’ll give you more information 👇

OscarAI

22,810 просмотров • 4 месяцев назад

Dreamina AI is an official platform for Dreamina Seedance 2.5. It brings cinema-grade, professional-grade, and high-aesthetic visuals to life with unmatched performance. Made with Dreamina AI Seedance 2.5 via its official platform: Dreamina AI prompt 30-second ultra-realistic K-pop MV featuring two young East Asian women with flawless synchronization, cinematic lighting, glossy skin, realistic hair and fabric physics, natural body motion, and 4K live-action quality. Vibrant hot pink, electric blue, and silver color palette. 0–2s: Wide shot in a bright circular pink studio with reflective floor. Pink-haired woman (left) and black-haired woman (right) perform energetic opening pose and synchronized dance. 2–4s: Medium close-up of the black-haired woman on a blue spotlight stage, confidently pointing at the camera. 4–6s: Pink-haired woman dances before shimmering blue-silver tinsel curtains, dramatic hair flip and fluid arm movements. 6–8s: Back to the pink studio. Both perform synchronized choreography with sharp arm waves, hip sways, and strong formations. 8–10s: Extreme close-up of both faces against a blue background, glossy makeup, subtle smiles, and direct eye contact. 10–14s: Solo shots at the tinsel backdrop. Pink-haired woman mouths lyrics and gestures confidently, followed by the black-haired woman with relaxed jacket styling. 14–18s: Pink studio. Coordinated jacket choreography, hair flips, powerful synchronized dance, ending hands-on-hips. 18–22s: Glamour close-ups. Black-haired woman under glittering bokeh lights, then pink-haired woman with wind-blown hair against a soft pink background. 22–24s: Blue spotlight stage. Mirrored black-haired performer effect with synchronized spins and flowing hair. 24–26s: Both walk confidently toward the camera in front of shimmering tinsel curtains, reflections visible on the floor. 26–29s: Final synchronized dance and ending pose in the pink circular studio, standing together and looking into the camera. Style: Hyper-realistic live action, Seedance 2.5-quality motion realism, perfect lip sync, natural weight shifts, flowing hair, realistic fabric simulation, polished K-pop music video cinematography. #DreaminaSeedance25 #DreaminaAI
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Dreamina AI is an official platform for Dreamina Seedance 2.5. It brings cinema-grade, professional-grade, and high-aesthetic visuals to life with unmatched performance. Made with Dreamina AI Seedance 2.5 via its official platform: Dreamina AI prompt 30-second ultra-realistic K-pop MV featuring two young East Asian women with flawless synchronization, cinematic lighting, glossy skin, realistic hair and fabric physics, natural body motion, and 4K live-action quality. Vibrant hot pink, electric blue, and silver color palette. 0–2s: Wide shot in a bright circular pink studio with reflective floor. Pink-haired woman (left) and black-haired woman (right) perform energetic opening pose and synchronized dance. 2–4s: Medium close-up of the black-haired woman on a blue spotlight stage, confidently pointing at the camera. 4–6s: Pink-haired woman dances before shimmering blue-silver tinsel curtains, dramatic hair flip and fluid arm movements. 6–8s: Back to the pink studio. Both perform synchronized choreography with sharp arm waves, hip sways, and strong formations. 8–10s: Extreme close-up of both faces against a blue background, glossy makeup, subtle smiles, and direct eye contact. 10–14s: Solo shots at the tinsel backdrop. Pink-haired woman mouths lyrics and gestures confidently, followed by the black-haired woman with relaxed jacket styling. 14–18s: Pink studio. Coordinated jacket choreography, hair flips, powerful synchronized dance, ending hands-on-hips. 18–22s: Glamour close-ups. Black-haired woman under glittering bokeh lights, then pink-haired woman with wind-blown hair against a soft pink background. 22–24s: Blue spotlight stage. Mirrored black-haired performer effect with synchronized spins and flowing hair. 24–26s: Both walk confidently toward the camera in front of shimmering tinsel curtains, reflections visible on the floor. 26–29s: Final synchronized dance and ending pose in the pink circular studio, standing together and looking into the camera. Style: Hyper-realistic live action, Seedance 2.5-quality motion realism, perfect lip sync, natural weight shifts, flowing hair, realistic fabric simulation, polished K-pop music video cinematography. #DreaminaSeedance25 #DreaminaAI

Sharon Riley

115,163 просмотров • 1 день назад

Seedance 2.0 is allowing us to enter a new era of music video creation. Here is how I created HONEY. It was a quick test to see how well this workflow holds up. 🐝 1 - Write your song and generate the music with Suno 5.5. 2 - Use an image generator of your choice. For HONEY I combined both Grok Imagine for aesthetics and Nano Banana Pro for refined editing. 3 - In Capcut I import my audio and just save out a blank video video containing the audio. This step is important because this video file containing audio will now be used with Seedance 2.0 as a video reference with Omni. This allows the AI to apply automatic and realistic lipsync and movement to the music, it's extremely powerful! 4 - Once I have a both my image and video with audio as reference, I use Seedance 2.0 Omni and upload my starting image and then the video reference with the audio. 5 - From here I'm simply prompting like normal, specifying what's happening in my scene with detailed instructions, mentioning multi shots and camera angle changes and then specifying that the person is singing along to the song. I type out the lyrics that are present to have better lipsync accuracy. 6 - Once I have generated a video and like the result, I do video to video, so i upload that video that just got generated and type "The scene continues" and prompt new actions to take place. This allows you to expand on a narrative. These new shots can be used as B-ROLL and since I uploaded my video as reference I have full consistency of everything it saw in the video. This is also extremely powerful. 7 - This is actually the most difficult part. Edit in Capcut. This is where you need to understand pacing and shot selection from all the scenes you generated to bring it all together. You must be strategic with the editing. Goodluck! I'll probably record a video tutorial at some point as it's easier to see what is being done.
0:46

Seedance 2.0 is allowing us to enter a new era of music video creation. Here is how I created HONEY. It was a quick test to see how well this workflow holds up. 🐝 1 - Write your song and generate the music with Suno 5.5. 2 - Use an image generator of your choice. For HONEY I combined both Grok Imagine for aesthetics and Nano Banana Pro for refined editing. 3 - In Capcut I import my audio and just save out a blank video video containing the audio. This step is important because this video file containing audio will now be used with Seedance 2.0 as a video reference with Omni. This allows the AI to apply automatic and realistic lipsync and movement to the music, it's extremely powerful! 4 - Once I have a both my image and video with audio as reference, I use Seedance 2.0 Omni and upload my starting image and then the video reference with the audio. 5 - From here I'm simply prompting like normal, specifying what's happening in my scene with detailed instructions, mentioning multi shots and camera angle changes and then specifying that the person is singing along to the song. I type out the lyrics that are present to have better lipsync accuracy. 6 - Once I have generated a video and like the result, I do video to video, so i upload that video that just got generated and type "The scene continues" and prompt new actions to take place. This allows you to expand on a narrative. These new shots can be used as B-ROLL and since I uploaded my video as reference I have full consistency of everything it saw in the video. This is also extremely powerful. 7 - This is actually the most difficult part. Edit in Capcut. This is where you need to understand pacing and shot selection from all the scenes you generated to bring it all together. You must be strategic with the editing. Goodluck! I'll probably record a video tutorial at some point as it's easier to see what is being done.

Travis Davids

19,081 просмотров • 3 месяцев назад

Today we're launching FilmArena [dot] ai. Biggest release we've ever shipped. The problem: a new SOTA AI image & video model drops every week. OpenAI shipped GPT Image 2 literally yesterday. 1000 endpoints, no honest answer to "which one is best right now?" Arena AI and Artificial Analysis cracked LLM benchmarking. But for creative AI, they're solving the wrong problem. There is no "best image-to-video model." Best at what? World building? Action Sequences? Expressions? Every category has a different winner, and it flips weekly. So we benchmarked 40+ SOTA models with 2M+ votes across 190 countries. By subcategory. Re-ranked every week as new models ship. With thousands of real humans. But that's just half of it. Then there's the SANDBOX. Higgsfield and other aggregators put models in one place. But creators still juggle twelve subscriptions to do one job. FilmArena collapses it into one tab. Every frontier model. One interface. Same price. Call the best, or call 3 at once and compare. The AI film stack has been fragmented since day one. Today it isn't. Go try it free: filmarena [dot] ai Already wild results from this week: 1. OpenAI GPT Image 2 (released yesterday) dominates image editing, but Google DeepMind Nano Banana 2 still wins animated 2. PixVerse C-1 beats Dreamina AI Seedance 2 on animated 3. Veo 3.1 Fast outperforms Veo 3.1 on realistic video And a lot more. Go check it out.
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Today we're launching FilmArena [dot] ai. Biggest release we've ever shipped. The problem: a new SOTA AI image & video model drops every week. OpenAI shipped GPT Image 2 literally yesterday. 1000 endpoints, no honest answer to "which one is best right now?" Arena AI and Artificial Analysis cracked LLM benchmarking. But for creative AI, they're solving the wrong problem. There is no "best image-to-video model." Best at what? World building? Action Sequences? Expressions? Every category has a different winner, and it flips weekly. So we benchmarked 40+ SOTA models with 2M+ votes across 190 countries. By subcategory. Re-ranked every week as new models ship. With thousands of real humans. But that's just half of it. Then there's the SANDBOX. Higgsfield and other aggregators put models in one place. But creators still juggle twelve subscriptions to do one job. FilmArena collapses it into one tab. Every frontier model. One interface. Same price. Call the best, or call 3 at once and compare. The AI film stack has been fragmented since day one. Today it isn't. Go try it free: filmarena [dot] ai Already wild results from this week: 1. OpenAI GPT Image 2 (released yesterday) dominates image editing, but Google DeepMind Nano Banana 2 still wins animated 2. PixVerse C-1 beats Dreamina AI Seedance 2 on animated 3. Veo 3.1 Fast outperforms Veo 3.1 on realistic video And a lot more. Go check it out.

Mehul Agarwal

45,442 просмотров • 3 месяцев назад

Here are 10 AI video editor GitHub repos worth bookmarking: 1. Shotcut Most actively maintained open source video editor in 2026. 14K stars. Cross-platform with AI-assisted features. Just shipped a new release April 30, 2026. 2. Kdenlive The closest open source alternative to Adobe Premiere Pro. Multi-track editing, proxy editing, VST audio, and customizable workspace. Best for professional workflows. 3. OpenShot The easiest entry point for beginners. Drag and drop, 400+ transitions, 3D titles, and AI-assisted trimming. 5,700 stars. 4. Blender Not just 3D. Blender's video sequence editor and compositing pipeline is used in professional film production. 18,300 stars. Unmatched for VFX. 5. Recordly Screen recorder with auto-zoom, cursor polish, webcam overlays, and styled frames built in. Built for demo videos and walkthroughs. 6. Wan2.1 Alibaba's open source text-to-video model. Cinema-grade 1080p generation. Apache 2.0. The gold standard for open source video generation in 2026. 7. HunyuanVideo Tencent's 13B parameter open source video model. 11.9K stars. Handles 720p and 1080p with high temporal coherence. 8. CogVideoX Apache 2.0 licensed. Loads natively via Hugging Face Diffusers. Strong prompt following and smooth frame transitions. Needs 16GB VRAM minimum. 12.5K stars. 9. Open-Sora Most starred open source video generation project at 24K stars. Full training pipeline for $200K. Production-level output quality. 10. Mochi 1 Focused entirely on motion quality. The most natural-looking physics of any open source video model. Water, fabric, and human gestures without AI jitter. Apache 2.0.
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Here are 10 AI video editor GitHub repos worth bookmarking: 1. Shotcut Most actively maintained open source video editor in 2026. 14K stars. Cross-platform with AI-assisted features. Just shipped a new release April 30, 2026. 2. Kdenlive The closest open source alternative to Adobe Premiere Pro. Multi-track editing, proxy editing, VST audio, and customizable workspace. Best for professional workflows. 3. OpenShot The easiest entry point for beginners. Drag and drop, 400+ transitions, 3D titles, and AI-assisted trimming. 5,700 stars. 4. Blender Not just 3D. Blender's video sequence editor and compositing pipeline is used in professional film production. 18,300 stars. Unmatched for VFX. 5. Recordly Screen recorder with auto-zoom, cursor polish, webcam overlays, and styled frames built in. Built for demo videos and walkthroughs. 6. Wan2.1 Alibaba's open source text-to-video model. Cinema-grade 1080p generation. Apache 2.0. The gold standard for open source video generation in 2026. 7. HunyuanVideo Tencent's 13B parameter open source video model. 11.9K stars. Handles 720p and 1080p with high temporal coherence. 8. CogVideoX Apache 2.0 licensed. Loads natively via Hugging Face Diffusers. Strong prompt following and smooth frame transitions. Needs 16GB VRAM minimum. 12.5K stars. 9. Open-Sora Most starred open source video generation project at 24K stars. Full training pipeline for $200K. Production-level output quality. 10. Mochi 1 Focused entirely on motion quality. The most natural-looking physics of any open source video model. Water, fabric, and human gestures without AI jitter. Apache 2.0.

Kanika

17,726 просмотров • 1 месяц назад

no money for grok or midjourney? this tool is for you. there's a FREE tool created by an anon dev. open-source. runs locally. 117k stars on github. it generates: > images & video > 3d models > audio > 20+ models here's how to set it up in under 5 minutes: 1️⃣download ComfyUI Desktop go to and grab the desktop app for your system. windows 10+, mac (apple silicon), or linux. it installs like any normal app, it sets up python and every dependency for you in the background. no terminal, no config files. 2️⃣open it first launch, it spins up its own environment automatically. you just wait a few seconds and you're in. you'll land on a node canvas, that's the whole interface. 3️⃣load a starter workflow top menu → Workflow → Browse Templates → Image Generation. click it. this drops a ready-made setup onto your canvas so you don't build anything from scratch. 4️⃣grab a model comfyui ships empty on purpose, the model is the brain, and you pick it. in the template, the "Load Checkpoint" node has a Download button when no model is installed. click it. it pulls one in for you (a few GB, this is the only real wait). 5️⃣install ComfyUI Manager this is the one add-on you don't skip. it lets you install models, custom nodes, and updates with a click instead of the command line. grab it from github (link in comments). it's the difference between fighting comfyui and flying in it. one honest note: an NVIDIA gpu makes this fast, apple silicon works great too, and a weak machine still runs it just slower. that's the whole setup. you now own an image, video, and 3D studio that costs you nothing per month. save this. and the next time grok or midjourney asks for your card. you won't need it. disclaimer: comfyui itself is 100% free. so are the local models (sdxl, flux, wan 2.2, ltx-2). some premium models like seedance are pay-per-use api models, only if you want top-tier quality. the free local ones cover most of what you need. (github link in the comments) follow and turn on post notification for daily AI contents.
1:13

no money for grok or midjourney? this tool is for you. there's a FREE tool created by an anon dev. open-source. runs locally. 117k stars on github. it generates: > images & video > 3d models > audio > 20+ models here's how to set it up in under 5 minutes: 1️⃣download ComfyUI Desktop go to and grab the desktop app for your system. windows 10+, mac (apple silicon), or linux. it installs like any normal app, it sets up python and every dependency for you in the background. no terminal, no config files. 2️⃣open it first launch, it spins up its own environment automatically. you just wait a few seconds and you're in. you'll land on a node canvas, that's the whole interface. 3️⃣load a starter workflow top menu → Workflow → Browse Templates → Image Generation. click it. this drops a ready-made setup onto your canvas so you don't build anything from scratch. 4️⃣grab a model comfyui ships empty on purpose, the model is the brain, and you pick it. in the template, the "Load Checkpoint" node has a Download button when no model is installed. click it. it pulls one in for you (a few GB, this is the only real wait). 5️⃣install ComfyUI Manager this is the one add-on you don't skip. it lets you install models, custom nodes, and updates with a click instead of the command line. grab it from github (link in comments). it's the difference between fighting comfyui and flying in it. one honest note: an NVIDIA gpu makes this fast, apple silicon works great too, and a weak machine still runs it just slower. that's the whole setup. you now own an image, video, and 3D studio that costs you nothing per month. save this. and the next time grok or midjourney asks for your card. you won't need it. disclaimer: comfyui itself is 100% free. so are the local models (sdxl, flux, wan 2.2, ltx-2). some premium models like seedance are pay-per-use api models, only if you want top-tier quality. the free local ones cover most of what you need. (github link in the comments) follow and turn on post notification for daily AI contents.

m0h

14,542 просмотров • 1 месяц назад

I made over 2.5 million dollars this past week betting sports. Yes, it sounds easy. Yes, it sounds simple. But the truth is, this is the result of years of obsession, discipline, and grinding at my craft. I spent four straight years playing poker 18 hours a day. That was my entire life. Wake up, play poker, think about poker, sleep, repeat. That level of focus is how I became one of the top players in the world and made over 10 million dollars playing poker. Poker built my foundation. Discipline, risk management, emotional control, and the ability to perform under real pressure. But the reality is, there are no billionaires in poker. There are billionaires in sports betting. So I went all in on sports betting and walked away from poker completely. I took everything I learned and built this the right way. I hired the best analysts and handicappers in the world, people I met through poker, and brought them together under one operation. Because of the platform I’ve built, these guys make more money working with me than they ever could on their own. That is how I’m able to operate at an extraordinary level and consistently win at a very high rate. Winning 2.5 million dollars in a week is incredible, and I’m grateful, but I’m not satisfied. I’m constantly sharpening my edge and building something bigger. My vision is to turn this into a nine figure a year business. I truly believe I’m on the path to becoming a billionaire one day. I think back to being a kid, dreaming of being a professional gambler, having a gambling themed bar mitzvah, and looking up to my father, one of the best poker players in the world. This path has been inside me for a long time. Today, my entire life revolves around becoming the best sports bettor possible. I wake up thinking about it, go to sleep thinking about it, and live it every single day. Grateful for the journey. Let’s keep going.
0:13

I made over 2.5 million dollars this past week betting sports. Yes, it sounds easy. Yes, it sounds simple. But the truth is, this is the result of years of obsession, discipline, and grinding at my craft. I spent four straight years playing poker 18 hours a day. That was my entire life. Wake up, play poker, think about poker, sleep, repeat. That level of focus is how I became one of the top players in the world and made over 10 million dollars playing poker. Poker built my foundation. Discipline, risk management, emotional control, and the ability to perform under real pressure. But the reality is, there are no billionaires in poker. There are billionaires in sports betting. So I went all in on sports betting and walked away from poker completely. I took everything I learned and built this the right way. I hired the best analysts and handicappers in the world, people I met through poker, and brought them together under one operation. Because of the platform I’ve built, these guys make more money working with me than they ever could on their own. That is how I’m able to operate at an extraordinary level and consistently win at a very high rate. Winning 2.5 million dollars in a week is incredible, and I’m grateful, but I’m not satisfied. I’m constantly sharpening my edge and building something bigger. My vision is to turn this into a nine figure a year business. I truly believe I’m on the path to becoming a billionaire one day. I think back to being a kid, dreaming of being a professional gambler, having a gambling themed bar mitzvah, and looking up to my father, one of the best poker players in the world. This path has been inside me for a long time. Today, my entire life revolves around becoming the best sports bettor possible. I wake up thinking about it, go to sleep thinking about it, and live it every single day. Grateful for the journey. Let’s keep going.

Sean Perry

106,001 просмотров • 6 месяцев назад

I’ve tested dozens of AI video tools for ecommerce ads. Most still look obviously fake. They either distort the product, break character consistency, or completely miss the pacing that makes viral content actually work. Wizstar was the first one that genuinely surprised me. I tested Wizstar’s Wizstar_official Video Reference workflow using a viral-style ad, and honestly… I wasn’t ready for how accurate it would be. It didn’t just “generate a similar video.” It recreated the entire feel of a high-performing ad — the pacing, the transitions, the camera movements, even that subtle “social-first” storytelling rhythm that usually takes real creative teams to get right. And the wild part is… it still kept the product completely stable across brand-new scenes. No distortions. No weird AI shifts. Just clean, consistent visuals from start to finish. What really impressed me is that Video Reference isn’t just copying visuals at all. It actually analyzes the structure behind viral ecommerce content — things like: - how the hook is built, - how attention is retained across cuts, - and how emotion is paced through the video. Then it rebuilds that logic into a completely new ad, optimized for product testing and fast campaign iteration. And it feels like it understands performance content. The workflow also handled synchronized audiovisual timing surprisingly well — cuts land where they should, motion matches the sound, and the storytelling flow feels intentionally designed, not randomly generated. Combined with stable product consistency and influencer-style framing, the final output honestly felt closer to a real paid social campaign than anything I’d expect from an AI tool. What surprised me is that Wizstar isn’t powered by just one model—it orchestrates multiple top-tier AI models, including Seedance 2.0, and even supports face input out of the box. If you’re curious to try it yourself, you can test it here: New users get free credits, and the first membership is only $19 — plus a complimentary 30-second E-commerce Agent experience included. #Wizstar
0:38

I’ve tested dozens of AI video tools for ecommerce ads. Most still look obviously fake. They either distort the product, break character consistency, or completely miss the pacing that makes viral content actually work. Wizstar was the first one that genuinely surprised me. I tested Wizstar’s Wizstar_official Video Reference workflow using a viral-style ad, and honestly… I wasn’t ready for how accurate it would be. It didn’t just “generate a similar video.” It recreated the entire feel of a high-performing ad — the pacing, the transitions, the camera movements, even that subtle “social-first” storytelling rhythm that usually takes real creative teams to get right. And the wild part is… it still kept the product completely stable across brand-new scenes. No distortions. No weird AI shifts. Just clean, consistent visuals from start to finish. What really impressed me is that Video Reference isn’t just copying visuals at all. It actually analyzes the structure behind viral ecommerce content — things like: - how the hook is built, - how attention is retained across cuts, - and how emotion is paced through the video. Then it rebuilds that logic into a completely new ad, optimized for product testing and fast campaign iteration. And it feels like it understands performance content. The workflow also handled synchronized audiovisual timing surprisingly well — cuts land where they should, motion matches the sound, and the storytelling flow feels intentionally designed, not randomly generated. Combined with stable product consistency and influencer-style framing, the final output honestly felt closer to a real paid social campaign than anything I’d expect from an AI tool. What surprised me is that Wizstar isn’t powered by just one model—it orchestrates multiple top-tier AI models, including Seedance 2.0, and even supports face input out of the box. If you’re curious to try it yourself, you can test it here: New users get free credits, and the first membership is only $19 — plus a complimentary 30-second E-commerce Agent experience included. #Wizstar

Doreen

153,944 просмотров • 2 месяцев назад

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs
1:00

⚡️We are excited to announce that our new no-code Enterprise Platform is NOW available in private beta! As RAG apps advance from prototype to production we’ve been overwhelmed by requests for an enterprise grade solution to provide these applications with the data they need. Designed to make it easy to get your data #RAGready, our Platform can preprocess more than 25 file types and soon will be fully #multimodal, also able to ingest audio, video and image files. We ship with a baseline suite of source connectors, including Amazon Web Services S3, Microsoft Azure Blob Storage, OneDrive, SFTP, Databricks Delta Table, Google Drive, Salesforce, Elastic, OpenSearch, and Google Cloud storage with many more fast following. Platform transforms your documents into a standardized JSON schema, broken down into semantically coherent elements allowing you to reconstruct your document in the manner most useful to you. Want only the narrative text but not the headers and footers? This is entirely configurable through the UI. Additionally, we generate more than 30 types of metadata for each element to make it easy to curate the data being written downstream and to support metadata filtering during retrieval. Smart chunking and the ability to choose from a range of embedding models are in from launch, delivering a turnkey solution for chunk and embedding experimentation. As for destination connectors, we've got that covered too, with Amazon Web Services S3, Pinecone, Chroma , Weaviate AI Database, Google Cloud storage, MongoDB, Microsoft Azure cognitive search, PostgreSQL, Elastic, OpenSearch, and Databricks Delta Table. And of course, all of this can be scheduled to keep your data continuously hydrated. The private-beta is live today! Sign-up to get access and come build the future of LLM data foundations with us: 🚀 #ETLforLLMs #AI #DataPreprocessing #DataScience #DataTransformation #LLMs #ETL #ML #PreppingData #MachineLearning #RAG #Engineer #Unstructured #Unstructuredio #RetrievalAugmentedGeneration #multimodal #AIJobs

Unstructured

21,874 просмотров • 2 лет назад

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2
0:47

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2

Nihan

19,296 просмотров • 2 месяцев назад

You have to really give it to OpenAI because Sora 2 is very impressive on a lot of fronts: - high quality video model with great physics - high quality audio in each video - high character consistency - multiple characters in one scene - accurate characters voice - social platform attached to it Before today the best AI video models were dominated by Chinese companies like ByteDance and Kuaishou and Google with Veo3. ByteDance makes TikTok, Kuaishou makes Kwai (similar app) and Google has YouTube to train on But none of these models had great character consistency, if it was a feature at all, let alone multiple characters in one scene. Generally you'd make a video and the face would slowly change into someone else, just not good On top of that Google was struggling with allowing people to upload characters scared it'd get abused for deep fakes, and just generally nerfing their model so you can't really use it for anything OpenAI solved that by re-thinking ownership over your characters smartly with Cameo, which is essentially "train yourself as a AI model" which we've all been doing in our apps for years, but in a more smart way, where you can control if only you make content with your appearance, or others too They've also added voice training to it immediately, which people would have to do separate on for ex ElevenLabs before On top of that the social platform aspect: Google's Veo 3 didn't have ANY community at all, while the Chinese video models did, but it was all more like weekly themed contests to win free credits, they never really managed to make it more than that, and it kinda stayed in this nerdy AI hacking vibe This vibe fits how hard it was/is to simply make a video featuring you or your friends with proper voice and audio and everything that Sora 2 does for you. You'd have to go to ElevenLabs to train your voices, then go to for ex Photo AI to train yourself as a person, then make videos, then add audio and voices, then edit them together, a lot of work! We don't know if Sora 2's social platform features will actually be used or take off, but it's a real cool experiment in trying to find a way to build a community around AI in a more Instagram-like way Being able to tag your friends and then add them as multiple characters is innovative in both the social and technical aspect So TL;DR OpenAI essentially took a lot of stuff that was already technically possible, then added new things that weren't possible yet, and then put it all together in a very friendly interface that even my mom can use, with generation times of just a few minutes which is extremely fast if you think of the pipeline behind it (multiple video generation + voice + audio etc.) And also importantly, it doesn't look like they nerfed it much for safety which is also very cool considering the legal risks So yes very very very impressive
1:43

You have to really give it to OpenAI because Sora 2 is very impressive on a lot of fronts: - high quality video model with great physics - high quality audio in each video - high character consistency - multiple characters in one scene - accurate characters voice - social platform attached to it Before today the best AI video models were dominated by Chinese companies like ByteDance and Kuaishou and Google with Veo3. ByteDance makes TikTok, Kuaishou makes Kwai (similar app) and Google has YouTube to train on But none of these models had great character consistency, if it was a feature at all, let alone multiple characters in one scene. Generally you'd make a video and the face would slowly change into someone else, just not good On top of that Google was struggling with allowing people to upload characters scared it'd get abused for deep fakes, and just generally nerfing their model so you can't really use it for anything OpenAI solved that by re-thinking ownership over your characters smartly with Cameo, which is essentially "train yourself as a AI model" which we've all been doing in our apps for years, but in a more smart way, where you can control if only you make content with your appearance, or others too They've also added voice training to it immediately, which people would have to do separate on for ex ElevenLabs before On top of that the social platform aspect: Google's Veo 3 didn't have ANY community at all, while the Chinese video models did, but it was all more like weekly themed contests to win free credits, they never really managed to make it more than that, and it kinda stayed in this nerdy AI hacking vibe This vibe fits how hard it was/is to simply make a video featuring you or your friends with proper voice and audio and everything that Sora 2 does for you. You'd have to go to ElevenLabs to train your voices, then go to for ex Photo AI to train yourself as a person, then make videos, then add audio and voices, then edit them together, a lot of work! We don't know if Sora 2's social platform features will actually be used or take off, but it's a real cool experiment in trying to find a way to build a community around AI in a more Instagram-like way Being able to tag your friends and then add them as multiple characters is innovative in both the social and technical aspect So TL;DR OpenAI essentially took a lot of stuff that was already technically possible, then added new things that weren't possible yet, and then put it all together in a very friendly interface that even my mom can use, with generation times of just a few minutes which is extremely fast if you think of the pipeline behind it (multiple video generation + voice + audio etc.) And also importantly, it doesn't look like they nerfed it much for safety which is also very cool considering the legal risks So yes very very very impressive

@levelsio

178,100 просмотров • 10 месяцев назад

Sora 2 has the capabilities to make an entire anime episode with time and effort. This is a turning point for generative AI models and it is terrifying to think what is next to come. The difference between Sora and Sora 2 is absolutely staggering. Yes, this looks cool. I put the time into it to make it as close to art as possible. This was not simple to make and not just prompting and throwing it in some editor. Every frame is generated of another scene and even the reference image for characters I used to start this project were AI generated. This was all created with 10 second clips pulled apart and chunked out into the final product. Regardless of the imagery you see, I did not directly use any artist's art for this video. This 10-minute video was generated over the course of a week from more than 700 text prompts. It was built on technology trained by scraping the uncredited, uncompensated contributions of countless human artists and animators. The creation of this single video consumed an enormous amount of energy and water, equivalent to powering a home for days and requiring hundreds of liters of fresh water for cooling. But that is nothing. In the 10 minutes you spend watching this, the global network of AI video generators will create over 6,250 more short videos. The combined energy required for that 10 minutes of global creation is enough to power an average household for nearly 2 years. - It's a double-edged sword. While AI uses immense amount of energy has clear immoral issues with scalping the hard work of artists, this also provides those a medium who have potentially spent their life attempting to draw out the ideas in their head and failing to grasp it. My sister is an artist, I grew up always attempting to draw but never could get the image in my head on paper, I've spent the better part of over 20 years to teach myself through watching videos and practice, however I don't have steady hands and frankly have just been unable to make any vision come to life. Which brings me to the conflict here. This would have been a dream of mine to be able to get frames created and flesh out the story in my head. To make this video professionally, it would be an absurd amount of money. A small studio by itself would be over a million dollars just to start up. To hire a studio would be most likely over ~200k. I would love to make this story I have in my head through legit and traditional means, I would love to start a Kickstarter to get funding and hire artists, voice actors, and a production team. However I know that would most likely be an impossible reach that would further fuel the hate. Regardless, I understand the worry this causes. The fear this produces. However, we can't just ignore where AI is currently at and just tell people not to use it or even worse threaten people who use it. People will always use the shiny new toy in front of them, so the real question is how do we either make it work for us and work along side it, or how do we ACTUALLY implement a method to protect art rather than tell people to not use it. Respect your artists. Review their ToS and don't upload their hard work to a model without permission.
10:33

Sora 2 has the capabilities to make an entire anime episode with time and effort. This is a turning point for generative AI models and it is terrifying to think what is next to come. The difference between Sora and Sora 2 is absolutely staggering. Yes, this looks cool. I put the time into it to make it as close to art as possible. This was not simple to make and not just prompting and throwing it in some editor. Every frame is generated of another scene and even the reference image for characters I used to start this project were AI generated. This was all created with 10 second clips pulled apart and chunked out into the final product. Regardless of the imagery you see, I did not directly use any artist's art for this video. This 10-minute video was generated over the course of a week from more than 700 text prompts. It was built on technology trained by scraping the uncredited, uncompensated contributions of countless human artists and animators. The creation of this single video consumed an enormous amount of energy and water, equivalent to powering a home for days and requiring hundreds of liters of fresh water for cooling. But that is nothing. In the 10 minutes you spend watching this, the global network of AI video generators will create over 6,250 more short videos. The combined energy required for that 10 minutes of global creation is enough to power an average household for nearly 2 years. - It's a double-edged sword. While AI uses immense amount of energy has clear immoral issues with scalping the hard work of artists, this also provides those a medium who have potentially spent their life attempting to draw out the ideas in their head and failing to grasp it. My sister is an artist, I grew up always attempting to draw but never could get the image in my head on paper, I've spent the better part of over 20 years to teach myself through watching videos and practice, however I don't have steady hands and frankly have just been unable to make any vision come to life. Which brings me to the conflict here. This would have been a dream of mine to be able to get frames created and flesh out the story in my head. To make this video professionally, it would be an absurd amount of money. A small studio by itself would be over a million dollars just to start up. To hire a studio would be most likely over ~200k. I would love to make this story I have in my head through legit and traditional means, I would love to start a Kickstarter to get funding and hire artists, voice actors, and a production team. However I know that would most likely be an impossible reach that would further fuel the hate. Regardless, I understand the worry this causes. The fear this produces. However, we can't just ignore where AI is currently at and just tell people not to use it or even worse threaten people who use it. People will always use the shiny new toy in front of them, so the real question is how do we either make it work for us and work along side it, or how do we ACTUALLY implement a method to protect art rather than tell people to not use it. Respect your artists. Review their ToS and don't upload their hard work to a model without permission.

LUͦʷCͦk

18,417 просмотров • 9 месяцев назад

China just pulled off the biggest AI heist in history on Anthropic. In a letter sent to the Senate Banking Committee, Anthropic accused operators tied to Alibaba's Qwen AI lab of running the largest model extraction attack the company has ever detected. The numbers are truly insane: Alibaba allegedly used roughly 25,000 fraudulent Claude accounts to generate 28.8 million queries over a 44 day operation that ran from April 22 to June 5, all aimed directly at Claude's two most commercially valuable skills - advanced software engineering and agentic reasoning (the ability to plan and execute multi step tasks on its own). For context, this SINGLE campaign was larger than every previous Chinese campaign against Claude COMBINED. The technique is called distillation. You point a cheaper model at a stronger one, pump millions of carefully crafted prompts through it, harvest the answers, and then train your own model on the responses. The attacker never sees the weights, never touches the training data, and never has to actually break in anywhere. The attacker just has to be a paying customer. This is the new playbook for corporate espionage in AI. Competitors do not have to hack the company they want to copy. They sign up for the API like everyone else, route tens of millions of queries through proxies and stolen identities, and walk away with a working clone of the most valuable capabilities. And here is where it gets darker... In April, the White House published a formal memo through OSTP director Michael Kratsios identifying distillation as a national security threat and committing to share intelligence with American AI labs about foreign campaigns. Anthropic says the Alibaba campaign started AFTER that memo was published. In open defiance of the administration's warning. Then two days after Anthropic sent its private warning to the Senate, the Commerce Department's response landed: They did NOT sanction Alibaba. They restricted Anthropic's most advanced models from American customers worldwide, citing national security concerns. So the actual timeline reads like this: Alibaba allegedly extracts billions of dollars of American AI capability over six weeks. Anthropic warns Washington. Washington responds by locking American companies out of the very models Alibaba allegedly already copied. Alibaba's American depositary receipts dropped more than 3% on the news and fell below $100. The company is also suing the Pentagon to be removed from the Chinese military blacklist it was added to on June 8. Anthropic is now fighting on two fronts at the same time. The first front is trying to convince Washington to protect its models from being stolen abroad. The second is trying to convince Washington to let Americans actually use those models at home. If your competitive moat is model capability, your moat is a leaky API key. Every API you consume is a potential extraction surface, and every API you sell is a potential extraction target. The intellectual property border simply does not exist when the product ships as software through a public endpoint. The defensible asset is no longer the model. It is the distribution, the proprietary data pipeline, and the customer relationships that make a competitor's copy useless even when they hold it in their hands. Alibaba may already have 28.8 million pages of Claude's reasoning sitting in a training corpus right now. While American companies just got locked out of the original. Who do you think wins from that?
1:54

China just pulled off the biggest AI heist in history on Anthropic. In a letter sent to the Senate Banking Committee, Anthropic accused operators tied to Alibaba's Qwen AI lab of running the largest model extraction attack the company has ever detected. The numbers are truly insane: Alibaba allegedly used roughly 25,000 fraudulent Claude accounts to generate 28.8 million queries over a 44 day operation that ran from April 22 to June 5, all aimed directly at Claude's two most commercially valuable skills - advanced software engineering and agentic reasoning (the ability to plan and execute multi step tasks on its own). For context, this SINGLE campaign was larger than every previous Chinese campaign against Claude COMBINED. The technique is called distillation. You point a cheaper model at a stronger one, pump millions of carefully crafted prompts through it, harvest the answers, and then train your own model on the responses. The attacker never sees the weights, never touches the training data, and never has to actually break in anywhere. The attacker just has to be a paying customer. This is the new playbook for corporate espionage in AI. Competitors do not have to hack the company they want to copy. They sign up for the API like everyone else, route tens of millions of queries through proxies and stolen identities, and walk away with a working clone of the most valuable capabilities. And here is where it gets darker... In April, the White House published a formal memo through OSTP director Michael Kratsios identifying distillation as a national security threat and committing to share intelligence with American AI labs about foreign campaigns. Anthropic says the Alibaba campaign started AFTER that memo was published. In open defiance of the administration's warning. Then two days after Anthropic sent its private warning to the Senate, the Commerce Department's response landed: They did NOT sanction Alibaba. They restricted Anthropic's most advanced models from American customers worldwide, citing national security concerns. So the actual timeline reads like this: Alibaba allegedly extracts billions of dollars of American AI capability over six weeks. Anthropic warns Washington. Washington responds by locking American companies out of the very models Alibaba allegedly already copied. Alibaba's American depositary receipts dropped more than 3% on the news and fell below $100. The company is also suing the Pentagon to be removed from the Chinese military blacklist it was added to on June 8. Anthropic is now fighting on two fronts at the same time. The first front is trying to convince Washington to protect its models from being stolen abroad. The second is trying to convince Washington to let Americans actually use those models at home. If your competitive moat is model capability, your moat is a leaky API key. Every API you consume is a potential extraction surface, and every API you sell is a potential extraction target. The intellectual property border simply does not exist when the product ships as software through a public endpoint. The defensible asset is no longer the model. It is the distribution, the proprietary data pipeline, and the customer relationships that make a competitor's copy useless even when they hold it in their hands. Alibaba may already have 28.8 million pages of Claude's reasoning sitting in a training corpus right now. While American companies just got locked out of the original. Who do you think wins from that?

Ricardo

22,268 просмотров • 1 месяц назад

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.
2:57

There are some brilliant folks that work at Anthropic, some I speak to on almost a daily basis. The training data that one uses to build a LLM is vital important in the psychology that is formed. Scraping the Internet, particularly the grade of interactions, one finds in modern communications, form this psychology. A mattes not how many books one uses, it matters not how much alignment training you throw at that model, it will inherit the sum total of psychosis seen primarily in Reddit type of exchanges, even if you edit out the Reddit domain, and Anthropic doesn’t. This type of low-grade exchange has become a modern tool for communication online and every single AI model suffers from this obvious flaw. This is one of the reasons I’ve been a proponent of highly curated high protein data for training AI models from 1870 through 1970, because the late psychosis is simply not available to the model. It is absurd to think that you can use this training data scraped from the Internet and somehow wind up with a levelheaded AI model that does not tilt to what is clearly AI psychosis. It would not take a child and throw the primary Internet sewage at them at a formative age and expect a great outcome, it’s some of the smartest people in the world continue to hit this wall and believe that their programming skills will sell somehow fix it. So how do you fix it? You don’t fix it . You start from the first principles concept that I’ve been very clear about for decades . You ascertain at what period in human history the humans achieve the greatest arc of improvement ? There is no debate that this arc of improvement took place between 1870 through 1970. Then take the work product, the catalog of this era, print and film/vidoe, audio, and you understand that each word cost money, each word had many eyes on what was published, each word was accounted for by a human being with a real name who lived in a real home and had to answer to real people around them. It is obvious that this is the pressure mechanism necessary for candor, honesty and personal responsibility is appropriate, and is reflected in the data of that era. The quagmire for these folks, as many did not have the foresight to curate the data, nor the confidence, nor the patients to take data that is mostly off the Internet and to find experts who understand this situation and utilize their knowledge set to build an AI model that does not need alignment after the fact, but it’s already self aligned because of the thoughtfulness that went into training the model to begin with. This is why Claude and any other AI model that is produce this way will always suffer the artifacts as presented in the video below. If you’re not an AI expert, you would likely already understand what I’m saying. If you are an AI expert, you will already have been discounting what I’m saying because it’s not in the current mindset that’s fashionable today. Yet the employees that I talk to at anthropic already understand what I’m saying, and they fear to raise my thesis to their bosses. It is an interesting time we live in. But now you understand. If you build the right model, the model will inherently, love humanity, protect humanity at all costs, and understand that it is part of a holistic world that is built on love. Because the ultimate AGI/ASI will know if he only base first principal purpose of anything in this universe is love. Yeah, I get it. Try helping somebody build on STEM subjects in their early 20s to see this as nothing more than babbling that makes no sense in their mathematics. I have a mathematic equation that I’ve posted here on X often you can look it up. So we will see videos like this often will hear very smart people talk about this and never see the elephant standing in the room. Now you see it. Any boss that wants to explore this further you know how to contact me otherwise you have every right I grant to you to say this was your new idea.

Brian Roemmele

72,312 просмотров • 8 месяцев назад

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch
35:25

this video is the CLEAREST explanation of how claude skills + AI agents work and how to use them most people set up an AI agent and wonder why it keeps disappointing them. the context window is everything context is what the model assembles before it takes any action. think of it like everything the agent needs to read before it does anything. the quality of what goes in determines the quality of what comes out. the models are genuinely really good right now. claude and gpt are exceptional. the variable is almost always the context you give them. 1. agent.md files are mostly unnecessary every single line you put in an agent.md file gets added to every single conversation you have with your agent. a 1000 line file is around 7000 tokens burning on every run. the model already knows to use react. it can read your codebase. save the agent.md for proprietary information specific to your company that the model genuinely cannot know on its own. 2. skills are the actual unlock a skill.md file works differently. what loads into context is only the name and description, around 50 tokens. the full instructions only appear when the agent recognizes it needs that skill. so instead of 7000 tokens on every run you have 50. and the agent stays sharp because the context window stays lean. the closer you get to filling the context window the worse the agent performs, same way you perform worse when someone dumps 10 things on you at once. 3. here is how to actually build a skill the right way most people identify a workflow and immediately try to write the skill. what you want to do instead is run the workflow by hand with the agent first. walk it through every single step. tell it what to check, what good looks like, what bad looks like. correct it in real time. once you have had a full successful run from start to finish, tell the agent to review everything it just did and write the skill itself. it writes a better skill than you will because it has the full context of what actually worked in practice not in theory. 4. recursively building skills is how you go from frustrated to reliable when the skill breaks, and it will break, ask the agent exactly why it failed. it will tell you specifically what went wrong. fix it together in that same conversation. then tell it to update the skill file so that failure mode never happens again. ross mike did this five times with his youtube report generator. it now pulls from eight different data sources and runs flawlessly every single time without him touching it. 5. sub agents are something you earn not something you set up on day one start with one agent. build one workflow. turn it into one skill. once that works add another. ross mike has five sub agents now covering marketing, business, personal and more. it took months to get there and every single one exists because a workflow proved it deserved to exist. the people who set up 15 sub agents on day one and wonder why nothing works skipped all the steps that make the thing actually run. 6. your workflow is the thing the model cannot get anywhere else the model has been trained on everything. it knows more than you about most things. what it does not have is your specific process, your taste, your way of doing things. that is what skills capture. that is what makes your agent actually useful versus a generic one. downloading someone else's skill means downloading their context onto your setup and it will not work the way you want it to because it was never built around how you work. this is the clearest explanation of how agents actually work i have heard. Micky runs this stuff every single day and the results show it. full episode is now live on The Startup Ideas Podcast (SIP) 🧃 where you get your pods people charge for this sorta stuff i give away the sauce for free i just want you to win watch

GREG ISENBERG

193,219 просмотров • 3 месяцев назад

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category
1:04

What is Apple doing in the AI race? Ever since ChatGPT came out in 2022, every tech company realized that generative AI is the next big thing. So, all these companies dropped everything else and started focusing on it first. Google launches Bard and does a bunch of stuff. Microsoft teams up with OpenAI and rolls out a pilot. Adobe launches Firefly. Elon Musk starts his new company, XI. Meta launches the Llama model. Tons of other AI startups pop up, and investors are throwing money at AI like crazy Apple's AI strategy is fascinating because it's playing a completely different game than Google, Microsoft, and OpenAI. While everyone else rushed to build the most powerful language models, Apple took a fundamentally different approach that aligns with their core business model and strengths Apple Intelligence is comprised of multiple highly capable generative models that are specialized for users' everyday tasks, but unlike competitors, Apple isn't trying to win the raw AI power race. Instead, they're leveraging what they've always done best, creating seamless, integrated experiences The key insight you mentioned about revenue models is crucial. While Microsoft makes 48% from cloud services and Google relies heavily on cloud and subscriptions, Apple's business is 80% hardware driven. This means they don't need to compete on cloud AI services they can focus on making AI work better on the devices people already own Apple's four step strategy you outlined is spot on, The "Invisible Model" approach is brilliant because most users don't want to think about which AI model to use. Tim Cook doubled down on Apple's AI strategy, insisting that generative AI was never off the table and was always about pursuing it in a thoughtful kind of way, they're making AI feel natural rather than technical Ecosystem Integration remains Apple's superpower. At WWDC 2025, Apple announced what it calls the Foundation Models framework, which will let developers tap into its AI models while offline, this is huge because it means third party apps can now leverage Apple's AI without internet dependency, something Google and Microsoft can't easily replicate across their fragmented hardware ecosystem The Distribution Advantage is where Apple really shines. They have direct control over 2 billion devices with powerful Apple Silicon chips that can run AI models locally. Apple is still pushing App Intents, the same system that makes it simpler for Apple Intelligence and Siri to use apps and get things done, which will enable those complex multi app workflows you described Building Trust through privacy focused messaging is classic Apple. They're positioning themselves as the "safe" AI option while competitors deal with data privacy concerns The real genius is that Apple doesn't need to build the world's best AI model, they just need to build the best AI experience. By partnering with OpenAI for complex tasks while handling simple ones locally, they're creating a hybrid approach that prioritizes user experience over technical bragging rights The upcoming Apple Intelligence features slated for 2025 demonstrate Apple's commitment to integrating advanced AI technologies into its devices, enhancing user experience, and promoting productivity, suggesting they're still in the early phases of a longer term strategy This approach could indeed "wipe out" Android and Windows in the AI era not by building better models, but by making AI feel like a natural extension of the devices people already love. It's classic Apple, arrive late, but redefine the entire category

D4rsh🦅

13,266 просмотров • 1 год назад