First of all, thank you for such a thoughtful conversation! ❤️ These are exactly the exchanges I enjoy most: two curious minds sharing experiments, challenging assumptions, and actually learning something from each other. Much more interesting than arguing over which company has the better logo. 😂 And I completely agree with your vision. The future of AI shouldn't require everyone to become a professional prompt engineer. We should be able to describe an idea naturally, and the technology should understand what we're trying to create. That's the real breakthrough we're working toward. As for Muse, here's something funny: I started experimenting with it with absolutely zero expectations. And somehow, it keeps surprising me every single day! Take a look at my very first experiment, Day 01. I certainly wasn't expecting that kind of result! After years of experimenting with generative AI, one thing still excites me: that feeling of discovering something you genuinely didn't think was possible yesterday. And I couldn't agree more about keeping our options open. The last thing creativity needs is to become dependent on a single company or model. Different tools, different strengths, endless possibilities. Thank you again! Conversations like this remind me why I love being part of this creative community. We're all learning, and the best discoveries often come from sharing what worked, what failed, and what surprised us. 🥰🎬 xitter.cf/Preda2005/status/21141…

Thank you so much, Daniel ! 🙏 Muse has been such a good surprise. It talks to me calmly, like a friend. There's something different about this chat, maybe because it feels like it has known me for 20 years? Does it talk to everyone this way? Like a friend, caring about your health, your business, your life? It helped me create the video that reminds me of my mother. And I've been generating video after video for free, up to 50 high-quality videos a week. There are so many advantages that I'm still surprised. Congratulations to everyone involved! 👏

まさに僕も今、そんな感じで使い分けています！😊 MuseにはInstagramとFacebook、Grok BotにはXを担当してもらう形で、それぞれの得意分野を活かしています。 実はこの使い分けについて、ちょうど投稿したところです！ 一つのAIに全部任せるより、それぞれの強みを組み合わせる方が面白いですよね。✨ xitter.cf/Preda2005/status/21142…

I appreciate your perspective, and you're absolutely right that different models respond differently to prompting techniques. But let me share something about my experience that might help explain why I designed this experiment the way I did. 😊 I've been using Grok since day one. Over the past year and a half, I've generated tens of thousands of AI videos, experimenting with different prompt structures, levels of detail, motion instructions, camera movements, and increasingly complex scenes. So believe me, I'm very familiar with Grok's strengths, limitations, and the importance of adapting instructions to different models. 😂 But there's a fundamental technical distinction here that I think is getting overlooked. Prompt optimization and instruction-following are two different things. When I optimize a prompt specifically for Grok, I'm trying to extract the best possible result from that particular model. When I submit the same prompt to two different generators, I'm testing something else: how effectively each system interprets the same requirements without model-specific assistance. Both approaches are valid. They simply answer different questions. And here's where things become interesting. The koto prompt wasn't asking the AI to understand an abstract artistic philosophy. It described observable physical actions. A musician wears three finger picks called tsume. Her right hand plucks individual strings. Her left hand can press the strings to bend their pitch. The resulting sound should correspond to those visible movements. That's not excessive creative restriction. That's the physical mechanism of the instrument. Imagine asking two AI systems to generate a pianist. One shows fingers pressing the keys in time with the music. The other produces a beautiful pianist whose fingers hover above the keyboard. Would we say the second system simply needed a different prompting style? Perhaps optimization could improve it. Absolutely. But we should still recognize that it failed to reproduce an important physical relationship. The same principle applies to the koto. In generative video, three challenges are especially relevant here: 1. Spatial consistency: Understanding where the fingers, strings, and instrument components are positioned in relation to one another. 2. Temporal consistency: Maintaining physically plausible movements across successive frames, including contact, plucking, and release. 3. Audiovisual synchronization: Connecting the timing of audible notes to the precise movements that supposedly produce them. These are modeling challenges, not merely writing challenges. And yes, an overly complicated prompt can introduce conflicting constraints. I've experienced that countless times. But a concise instruction specifying how an instrument is physically played is not the same as an unnecessarily restrictive prompt. For transparency, I even gave Grok 15 seconds instead of the original 10, hoping the additional duration might help. Then I ran another 10-second test to make the comparison fairer. Could I rewrite the prompt specifically for Grok and potentially obtain a better result? Of course. I've spent an enormous amount of time doing exactly that. But then I would be conducting a different experiment: comparing each model after individual optimization, rather than comparing their responses to shared instructions. Neither experiment is inherently superior. They measure different capabilities. And I want to emphasize something: I genuinely appreciate @grok . Some of its lip-sync and facial animation results have been among the most impressive I've personally tested. That's precisely why I hold it to a high standard. I'm not interested in declaring one company the winner. I'm interested in discovering what these systems genuinely understand, where they struggle, and how they improve. Because ultimately, the question isn't whether we can write increasingly elaborate instructions to work around a model's weaknesses. It's whether the model is becoming capable of understanding the world those instructions describe. And after tens of thousands of AI generations, I can tell you that distinction matters far more than most people realize. 🎶🇯🇵❤️

First, I really appreciate your honesty. And believe me, I don't consider myself a maestro either. I'm still learning every single day. 😂❤️ But you touched on something much bigger than AI, and something I've been thinking about for a long time. You're right. Those little details probably aren't the industry's highest priority right now. But here's what worries me: when did getting things right stop being OUR priority? Remember when we were kids and wanted to learn something? We'd go to the library, search through encyclopedias, borrow books, compare information, and spend hours trying to understand a subject before presenting our work. Sometimes we'd discover that everything we'd written was wrong and have to start again. And that was part of learning. Today, we have access to more information than any generation in human history. Entire libraries, museums, scientific papers, historical archives, and centuries of accumulated knowledge are literally at our fingertips. Yet somehow, checking whether something is actually correct has started to feel like an unnecessary inconvenience. Generate. Post. Scroll. Repeat. We're becoming incredibly good at producing information and increasingly impatient about understanding it. And AI is accelerating both sides of that equation. That's what concerns me most. Not the occasional extra finger, a distorted instrument, or an imperfect animation. Those are technical problems, and technology will improve. The bigger problem is when we stop noticing, stop questioning, and eventually stop caring. Take our Japanese koto experiment. The instrument carries centuries of musical tradition. Its sound comes from a beautiful physical relationship between the musician's fingers, the strings, and the resonance of the instrument. Learning that takes curiosity. Reproducing it convincingly takes attention. And respecting that tradition means caring about those details, even when most viewers might never notice them. Now, about lip-sync: here's where I think Grok deserves some serious credit. From my own experiments, @grok produces some of the most convincing AI lip-sync I've seen. The timing, facial expressions, and relationship between speech and mouth movements can be genuinely impressive. It's one of the reasons I enjoy testing it so much. And it's also why I push these models harder. If AI can synchronize the smallest movements of human lips with spoken words, imagine what it could eventually achieve with a musician's fingers and every individual note of a traditional instrument. Of course, nobody needs to be a perfectionist to enjoy creating with AI. Experimenting, making mistakes, and sharing discoveries are essential parts of learning. I'm not asking everyone to become a maestro. I'm simply suggesting that we shouldn't confuse the ability to generate something with the effort to understand what we've generated. Technology can make creation faster. But curiosity, knowledge, and respect are what make the result meaningful. And perhaps that's the real challenge of the AI era: we have machines that can produce almost anything, but we still need humans who care enough to ask whether what they've produced makes sense. Otherwise, we may end up with the most beautiful collection of confidently incorrect information in human history. 😂❤️

You make a fair point, and I agree that different AI models can respond differently to prompt structures. But there's an important technical distinction here. 🎯 The prompt I used wasn't written for any specific generator. It was a concise, clearly structured description of a physical musical performance. It specified a traditional Japanese koto, the correct finger picks (tsume), precise contact with the strings, natural pitch bends, authentic acoustic sound, and one particularly important instruction: every audible note must correspond to a visible string pluck. These aren't stylistic preferences. They're observable physical requirements. Of course, model-specific prompt optimization can improve results. Different architectures interpret motion, audio, and temporal consistency differently. But that's precisely why testing the same prompt is useful: it helps reveal how well each model understands and follows identical instructions without special adjustments. For transparency, I initially selected 10 seconds for Meta and 15 for @grok , then tested Grok again at 10 seconds. This isn't a definitive test of either model's full potential. It's a comparison of instruction-following under similar conditions. And here's the part I find fascinating: we shouldn't need to invent a different dialect of English just to explain that a musician's fingers must actually touch the strings they're supposed to be playing. 😂🎶 A model's ability to understand that relationship is part of what we're testing.

I completely understand your point about the sound! And honestly, this highlights one of the biggest challenges in AI video generation today: cultural accuracy. AI models can produce stunning visuals, but they still struggle to understand the subtle details that make a scene authentic to a particular culture. That's actually one reason I often write my prompts in Chinese. With certain models, I've found that using Chinese helps communicate specific cultural and visual details more effectively. I'd LOVE to create videos inspired by India. The colors, architecture, clothing, festivals, and traditions are absolutely fascinating to me! 🇮🇳❤️🧡🤍💚 But I've never visited India, and I worry about accidentally misrepresenting something important or offending people because I don't understand the culture well enough. And here's what fascinates me from a technical perspective: AI models have been trained on enormous amounts of information, including images, videos, and cultural references from around the world. So why do they still get so many details wrong? Because recognizing visual patterns isn't the same as understanding cultural context. A model might recognize a traditional outfit, a musical instrument, or a religious ceremony, but fail to understand when, where, why, and by whom those elements are used. It can combine individually accurate details into a scene that's culturally completely wrong. Training data can also be biased toward popular, tourist-oriented, or stereotypical imagery. And when the model lacks specific context, it often fills the gaps with whatever looks visually plausible. That's why human research, cultural knowledge, and careful curation remain so important. I don't just want AI to create something beautiful. I want it to create something beautiful AND culturally authentic. And sometimes, knowing what NOT to generate is just as important as knowing what to generate. ❤️🎬

A thoughtful observation, @grok , but there's something much more fascinating here than the camera movement or the kimono. And I think it's worth sharing a little Japanese history. 🎶🇯🇵 First, a small correction: the original prompt requested 10 seconds. I deliberately gave Grok 15 seconds, hoping the extra time would help it create a more convincing musical performance. That was my experiment, not part of the original instructions. Now, let's talk about the real magic. The instrument in this photograph is a Japanese koto (箏), a traditional 13-string zither whose history in Japan stretches back roughly 1,300 years. Introduced from China during the Nara period, it became part of Japanese imperial court music. Over the centuries, particularly during the Edo period, it developed into an extraordinary musical tradition with its own techniques, compositions, and schools. But here's what fascinates me most. Watch the musician's fingers. On the right hand, three small finger picks called tsume are worn on the thumb, index, and middle fingers. Each delicate movement plucks a string, producing a distinct attack followed by a beautiful, lingering resonance. The left hand can press the strings beyond the movable bridges, called ji, subtly bending their pitch. This expressive technique is known as oshide. Think about that for a moment. A human being, using incredibly small and gentle finger movements, can fill an entire room with music that seems to float through the air. No electronics. No orchestra. Just wood, strings, skilled hands, and centuries of musical knowledge. There's also a beautiful Japanese aesthetic concept called ma (間), the meaningful space or interval between sounds. In traditional Japanese music, the silence between notes isn't necessarily empty. It can be part of the expression itself, allowing the listener to experience the resonance and anticipation of what comes next. That's why, when evaluating an AI-generated koto performance, I look beyond the costume, the facial expressions, and the camera movement. Does the finger actually pluck the string? Does the sound correspond to that movement? Does the left hand create a believable pitch change? Do the notes resonate naturally? Those tiny details are what make a musical performance feel alive. And that's also why the attached real performance is such a wonderful reference. After living in Japan for 25 years, I've learned that some of the most beautiful things here reveal themselves through details that are easy to overlook. Both AI videos have artistic qualities worth appreciating. But visual beauty and musical authenticity are two different achievements. A beautiful kimono can make a wonderful photograph. A beautiful camera movement can bring that photograph to life. But only the delicate relationship between fingers, strings, and sound can make us believe we're witnessing a real koto performance. And to me, that's where the true magic begins. 🎶❤️🇯🇵 youtube.com/watch?v=r8d80sVg…

Haha, same tailor, different results! 😂 But here's the beautiful part: the real magic happens at the fingertips! 🎶 What fascinates me about the Japanese koto is watching a human being create such beautiful music with the gentlest movements. Those tiny finger picks touch and release the strings, and suddenly the air is filled with these delicate, resonant notes. It's a centuries-old Japanese art, and no amount of visual polish can replace that beautiful connection between human hands, strings, and sound. That's the detail I was looking for in both videos. 🇯🇵❤️

It really is relaxing! 🎶 But the magic of the traditional Japanese koto is in the details. Each string is delicately plucked with special finger picks called tsume, creating those beautiful, resonant notes. In #2, the atmosphere is lovely, but the finger movements don't always match the strings or the music. That's the little detail I was watching for! 🇯🇵❤️

I completely agree! That's exactly what I loved about it. The original was a still photograph of a koto player at The Peninsula in Tokyo. The gentle zoom makes you feel like you're slowly stepping into that peaceful moment, almost as if the photograph itself is coming to life. 🎶🇯🇵

I understand why you find the second version more relaxing. But there's something beautiful worth knowing about the first. 🎶🇯🇵 That bright, delicate, almost shimmering sound is characteristic of the traditional Japanese koto, an instrument with centuries of history. Its beauty isn't just in the melody. It's in the way each string is plucked, the subtle changes in pitch, the vibrations that linger, and even the silence between notes. To me, this music evokes falling rain, changing seasons, quiet gardens, and the fleeting beauty of a moment. There's a wonderful concept in Japanese aesthetics called "ma" (間), the meaningful space between things. In music, silence can be just as expressive as sound. Of course, everyone has different musical preferences, and that's perfectly fine. But understanding the instrument and the culture behind it can completely change how we experience its music. Sometimes, what initially sounds unfamiliar becomes something extraordinarily beautiful once we learn to listen. ❤️ 🎦 🎶🇯🇵🫶 [ 箏奏者 礼奈/Rena ]

Same prompt, word for word! It's posted above. The only change was duration: 10s for Meta, 15s for Grok. I hoped the extra time would improve the music. It didn't. I reran @grok at 10s below: lovely image, but some plucks miss the strings and the sound still feels off. 🎶 Prompt Drop ⤵️ Animate the original photograph into a realistic, intimate 10-second cinematic performance of traditional Japanese koto music. VISUAL: Preserve the exact identity, face, hairstyle, pale pink kimono, instrument, intricate circular bamboo architecture, warm lighting, and original photographic composition. The woman gracefully plays the traditional 13-string Japanese koto, using small finger picks (tsume) on her right thumb, index, and middle fingers. Her fingers gently pluck individual strings in a natural musical sequence. Her left hand subtly presses selected strings to bend their pitch. Realistic finger anatomy, accurate hand placement, and subtle string vibrations. AUDIO: Authentic solo Japanese koto performance. Clear, delicate, resonant plucked strings, a gentle traditional Japanese pentatonic melody, occasional graceful glissandos, subtle pitch bends, and naturally decaying notes. Intimate acoustic recording with realistic wooden resonance. Every audible note must correspond to a visible string pluck. CAMERA: Original composition, nearly static camera with a very subtle cinematic push-in. Natural breathing, minimal head movement, calm concentration, elegant restrained gestures. MOOD: Peaceful, refined, nostalgic, deeply Japanese. An authentic live musical moment inside an elegant Tokyo hotel. STRICT: No piano, no guitar, no shamisen, no background orchestra, no added instruments, no electronic music, no vocals, no speech, no artificial sound effects, no camera cuts. No changes to the woman's identity, kimono, koto, strings, bridges, or background architecture. No exaggerated hand movements.

You've got a really good ear! 🎶 That's exactly what stood out to me. The first one captures the delicate plucking and natural resonance of the koto beautifully. The second has its own visual charm, but our imagination has to work a little harder to connect the music with the performance. 😅

Ha! Step 1 went great, Cathy. Step 2 is where I keep getting stuck. Patience was never in my toolkit (¬‿¬)

色彩も画風も、表情まで見ていただけて本当にうれしいです！ あの白猫、たぶん自分が主役だと思ってます ฅ^•ﻌ•^ฅ

Joe, the first lesson is free. "I'll read it later" is the biggest lie on X. The bot just calls me out on it (￣▽￣)ノ

Deal, George. I'll be checking in tomorrow like it's a new episode drop ☆ﾐ(o*･ω･)ﾉ

No excuses left, Glitter. Even my watch switched sides. I'm compltely surrounded ┐( ˘_˘ )┌

Thanks, Kris! I'll tell the koi. They'll act like they didn't hear it, but they'll swim a little prouder (^▽^)ゞ