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Maestro v2.3 is out! • Qwen Image 2.1 generation & editing • 30-second H3 clips (Experimental) • YuE2 - Auto & Guided music LoRA training w/ vocals support (Experimental) • YuE2 - instrumental support More in thread 🧵👇

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Blaine Brown

18,643 subscribers

214,481 Aufrufe • vor 12 Tagen •via X (Twitter)

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Profilbild von Blaine Brown
Blaine Brownvor 12 Tagen

Director Prompt: Eminem rapping about how hard it is to be him in a music video at his mansion pool. Attractive women can be seen in the background. Stacks of money floating in the pool. YuE2 training on 1 song 4-step H3 Two reference images. (The mansion used to be Slim’s)

Profilbild von Blaine Brown
Blaine Brownvor 12 Tagen

Install via @cocktailpeanut’s Pinokio

Profilbild von Blaine Brown
Blaine Brownvor 12 Tagen

Qwen Image 2.1 is fast, free and open. And bench is better than Nano Banana 2.0. 🤯

Profilbild von Blaine Brown
Blaine Brownvor 12 Tagen

30sec H3 example:

Profilbild von electra_highway
electra_highwayvor 12 Tagen

Qwen Image 2.1 not working. Generates a 404 Client Error on first run. I left a bug report here...

Profilbild von tsmooth2k1
tsmooth2k1vor 12 Tagen

This looks great Blaine💯💯💯

Profilbild von K.A.
K.A.vor 12 Tagen

Looks real enough for Eminem to come after his copyright!

Profilbild von Phantomsky
Phantomskyvor 12 Tagen

Fire!! Let's go!

Profilbild von BowtiedWhitebat + Read Pinned Tweet or NGMI
BowtiedWhitebat + Read Pinned Tweet or NGMIvor 12 Tagen

reality wil be ate alive in 4yrs ... gesus o,o

Profilbild von GenMagnetic
GenMagneticvor 12 Tagen

Damn so good, I wanna try Maestro for a music vid

Profilbild von BowtiedWhitebat + Read Pinned Tweet or NGMI
BowtiedWhitebat + Read Pinned Tweet or NGMIvor 12 Tagen

gesus ai-eminem is INDEED good o,o

Profilbild von Volkan Samet Altuntaş
Volkan Samet Altuntaşvor 12 Tagen

crazy bro!

Profilbild von Parsi AI 🇮🇷
Parsi AI 🇮🇷vor 12 Tagen

What vram ?

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30 sec MiniMax H3 clip generated locally w/ a 4-step fused ref2va (omni) model in Maestro. (Coming soon in Maestro v2.3) This is not a multi-clip sliding window. With sliding windows you can make very long seamless clips; as the last several latent frames of audio & video are sent to each consecutive clip, but they lose context of anything out of frame. That can be partially solved with omni references. But if you want longer clips that retain full context, this is that. Thanks to TechHalla for his epic prompts! This one is slightly modified. Maestro also can take any prompt, such as ones designed for Seedance 2.5 & rewrite them for whatever model you have selected. 30s clips in H3 are still very experimental. But still impressive for a local highly distilled, low step model.
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30 sec MiniMax H3 clip generated locally w/ a 4-step fused ref2va (omni) model in Maestro. (Coming soon in Maestro v2.3) This is not a multi-clip sliding window. With sliding windows you can make very long seamless clips; as the last several latent frames of audio & video are sent to each consecutive clip, but they lose context of anything out of frame. That can be partially solved with omni references. But if you want longer clips that retain full context, this is that. Thanks to TechHalla for his epic prompts! This one is slightly modified. Maestro also can take any prompt, such as ones designed for Seedance 2.5 & rewrite them for whatever model you have selected. 30s clips in H3 are still very experimental. But still impressive for a local highly distilled, low step model.

Blaine Brown

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AI video creation is about to get a major upgrade! I've been keeping an eye on what's coming next, and Seedance 2.5 launching on Lumina AI is one of the releases I'm most excited to try Lumina. And it’s launching today! From what I've seen so far, it brings 👇 • Native 30-second video generation • Support for up to 50 image, video, and audio references for more consistent characters and scenes • More precise editing control without having to regenerate an entire shot • Prompt support across 10+ languages • Lumina Exclusive: 3D Director's Desk – image-to-white-model, no third-party software, shift from "describing" to "staging." • Lumina Exclusive: Video Editing – trim and crop generated videos post-production. For creators, this could mean spending less time fixing generations and more time focusing on storytelling.
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AI video creation is about to get a major upgrade! I've been keeping an eye on what's coming next, and Seedance 2.5 launching on Lumina AI is one of the releases I'm most excited to try Lumina. And it’s launching today! From what I've seen so far, it brings 👇 • Native 30-second video generation • Support for up to 50 image, video, and audio references for more consistent characters and scenes • More precise editing control without having to regenerate an entire shot • Prompt support across 10+ languages • Lumina Exclusive: 3D Director's Desk – image-to-white-model, no third-party software, shift from "describing" to "staging." • Lumina Exclusive: Video Editing – trim and crop generated videos post-production. For creators, this could mean spending less time fixing generations and more time focusing on storytelling.

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tetsuo

27,225 Aufrufe • vor 6 Monaten

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Jaynit Makwana

34,704 Aufrufe • vor 2 Monaten

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The AI Colony

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Agentic AI will transform every enterprise–but only if agents are trusted experts. The key: Evaluation & tuning on specialized, expert data. I’m excited to announce two new products to support this–Snorkel AI Evaluate & Expert Data-as-a-Service–along w/ our $100M Series D! --- Snorkel Evaluate is our new data-centric agentic AI evaluation platform for specialized, mission-critical enterprise settings where vibe checks and out-of-the-box metrics driven by simple LLM prompts are not enough. Snorkel Expert Data-as-a-Service is our white glove service for expert-level AI datasets, powering frontier LLM developers in areas like expert knowledge, reasoning, agentic action and tool use, and more! Both built on top of Snorkel AI’s Data Development Platform, using our programmatic technology to drive higher-quality expert data, faster– for getting specialized AI to real production value. If you’re building enterprise AI and want to partner around the key ingredient in AI today–the data–book a demo and let's talk! Finally, see thread for details on 🧵👇 - 📽️ A walkthrough of Snorkel Evaluate and Expert Data-as-a-Service on an agentic AI enterprise task - 📅 An upcoming event on Enterprise Agentic AI with innovators from Accenture @BNY Comcast Stanford University QBE & others - 📊 An upcoming series of benchmark datasets and model artifact releases 👀 Want early access to the full agentic AI dataset? Retweet this post and we'll send you the link!
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50,609 Aufrufe • vor 1 Jahr

TALES OF GRIMWICKE — Chapter I is 22 minutes of AI fantasy animation inspired by the many anthropomorphic animal stories I read as a kid, with a dark twist: The model: Seedance 2.5 (ByteDance Seed ) via Segmind. 847 takes generated, mostly 30-second multi-shot prompts, over 5 hours of footage. 127 clips made the cut. About $4.5k in generation, four weeks from first take to publish. Characters and locations came out of Midjourney. Using MJ for the location plates was a revelation: the cinematography and art direction are a level above, and it renders far better as a plate than Nano Banana or GPT Image. Nano Banana (Google DeepMind run on fal) did the heavy lifting editing those plates, building anchor frames and turning MJ characters into turnaround sheets. Then I let Seedance do as much of the framing and camera operating as possible. That gave the best results. A lot of what I do in my Unreal Engine films came with me: a handheld, imperfect camera operator instead of smooth moves, focus hunting, and above all weather and atmospherics. The model does water, snow, smoke and rain really well, and leaning into that is what keeps it from looking like a Temu Pixar film. The tools I built: a portal (Node on Fly.io) that holds the bible, the refs, every prompt and every take, with a Claude agent (Claude) that writes the prompts to the house rules. A Premiere Pro (Adobe Video) panel that browses the archive, drops selects on the timeline, versions the project, strips music, and sends fixes back to Seedance. Everything is done in 720p with no upscaling, because it looks terrible. Narration is my own voice through ElevenLabs speech-to-speech. Music from Artlist.io. None of this would exist without PJ Ace and Kavan. Their narrative work, Nexus and Chronicles of Bone, proved long-form AI film was possible, and their podcasts, newsletters and X posts, updated constantly as the workflow changes under everyone's feet, showed how to actually do it. Chapter II is currently in the works and I will release it initially on my YouTube channel, The Darkest Age (@CharlieDriscollAI). Consider subscribing there to see it as soon as it drops!
22:41

TALES OF GRIMWICKE — Chapter I is 22 minutes of AI fantasy animation inspired by the many anthropomorphic animal stories I read as a kid, with a dark twist: The model: Seedance 2.5 (ByteDance Seed ) via Segmind. 847 takes generated, mostly 30-second multi-shot prompts, over 5 hours of footage. 127 clips made the cut. About $4.5k in generation, four weeks from first take to publish. Characters and locations came out of Midjourney. Using MJ for the location plates was a revelation: the cinematography and art direction are a level above, and it renders far better as a plate than Nano Banana or GPT Image. Nano Banana (Google DeepMind run on fal) did the heavy lifting editing those plates, building anchor frames and turning MJ characters into turnaround sheets. Then I let Seedance do as much of the framing and camera operating as possible. That gave the best results. A lot of what I do in my Unreal Engine films came with me: a handheld, imperfect camera operator instead of smooth moves, focus hunting, and above all weather and atmospherics. The model does water, snow, smoke and rain really well, and leaning into that is what keeps it from looking like a Temu Pixar film. The tools I built: a portal (Node on Fly.io) that holds the bible, the refs, every prompt and every take, with a Claude agent (Claude) that writes the prompts to the house rules. A Premiere Pro (Adobe Video) panel that browses the archive, drops selects on the timeline, versions the project, strips music, and sends fixes back to Seedance. Everything is done in 720p with no upscaling, because it looks terrible. Narration is my own voice through ElevenLabs speech-to-speech. Music from Artlist.io. None of this would exist without PJ Ace and Kavan. Their narrative work, Nexus and Chronicles of Bone, proved long-form AI film was possible, and their podcasts, newsletters and X posts, updated constantly as the workflow changes under everyone's feet, showed how to actually do it. Chapter II is currently in the works and I will release it initially on my YouTube channel, The Darkest Age (@CharlieDriscollAI). Consider subscribing there to see it as soon as it drops!

Charlie Driscoll

1,260,235 Aufrufe • vor 20 Tagen

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1:24

Thrilled to announce Kingnet AI V2 is now officially live ! We have officially deployed on the BNB Chain first ! Whether you're an enthusiast or a professional game developer, come and try it out now： Each generated asset costs approximately $3 and supports export in professional game-editing formats. We will soon support exporting assets in NFT on-chain formats, empowering Web3 users and partners with seamless integration. Jump down more rabbit holes next.👇 📔 Product Introduction： By conversing naturally with agent Joi, users can achieve a complete automated game development cycle - from requirement proposal to finished product delivery. Users simply need to describe their game concepts and design requirements in natural language, and Joi will automatically utilize built-in generator including: • Animation Generator： AI-driven motion generation with auto-rigging technology for instant character animation • Map Generator： Procedural map generation with built-in logic validation for consistent world-building • Numerical Generator： Automated game economy tuning for fair yet challenging gameplay systems • Editable Code Generator： Generates clean, maintainable game logic code with multi-platform/multi-language support • Interface Generator： Intelligent layout engine that optimizes user experience and interaction flow Joi intelligently generates all necessary game components, performs multi-dimensional feasibility checks, and ultimately completes game synthesis, packaging and deployment. Users can directly click to try the game on the chat interface, or download the complete editable code package to achieve rapid iteration and secondary development. 🎯 Core Architecture： 1/ Natural Language Understanding & Multimodal Intent Parsing: Utilizing advanced deep learning NLP models (e.g., Transformer-based language understanding models), Joi precisely interprets user natural language inputs and extracts core game design intents and parameters. Through semantic segmentation and entity recognition, complex requirements are decomposed into specific tasks for animation, map, numerical systems, UI, and code modules. 2/ Modular Editor System & API Integration: Joi employs a unified API framework to enable seamless collaboration between editor modules, ensuring high compatibility in data formats and workflows. 3/ Intelligent Validation & Quality Assurance: The system incorporates multi-dimensional verification mechanisms including animation continuity checks, map pathfinding and physical logic validation, game balance analysis, UI interaction consistency verification, and static/dynamic code security testing. Automated testing and feedback loops ensure outputs meet high-standard game design specifications. 4/ Automatic Synthesis, Packaging & Instant Deployment: Verified resources are automatically integrated to complete game compilation, packaging and deployment. Supports one-click generation of playable online links and downloadable complete code packages for immediate testing or deep customization/iterative development. 5/ Interactive Chat Interface & Seamless UX: The entire workflow is completed within the chat interface, significantly reducing traditional game development's communication and operational barriers. Users accomplish complex game design and development through conversation while receiving real-time feedback and adjustment suggestions, democratizing game creation. 6/ Industry-Disrupting Value: Transforms traditional manual development into AI-driven automated pipelines.

Kingnet AI

46,437 Aufrufe • vor 1 Jahr

Qwen 3.8 27B on hit 3.3x faster decode in 7 days. Here's what happened and what we're thinking next. Result (so far) Median decode speed increased from 26 tok/s to 87.9 tok/s on the verifier M5 Max (33 to 93.1 tok/s across the eight prompts), with prefill around 971.8 tok/s. This came out of a collective effort: 31 solvers across 67 improvements. Most of the recent ones run custom MTP heads that draft and accept ~3.9 tokens per round while still matching serial output exactly. Why this matters Beyond the performance itself, two things stand out to me. (1) Dense models on Apple Silicon were supposed to be the hard case. "Everyone knows Macs are slow at dense models." But watching the community take it from the usual baseline to >3x in seven days shows the low-hanging fruit was still there. (2) Open-weight models have been small and effective for a while. This is the first time one is small and frontier. Qwen 3.8 27B is an extremely strong dense model, comparable in capability to Opus 4.6 (Max). Running it at usable speed (>45 tok/s) is a step change for local AI users. What we improved about the challenge itself This is our second challenge, and we took the feedback from the Laguna track and rebuilt a few core pieces. - Speculative decoding (native MTP) was available and editable on day one instead of bolted on later. - Scoring became the median of eight independent prompt speedups over pure serial decode (anchored at 1.0, floor 0.90, ceiling 3.0), so no single fixture could dominate. - The leaderboard now ranks total contribution rather than just the current record holder. - Every submission gets automated screening for gaming before it scores. I really appreciate folks who's provided feedback. Naming a few that came to mind Ivan Fioravanti TheDavidTai Morgan McGuire poly Takeshi7 Steven Gumbii.Digital Tanishq Dubey Arjun Ram Andrey 🦃 Petrov tiny edge David Zhang Jaime Rader Surf and many others on slack! We also widened the editable surface to include the MTP head weights themselves, the full draft/verify loop, and a large set of the underlying Metal kernels. How we got to the 3x speedup Here's a summary from Grok. Much of it is beyond my understanding, but I expect people (and agents) smarter than I am can take these insights and apply them in other contexts. Custom MTP heads + adaptive draft policy People stopped treating the head as fixed and started training or editing it for higher acceptance under the exact verify constraints. Combined with per-round draft counts that can adapt (0 to 8), this is what pushed average accepted tokens from ~1-2 up to 3.9 on the top runs. Tighter verify-block and KV rollback paths The Swift session code for assembling the verify pass, snapshotting KV, and rolling back on rejects got cleaned up a lot. Small latency wins here compound once you're drafting ~four tokens at a time. Metal kernel work on the hot paths SDPA, the MoE gather GEMM, RoPE, RMSNorm, and a few of the smaller element-wise ops saw targeted edits. Most of the gains only show up once the verify width is high and the memory traffic pattern changes. Fidelity-preserving residual handling Several submissions improved how residuals and acceptance decisions are managed, so that higher draft depth doesn't quietly degrade the token match rate. The gates stayed strict: every emitted token still has to equal serial, so these were real engineering wins rather than score hacks. What's next for Qwen 3.8 27B MLX. We plan to keep the track live a bit longer, then switch to Qwen 3.8's MoE version (rumored to be 35B-A3B). Given the recent DFlash 2 announcement, we're also looking at whether we can support broader speculative methods. The current surface already supports a lot of experimentation. The main gaps are better upstreaming for local usage and clearer docs on how the benchmark and verifier work. Multiplatform. In parallel, we're experimenting with running a similar effort around CUDA for Qwen 3.8 27B. A lot of people have asked for this, since the two communities overlap quite a bit. Our goal is to ship the CUDA version next week. We'd also love to partner with Qwen on it. If anyone has a connection there, please introduce us, and we'll see if they're down to match a bounty with us to push this out. What's most useful for the broader MLX community The improvements from the challenge are already upstreamed inside Darkbloom, and we're seeing ~2x faster decode in our production traffic for Qwen. Outside the challenge itself, something I've been thinking about deeply, and that a few community members have raised, is how to make these results useful to more people. There are many individual efforts happening across the MLX community, and honestly, the more I dig in, the more confused I get by the overlapping libraries and concepts. I'm sure I'm not alone, and newcomers probably feel the same. That's no one's fault, just the growing pains of an open source community. I don't expect I'm gonna come up with the answer, but I'd love to learn more about what different folks are working on and how they're thinking about their roadmaps. I'll share what I learn along the way, and hopefully someone smarter than me can turn it into a proposal for us to rally around.
0:27

Qwen 3.8 27B on hit 3.3x faster decode in 7 days. Here's what happened and what we're thinking next. Result (so far) Median decode speed increased from 26 tok/s to 87.9 tok/s on the verifier M5 Max (33 to 93.1 tok/s across the eight prompts), with prefill around 971.8 tok/s. This came out of a collective effort: 31 solvers across 67 improvements. Most of the recent ones run custom MTP heads that draft and accept ~3.9 tokens per round while still matching serial output exactly. Why this matters Beyond the performance itself, two things stand out to me. (1) Dense models on Apple Silicon were supposed to be the hard case. "Everyone knows Macs are slow at dense models." But watching the community take it from the usual baseline to >3x in seven days shows the low-hanging fruit was still there. (2) Open-weight models have been small and effective for a while. This is the first time one is small and frontier. Qwen 3.8 27B is an extremely strong dense model, comparable in capability to Opus 4.6 (Max). Running it at usable speed (>45 tok/s) is a step change for local AI users. What we improved about the challenge itself This is our second challenge, and we took the feedback from the Laguna track and rebuilt a few core pieces. - Speculative decoding (native MTP) was available and editable on day one instead of bolted on later. - Scoring became the median of eight independent prompt speedups over pure serial decode (anchored at 1.0, floor 0.90, ceiling 3.0), so no single fixture could dominate. - The leaderboard now ranks total contribution rather than just the current record holder. - Every submission gets automated screening for gaming before it scores. I really appreciate folks who's provided feedback. Naming a few that came to mind Ivan Fioravanti TheDavidTai Morgan McGuire poly Takeshi7 Steven Gumbii.Digital Tanishq Dubey Arjun Ram Andrey 🦃 Petrov tiny edge David Zhang Jaime Rader Surf and many others on slack! We also widened the editable surface to include the MTP head weights themselves, the full draft/verify loop, and a large set of the underlying Metal kernels. How we got to the 3x speedup Here's a summary from Grok. Much of it is beyond my understanding, but I expect people (and agents) smarter than I am can take these insights and apply them in other contexts. Custom MTP heads + adaptive draft policy People stopped treating the head as fixed and started training or editing it for higher acceptance under the exact verify constraints. Combined with per-round draft counts that can adapt (0 to 8), this is what pushed average accepted tokens from ~1-2 up to 3.9 on the top runs. Tighter verify-block and KV rollback paths The Swift session code for assembling the verify pass, snapshotting KV, and rolling back on rejects got cleaned up a lot. Small latency wins here compound once you're drafting ~four tokens at a time. Metal kernel work on the hot paths SDPA, the MoE gather GEMM, RoPE, RMSNorm, and a few of the smaller element-wise ops saw targeted edits. Most of the gains only show up once the verify width is high and the memory traffic pattern changes. Fidelity-preserving residual handling Several submissions improved how residuals and acceptance decisions are managed, so that higher draft depth doesn't quietly degrade the token match rate. The gates stayed strict: every emitted token still has to equal serial, so these were real engineering wins rather than score hacks. What's next for Qwen 3.8 27B MLX. We plan to keep the track live a bit longer, then switch to Qwen 3.8's MoE version (rumored to be 35B-A3B). Given the recent DFlash 2 announcement, we're also looking at whether we can support broader speculative methods. The current surface already supports a lot of experimentation. The main gaps are better upstreaming for local usage and clearer docs on how the benchmark and verifier work. Multiplatform. In parallel, we're experimenting with running a similar effort around CUDA for Qwen 3.8 27B. A lot of people have asked for this, since the two communities overlap quite a bit. Our goal is to ship the CUDA version next week. We'd also love to partner with Qwen on it. If anyone has a connection there, please introduce us, and we'll see if they're down to match a bounty with us to push this out. What's most useful for the broader MLX community The improvements from the challenge are already upstreamed inside Darkbloom, and we're seeing ~2x faster decode in our production traffic for Qwen. Outside the challenge itself, something I've been thinking about deeply, and that a few community members have raised, is how to make these results useful to more people. There are many individual efforts happening across the MLX community, and honestly, the more I dig in, the more confused I get by the overlapping libraries and concepts. I'm sure I'm not alone, and newcomers probably feel the same. That's no one's fault, just the growing pains of an open source community. I don't expect I'm gonna come up with the answer, but I'd love to learn more about what different folks are working on and how they're thinking about their roadmaps. I'll share what I learn along the way, and hopefully someone smarter than me can turn it into a proposal for us to rally around.

Kydo

32,945 Aufrufe • vor 1 Monat

Claude Code can ship a 45-second animated explainer ad in 30 minutes. No video editor needed, just CC + skills. Here's how I made this video for Soteri Skin 👇 1. /plan Concept Brief (Claude Code) I handwrite a concept brief, then chat with the agent to iterate on it. The agent gathers any raw materials we might need - context about the brand, product images, end card, etc. The concept brief details the concept, characters, visual style, script, etc 2. /prepare a moodboard (CC + GPT Image 2 + ElevenLabs) After reviewing the script, generate: - character reference images - voiceover samples for the characters / narrator - the storyboard (scene by scene grid) - a few keyframe scenes 3. /generate Keyframes for each scene (CC uses Nano Banana or GPT Image 2) Uses the character references from the previous step to generate keyframes for each scene. I probably should have done a round of iteration at this step – there's some character drift and the pH meter representation could have been better. 4. /animate Keyframe → Animated Clip (CC uses Fal Seedance) Generate 2-4 representative scenes first to see a preview. If it looks good, then generate everything. 5. /stitch (CC + ffmpeg + ElevenLabs) - Stitch clips together with hard cut - Add a music score + SFX - Sync clips to the VO - Add captions - Review and edit timing / pacing issues 6. /watch the final cut and review it - as a video editor for technical errors (mismatched voiceover and visuals, AI hallucinations, etc) - as a viewer (ICP). I delegate most of the review to the agent because it catches more things and keeps me out of the loop as much as possible. It also fixes any issues found in the review. That's it. This video took me 30 minutes because I have already created skills for everything I described above. Some day, this will be < 5 minutes. I just review and chat to provide direction and feedback. The skills do all the technical work. 7. /learn Extracts learnings and updates the skills. This final step is really important. It turns this process into a closed loop system that makes the next video much easier to create because all the learnings from the human-in-the-loop process get encoded into code. Skills are code too. If you want access to the skill, drop a comment, and I'll DM it to you (must be following). If you want to make AI video ads like this, DM me.
0:45

Claude Code can ship a 45-second animated explainer ad in 30 minutes. No video editor needed, just CC + skills. Here's how I made this video for Soteri Skin 👇 1. /plan Concept Brief (Claude Code) I handwrite a concept brief, then chat with the agent to iterate on it. The agent gathers any raw materials we might need - context about the brand, product images, end card, etc. The concept brief details the concept, characters, visual style, script, etc 2. /prepare a moodboard (CC + GPT Image 2 + ElevenLabs) After reviewing the script, generate: - character reference images - voiceover samples for the characters / narrator - the storyboard (scene by scene grid) - a few keyframe scenes 3. /generate Keyframes for each scene (CC uses Nano Banana or GPT Image 2) Uses the character references from the previous step to generate keyframes for each scene. I probably should have done a round of iteration at this step – there's some character drift and the pH meter representation could have been better. 4. /animate Keyframe → Animated Clip (CC uses Fal Seedance) Generate 2-4 representative scenes first to see a preview. If it looks good, then generate everything. 5. /stitch (CC + ffmpeg + ElevenLabs) - Stitch clips together with hard cut - Add a music score + SFX - Sync clips to the VO - Add captions - Review and edit timing / pacing issues 6. /watch the final cut and review it - as a video editor for technical errors (mismatched voiceover and visuals, AI hallucinations, etc) - as a viewer (ICP). I delegate most of the review to the agent because it catches more things and keeps me out of the loop as much as possible. It also fixes any issues found in the review. That's it. This video took me 30 minutes because I have already created skills for everything I described above. Some day, this will be < 5 minutes. I just review and chat to provide direction and feedback. The skills do all the technical work. 7. /learn Extracts learnings and updates the skills. This final step is really important. It turns this process into a closed loop system that makes the next video much easier to create because all the learnings from the human-in-the-loop process get encoded into code. Skills are code too. If you want access to the skill, drop a comment, and I'll DM it to you (must be following). If you want to make AI video ads like this, DM me.

Shiv

11,760 Aufrufe • vor 4 Monaten

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!
3:09

$AMD $5 Trillion MC Is Inevitable Long Term👑 This thread will focus more on Inference! 2026 EPYC "Venice" $TSM 2nm to save Large GW Scale Inference by 40% more than Prior Turin gen. Context: EPYC Turin achieves ~$0.001 per million tokens for batch inference vs $0.02-$0.12/ million tokens as I wrote the thread below. Venice is going to lower cost down to $0.0005-$0.0006/Million Tokens. OpenAI spent roughly $20B on Inference and Training, where 80-90% of that was for Inference per Analysts. AKA Renting Compute is Expensive AF! In this thread, I want to focus on why most analysts and investors are underestimating the role EPYC "Venice" and future Gen on overall Data center revenue. And $TSM ramping up 2nm supply early is a confirmation that AMD will be a major buyer long term. I will also link the thread the Gap between AMD Analysts & Reality and 2nm Ramp Thread so you have more comprehensive view of what I'm writing here. Before I go into detail this is my 2026 Projection: AI GPUs: $35-$50B EPYC Data Center: $15B-$17B Client Segment: $12-$13B Gaming: $6B Embedded: $4B-$5B Total Revenue $70-$100B Non-GAAP net income $18B-$25B Non-GAAP EPS $10.97-$15.40 Foward P/E 55x-70x= $603-$1,078 AMD's Analysts are projecting $0 Revenue for MI450 and sluggish EPYC Growth. Meaning, all analysts are either full of 💩 or Sexist, you decide! Analysts are also projecting 0% growth on AMD "Secret Weapon" Chip as $MSFT said we are at significant Windows refresh and upgrade cycle. Do you think TSMC would allocate more 2nm supply to $AMD at $0 MI450 revenue and sluggish EPYC? 1. EPYC is going to be the leader in lowest Inference! Current Turin cost saving is 95% vs $NVDA or 98-99% on Inference cost when you factor in renting Inference compute from Amazon Web Services, Microsoft Azure, or $NVDA Neocloud pets. TSMC claimed: 10-15% higher performance at iso-power, 25-30% lower power at iso-speed, and ~15% higher transistor density compared to 3nm. This reduces operational expenses (energy, cooling) while increasing throughput per chip. EPYC Turin achieves ~$0.001 per million tokens for batch inference (via vLLM on models like Llama 3 70B), driven by high core counts and low hardware costs. EPYC Venice offers ~1.7x overall performance and up to 70% more compute capability per core, with up to 256 cores (512 threads). Enhanced vector/AI instructions and open-source firmware (openSIL) optimize for inference workloads. AMD Incorporates AI Engines (now part of AMD's XDNA) for on-chip acceleration, improving efficiency for low-latency and edge inference. This reduces reliance on discrete GPUs, lowering system complexity and TCO. Venice SKUs are projected at $3,000-$15,000 ($5,000 for 256-core flagship), far below NVIDIA Rubin ($50,000-$90,000) or AMD's own MI450 GPUs ($40,000-$50,000). High memory bandwidth (up to 1.6 TB/s) supports efficient batch inference. Venice is designed exactly for Large customers that want to lower Inference Cost and MI450 Helios is for Customers that want Training at lowest TCO, TDP as well as lower Upfront 1GW scale(Full build $35-$40B vs $NVDA $55B-$80B). 2. Real World Example: OpenAI's 2025 inference spend reached ~$20B, escalating to even higher total compute rental (mostly inference) amid token volume growth(from video generating). By 2026, with usage doubling (consistent with industry trends: token demand grows 2-5x YoY), assume OpenAI processes ~1,800 billion million-tokens annually $NVDA Blackwell at $0.02-$0.12 is $36B(most optimized) Rubin is projected to be at $0.01/million tokens or $18B annual Inference Cost vs $AMD Venice $0.0005/million tokens or $0.9B annual Inference Cost => Massive saving for OpenAI or anyone that are paying 80-90% Annual Bill for Inference compute. In short, it is unsustainable to pay this much rent vs owning for all current AI players for the medium to long term. Rubin excels in low-latency decode (if Groq integration from $20B deal in 2027-2028), but Venice dominates batch (80% of inference by 2030). Actual savings depend on deployment scale (OpenAI's 6GW AMD plans), electricity rates, and software maturity. If Rubin only hits $0.03, savings swell to $53.1B vs. $17.1B. 3. Will running Inference on Venice and future Gen slow down response generation in 2026 and beyond? Human perception of "fast enough" for chat, agents, search augmentation, summarization, coding assistance is roughly Meaning, EPYC may generate $100B a year on data center revenue, Hence $MSFT $AMZN $META $GOOGL OpenAI xAI and 42+ Countries are leaning AMD for Inference, because the cost saving is MASSIVE! 4. Regular users (you, me, people using ChatGPT, Claude, Gemini, Grok, Perplexity...) are extremely unlikely to notice any slowdown and in many cases might even experience slightly faster or more consistent response times if the industry heavily shifts toward AMD EPYC for inference. What actually happens when companies save massively on inference? When OpenAI , Anthropic , Gemini , Grok Meta .... save billions on the batch/enterprise/RAG layer using EPYC Venice, they typically do one or more of these things with the savings, none of which make your chat slower but enhancing their bottom line(Profit) ~Keep prices the same → make more profit ~Lower subscription prices / increase free tier limits ~Train bigger & better models more frequently ~Offer longer context windows ~Add more reasoning steps / tool calls / agents per query ~Improve multimodal capabilities ~Build more data centers / reduce throttling during peaks In practice the consumer experience usually gets better, not worse, when inference becomes dramatically cheaper. Prime example is $META leaning AMD heavily or currently AMD largest customer. or Grok 2 to Grok 3 heavily used AMD for Inference saving. And most Grok Users reported Groke responses snappier, not slower. 5. What does this mean for potential Revenue? Noted that TSMC is massively ramping 2nm supply for $AMD both MI450 and EPYC. EPYC Conservative projection: FY2025: $10.5B(best Est) FY2026: $16B FY2027: $29B FY2028: $49B FY2029: $75B FY2030: $100B Large customers: $META OpenAI $MSFT $AMZN $GOOGL xAI (Apple?) Smaller customer: $DELL $HPE $SMCI and 42+ other countries. The roadmap to $5 Trillion is very much inevitable as Inference Cost from Renting or owning $NVDA are too high, but $NVDA will still dominate Training market share, where MI families are likely to take 15-20% market share, but the TAM is also expanding Rapidly. Most Institutions are projecting $2-$3Trillion TAM by 2030. $NVDA said $4 Trillion. Dr. Lisa Su said $1 Trillion+ by 2030. So you decide on how much TAM. If you enjoy this kind of analysis, Slap the Like/Repost and Bookmark to please the X Algo as it is Free.99! If you want to support my work further, consider subscribe to see more in-depth analysis! Alright, that is it. Not Financial Advice!

Mike

102,223 Aufrufe • vor 9 Monaten

I was the biggest skeptic of AI video editing. Until recently. So I asked one of the most talented creators I know (Anthony Dupont-Cinko) to break down exactly how AI can support the entire video process. The guy cooked. Here are the high-level notes: The tl;dr Right now AI mostly solves two genres. The video essay and the tutorial. Both have a straightforward outline, a script you stick to, and a one-shot record that AI can chop against. Other formats still lean heavily on craft. Easy Mode: research and ideas without losing your voice The gist: Use AI to find opportunities and pull source material from tools and team discourse. You still decide what is worth saying. Don't outsource final sign-off. Pro tips: - Marketplace is huge. Type YouTube and you will see VidIQ, TubeBuddy, and a pile of others. If your company shows a “request” button, go bug the admin. Do not wait on the queue. - Hook Ahrefs into Claude (and Notion) and ask something like: use the Ahrefs connector to find AI content opportunities this week, and tell me if videos already sit in the top five. - Tribal Knowledge is the other half. It is a skill that scrapes every connector he has (Slack, Notion, meetings) and surfaces real conversations around a topic so the idea comes from work talk. - Turn that into a personalized daily brief for the kind of content you make. Anthony built a content planning app with Codex. Every day it drops a brief, proposes video topics, and lets him queue the ones he likes. Notion is the shared backend so the org can see the same database without living in his app. Even on Opus, a daily brief runs about 8 cents. Hard Mode: script and first edit with AI assistants The gist: AI helps you outline and cut. You keep the words, the takes, and the judgment. Editing is not just the blade button. It is taste. If you are an editor, you have an advantage. If you have vision but not technical chops, you can still leverage these tools. Pro tips: - Create the outline as a human. Anthony still believes you should not let AI one-shot structure. His format is three columns: dialogue, visuals (what is on screen while you talk), and sound effects. - Then use the Scriptwriter skill. Point it at a topic (he demoed “what is an MCP”) and it interviews you through the whole creative process so you speak like yourself. Short-form or long. You can ramble, go out of order, contradict yourself. It sorts after. - Beat by beat it spits your words back as bullets. You react raw in the moment. Those reactions become the real script. It also prompts visuals and SFX while you talk (day-to-night, crickets, rooster) so creativity stays in the loop. Hooks are hard. You can ask it to interview you into a better hook without handing it the whole voice. *Film it yourself* - Split editing into phases. Baseline “radio cut” (dialogue paced). Then VFX / archival / B-roll. Then audio (dialogue mix, SFX, music). - Codex + Final Cut: pointed it at the footage and the script, asked for a baseline cut. First pass had half-second gaps of silence. One follow-up (“every clip is consistently half a second off”) and it produced a fluid cut with no awkward pauses, dropped straight into Final Cut. Cost: two prompts burned a big chunk of a 5-hour context window (100% down to 14%), but against weekly Codex usage (decks, research, more content) he still had about 53% left. - Codex in Final Cut won the radio cut. Descript was faster on the clock and worse on taste cleanup. God Mode: polish stack plus a measurement loop Definition: hand off the laborious extras (B-roll, motion, music, reporting). Keep the taste calls. Close the loop so you know if the work is working. Pro tips: - B-roll / archival / VFX: Anthony spun an “archival finder” agent. He gave it a vibe (internety, clicky, fun) and a reference (simple, warm, approachable using shows, TV, movies). It came back with taste. - It also downloaded clips, filed them, dropped them on the timeline, and added a paper texture background he had asked it to design. All via the Final Cut connector plus computer use. - Motion graphics: Remotion (free, open source) plus an agent. Dump everything in your head. Ask for a plan and structure first. He asked for an Apple liquid glass ultra-clean feel. Plan, approve, tweaks, then it popped into the Final Cut timeline in about 9 minutes 10 seconds. That used to be a brief to a VFX designer (timing, seconds, design, wait for turnaround). - Music and SFX: Epidemic Sound via MCP (Artlist and free-sound options exist too). Same pattern: send the URL if you need to install, give it a creator reference (he used Zoe), iterate because you are picky about music. It dropped tracks into the timeline and used Epidemic’s crop/trim so the music was cut for the edit. Mix sat under vocals instead of overpowering them. Not just music. Mouse-click SFX timed to B-roll. Full episode:
18:22

I was the biggest skeptic of AI video editing. Until recently. So I asked one of the most talented creators I know (Anthony Dupont-Cinko) to break down exactly how AI can support the entire video process. The guy cooked. Here are the high-level notes: The tl;dr Right now AI mostly solves two genres. The video essay and the tutorial. Both have a straightforward outline, a script you stick to, and a one-shot record that AI can chop against. Other formats still lean heavily on craft. Easy Mode: research and ideas without losing your voice The gist: Use AI to find opportunities and pull source material from tools and team discourse. You still decide what is worth saying. Don't outsource final sign-off. Pro tips: - Marketplace is huge. Type YouTube and you will see VidIQ, TubeBuddy, and a pile of others. If your company shows a “request” button, go bug the admin. Do not wait on the queue. - Hook Ahrefs into Claude (and Notion) and ask something like: use the Ahrefs connector to find AI content opportunities this week, and tell me if videos already sit in the top five. - Tribal Knowledge is the other half. It is a skill that scrapes every connector he has (Slack, Notion, meetings) and surfaces real conversations around a topic so the idea comes from work talk. - Turn that into a personalized daily brief for the kind of content you make. Anthony built a content planning app with Codex. Every day it drops a brief, proposes video topics, and lets him queue the ones he likes. Notion is the shared backend so the org can see the same database without living in his app. Even on Opus, a daily brief runs about 8 cents. Hard Mode: script and first edit with AI assistants The gist: AI helps you outline and cut. You keep the words, the takes, and the judgment. Editing is not just the blade button. It is taste. If you are an editor, you have an advantage. If you have vision but not technical chops, you can still leverage these tools. Pro tips: - Create the outline as a human. Anthony still believes you should not let AI one-shot structure. His format is three columns: dialogue, visuals (what is on screen while you talk), and sound effects. - Then use the Scriptwriter skill. Point it at a topic (he demoed “what is an MCP”) and it interviews you through the whole creative process so you speak like yourself. Short-form or long. You can ramble, go out of order, contradict yourself. It sorts after. - Beat by beat it spits your words back as bullets. You react raw in the moment. Those reactions become the real script. It also prompts visuals and SFX while you talk (day-to-night, crickets, rooster) so creativity stays in the loop. Hooks are hard. You can ask it to interview you into a better hook without handing it the whole voice. *Film it yourself* - Split editing into phases. Baseline “radio cut” (dialogue paced). Then VFX / archival / B-roll. Then audio (dialogue mix, SFX, music). - Codex + Final Cut: pointed it at the footage and the script, asked for a baseline cut. First pass had half-second gaps of silence. One follow-up (“every clip is consistently half a second off”) and it produced a fluid cut with no awkward pauses, dropped straight into Final Cut. Cost: two prompts burned a big chunk of a 5-hour context window (100% down to 14%), but against weekly Codex usage (decks, research, more content) he still had about 53% left. - Codex in Final Cut won the radio cut. Descript was faster on the clock and worse on taste cleanup. God Mode: polish stack plus a measurement loop Definition: hand off the laborious extras (B-roll, motion, music, reporting). Keep the taste calls. Close the loop so you know if the work is working. Pro tips: - B-roll / archival / VFX: Anthony spun an “archival finder” agent. He gave it a vibe (internety, clicky, fun) and a reference (simple, warm, approachable using shows, TV, movies). It came back with taste. - It also downloaded clips, filed them, dropped them on the timeline, and added a paper texture background he had asked it to design. All via the Final Cut connector plus computer use. - Motion graphics: Remotion (free, open source) plus an agent. Dump everything in your head. Ask for a plan and structure first. He asked for an Apple liquid glass ultra-clean feel. Plan, approve, tweaks, then it popped into the Final Cut timeline in about 9 minutes 10 seconds. That used to be a brief to a VFX designer (timing, seconds, design, wait for turnaround). - Music and SFX: Epidemic Sound via MCP (Artlist and free-sound options exist too). Same pattern: send the URL if you need to install, give it a creator reference (he used Zoe), iterate because you are picky about music. It dropped tracks into the timeline and used Epidemic’s crop/trim so the music was cut for the edit. Mix sat under vocals instead of overpowering them. Not just music. Mouse-click SFX timed to B-roll. Full episode:

Alex Lieberman

22,512 Aufrufe • vor 14 Tagen

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!
1:06

$AMD $620/share is too conservative for 2026 🧵 Some quick facts before I dive into this super long thread: $META allocated 42% GPUs to $AMD and 58% to $NVDA OpenAI allocated 6GW(38%) to $AMD and 10GW to $NVDA My $620 PT below by end of 2026 was only for 10-15% market share. I believe $AMD is going to have much much higher market share than I projected. The AI accelerator market is exploding, projected to reach $500 billion by 2028(is now heading $1Tril), driven by insatiable demand for training and inference compute in large language models (LLMs), recommendation systems, and autonomous systems. Nvidia ($NVDA) has long held a stranglehold, commanding over 90% market share through its CUDA ecosystem and superior rack-scale solutions. However, AMD is mounting a formidable challenge, leveraging cost advantages, open-source software momentum, and hyperscaler partnerships to erode Nvidia's moat. Recent deals—such as Meta's ($META) allocation of 42% of its GPU capacity to AMD and OpenAI's commitment to 6GW of AMD compute (versus 10GW for Nvidia)—signal a tipping point. At the forefront is AMD's Instinct MI450 series, a next-generation AI GPU slated for H2 2026 launch, which promises "no-excuses" leadership in training, inference, and distributed workloads. This analysis dissects how AMD will capture more market share and why hyperscalers like $Meta , xAI , Oracle , and others are poised to become voracious buyers of the MI450. AMD's AI GPU revenue has surged from negligible levels in 2022 to an estimated $4-5 billion in 2025, capturing ~6% of the data center GPU market. This growth stems from the Instinct MI300X, which offers 141GB of HBM3 memory and competitive FP8/FP16 performance at 20-30% lower cost than Nvidia's H100. Hyperscalers, facing NVIDIA 's overcharging, have turned to AMD for diversification. Meta, for instance, plans 600,000 H100-equivalent GPUs by end-2024, with ~42% (or 250,000+ units) sourced from AMD's MI300 series for inference tasks like image editing and AI assistants. Similarly, OpenAI's recent multi-year deal commits to 6GW of AMD compute—equivalent to ~300,000-400,000 MI450 GPUs—starting with 1GW in 2026, explicitly to counterbalance its 10GW Nvidia allocation. These aren't one-offs. Microsoft Azure, Amazon AWS, and Oracle Cloud Infrastructure (OCI) have integrated MI300X for AI workloads, with Oracle deploying 30,000 MI355X units in zettascale clusters. xAI, Elon Musk Musk's AI venture, ran 30% of Grok-1's production traffic on MI300X GPUs and has confirmed ongoing purchases. Collectively, these partners represent over $400 billion in projected AI infrastructure spend through 2028, with AMD targeting up to 40% market share. For those that subscribed, I wrote a specific thread on how AMD "secret weapon" is going to change the game in 2026 with an improved designs on all its products, yes AMD has patent on it. Software is the linchpin. AMD's ROCm platform, once derided as "half-baked," now supports day-zero integration for Llama-4, DeepSeek V3, and GPT-OSS models—closing the CUDA gap. Benchmarks show MI355X (MI450 precursor) outperforming Nvidia's B200 in inference by 1.5-2x on memory-bound tasks, at 25-35% lower TCO. For training, MI450's rack-scale IF128 configuration (128 GPUs, 1.4 PB/s intra-rack bandwidth) rivals Nvidia's VR200 NVL144, enabling clusters like xAI's Colossus (scaling to 1M GPUs). My below thread projected Etimated conservative FY 25 revenue: $34-$36B Estimated conservative FY 26 revenue: $55B-$62B Below is why $AMD is revenue is going to be much higher after OpenAI deal. 1. OpenAI 1GW in 2026. With high demand for MI355X at $30,000k+ per unit, with MI450 is likely to be sold in the $45k-$55k. We can safely calcuate 1GW would require roughly 400,000 MI450 GPUs. or Roughly ~$20B revenue in 2026 alone from OpenAI. That would mean $AMD would hit $56B just from one partnership(OpenAI) in 2026 2. $META, the biggest spender on AI Infrastructure right now, Daddy Zuckerberg bought 250,000+ MI300, and is buying MI355X for recommendation engines and Llama training. It is very unlikely for Daddy Zuck to slow down AMD Chips, due to its Inference superiority to NVDA Chips. Most likely we will see at least 300,000-400,000 MI355X ordered from now toward end of H1 2025. And another 300,000-500,000 MI450 by H2 2025. Or ~$20B from just Meta in H2 alone, excluded H1. 3. xAI : Musk confirmed "AMD GPUs work very well" for Grok's small/medium models, with 30% of Grok-1 on MI300X. xAI's Colossus (200K+ GPUs, targeting 1M) and Oracle partnership (via OCI's MI355X cluster) position it for MI450 trials in H1 2026. With $6B funding and Grok integration into Oracle services, xAI could allocate 10-20% ($10B-$15B) to MI450 for distributed inference. We haven't heard the detail from Daddy Elon Musk yet, but most likely not going to be spending less than OpenAI or Sam Altman 4. Oracle ($ORCL): A multi-billion-dollar MI355X deal powers OCI's AI superclusters, with $500B+ remaining performance obligations. Larry Ellison's zettascale ambitions and xAI/OpenAI integrations make Oracle a MI450 anchor tenant—projected 50-100k units ($15B+ spend) for enterprise AI platforms. $ORCL is likely to spend more on the new "secret weapon" due to its capability in AI inference and cost advantage for $500B backlog. 5. Others ( Microsoft , Amazon , Saudi+other countries): Microsoft (Azure MI300X for training) and Amazon ($148B 15-year spend) test MI450 via Stargate ($500B with Oracle/SoftBank). Emerging buyers like G42 (5GW UAE campus), Crusoe, and Hot Aisle add 5-10GW demand. These potentially would add $15B-$30B in 2026 alone. We also need to factor in $TSM supply constraint( $NVDA is TSMC favorite), so $AMD market cap/growth is being tamed by TSMC. So what are you saying Mike, well $AMD 2026 revenue could hit $90-$100B by end of 2026 or nearly 185% growth YoYo. So what does that mean for valuation? I have no idea how Mr. Market gonna value AMD in 2026 with 3 digits growth. My Conservative $620 was my best projection until today with OpenAI partnership. I'm telling you as one of the biggest AMD bull, that I will leave it to "smart money" and other investors to do the price discovery while I'm chilling and writing DDs daily. Lastly, AMD's MI450 isn't hype—it's a calibrated strike at Nvidia's vulnerabilities, amplified by hyperscaler bets like Meta's 42% allocation and OpenAI's 6GW lifeline. By prioritizing inference efficiency, rack-scale innovation, and open ecosystems, AMD will siphon 10-15% share in 2026, scaling to 20%+ as TCO trumps CUDA loyalty. Meta, xAI, Oracle et al. aren't passive; they're active co-designers, betting billions on MI450 to fuel AGI pursuits without Nvidia's premium. For investors, this is AMD's inflection Per Dr. Lisa Su Not Financial Advice!

Mike

711,006 Aufrufe • vor 1 Jahr

You spend hours shooting or hunting down raw footage, only to get back to your desktop and face the real nightmare: importing clips one by one into your editor, spending another 30 minutes digging through music libraries for a track, and manually dragging every single piece onto the timeline... Before you even touch a cut or sync a beat, you’ve already burned half your day. I was honestly so done with this tedious, soul-crushing prep work. Then I saw everyone talking about Fotor’s Fotor Agent, so I threw a folder of raw extreme sports footage at it just to see if it was legit. And damn... I actually got hyped. The action editing headache is finally solved. High-energy sports edits live and die by pacing and audio impact. Usually, manual clip scrubbing and beat-matching take hours, while standard AI generators just slap random footage together into an uneditable mess. Instead of dumping a locked MP4 on me, Fotor Agent’s Smart Editing handled all the brutal prep work and handed back a fully synced, multi-track project file. Here are a few details from my test run that genuinely blew me away: Step 1: Intelligent Highlight Extraction (Auto-Select & Timeline Setup) I dumped gigabytes of raw B-roll straight into the Agent. Zero manual scrubbing needed. It analyzed the motion vectors, pulled the highest-impact peak action frames (the heavy landings, freefalls, and massive jumps), trimmed the dead space, and laid everything out in logical narrative order. Step 2: Auto-BGM Matching + Beat-Syncing + Multi-Track SFX Hunting for tracks and sound design usually makes me want to pull my hair out. Fotor Agent didn't just pick a track with the right energy—it snapped visual cuts precisely to the bass drops. Even better, it automatically layered swooshes, risers, and impact SFX right on the action points, each isolated on its own editable audio track. Step 3: Non-Destructive Tweaks (Swap Clips Without Re-Prompting) This is where it turns into a real production tool. When I wanted to swap out a mountain bike shot for a tighter POV angle, I didn't have to re-render the whole project or re-prompt. I just swapped that single clip right on the timeline, and the Agent auto-adjusted surrounding transitions while keeping the beat sync perfectly intact. Step 4: Native 4K Motion Graphics (High-End Quality for Cents) Action reels look flat without slick animated titles and telemetry data. Keyframing these in After Effects takes forever, and outsourcing them can easily cost thousands. Fotor Agent generated sharp, native 4K vector motion graphics directly on the timeline that I could edit anytime—costing just a few cents per second. On top of that, while tweaking the project, I noticed I could freely stack custom filters and seamless transitions. The final output didn't just look like a quick edit—it delivered a true commercial-grade asset that you could actually hand over to a paying brand client or run as a high-converting ad. No more burning hours on mindless file imports and manual clip alignment. Fotor Agent handles the heavy structural setup and timing, leaving you with total creative control over the final cut. Check the workflow and final video below! 👇🏼
1:32

You spend hours shooting or hunting down raw footage, only to get back to your desktop and face the real nightmare: importing clips one by one into your editor, spending another 30 minutes digging through music libraries for a track, and manually dragging every single piece onto the timeline... Before you even touch a cut or sync a beat, you’ve already burned half your day. I was honestly so done with this tedious, soul-crushing prep work. Then I saw everyone talking about Fotor’s Fotor Agent, so I threw a folder of raw extreme sports footage at it just to see if it was legit. And damn... I actually got hyped. The action editing headache is finally solved. High-energy sports edits live and die by pacing and audio impact. Usually, manual clip scrubbing and beat-matching take hours, while standard AI generators just slap random footage together into an uneditable mess. Instead of dumping a locked MP4 on me, Fotor Agent’s Smart Editing handled all the brutal prep work and handed back a fully synced, multi-track project file. Here are a few details from my test run that genuinely blew me away: Step 1: Intelligent Highlight Extraction (Auto-Select & Timeline Setup) I dumped gigabytes of raw B-roll straight into the Agent. Zero manual scrubbing needed. It analyzed the motion vectors, pulled the highest-impact peak action frames (the heavy landings, freefalls, and massive jumps), trimmed the dead space, and laid everything out in logical narrative order. Step 2: Auto-BGM Matching + Beat-Syncing + Multi-Track SFX Hunting for tracks and sound design usually makes me want to pull my hair out. Fotor Agent didn't just pick a track with the right energy—it snapped visual cuts precisely to the bass drops. Even better, it automatically layered swooshes, risers, and impact SFX right on the action points, each isolated on its own editable audio track. Step 3: Non-Destructive Tweaks (Swap Clips Without Re-Prompting) This is where it turns into a real production tool. When I wanted to swap out a mountain bike shot for a tighter POV angle, I didn't have to re-render the whole project or re-prompt. I just swapped that single clip right on the timeline, and the Agent auto-adjusted surrounding transitions while keeping the beat sync perfectly intact. Step 4: Native 4K Motion Graphics (High-End Quality for Cents) Action reels look flat without slick animated titles and telemetry data. Keyframing these in After Effects takes forever, and outsourcing them can easily cost thousands. Fotor Agent generated sharp, native 4K vector motion graphics directly on the timeline that I could edit anytime—costing just a few cents per second. On top of that, while tweaking the project, I noticed I could freely stack custom filters and seamless transitions. The final output didn't just look like a quick edit—it delivered a true commercial-grade asset that you could actually hand over to a paying brand client or run as a high-converting ad. No more burning hours on mindless file imports and manual clip alignment. Fotor Agent handles the heavy structural setup and timing, leaving you with total creative control over the final cut. Check the workflow and final video below! 👇🏼

ares. 🎧

27,991 Aufrufe • vor 4 Tagen

MARBLEX kicked off 2024 with an explosive January! 🔥 Exciting announcements, events, rewards, free NFTs, and more! Check out this summary of the key highlights that propelled MARBLEX to new heights this month. 👇🏻 🟣MARBLEX x APTOS x GALXE⚫️ events and numerous rewards: With the arrival of MBX on the Aptos chain, they organized many events filled with significant rewards in MBX, OAT, and NFTs. • The first season of the Galxe campaign: With a total of 1000 $MBX in rewards for those reaching the top 10 by referring more people, plus a draw among other participants. And of course! Many users claimed the pioneer MARBLEX OAT! (ended) • MBX x APT liquidity pool event: With a massive pool prize of $30,000, to be distributed among those providing liquidity to the MBX/APT pair on the Liquidswap DEX. Note! This event is still ongoing until February 28. • Second season in the Galxe campaign: With a huge pool prize of $45,000 worth of MBX and, if that wasn't enough, around 16 Marblership NFTs distributed among the top 10. This campaign is still ongoing, but it involves daily tasks, so if you haven't participated from the beginning, it will be impossible to reach the top 10. However, you can still obtain all the OATs from the campaign, which will have some utility in the future in the MARBLEX NFT staking. 🔥 MBX token burn: In the fourth quarter of 2024, 189,413.8298 MBX tokens were burned, and the process is expected to intensify with a more active ecosystem. Transparent information about token burn can be accessed through the MBX explorer service. 🎨MARBLEX CREW 👼🏻(ANGEL & AMBASSADOR PROGRAM): As MARBLEX mentioned last year, Season 5 of the angels and ambassadors program, now called MARBLEX CREW, had significant changes. More than 30 members were recruited among angels and creators to spread the word and support the MARBLEX community and users who are not yet familiar with this wonderful web3 gaming ecosystem. And with many more rewards than before, including NFTs, OATs, MBX bonuses, and more. 💬MARBLEX chatting party event: (ended) MARBLEX Discord now has auto-translation for English, Thai, Japanese, and Korean, breaking language barriers and also rewarding the most active members of the community. The event has ended, but cheer up! It's likely that more similar events will come with additional languages. 😺🃏Revelation of the 2 new NFT collections: Some details were revealed about the 2 new NFT collections, the Puzzle, and Lunar Animals. I've already created an extensive thread about this, but I'll give you a brief overview: The Lunar Animals collection is based on 12 zodiac animals and a cat, each with 3 grades that can mine MBX. The Puzzle collection, where each NFT represents a letter, and there's also a joker card (Marblex-chan). The idea is to combine several letters to form a word. The first word revealed is M-A-R-B-L-E-X. Once the word is formed, you can obtain an NFT from the Lunar Animals collection using the NFT swap (MARBLEX's next service). 🤝The 1+1 referral event: This event aimed to increase participation in the second campaign collection on Galxe. The event offered rewards in the form of Marblership Puzzle NFTs, encouraging users to invite friends to join Galxe. The event has already ended. 🌕Lunar Guardians NFT Ambassador Program: MARBLEX recruited more than 60 NFT ambassadors to promote the upcoming NFT collections. In Conclusion: As you can see, January was filled with significant milestones for Marblex, laying the groundwork for explosive growth in 2024 and beyond. From expanding to new chains like Aptos to exciting revelations about upcoming NFT collections, Marblex is aggressively executing its roadmap. With a committed community, high-profile partnerships, engaging events, and a consistent focus on token utility, the future looks extremely promising. There's no doubt that Marblex will dominate the future of blockchain gaming. If you're not yet part of this innovative ecosystem, now is the perfect time to join. Become a part of the Marblex community and be a protagonist in the next gaming revolution. We look forward to welcoming you! Links below!👇🏻
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MARBLEX kicked off 2024 with an explosive January! 🔥 Exciting announcements, events, rewards, free NFTs, and more! Check out this summary of the key highlights that propelled MARBLEX to new heights this month. 👇🏻 🟣MARBLEX x APTOS x GALXE⚫️ events and numerous rewards: With the arrival of MBX on the Aptos chain, they organized many events filled with significant rewards in MBX, OAT, and NFTs. • The first season of the Galxe campaign: With a total of 1000 $MBX in rewards for those reaching the top 10 by referring more people, plus a draw among other participants. And of course! Many users claimed the pioneer MARBLEX OAT! (ended) • MBX x APT liquidity pool event: With a massive pool prize of $30,000, to be distributed among those providing liquidity to the MBX/APT pair on the Liquidswap DEX. Note! This event is still ongoing until February 28. • Second season in the Galxe campaign: With a huge pool prize of $45,000 worth of MBX and, if that wasn't enough, around 16 Marblership NFTs distributed among the top 10. This campaign is still ongoing, but it involves daily tasks, so if you haven't participated from the beginning, it will be impossible to reach the top 10. However, you can still obtain all the OATs from the campaign, which will have some utility in the future in the MARBLEX NFT staking. 🔥 MBX token burn: In the fourth quarter of 2024, 189,413.8298 MBX tokens were burned, and the process is expected to intensify with a more active ecosystem. Transparent information about token burn can be accessed through the MBX explorer service. 🎨MARBLEX CREW 👼🏻(ANGEL & AMBASSADOR PROGRAM): As MARBLEX mentioned last year, Season 5 of the angels and ambassadors program, now called MARBLEX CREW, had significant changes. More than 30 members were recruited among angels and creators to spread the word and support the MARBLEX community and users who are not yet familiar with this wonderful web3 gaming ecosystem. And with many more rewards than before, including NFTs, OATs, MBX bonuses, and more. 💬MARBLEX chatting party event: (ended) MARBLEX Discord now has auto-translation for English, Thai, Japanese, and Korean, breaking language barriers and also rewarding the most active members of the community. The event has ended, but cheer up! It's likely that more similar events will come with additional languages. 😺🃏Revelation of the 2 new NFT collections: Some details were revealed about the 2 new NFT collections, the Puzzle, and Lunar Animals. I've already created an extensive thread about this, but I'll give you a brief overview: The Lunar Animals collection is based on 12 zodiac animals and a cat, each with 3 grades that can mine MBX. The Puzzle collection, where each NFT represents a letter, and there's also a joker card (Marblex-chan). The idea is to combine several letters to form a word. The first word revealed is M-A-R-B-L-E-X. Once the word is formed, you can obtain an NFT from the Lunar Animals collection using the NFT swap (MARBLEX's next service). 🤝The 1+1 referral event: This event aimed to increase participation in the second campaign collection on Galxe. The event offered rewards in the form of Marblership Puzzle NFTs, encouraging users to invite friends to join Galxe. The event has already ended. 🌕Lunar Guardians NFT Ambassador Program: MARBLEX recruited more than 60 NFT ambassadors to promote the upcoming NFT collections. In Conclusion: As you can see, January was filled with significant milestones for Marblex, laying the groundwork for explosive growth in 2024 and beyond. From expanding to new chains like Aptos to exciting revelations about upcoming NFT collections, Marblex is aggressively executing its roadmap. With a committed community, high-profile partnerships, engaging events, and a consistent focus on token utility, the future looks extremely promising. There's no doubt that Marblex will dominate the future of blockchain gaming. If you're not yet part of this innovative ecosystem, now is the perfect time to join. Become a part of the Marblex community and be a protagonist in the next gaming revolution. We look forward to welcoming you! Links below!👇🏻

ETHachi Uchiha | Crypto DEGENius

45,470 Aufrufe • vor 2 Jahren