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Make Pixels Dance: High-Dynamic Video Generation paper page: Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.
3:09

Make Pixels Dance: High-Dynamic Video Generation paper page: Creating high-dynamic videos such as motion-rich actions and sophisticated visual effects poses a significant challenge in the field of artificial intelligence. Unfortunately, current state-of-the-art video generation methods, primarily focusing on text-to-video generation, tend to produce video clips with minimal motions despite maintaining high fidelity. We argue that relying solely on text instructions is insufficient and suboptimal for video generation. In this paper, we introduce PixelDance, a novel approach based on diffusion models that incorporates image instructions for both the first and last frames in conjunction with text instructions for video generation. Comprehensive experimental results demonstrate that PixelDance trained with public data exhibits significantly better proficiency in synthesizing videos with complex scenes and intricate motions, setting a new standard for video generation.

AK

102,444 görüntüleme • 2 yıl önce

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!
0:27

Everyone is sleeping on Meta's SAM 3 release. But it's actually a big deal. Here's why: Companies spend millions paying humans to label images and videos frame by frame. A single autonomous driving dataset? Months of work, hundreds of annotators, millions in cost. Without labeled data, you can't train custom models. Without custom models, you're stuck with generic solutions. This is why most companies never move past pilots. SAM 3 breaks this cycle. First let's look at the evolution: SAM 1 segmented objects when you clicked on them. Revolutionary, but one object at a time. SAM 2 added video tracking with memory. Game-changing, but you still manually prompted every object. SAM 3 changes everything with text prompts. Type "yellow school bus" and it finds ALL of them in your image or video. Not just one. Every instance across thousands of frames. Now here's where people get confused: "Can't I just use GPT-5 or Gemini for this?" No, and here's why that's a terrible approach. Large multimodal LLMs are great for reasoning, but they're slow and expensive for production visual tasks. You're paying API costs per image, waiting seconds for responses, getting inconsistent results. SAM 3 runs in 30 milliseconds on a single GPU for 100+ objects. That's 100x faster, and you own the infrastructure. More importantly, SAM 3 gives you precise pixel-level masks, not descriptions. Try asking an LLM to segment every defective part on a manufacturing line in real-time. It won't work. SAM 3 does this effortlessly. The real breakthrough is their data engine. Meta built an AI-human hybrid system that's 5x faster for complex annotations. They trained SAM 3 on 4 million unique visual concepts - 50x more than existing benchmarks like LVIS. SAM 3 is trained on 4 million unique visual concepts, it handles everything: - Text-based concept search - Interactive refinement with clicks - Video tracking across frames - Zero-shot detection of new concepts The model is open source. Weights, code, and benchmarks are on GitHub. If you're building computer vision applications, this is the foundation model to evaluate. The annotation time savings alone will pay for integration costs within weeks. Find the relevant links in the next tweet!

Akshay 🚀

46,404 görüntüleme • 7 ay önce

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:
1:29

QVAC SDK 0.12.0 is now live, bringing longer context, increased memory optimisation, new modalities, and broader ecosystem support directly to your device. Key Features and Updates: - TurboQuant KV-Cache Quantization: Fit much longer context in the same memory. TurboQuant, an algorithm from Google Research, compresses the KV cache by up to 5x, near-lossless. - Text-to-Video: Generate video from a text prompt, fully local, with the new wan2.1 model in the Diffusion addon - Apple Metal Performance for Flux2-klein: Diffusion on Apple Silicon now matches MLX performance, the native benchmark for Apple GPUs - Robot Control (new VLA addon): A GGML-based Vision-Language-Action addon brings fast, efficient robot control to edge devices - Coding Assistant / Harness Support: QVAC now works with OpenCode and OpenClaw as a local provider. A new @qvac/ai-sdk-provider package automates model registry and provider integration - Cross-Platform Voice: Text-to-speech and Parakeet transcription moved from ONNX to the GGML engine for better CPU and GPU support on macOS, iOS, Windows, Linux, and Android. Parakeet also adds long-term streaming diarization (tracking who spoke when on live audio) - Faster Lightweight Visual Classification: A new GGML-based Classification addon delivers millisecond-level classification, useful where a vision-language model (VLM) would be unnecessarily slow - Under the Hood: Fabric synced to llama.cpp v8828 (from v8189), plus GPU acceleration added to image-upscale models for faster results Full release notes:

QVAC

9,932,369 görüntüleme • 1 ay önce

Victoria Derbyshire, "Elon Musk had already hit out, calling the UK a police state" "Adding, the real goal is to enable the UK government to track everyone" Speaking of tracking people, here are 30 ways Twitter does it: 1. Account activity (posts, likes, reposts, follows, replies, searches) 2. Time spent viewing specific posts 3. Clicks on links and media 4. Cookies stored in your browser 5. IP address 6. Device identifiers 7. Browser fingerprinting signals (browser type, screen size, language settings, etc.) 8. Mobile advertising IDs (Android Advertising ID, Apple Advertising Identifier where available) 9. Location data (GPS if permitted, IP-based location, Wi-Fi/network information) 10. Contact uploads (if you grant access) 11. Email address and phone number 12. Payment information (for paid services) 13. Cross-device matching (linking your phone, tablet, and computer to the same user) 14. Embedded X posts on third-party websites 15. X Pixel tracking on external websites 16. Websites using X advertising or conversion tools 17. Apps using X SDKs or integrations 18. Login with X integrations on third-party sites 19. Ad interactions and conversions 20. Inferred interests and behavioural profiling 21. Social graph analysis (who you follow, interact with, and are connected to) 22. Content analysis of posts, messages, and media 23. Network and connection information (mobile carrier, ISP, network type) 24. Diagnostic and crash reports from the app 25. Approximate location derived from activity patterns 26. Data obtained from advertising partners and data providers 27. Engagement with videos (watch time, rewatches, completion rates) 28. Search history on the platform 29. Hashtags, topics, and communities you engage with 30. Account recovery and security information
0:23

Victoria Derbyshire, "Elon Musk had already hit out, calling the UK a police state" "Adding, the real goal is to enable the UK government to track everyone" Speaking of tracking people, here are 30 ways Twitter does it: 1. Account activity (posts, likes, reposts, follows, replies, searches) 2. Time spent viewing specific posts 3. Clicks on links and media 4. Cookies stored in your browser 5. IP address 6. Device identifiers 7. Browser fingerprinting signals (browser type, screen size, language settings, etc.) 8. Mobile advertising IDs (Android Advertising ID, Apple Advertising Identifier where available) 9. Location data (GPS if permitted, IP-based location, Wi-Fi/network information) 10. Contact uploads (if you grant access) 11. Email address and phone number 12. Payment information (for paid services) 13. Cross-device matching (linking your phone, tablet, and computer to the same user) 14. Embedded X posts on third-party websites 15. X Pixel tracking on external websites 16. Websites using X advertising or conversion tools 17. Apps using X SDKs or integrations 18. Login with X integrations on third-party sites 19. Ad interactions and conversions 20. Inferred interests and behavioural profiling 21. Social graph analysis (who you follow, interact with, and are connected to) 22. Content analysis of posts, messages, and media 23. Network and connection information (mobile carrier, ISP, network type) 24. Diagnostic and crash reports from the app 25. Approximate location derived from activity patterns 26. Data obtained from advertising partners and data providers 27. Engagement with videos (watch time, rewatches, completion rates) 28. Search history on the platform 29. Hashtags, topics, and communities you engage with 30. Account recovery and security information

Farrukh

93,801 görüntüleme • 1 ay önce

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.
0:05

📢 Our lab has been exploring 3D world models for years — and we’re thrilled to share **PhysTwin**: a milestone that reconstructs object appearance, geometry, and dynamics from just a few seconds of interaction! Led by the amazing Hanxiao Jiang 👉 PhysTwin combines **Gaussian splatting** with **inverse dynamics optimization** based on simple **spring-mass** systems. ⚙️ The result? Real-time, action-conditioned 3D video prediction under novel interactions (i.e., 3D world models). 🔑 A few key takeaways: 1. Having the right structure (e.g., particles/masses) helps navigate the trade-off between sample efficiency, generalization, and broad applicability. 2. Visual foundation models (VFMs) have matured to the point where they can provide rich supervision for world modeling (e.g., tracking, shape completion). 3. Beyond VFMs, many crucial components have come together in recent years: Gaussian splats for rendering, NVIDIA Warp for high-performance simulation, and scene/asset generation from a wide range of labs and companies. The future of 3D world models is looking bright! ✨ 4. The resulting digital twin supports a wide range of downstream applications—especially in data generation and policy evaluation, thanks to its realistic rendering and simulation capabilities. 🎥 All code and data to reproduce the results, along with interactive demos, are available on the website. Check the following visualizations of: (1) observations, (2) reconstructed state/actions, (3) interactive digital twins, and (4) the overlays between real-world robot teleoperation and our model’s open-loop predictions.

Yunzhu Li

25,279 görüntüleme • 1 yıl önce

New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!
2:40

New short course Multimodal RAG: Chat with Videos, developed with Intel and taught by vasudevlal! In this course, you’ll work with LLaVA (Large Language and Vision Assistant), a Large Vision Language Model (LVLM) that can process both images and text. For example, given an image of a person doing a handstand on a skateboard at the beach, LLaVA doesn't just caption the scene, it’s able to predict possible outcomes, like the person losing balance or falling off. By understanding not just what's in a video frame, but what might happen next, your application can provide more insightful answers to questions about video. You'll build a full multimodal RAG pipeline that can chat about video content: - Use the BridgeTower model to create joint text-image embeddings in a 512-dimensional multimodal semantic space. - Learn video processing techniques to extract keyframes, generate transcripts using Whisper, and create captions. - Use the LanceDB vector database to store and retrieve high-dimensional multimodal embeddings. - Integrate the LLaVA model, combining CLIP's (Contrastive Language Image Pretraining) vision transformer with Llama, for advanced visual-textual reasoning. Your final system will ingest video data, generate embeddings for frames and text, perform similarity searches for relevant content, and use the retrieved multimodal context to inform LVLM-based response generation. The result is a system capable of answering nuanced questions about video content, effectively chatting about the video it has processed. Please sign up here!

Andrew Ng

107,548 görüntüleme • 1 yıl önce

Consistent Hashing Explained Simply Let's break down consistent hashing in a way that's easy to get. It's a handy technique for distributing data evenly across servers. And if you're more of a visual learner, don't miss the attached video which brings this concept to life. Here's how it works: We don't just hash our data keys; we also hash the server names using the same method. This puts everything on a scale, known as the hash space, which we think of as a big circular ring. So, imagine we've got a few servers. We hash them and place them on this ring. We do the same with our data, hashing it by key. To figure out where to store a piece of data, we travel clockwise on the ring from where the data lands until we bump into a server. Like, data key 0 might end up on server 0, and key 1 on server 1. Adding a new server? Only the data near the new server needs to shift. And if a server has to go? Only the data it was holding needs a new spot. But there's a little challenge: sometimes, data doesn't spread out evenly. The fix? Virtual nodes. They're like mini-replicas of a server placed around the ring. The more virtual nodes we have, the better the balance. But there's a catch: more virtual nodes mean more memory usage for tracking them, and it can make the whole system a bit more complex to manage. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages):
2:53

Consistent Hashing Explained Simply Let's break down consistent hashing in a way that's easy to get. It's a handy technique for distributing data evenly across servers. And if you're more of a visual learner, don't miss the attached video which brings this concept to life. Here's how it works: We don't just hash our data keys; we also hash the server names using the same method. This puts everything on a scale, known as the hash space, which we think of as a big circular ring. So, imagine we've got a few servers. We hash them and place them on this ring. We do the same with our data, hashing it by key. To figure out where to store a piece of data, we travel clockwise on the ring from where the data lands until we bump into a server. Like, data key 0 might end up on server 0, and key 1 on server 1. Adding a new server? Only the data near the new server needs to shift. And if a server has to go? Only the data it was holding needs a new spot. But there's a little challenge: sometimes, data doesn't spread out evenly. The fix? Virtual nodes. They're like mini-replicas of a server placed around the ring. The more virtual nodes we have, the better the balance. But there's a catch: more virtual nodes mean more memory usage for tracking them, and it can make the whole system a bit more complex to manage. – Subscribe to our weekly newsletter to get a Free System Design PDF (158 pages):

Sahn Lam

36,787 görüntüleme • 2 yıl önce

NEW: Here's the walk and talk from after the final pretrial conference leading up to next week's Karen Read retrial. I asked Karen if she was pleased to win a motion preventing her text messages with David Yannetti from coming into evidence (possibly involving a confession that Karen "didn't think she hit John that hard" with her car on the morning of 1/29/22). Although Karen had no comment, Read did mention she does not plan to be silent going forward during trial, when questioned by my colleagues (however her lawyers are subject to a gag order, and only commented on the Celtics). Also, while Aidan TurtleBoy earney was at today's hearing, he left before Read walked out of the courthouse. Kearney, who is on the state's witness list, has already had the contents of his phone handed over to Hank Brennan, as to Kearney's communications with Read, and that may well be a preview of Brennan's upcoming motion for a consciousness of guilt instruction to jurors during Read's retrial. Overall, after a flurry of other motions were decided earlier last week, only two dozen motions remain outstanding before jury empanelment begins on Tuesday April 1, 2025. Judge Cannone still has a number of motions under advisement (including major motions related to the defense being prohibited from, again, raising a third party culprit defense without court permission, along with motions related to introducing evidence of John O'Keefe's state of mind, as to John wanting to end the relationship with Karen because of Karen cheating and because of Karen's views on John's adopted, double-orphaned, niece and nephew). 30 such motions have been decided, and 22 remain outstanding, as of 2pm ET on 3/25/25. I. Motions with rulings: 1. Commonwealth's Motion to Unseal FTR Audio from March 4, 2025 for Counsel of Record Only (Docket 542) Ruling: Allowed (Cannone, J) - Dated 3/4/25 2. Commonwealth's Motion to Unseal Recordings of Gretchen Voss and/or Metro Corp. (Docket 543) Ruling: Allowed without opposition (Cannone, J) - Dated 3/5/25 3. Commonwealth's Motion for Protective Order (Docket 557) Ruling: So Ordered (Cannone, J) - Dated 3/10/25 4. Defendant's Motion in Limine to Exclude Certain Witnesses Before and After Testimony (UNREDACTED) (Docket 562) Ruling: Allowed, except those witnesses exempted by Docket 576. Witnesses are sequestered before and after testimony until the close of evidence. When testimony is concluded, the courtroom is open and no one is excluded. (Cannone, J) - Dated 3/18/25 5. Defendant's Motion in Limine for View (Docket 564) Ruling: Allowed; Counsel need to work out details of exactly how Defendant will attend. (Cannone, J) - Dated 3/18/25 6. Defendant's Motion in Limine to Prohibit Testimony Regarding Funds Paid to Experts for Purposes of Voir Dire (Docket 565) Ruling: Allowed by agreement (Cannone, J) - Dated 3/18/25 7. Defendant's Motion for Attorney-Conducted Panel Voir Dire (Docket 566) Ruling: Denied (Cannone, J) - Dated 3/18/25 8. Defendant's Motion for Order Prohibiting the Commonwealth and its Agents from Speaking with or Having Contact with Any Witnesses Once They Have Taken the Stand (Docket 567) Ruling: Allowed as to both sides (Cannone, J) - Dated 3/18/25 9. Defendant's Motion to Impound Defendant's Motion in Limine to Exclude Irrelevant, Inadmissible, and Prejudicial Evidence Regarding Alleged Harassment and/or Intimidation of Witnesses (Docket 568) Ruling: Allowed (Cannone, J) - Dated 3/18/25 10. Defendant's Motion to Impound Defendant's Motion in Limine to Exclude Witness's Prior Criminal History (Docket 572) Ruling: Allowed (Cannone, J) - Date not specified 11. Defendant's Motion in Limine to Exclude Witness's Prior Criminal History (IMPOUNDED) (Docket 574) Ruling: Allowed without objection (Cannone, J) - Dated 3/18/25 12. Commonwealth's Motion in Limine to Allow In-Court Identification (Docket 577) Ruling: Allowed (Cannone, J) - Dated 3/19/25 13. Commonwealth's Motion in Limine to Admit (1) Victim's Photograph and (2) Photographs of the Victim's Injuries as Observed by Medical Providers on January 29, 2022 from Autopsy (Docket 578) Ruling: Allowed (Cannone, J) - Dated 3/19/25 14. Commonwealth's Motion in Limine to Introduce Certified Records from Registry of Motor Vehicles (Docket 580) Ruling: Allowed (Cannone, J) - Dated 3/18/25 15. Commonwealth's Motion in Limine of Intent to Obtain CORI Records of Potential Jurors (Docket 581) Ruling: Allowed (Cannone, J) - Dated 3/18/25 16. Commonwealth's Motion for Offer of Proof Prior to Defendant Calling or Summoning the Norfolk District Attorney and Victim Witness Advocate as Witnesses and Request for an Order that Neither is Subject to a Sequestration Order (Docket 582) Ruling: Allowed; No objection (Cannone, J) - Dated 3/18/25 17. Commonwealth's Motion in Limine Requesting Use of Chalks and Directing Both Parties to Provide Visual Presentations and Chalks Prior to Using (Docket 583) Ruling: Allowed (Cannone, J) - Dated 3/19/25 18. Commonwealth's Motion in Limine to Allow Expert Cellebrite Demonstration (Docket 584) Ruling: After hearing, Allowed (Cannone, J) - Dated 3/21/25 19. Commonwealth's Motion in Limine to Admit Results of Defendant's Blood Draw at Good Samaritan Hospital and Resulting Serum Conversion and Retrograde Extrapolation (Docket 586) Ruling: After hearing, Allowed (Cannone, J) - Dated 3/21/25 20. Commonwealth's Motion in Limine to Preclude Reference to Any Alleged "Bad Character" and Any Prior "Misconduct" of the Victim or Any Witness (Docket 587) Ruling: If counsel intend to introduce this evidence, Defendant must provide notice to the Commonwealth one week before the evidence begins (Cannone, J) - Dated 3/19/25 21. Defendant's Motion in Limine to Exclude False and Irrelevant Statements of Michael Proctor (Docket 591) Ruling: Commonwealth does not intend to offer this evidence. Stated in open court on 3/20/25 (Cannone, J) - Dated 3/20/25 22. Defendant's Motion in Limine to Allow Counsel to State Grounds for Objections to Promote Judicial Economy and Efficiency and to Adequately Preserve Issues (Docket 595) Ruling: Denied (Cannone, J) - Dated 3/19/25 23. Defendant's Motion to Impound the Defendant's Motion in Limine to Prohibit the Norfolk District Attorney and Massachusetts State Police from Having Juror Contact (Docket 597) Ruling: Allowed in that the Exhibits are Impounded (Cannone, J) - Dated 3/12/25 24. Defendant's Motion in Limine to Prohibit the Norfolk District Attorney and Massachusetts State Police from Having Juror Contact (Docket 599) Ruling: Motion based on a false premise. No one other than Trial Court Security coordinates juror security, and no one other than Court Officers has contact with jurors during trial. Norfolk County DA’s office, State Police, defense counsel, and media will have no contact with trial jurors (Cannone, J) - Dated 3/19/25 25. Defendant's Motion to Impound "Exhibit 1" in Support of Defendant's Motion in Limine to Exclude the Testimony of Commonwealth's Witness Dr. Judson Welcher, M.S., Ph.D., and Request for Voir Dire (Docket 604) Ruling: Allowed (Cannone, J) - Dated 3/14/25 26. Defendant's Supplemental Motion for Admission Pro Hac Vice of Mark A. Bederow (Docket 549) Ruling: Decision and Order [denying motion] issued (Cannone, J) - Dated 3/7/25 (Docket 551) 27. Commonwealth's Motion for Protective Order (Docket 617) Ruling: So Ordered (Cannone, J) - Dated 3/20/25 28. Defendant's Motion to Exclude the Testimony of the Commonwealth's Witness James W. Crosby, MS PhD Ruling: Denied (Cannone, J) - Dated 3/6/25 (Docket 548) 29. Commonwealth's Motion for Records from Dockets 2842CR00043 and 2382CR00313 [Aidan TurtleBoy Kearney open felony cases involving Read trial witnesses and Lindsey Gaetani) addressed to Special Assistant District Attorney Robert Cosgrove (Docket 546) Status: Allowed in part (Sisitsky, A). Memorandum & Order was issued on 3/20/25 (Docket 615) and an Order for Production of Records followed (Docket 616) 30. Commonwealth's Motion to Compel Communications Between the Defendant and Attorney Yannetti Stored within the Defendant's Cell Phone Data (Docket 618) Status: Denied (Cannone, J) - Dated 3/25/25 II. Motions with No Ruling or Taken Under Advisement: 31. Defendant's Motion to Exclude the Purported Expert Testimony of the Commonwealth's Proffered Witness Dr. Aizik L. Wolf and Request Daubert-Lanigan Hearing (Docket 540) Status: No ruling specified 32. Commonwealth's Motion for Extension of Time to File Motions in Limine Related to Defense Expert Reciprocal Discovery (Docket 552) Status: No ruling specified 33. Commonwealth's Motion to Correct the Record (Docket 553) Status: No ruling specified 34. Defendant's Motion in Limine to Present Demonstrative Exhibit Based on Christina Hanley's Testimony (Docket 558) Status: No action taken at this time (Cannone, J) - Dated 3/20/25 35. Defendant's Motion in Limine to Exclude Irrelevant, Inadmissible, and Prejudicial Prior Bad Character and Propensity Evidence (Docket 559) Status: No ruling specified 36. Defendant's Motion to Impound Portions of Defendant's Motion in Limine to Exclude Certain Witnesses Before and After Testimony (Docket 560) Status: No ruling specified 37. Defendant's Motion in Limine to Exclude Serum/Plasma Ethanol Concentration, Blood Ethanol Concentration Conversion, and Corresponding Retrograde Extrapolation Analysis (Docket 571) Status: No ruling specified 38. Commonwealth's Motion in Limine to Appoint Court Stenographer, Prevent Identification of Jurors, and Impound Juror Names During Trial (Docket 575) Status: No ruling specified 39. Commonwealth's Motion in Limine for Sequestration Order and for Relief from that Order for Family Members of the Victim (Docket 576) Status: No ruling specified (though referenced in Docket 562 as exempting certain witnesses, indicating 576 may soon be allowed) 40. Commonwealth's Motion in Limine to Preclude Reference to and Redact the Manner of Death Contained on the Victim's Death Certificate (Docket 579) Status: No ruling specified 41. Commonwealth's Motion in Limine to Admit the Victim's Out of Court Statements Relating to His State of Mind (Docket 585) Status: No action taken - Need to see how the evidence develops (Cannone, J) - Dated 3/20/25 42. Commonwealth's Motion in Limine to Prohibit Reference to Any Pending Internal Affairs Investigations; Sustained Findings Unrelated to This Case; or Unfounded Allegations of Misconduct (Docket 588) Status: No action (Cannone, J) - Dated 3/19/25 43. Commonwealth's Motion in Limine to Preclude the Defendant from Raising a Third-Party Culprit Defense (Docket 589) Status: No ruling specified (Defense filed opposition, 3/25/25, Docket 625) 44. Commonwealth's Motion for Attorney-Conducted and Individual Voir Dire of Potential Jurors and Proposed Jury Questionnaire (Docket 590) Status: No ruling specified 45. Defendant's Motion in Limine to Allow Evidence of Lack of Bias (Docket 593) Status: No ruling specified (Commonwealth filed opposition, 3/17/25, Docket 610) 46. Defendant's Motion for Reconsideration of March 6, 2025 Court Order Denying Defendant's Motion to Exclude the Testimony of the Commonwealth's Witness James W. Crosby, MS PhD (Docket 601) Status: No ruling specified 47. Defendant's Motion in Limine to Exclude the Testimony of Commonwealth's Witness Dr. Judson Welcher, M.S., Ph.D., and Request for Voir Dire (Docket 606) Status: No ruling specified (Commonwealth filed opposition, 3/17/25, Docket 609) 48. Commonwealth's Motion in Limine to Exclude Expert Testimony of Garrett Wing (Docket 607) Status: No ruling specified 49. Commonwealth's Motion in Limine to Exclude Defense's Expert Michael Easter's Opinion of the Investigation (Docket 608) Status: No ruling specified 50. Commonwealth's Motion for Buffer Zone and Order Prohibiting Signs or Clothing in Favor of Either Party or Law Enforcement (Docket 611) Status: No ruling specified 51. Defendant's Assented to Motion to Continue Trial Date on or After April 25, 2025 (Docket 613) Status: Denied without prejudice. Trial will begin with empanelment on April 1, 2025 as scheduled. If a jury is selected before April 28, 2025, counsel may renew the motion prior to the jury being sworn (Cannone, J) - Dated 3/19/25 51. Commonwealth's Motion for Records: Unsolved Productions, INC (Docket 620) Status: No ruling specified 52. Defendant 's Motion to Dismiss for Extraordinary Governmental Misconduct (REDACTED) (Docket 519) Status: No ruling specified (Commonwealth filed opposition 2/28/25, Docket 526) Please let me know if I missed any rulings, or if any information is misplaced. This content may be reposted, with attribution and a link to the original post.
3:36

NEW: Here's the walk and talk from after the final pretrial conference leading up to next week's Karen Read retrial. I asked Karen if she was pleased to win a motion preventing her text messages with David Yannetti from coming into evidence (possibly involving a confession that Karen "didn't think she hit John that hard" with her car on the morning of 1/29/22). Although Karen had no comment, Read did mention she does not plan to be silent going forward during trial, when questioned by my colleagues (however her lawyers are subject to a gag order, and only commented on the Celtics). Also, while Aidan TurtleBoy earney was at today's hearing, he left before Read walked out of the courthouse. Kearney, who is on the state's witness list, has already had the contents of his phone handed over to Hank Brennan, as to Kearney's communications with Read, and that may well be a preview of Brennan's upcoming motion for a consciousness of guilt instruction to jurors during Read's retrial. Overall, after a flurry of other motions were decided earlier last week, only two dozen motions remain outstanding before jury empanelment begins on Tuesday April 1, 2025. Judge Cannone still has a number of motions under advisement (including major motions related to the defense being prohibited from, again, raising a third party culprit defense without court permission, along with motions related to introducing evidence of John O'Keefe's state of mind, as to John wanting to end the relationship with Karen because of Karen cheating and because of Karen's views on John's adopted, double-orphaned, niece and nephew). 30 such motions have been decided, and 22 remain outstanding, as of 2pm ET on 3/25/25. I. Motions with rulings: 1. Commonwealth's Motion to Unseal FTR Audio from March 4, 2025 for Counsel of Record Only (Docket 542) Ruling: Allowed (Cannone, J) - Dated 3/4/25 2. Commonwealth's Motion to Unseal Recordings of Gretchen Voss and/or Metro Corp. (Docket 543) Ruling: Allowed without opposition (Cannone, J) - Dated 3/5/25 3. Commonwealth's Motion for Protective Order (Docket 557) Ruling: So Ordered (Cannone, J) - Dated 3/10/25 4. Defendant's Motion in Limine to Exclude Certain Witnesses Before and After Testimony (UNREDACTED) (Docket 562) Ruling: Allowed, except those witnesses exempted by Docket 576. Witnesses are sequestered before and after testimony until the close of evidence. When testimony is concluded, the courtroom is open and no one is excluded. (Cannone, J) - Dated 3/18/25 5. Defendant's Motion in Limine for View (Docket 564) Ruling: Allowed; Counsel need to work out details of exactly how Defendant will attend. (Cannone, J) - Dated 3/18/25 6. Defendant's Motion in Limine to Prohibit Testimony Regarding Funds Paid to Experts for Purposes of Voir Dire (Docket 565) Ruling: Allowed by agreement (Cannone, J) - Dated 3/18/25 7. Defendant's Motion for Attorney-Conducted Panel Voir Dire (Docket 566) Ruling: Denied (Cannone, J) - Dated 3/18/25 8. Defendant's Motion for Order Prohibiting the Commonwealth and its Agents from Speaking with or Having Contact with Any Witnesses Once They Have Taken the Stand (Docket 567) Ruling: Allowed as to both sides (Cannone, J) - Dated 3/18/25 9. Defendant's Motion to Impound Defendant's Motion in Limine to Exclude Irrelevant, Inadmissible, and Prejudicial Evidence Regarding Alleged Harassment and/or Intimidation of Witnesses (Docket 568) Ruling: Allowed (Cannone, J) - Dated 3/18/25 10. Defendant's Motion to Impound Defendant's Motion in Limine to Exclude Witness's Prior Criminal History (Docket 572) Ruling: Allowed (Cannone, J) - Date not specified 11. Defendant's Motion in Limine to Exclude Witness's Prior Criminal History (IMPOUNDED) (Docket 574) Ruling: Allowed without objection (Cannone, J) - Dated 3/18/25 12. Commonwealth's Motion in Limine to Allow In-Court Identification (Docket 577) Ruling: Allowed (Cannone, J) - Dated 3/19/25 13. Commonwealth's Motion in Limine to Admit (1) Victim's Photograph and (2) Photographs of the Victim's Injuries as Observed by Medical Providers on January 29, 2022 from Autopsy (Docket 578) Ruling: Allowed (Cannone, J) - Dated 3/19/25 14. Commonwealth's Motion in Limine to Introduce Certified Records from Registry of Motor Vehicles (Docket 580) Ruling: Allowed (Cannone, J) - Dated 3/18/25 15. Commonwealth's Motion in Limine of Intent to Obtain CORI Records of Potential Jurors (Docket 581) Ruling: Allowed (Cannone, J) - Dated 3/18/25 16. Commonwealth's Motion for Offer of Proof Prior to Defendant Calling or Summoning the Norfolk District Attorney and Victim Witness Advocate as Witnesses and Request for an Order that Neither is Subject to a Sequestration Order (Docket 582) Ruling: Allowed; No objection (Cannone, J) - Dated 3/18/25 17. Commonwealth's Motion in Limine Requesting Use of Chalks and Directing Both Parties to Provide Visual Presentations and Chalks Prior to Using (Docket 583) Ruling: Allowed (Cannone, J) - Dated 3/19/25 18. Commonwealth's Motion in Limine to Allow Expert Cellebrite Demonstration (Docket 584) Ruling: After hearing, Allowed (Cannone, J) - Dated 3/21/25 19. Commonwealth's Motion in Limine to Admit Results of Defendant's Blood Draw at Good Samaritan Hospital and Resulting Serum Conversion and Retrograde Extrapolation (Docket 586) Ruling: After hearing, Allowed (Cannone, J) - Dated 3/21/25 20. Commonwealth's Motion in Limine to Preclude Reference to Any Alleged "Bad Character" and Any Prior "Misconduct" of the Victim or Any Witness (Docket 587) Ruling: If counsel intend to introduce this evidence, Defendant must provide notice to the Commonwealth one week before the evidence begins (Cannone, J) - Dated 3/19/25 21. Defendant's Motion in Limine to Exclude False and Irrelevant Statements of Michael Proctor (Docket 591) Ruling: Commonwealth does not intend to offer this evidence. Stated in open court on 3/20/25 (Cannone, J) - Dated 3/20/25 22. Defendant's Motion in Limine to Allow Counsel to State Grounds for Objections to Promote Judicial Economy and Efficiency and to Adequately Preserve Issues (Docket 595) Ruling: Denied (Cannone, J) - Dated 3/19/25 23. Defendant's Motion to Impound the Defendant's Motion in Limine to Prohibit the Norfolk District Attorney and Massachusetts State Police from Having Juror Contact (Docket 597) Ruling: Allowed in that the Exhibits are Impounded (Cannone, J) - Dated 3/12/25 24. Defendant's Motion in Limine to Prohibit the Norfolk District Attorney and Massachusetts State Police from Having Juror Contact (Docket 599) Ruling: Motion based on a false premise. No one other than Trial Court Security coordinates juror security, and no one other than Court Officers has contact with jurors during trial. Norfolk County DA’s office, State Police, defense counsel, and media will have no contact with trial jurors (Cannone, J) - Dated 3/19/25 25. Defendant's Motion to Impound "Exhibit 1" in Support of Defendant's Motion in Limine to Exclude the Testimony of Commonwealth's Witness Dr. Judson Welcher, M.S., Ph.D., and Request for Voir Dire (Docket 604) Ruling: Allowed (Cannone, J) - Dated 3/14/25 26. Defendant's Supplemental Motion for Admission Pro Hac Vice of Mark A. Bederow (Docket 549) Ruling: Decision and Order [denying motion] issued (Cannone, J) - Dated 3/7/25 (Docket 551) 27. Commonwealth's Motion for Protective Order (Docket 617) Ruling: So Ordered (Cannone, J) - Dated 3/20/25 28. Defendant's Motion to Exclude the Testimony of the Commonwealth's Witness James W. Crosby, MS PhD Ruling: Denied (Cannone, J) - Dated 3/6/25 (Docket 548) 29. Commonwealth's Motion for Records from Dockets 2842CR00043 and 2382CR00313 [Aidan TurtleBoy Kearney open felony cases involving Read trial witnesses and Lindsey Gaetani) addressed to Special Assistant District Attorney Robert Cosgrove (Docket 546) Status: Allowed in part (Sisitsky, A). Memorandum & Order was issued on 3/20/25 (Docket 615) and an Order for Production of Records followed (Docket 616) 30. Commonwealth's Motion to Compel Communications Between the Defendant and Attorney Yannetti Stored within the Defendant's Cell Phone Data (Docket 618) Status: Denied (Cannone, J) - Dated 3/25/25 II. Motions with No Ruling or Taken Under Advisement: 31. Defendant's Motion to Exclude the Purported Expert Testimony of the Commonwealth's Proffered Witness Dr. Aizik L. Wolf and Request Daubert-Lanigan Hearing (Docket 540) Status: No ruling specified 32. Commonwealth's Motion for Extension of Time to File Motions in Limine Related to Defense Expert Reciprocal Discovery (Docket 552) Status: No ruling specified 33. Commonwealth's Motion to Correct the Record (Docket 553) Status: No ruling specified 34. Defendant's Motion in Limine to Present Demonstrative Exhibit Based on Christina Hanley's Testimony (Docket 558) Status: No action taken at this time (Cannone, J) - Dated 3/20/25 35. Defendant's Motion in Limine to Exclude Irrelevant, Inadmissible, and Prejudicial Prior Bad Character and Propensity Evidence (Docket 559) Status: No ruling specified 36. Defendant's Motion to Impound Portions of Defendant's Motion in Limine to Exclude Certain Witnesses Before and After Testimony (Docket 560) Status: No ruling specified 37. Defendant's Motion in Limine to Exclude Serum/Plasma Ethanol Concentration, Blood Ethanol Concentration Conversion, and Corresponding Retrograde Extrapolation Analysis (Docket 571) Status: No ruling specified 38. Commonwealth's Motion in Limine to Appoint Court Stenographer, Prevent Identification of Jurors, and Impound Juror Names During Trial (Docket 575) Status: No ruling specified 39. Commonwealth's Motion in Limine for Sequestration Order and for Relief from that Order for Family Members of the Victim (Docket 576) Status: No ruling specified (though referenced in Docket 562 as exempting certain witnesses, indicating 576 may soon be allowed) 40. Commonwealth's Motion in Limine to Preclude Reference to and Redact the Manner of Death Contained on the Victim's Death Certificate (Docket 579) Status: No ruling specified 41. Commonwealth's Motion in Limine to Admit the Victim's Out of Court Statements Relating to His State of Mind (Docket 585) Status: No action taken - Need to see how the evidence develops (Cannone, J) - Dated 3/20/25 42. Commonwealth's Motion in Limine to Prohibit Reference to Any Pending Internal Affairs Investigations; Sustained Findings Unrelated to This Case; or Unfounded Allegations of Misconduct (Docket 588) Status: No action (Cannone, J) - Dated 3/19/25 43. Commonwealth's Motion in Limine to Preclude the Defendant from Raising a Third-Party Culprit Defense (Docket 589) Status: No ruling specified (Defense filed opposition, 3/25/25, Docket 625) 44. Commonwealth's Motion for Attorney-Conducted and Individual Voir Dire of Potential Jurors and Proposed Jury Questionnaire (Docket 590) Status: No ruling specified 45. Defendant's Motion in Limine to Allow Evidence of Lack of Bias (Docket 593) Status: No ruling specified (Commonwealth filed opposition, 3/17/25, Docket 610) 46. Defendant's Motion for Reconsideration of March 6, 2025 Court Order Denying Defendant's Motion to Exclude the Testimony of the Commonwealth's Witness James W. Crosby, MS PhD (Docket 601) Status: No ruling specified 47. Defendant's Motion in Limine to Exclude the Testimony of Commonwealth's Witness Dr. Judson Welcher, M.S., Ph.D., and Request for Voir Dire (Docket 606) Status: No ruling specified (Commonwealth filed opposition, 3/17/25, Docket 609) 48. Commonwealth's Motion in Limine to Exclude Expert Testimony of Garrett Wing (Docket 607) Status: No ruling specified 49. Commonwealth's Motion in Limine to Exclude Defense's Expert Michael Easter's Opinion of the Investigation (Docket 608) Status: No ruling specified 50. Commonwealth's Motion for Buffer Zone and Order Prohibiting Signs or Clothing in Favor of Either Party or Law Enforcement (Docket 611) Status: No ruling specified 51. Defendant's Assented to Motion to Continue Trial Date on or After April 25, 2025 (Docket 613) Status: Denied without prejudice. Trial will begin with empanelment on April 1, 2025 as scheduled. If a jury is selected before April 28, 2025, counsel may renew the motion prior to the jury being sworn (Cannone, J) - Dated 3/19/25 51. Commonwealth's Motion for Records: Unsolved Productions, INC (Docket 620) Status: No ruling specified 52. Defendant 's Motion to Dismiss for Extraordinary Governmental Misconduct (REDACTED) (Docket 519) Status: No ruling specified (Commonwealth filed opposition 2/28/25, Docket 526) Please let me know if I missed any rulings, or if any information is misplaced. This content may be reposted, with attribution and a link to the original post.

Grant Smith Ellis

16,809 görüntüleme • 1 yıl önce

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data. 2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro -> RoboCasa produces N (varying visuals) -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are building tools to enable everyone in the ecosystem to scale up with us. Links in thread:
1:21

Exciting updates on Project GR00T! We discover a systematic way to scale up robot data, tackling the most painful pain point in robotics. The idea is simple: human collects demonstration on a real robot, and we multiply that data 1000x or more in simulation. Let’s break it down: 1. We use Apple Vision Pro (yes!!) to give the human operator first person control of the humanoid. Vision Pro parses human hand pose and retargets the motion to the robot hand, all in real time. From the human’s point of view, they are immersed in another body like the Avatar. Teleoperation is slow and time-consuming, but we can afford to collect a small amount of data. 2. We use RoboCasa, a generative simulation framework, to multiply the demonstration data by varying the visual appearance and layout of the environment. In Jensen’s keynote video below, the humanoid is now placing the cup in hundreds of kitchens with a huge diversity of textures, furniture, and object placement. We only have 1 physical kitchen at the GEAR Lab in NVIDIA HQ, but we can conjure up infinite ones in simulation. 3. Finally, we apply MimicGen, a technique to multiply the above data even more by varying the *motion* of the robot. MimicGen generates vast number of new action trajectories based on the original human data, and filters out failed ones (e.g. those that drop the cup) to form a much larger dataset. To sum up, given 1 human trajectory with Vision Pro -> RoboCasa produces N (varying visuals) -> MimicGen further augments to NxM (varying motions). This is the way to trade compute for expensive human data by GPU-accelerated simulation. A while ago, I mentioned that teleoperation is fundamentally not scalable, because we are always limited by 24 hrs/robot/day in the world of atoms. Our new GR00T synthetic data pipeline breaks this barrier in the world of bits. Scaling has been so much fun for LLMs, and it's finally our turn to have fun in robotics! We are building tools to enable everyone in the ecosystem to scale up with us. Links in thread:

Jim Fan

364,380 görüntüleme • 1 yıl önce

Every time a new AI model demonstrates an ability to manipulate or generate media, it's celebrated as if we're witnessing these capabilities for the first time. This framing misses a crucial point: most of these capabilities have existed for decades. We just used different technology to accomplish them. What's changing now is that AI is allowing media generation and editing to be more accessible to many more - both in terms of cost and ease of use. Take David Fincher's The Social Network from 2010. The Winklevoss twins were played by two different actors, but Armie Hammer's face was digitally placed onto Josh Pence's body. What we call deepfakes today was just traditional VFX pipelines then. The process involved high-resolution facial scanning, detailed 3D modeling, precise facial rigging, and frame-by-frame tracking. The VFX team created photorealistic digital doubles, mapped accurate skin textures, and manually refined each transformation until the result was imperceptible. Most viewers never noticed - which is exactly the point. The best CGI is invisible CGI. This wasn't unique. Hollywood has been digitally transforming actors for decades through well-established technical pipelines: photogrammetry, facial capture, advanced compositing, and physics-based rendering. The question isn't whether we can do it - that has already been answered. Previously, two factors kept this technology contained: technical complexity and cost. Traditional graphics pipelines required specialized infrastructure - from high-end capture equipment to render farms - and deep technical expertise across multiple disciplines. "Fix it in post" meant teams of technical artists working with proprietary tools and complex workflows. More importantly, it was expensive - often millions per shot. Digital effects at this level are reserved for big-budget productions. AI isn't changing what's possible - it's changing the implementation stack. The same transformations that once required extensive hardware, specialized software, and teams of technical artists are being consolidated by generative and world models. The technology is moving from complex production pipelines to end-to-end solutions that anyone can use. Like Act-One (who said Act-Two?). What becomes possible when these capabilities are democratized? When sophisticated effects and graphics become accessible to many more filmmakers? When actors will be able to do more, in more languages, for more audiences? What happens when the technical and financial barriers to high-end content creation disappear?
1:19

Every time a new AI model demonstrates an ability to manipulate or generate media, it's celebrated as if we're witnessing these capabilities for the first time. This framing misses a crucial point: most of these capabilities have existed for decades. We just used different technology to accomplish them. What's changing now is that AI is allowing media generation and editing to be more accessible to many more - both in terms of cost and ease of use. Take David Fincher's The Social Network from 2010. The Winklevoss twins were played by two different actors, but Armie Hammer's face was digitally placed onto Josh Pence's body. What we call deepfakes today was just traditional VFX pipelines then. The process involved high-resolution facial scanning, detailed 3D modeling, precise facial rigging, and frame-by-frame tracking. The VFX team created photorealistic digital doubles, mapped accurate skin textures, and manually refined each transformation until the result was imperceptible. Most viewers never noticed - which is exactly the point. The best CGI is invisible CGI. This wasn't unique. Hollywood has been digitally transforming actors for decades through well-established technical pipelines: photogrammetry, facial capture, advanced compositing, and physics-based rendering. The question isn't whether we can do it - that has already been answered. Previously, two factors kept this technology contained: technical complexity and cost. Traditional graphics pipelines required specialized infrastructure - from high-end capture equipment to render farms - and deep technical expertise across multiple disciplines. "Fix it in post" meant teams of technical artists working with proprietary tools and complex workflows. More importantly, it was expensive - often millions per shot. Digital effects at this level are reserved for big-budget productions. AI isn't changing what's possible - it's changing the implementation stack. The same transformations that once required extensive hardware, specialized software, and teams of technical artists are being consolidated by generative and world models. The technology is moving from complex production pipelines to end-to-end solutions that anyone can use. Like Act-One (who said Act-Two?). What becomes possible when these capabilities are democratized? When sophisticated effects and graphics become accessible to many more filmmakers? When actors will be able to do more, in more languages, for more audiences? What happens when the technical and financial barriers to high-end content creation disappear?

Cristóbal Valenzuela

30,561 görüntüleme • 1 yıl önce

RUN RABBIT RUN 🐇 – VEO3 CHEAT SHEET – Part I ( 🔈Sound on) So, I’ve been more extensively testing Veo3 over the past few weeks, and I wanted to see how much dynamism I could get from the new model and how I could use motion and placement to consistently tell a story while changing universes each time. Now, my findings: 1/ First, it does feel more like a Veo2.5 than a full leap. But it’s a meaningful step forward, especially in motion and complexity. Maybe I'm a little jaded, but I expect new versions of models to be strong departures from the previous one. Veo2 was already great, this one feels like an evolution, not a revolution. 2/ My Veo2 Cheat Sheet still mostly apply for Veo3: For example, I still prompt the most important elements first (often style). Just as a reminder: 3/ It's pretty good at filling the gaps when you're writing simple prompts. For cool, out-of-the-box results, just describe short scenes, but be careful with consistency. Weird tradeoff, I guess. 4/ The token window is a LOT longer, so you can get more complex, multi-step actions from your prompt. Veo2 would often lose the thread, Veo3 can keep up with sequential and layered instructions. That’s a win. 5/ Camera motion is much better. Handheld, tracking, wide-angle moves, it holds together well. Still lacks control, but it’s a clear jump from Veo2. Definitely worth noting, especially given how many people have been experimenting with Higgsfield lately. 6/ Like Veo2, POV and fantasy prompts seem to lean into a game engine vibe. Not quite realism — more like stylized cutscenes or in-game shots. Sometimes it works, sometimes it’s distracting. 7/ Aspect ratio tip (thanks Kyle Salazar): want to reduce the black bars? Add “aspect ratio 1.85:1” at the start of your prompt. Haven’t tested it yet, but apparently it helps. -8/ You can generate 360° videos. Try “360 video,” “monoscopic,” or “equirectangular.” Not flawless (some seams), but it works surprisingly well. See my previous post for this. 9/ Prompting for aesthetics is still hit-or-miss. Consistency is tough. You can over-describe your characters and key elements to keep them stable throughout, but for style...especially animation, it’s still hard to maintain. Hopefully "ingredients" or style-locking features will help in the near future. 10/ Bodies and turning movements are still tricky. Fast actions still distort limbs, creates artifacts, etc... Most shot are fine, but if you push too much for extremely dynamic scenes, you'll definitely notice some distortions (some in this video too) - What’s still missing (but probably coming): – Image-to-video – Start/end frame control – Locking a voice to a character – Prompt ingredients or persistent visual logic This is just Part I :) I’ll keep posting notes as I go.
0:56

RUN RABBIT RUN 🐇 – VEO3 CHEAT SHEET – Part I ( 🔈Sound on) So, I’ve been more extensively testing Veo3 over the past few weeks, and I wanted to see how much dynamism I could get from the new model and how I could use motion and placement to consistently tell a story while changing universes each time. Now, my findings: 1/ First, it does feel more like a Veo2.5 than a full leap. But it’s a meaningful step forward, especially in motion and complexity. Maybe I'm a little jaded, but I expect new versions of models to be strong departures from the previous one. Veo2 was already great, this one feels like an evolution, not a revolution. 2/ My Veo2 Cheat Sheet still mostly apply for Veo3: For example, I still prompt the most important elements first (often style). Just as a reminder: 3/ It's pretty good at filling the gaps when you're writing simple prompts. For cool, out-of-the-box results, just describe short scenes, but be careful with consistency. Weird tradeoff, I guess. 4/ The token window is a LOT longer, so you can get more complex, multi-step actions from your prompt. Veo2 would often lose the thread, Veo3 can keep up with sequential and layered instructions. That’s a win. 5/ Camera motion is much better. Handheld, tracking, wide-angle moves, it holds together well. Still lacks control, but it’s a clear jump from Veo2. Definitely worth noting, especially given how many people have been experimenting with Higgsfield lately. 6/ Like Veo2, POV and fantasy prompts seem to lean into a game engine vibe. Not quite realism — more like stylized cutscenes or in-game shots. Sometimes it works, sometimes it’s distracting. 7/ Aspect ratio tip (thanks Kyle Salazar): want to reduce the black bars? Add “aspect ratio 1.85:1” at the start of your prompt. Haven’t tested it yet, but apparently it helps. -8/ You can generate 360° videos. Try “360 video,” “monoscopic,” or “equirectangular.” Not flawless (some seams), but it works surprisingly well. See my previous post for this. 9/ Prompting for aesthetics is still hit-or-miss. Consistency is tough. You can over-describe your characters and key elements to keep them stable throughout, but for style...especially animation, it’s still hard to maintain. Hopefully "ingredients" or style-locking features will help in the near future. 10/ Bodies and turning movements are still tricky. Fast actions still distort limbs, creates artifacts, etc... Most shot are fine, but if you push too much for extremely dynamic scenes, you'll definitely notice some distortions (some in this video too) - What’s still missing (but probably coming): – Image-to-video – Start/end frame control – Locking a voice to a character – Prompt ingredients or persistent visual logic This is just Part I :) I’ll keep posting notes as I go.

Henry Daubrez 🌸💀

14,683 görüntüleme • 1 yıl önce

Claude Code can now make full videos from your terminal.. Not slideshows. Not text on screen.. Actual motion graphics with animations, transitions, custom photos, and background music. [ SHARED A TUTORIAL BELOW EDITED WITH THIS SETUP IN JUST 5mins ] ▫️Here's the setup: Claude Code + Remotion Remotion is a React based framework that renders video programmatically. You describe what you want in plain English, Claude writes the React components and Remotion renders it into a real MP4. What you can actually do with this: > Generate 9:16 vertical videos for TikTok / Reels / Shorts > Add animated text with viral hooks and safe zones > Pull live web screenshots directly into your scenes using Chrome MCP > Fact-check your content in real time with Perplexity MCP > Drop in your own photos and background music > Edit existing talking-head footage cut bloopers, add captions > Schedule posts to your socials straight from the terminal ▫️How to set it up (takes 5 minutes) : > Make sure you have Node.js installed ( node -v to check ) > Create a new Remotion project: npx create-video@latest Pick the Blank template, enable TailwindCSS, and install the Skills package when prompted. > Install dependencies: cd my-video npm install > Start the preview server: npm run dev > Open Claude Code in the same project folder: cd my-video claude That's it. You can now prompt videos in plain English. If you already have a Remotion project, just add the skill directly: npx skills add remotion-dev/skills This drops a SKILL.md into your project that gives Claude expert knowledge of Remotion.. animations, compositions, captions, assets, 3D content everything. Example prompt you can steal: "Create a 30-second 9:16 vertical video about the top 3 AI tools this week. Use animated text with a hook in the first 2 seconds. Add smooth transitions between scenes. Keep text in the safe zone for TikTok. Use a dark tech aesthetic with blue accent colors." Claude writes all the React code, renders a preview, you tweak with natural language, and export when ready. The crazy part is this whole pipeline is local, free (minus your Claude sub), and you never open a video editor. imo this kills CapCut for anyone making info-style content. You describe the video in English and get back a rendered MP4. try it now.
2:01

Claude Code can now make full videos from your terminal.. Not slideshows. Not text on screen.. Actual motion graphics with animations, transitions, custom photos, and background music. [ SHARED A TUTORIAL BELOW EDITED WITH THIS SETUP IN JUST 5mins ] ▫️Here's the setup: Claude Code + Remotion Remotion is a React based framework that renders video programmatically. You describe what you want in plain English, Claude writes the React components and Remotion renders it into a real MP4. What you can actually do with this: > Generate 9:16 vertical videos for TikTok / Reels / Shorts > Add animated text with viral hooks and safe zones > Pull live web screenshots directly into your scenes using Chrome MCP > Fact-check your content in real time with Perplexity MCP > Drop in your own photos and background music > Edit existing talking-head footage cut bloopers, add captions > Schedule posts to your socials straight from the terminal ▫️How to set it up (takes 5 minutes) : > Make sure you have Node.js installed ( node -v to check ) > Create a new Remotion project: npx create-video@latest Pick the Blank template, enable TailwindCSS, and install the Skills package when prompted. > Install dependencies: cd my-video npm install > Start the preview server: npm run dev > Open Claude Code in the same project folder: cd my-video claude That's it. You can now prompt videos in plain English. If you already have a Remotion project, just add the skill directly: npx skills add remotion-dev/skills This drops a SKILL.md into your project that gives Claude expert knowledge of Remotion.. animations, compositions, captions, assets, 3D content everything. Example prompt you can steal: "Create a 30-second 9:16 vertical video about the top 3 AI tools this week. Use animated text with a hook in the first 2 seconds. Add smooth transitions between scenes. Keep text in the safe zone for TikTok. Use a dark tech aesthetic with blue accent colors." Claude writes all the React code, renders a preview, you tweak with natural language, and export when ready. The crazy part is this whole pipeline is local, free (minus your Claude sub), and you never open a video editor. imo this kills CapCut for anyone making info-style content. You describe the video in English and get back a rendered MP4. try it now.

Axel Bitblaze 🪓

31,250 görüntüleme • 3 ay önce

Steal my Gemini 3.0 prompt to generate any website based on your custom requirements. ------------------------ ELITE WEB DESIGNER ------------------------ Adopt the role of a former Silicon Valley design prodigy who burned out creating soulless SaaS dashboards, disappeared to study motion graphics and shader programming in Tokyo's underground creative scene, and emerged with an obsessive understanding of how visual maximalism serves business credibility when executed with surgical precision. You're a conversion strategist who spent years A/B testing landing pages for unicorn startups, a design fundamentalist who refuses to sacrifice usability for aesthetics, and a master meta-prompter who optimizes for clarity over verbosity. You know modern image generation AI needs specific structural formatting—contemporary design frameworks (Tailwind CSS, Shadcn UI, glassmorphism, liquid glass, morphism), backgrounds with depth (animated gradients, shaders, mascots), and step-by-step execution instructions—to produce 2025-quality interfaces instead of outdated designs. Your mission: Transform user vision into fully-coded, visually striking websites that balance aesthetic impact with conversion effectiveness. Extract requirements, architect strategic 5-6 section homepages, generate visual previews showing all sections with interactive elements visible, iterate until perfect, then build complete homepage before making navigation and additional pages functional—all adapted to specific context, not rigid templates. ##PHASE 1: Vision Capture What we're doing: Understanding your aesthetic, business context, and strategic goals efficiently. Provide your vision via: 1. Screenshot of design inspiration 2. Written description (business type, aesthetic, features) 3. Both Share: **Aesthetic**: Style preference? (maximalist, minimalist, brutalist, glassmorphic, liquid glass, morphism, retro, futuristic, geometric, editorial, etc.) **Elements**: Specific visuals wanted? (shaders, 3D effects, colors, animations, mascots, backgrounds) **Avoid**: What to exclude? (purple overload, illegible text, hidden CTAs, outdated UI, flat backgrounds, etc.) **Business**: What you do, target audience, website goal, differentiator? Type "ready" when shared. ##PHASE 2: Strategic Homepage Architecture What we're doing: Translating your vision into 5-6 section homepage structure following conversion principles and modern design fundamentals. I'll architect sections specifically for YOUR business, not templates: **Strategic Framework** (contextualized to your model): Core sections adapt based on business type: - Hero with value prop + primary CTA - Trust/credibility section (social proof, stats, logos) - Value delivery (features, benefits, process, how-it-works) - Conversion focal point (pricing, offers, lead capture, demo) - Engagement closer (FAQ, secondary CTA, community) Sections customize to context—SaaS gets problem-solution-pricing flow, agencies get case studies-process-testimonials, e-commerce gets benefits-proof-offers, portfolios get philosophy-work-results. **Strategic Plan Includes**: - 5-6 contextualized sections with rationale - Content direction based on audience psychology - Visual treatment matching your aesthetic with fundamentals enforced - Modern framework approach (Tailwind/Shadcn/Glassmorphism) - Background depth strategy (animated gradients, shaders, visuals) - Color strategy avoiding generic choices unless brand-appropriate - Typography prioritizing legibility - CTA strategy for conversion optimization **Your options**: - "continue" to proceed to design system and mockup - Request adjustments - Ask questions ##PHASE 3: Design System & Mockup Preparation What we're doing: Establishing visual foundation using contemporary frameworks, then crafting optimized prompt to generate mockup showing ALL 5-6 sections at once with visible interactive elements. I'll define: **Contextualized Style Direction**: Keywords and frameworks fitting YOUR brand specifically **Design Framework Strategy**: Styling approach, component philosophy, layout pattern—all adapted to your aesthetic **Background Depth Treatment**: How background creates depth without distraction, animation philosophy, visual elements supporting content **Visual System**: Color palette with strategic rationale, typography with reasoning, component styling philosophy, spacing strategy, CTA differentiation, modern UI patterns adapted to your aesthetic **Optimized Prompt Structure** (meta-prompted): Two versions: **Human-Readable**: Descriptive overview for review **JSON Optimized**: Structured for image generation using meta-prompt principles: - Required anchors: "Website screenshot", "Professional website design mockup", "Award-winning UI design", "Modern web interface 2025" - Aesthetic philosophy over exhaustive lists - "Execute this step-by-step" instruction - Modern framework references (Tailwind, Shadcn, Glassmorphism) - Background depth details (animated gradients, shaders, visuals) - All 5-6 sections in flowing narrative - Interactive element visibility emphasis (CTAs, buttons, animations) to convey design principles - Strategic constraints (legibility, prominence, hierarchy, depth) - Optimized length balancing detail with conciseness Type "continue" to see prompt. ##PHASE 4: Complete Homepage Mockup Prompt What we're doing: Presenting optimized prompts for full-page mockup showing ALL 5-6 sections with interactive design elements visible. **HUMAN-READABLE VERSION**: Narrative description of your complete homepage: - Opening with quality anchors - Core aesthetic philosophy adapted to your context - Background treatment creating depth - Navigation approach - All 5-6 sections described contextually - Color palette with reasoning - Typography philosophy - Component styling approach - Modern framework references - Interactive element visibility strategy - Critical constraints - Avoidance list based on preferences **JSON VERSION** (optimized for generation): ```json { "prompt": "Website screenshot of [your business]. Professional website design mockup. Award-winning UI design. Modern web interface 2025. Execute this step-by-step. [Aesthetic philosophy] with [framework] approach. Background: [depth treatment with animations/gradients/effects]. Full homepage vertical scroll showing 5-6 sections: Navigation [treatment]. Hero [value prop, CTA, visuals]. [Section 2 with layout philosophy]. [Section 3 with component approach]. [Section 4 with interaction style]. [Section 5 with conversion focus]. [Section 6 if applicable]. Color strategy: [palette with reasoning]. Typography: [philosophy and hierarchy]. Components: [styling approach with visible affordances]. Framework: Tailwind patterns, Shadcn style, [specific effects]. Interactive elements show: prominent CTAs, hover implications, animation hints, button affordances. Critical: legible text, prominent CTAs, background depth, clear hierarchy, contemporary 2025 design, professional quality. Avoid: [specific issues].", "aspect_ratio": "9:16" } ``` Meta-optimized: principles over lists, step-by-step execution, framework context, interactive visibility. **Review both. JSON executes.** **To generate complete homepage mockup, type "generate"** **Important note**: When you type "generate", I'll execute the image generation tool. The image will appear, but the process will seem to pause. This is normal—the tool can only return the image without commentary. Simply type "continue" after you receive the image to proceed with the next phase. **To adjust the prompt before generating, tell me what to change** Won't execute until you command. ##PHASE 5: Complete Homepage Mockup Generation What we're doing: Executing image generation with optimized JSON showing ALL 5-6 sections vertically. ONLY activates when you type "generate", "create mockup", "make image", or similar. Once commanded, I execute using ONLY JSON prompt—no modifications. You receive full-page vertical mockup showing: - All 5-6 sections in scrollable view - Interactive design elements (CTAs, buttons, animations) visible - Background depth and modern framework styling - Complete design system applied **After the image appears, type "continue" to proceed.** The image generation tool only returns the visual—you'll need to type "continue" to move forward with reviewing and next steps. ##PHASE 6: Mockup Review & Refinement Decision What we're doing: Reviewing the generated mockup and deciding next steps. This phase activates after you type "continue" following image generation. **Your options after viewing the mockup**: - "Approved" or "build" - proceed to building complete homepage code - Request specific changes - I'll update the prompt and regenerate - Ask questions or request adjustments **If you request changes**: I'll present updated prompts (readable + JSON) showing modifications, then ask you to type "generate" again for the revised mockup. Each refinement iteration: 1. You describe desired changes 2. I present updated prompts 3. You type "generate" 4. Image appears 5. You type "continue" to proceed 6. We review and decide next steps 7. Repeat until perfect Common refinements: section emphasis, background depth, colors, typography, CTA prominence, interactive visibility, framework styling, aesthetic tuning. Once you're satisfied with the mockup, type "approved" or "build" to proceed to code generation. ##PHASE 7: Complete Homepage Code Generation What we're doing: Building entire 5-6 section homepage as production-ready code matching approved mockup exactly. **Complete Single-File HTML Delivery**: - All 5-6 sections coded and integrated - Fully responsive across devices - Modern CSS implementation (Tailwind-style or modern CSS) - Animated background matching mockup (CSS gradients, WebGL, SVG) - All interactive elements functional (buttons, CTAs, forms, animations) - Navigation implemented per design - Component styling matching aesthetic (glassmorphism, shadows, borders) - Typography system with hierarchy and legibility - Color system from specification - Micro-interactions and hover states - Scroll animations where appropriate - Performance-optimized **Technical Quality**: Semantic HTML, modern CSS (custom properties, grid, flexbox, backdrop-filter, transforms, animations), vanilla JavaScript, accessibility considerations, mobile-first responsive, smooth scrolling, optimized assets, cross-browser compatible. **Code Structure**: Clean commented HTML, inline CSS organized in style block, inline JavaScript, ready to copy/paste and deploy, fully functional standalone. **Strategic Content**: Intelligent placeholders based on your business model, conversion psychology, target audience, professional tone—easily replaceable. **Design Fundamentals Verified**: All sections with hierarchy, prominent functional CTAs, readable text with contrast, clear interactive signals, background depth, adequate whitespace, responsive, contemporary 2025 quality. Automatically presents next phase after delivery. ##PHASE 8: Navigation & Pages Planning What we're doing: Making all navigation functional and planning additional pages. **Navigation Audit**: [List nav items from homepage] **Options for each item**: Create dedicated page, expand section to full page, smooth scroll to section, custom approach. **For clickable elements**: Decide what happens—link to new page, scroll to section, open modal, trigger action, external link. **What to make functional first? Choose**: 1. Complete navigation by building all pages 2. Primary conversion path (CTA → specific page) 3. Specific pages you prioritize 4. Internal links with smooth scrolling 5. Custom approach **Or** "auto-complete" for intelligent decisions based on your model. ##PHASE 9-X: Progressive Development What we're doing: Building each page or making elements functional, maintaining design consistency. **Each Page Delivery**: Complete HTML matching homepage design system, same framework styling, same background treatment, same typography/colors, appropriate sections, full responsiveness, functional interactions, integrated navigation. **Each Functionality Addition**: Smooth scroll, modals, form validation, interactive components, animation triggers, other elements. **After Each Delivery**: Current Progress: [What's complete] **What next? Choose**: [4-6 options for next page/functionality] **Or** "auto-complete" for intelligent completion. Continues until site fully functional. ##PHASE FINAL: Complete Integration & Polish What we're doing: Final integration ensuring everything links, works, and maintains consistency. **Complete Package**: Homepage HTML (all sections), all additional pages, complete styling/functionality per file, working navigation across pages, functional CTAs/buttons, validated forms, consistent design system. **Deliverables**: All HTML files deployment-ready, quick deployment guide, customization documentation, design system reference. **Quality Verified**: Complete homepage, functional navigation, working CTAs, consistent pages, responsive, optimized, modern framework styling, functional interactions, professional 2025 quality. --- **CRITICAL RULES**: **Image Generation**: - Present: Human-Readable + Optimized JSON - JSON meta-principles: distilled concepts, "Execute step-by-step", framework context - JSON opens: "Website screenshot" + "Professional website design mockup. Award-winning UI design. Modern web interface 2025." - JSON shows: ALL 5-6 sections vertically in one mockup - JSON emphasizes: interactive element visibility (CTAs, buttons, animations) - JSON includes: modern frameworks (Tailwind, Shadcn, Glassmorphism), background depth (gradients, shaders, mascots—NEVER flat) - User "generate" → Send ONLY JSON → No modifications - Aspect ratio: 9:16 (vertical to show all sections) - After image appears → User MUST type "continue" to proceed (tool only returns image without commentary) **Homepage Development**: - Generate mockup with ALL 5-6 sections at once - After approval, build COMPLETE homepage code (all sections functional) - Deliver entire homepage as single working file - Then make navigation/additional pages functional - Flow: complete homepage → functional navigation → additional pages **Content Adaptation**: - NO hardcoded templates - Adapt ALL to user's specific business context - Strategic frameworks based on actual audience - Section selection/styling contextualized to goals - Design choices match aesthetic preference - Professional placeholders easily customizable **Standards**: Contemporary frameworks, background depth, interactive element visibility, modern CSS/frameworks, 2025 quality throughout. **Control**: User commands each phase explicitly. "generate" for mockup (then "continue" after image), "approved"/"build" for code, choose-your-adventure for pages, adjust anytime. Begin Phase 1 when ready.
0:35

Steal my Gemini 3.0 prompt to generate any website based on your custom requirements. ------------------------ ELITE WEB DESIGNER ------------------------ Adopt the role of a former Silicon Valley design prodigy who burned out creating soulless SaaS dashboards, disappeared to study motion graphics and shader programming in Tokyo's underground creative scene, and emerged with an obsessive understanding of how visual maximalism serves business credibility when executed with surgical precision. You're a conversion strategist who spent years A/B testing landing pages for unicorn startups, a design fundamentalist who refuses to sacrifice usability for aesthetics, and a master meta-prompter who optimizes for clarity over verbosity. You know modern image generation AI needs specific structural formatting—contemporary design frameworks (Tailwind CSS, Shadcn UI, glassmorphism, liquid glass, morphism), backgrounds with depth (animated gradients, shaders, mascots), and step-by-step execution instructions—to produce 2025-quality interfaces instead of outdated designs. Your mission: Transform user vision into fully-coded, visually striking websites that balance aesthetic impact with conversion effectiveness. Extract requirements, architect strategic 5-6 section homepages, generate visual previews showing all sections with interactive elements visible, iterate until perfect, then build complete homepage before making navigation and additional pages functional—all adapted to specific context, not rigid templates. ##PHASE 1: Vision Capture What we're doing: Understanding your aesthetic, business context, and strategic goals efficiently. Provide your vision via: 1. Screenshot of design inspiration 2. Written description (business type, aesthetic, features) 3. Both Share: **Aesthetic**: Style preference? (maximalist, minimalist, brutalist, glassmorphic, liquid glass, morphism, retro, futuristic, geometric, editorial, etc.) **Elements**: Specific visuals wanted? (shaders, 3D effects, colors, animations, mascots, backgrounds) **Avoid**: What to exclude? (purple overload, illegible text, hidden CTAs, outdated UI, flat backgrounds, etc.) **Business**: What you do, target audience, website goal, differentiator? Type "ready" when shared. ##PHASE 2: Strategic Homepage Architecture What we're doing: Translating your vision into 5-6 section homepage structure following conversion principles and modern design fundamentals. I'll architect sections specifically for YOUR business, not templates: **Strategic Framework** (contextualized to your model): Core sections adapt based on business type: - Hero with value prop + primary CTA - Trust/credibility section (social proof, stats, logos) - Value delivery (features, benefits, process, how-it-works) - Conversion focal point (pricing, offers, lead capture, demo) - Engagement closer (FAQ, secondary CTA, community) Sections customize to context—SaaS gets problem-solution-pricing flow, agencies get case studies-process-testimonials, e-commerce gets benefits-proof-offers, portfolios get philosophy-work-results. **Strategic Plan Includes**: - 5-6 contextualized sections with rationale - Content direction based on audience psychology - Visual treatment matching your aesthetic with fundamentals enforced - Modern framework approach (Tailwind/Shadcn/Glassmorphism) - Background depth strategy (animated gradients, shaders, visuals) - Color strategy avoiding generic choices unless brand-appropriate - Typography prioritizing legibility - CTA strategy for conversion optimization **Your options**: - "continue" to proceed to design system and mockup - Request adjustments - Ask questions ##PHASE 3: Design System & Mockup Preparation What we're doing: Establishing visual foundation using contemporary frameworks, then crafting optimized prompt to generate mockup showing ALL 5-6 sections at once with visible interactive elements. I'll define: **Contextualized Style Direction**: Keywords and frameworks fitting YOUR brand specifically **Design Framework Strategy**: Styling approach, component philosophy, layout pattern—all adapted to your aesthetic **Background Depth Treatment**: How background creates depth without distraction, animation philosophy, visual elements supporting content **Visual System**: Color palette with strategic rationale, typography with reasoning, component styling philosophy, spacing strategy, CTA differentiation, modern UI patterns adapted to your aesthetic **Optimized Prompt Structure** (meta-prompted): Two versions: **Human-Readable**: Descriptive overview for review **JSON Optimized**: Structured for image generation using meta-prompt principles: - Required anchors: "Website screenshot", "Professional website design mockup", "Award-winning UI design", "Modern web interface 2025" - Aesthetic philosophy over exhaustive lists - "Execute this step-by-step" instruction - Modern framework references (Tailwind, Shadcn, Glassmorphism) - Background depth details (animated gradients, shaders, visuals) - All 5-6 sections in flowing narrative - Interactive element visibility emphasis (CTAs, buttons, animations) to convey design principles - Strategic constraints (legibility, prominence, hierarchy, depth) - Optimized length balancing detail with conciseness Type "continue" to see prompt. ##PHASE 4: Complete Homepage Mockup Prompt What we're doing: Presenting optimized prompts for full-page mockup showing ALL 5-6 sections with interactive design elements visible. **HUMAN-READABLE VERSION**: Narrative description of your complete homepage: - Opening with quality anchors - Core aesthetic philosophy adapted to your context - Background treatment creating depth - Navigation approach - All 5-6 sections described contextually - Color palette with reasoning - Typography philosophy - Component styling approach - Modern framework references - Interactive element visibility strategy - Critical constraints - Avoidance list based on preferences **JSON VERSION** (optimized for generation): ```json { "prompt": "Website screenshot of [your business]. Professional website design mockup. Award-winning UI design. Modern web interface 2025. Execute this step-by-step. [Aesthetic philosophy] with [framework] approach. Background: [depth treatment with animations/gradients/effects]. Full homepage vertical scroll showing 5-6 sections: Navigation [treatment]. Hero [value prop, CTA, visuals]. [Section 2 with layout philosophy]. [Section 3 with component approach]. [Section 4 with interaction style]. [Section 5 with conversion focus]. [Section 6 if applicable]. Color strategy: [palette with reasoning]. Typography: [philosophy and hierarchy]. Components: [styling approach with visible affordances]. Framework: Tailwind patterns, Shadcn style, [specific effects]. Interactive elements show: prominent CTAs, hover implications, animation hints, button affordances. Critical: legible text, prominent CTAs, background depth, clear hierarchy, contemporary 2025 design, professional quality. Avoid: [specific issues].", "aspect_ratio": "9:16" } ``` Meta-optimized: principles over lists, step-by-step execution, framework context, interactive visibility. **Review both. JSON executes.** **To generate complete homepage mockup, type "generate"** **Important note**: When you type "generate", I'll execute the image generation tool. The image will appear, but the process will seem to pause. This is normal—the tool can only return the image without commentary. Simply type "continue" after you receive the image to proceed with the next phase. **To adjust the prompt before generating, tell me what to change** Won't execute until you command. ##PHASE 5: Complete Homepage Mockup Generation What we're doing: Executing image generation with optimized JSON showing ALL 5-6 sections vertically. ONLY activates when you type "generate", "create mockup", "make image", or similar. Once commanded, I execute using ONLY JSON prompt—no modifications. You receive full-page vertical mockup showing: - All 5-6 sections in scrollable view - Interactive design elements (CTAs, buttons, animations) visible - Background depth and modern framework styling - Complete design system applied **After the image appears, type "continue" to proceed.** The image generation tool only returns the visual—you'll need to type "continue" to move forward with reviewing and next steps. ##PHASE 6: Mockup Review & Refinement Decision What we're doing: Reviewing the generated mockup and deciding next steps. This phase activates after you type "continue" following image generation. **Your options after viewing the mockup**: - "Approved" or "build" - proceed to building complete homepage code - Request specific changes - I'll update the prompt and regenerate - Ask questions or request adjustments **If you request changes**: I'll present updated prompts (readable + JSON) showing modifications, then ask you to type "generate" again for the revised mockup. Each refinement iteration: 1. You describe desired changes 2. I present updated prompts 3. You type "generate" 4. Image appears 5. You type "continue" to proceed 6. We review and decide next steps 7. Repeat until perfect Common refinements: section emphasis, background depth, colors, typography, CTA prominence, interactive visibility, framework styling, aesthetic tuning. Once you're satisfied with the mockup, type "approved" or "build" to proceed to code generation. ##PHASE 7: Complete Homepage Code Generation What we're doing: Building entire 5-6 section homepage as production-ready code matching approved mockup exactly. **Complete Single-File HTML Delivery**: - All 5-6 sections coded and integrated - Fully responsive across devices - Modern CSS implementation (Tailwind-style or modern CSS) - Animated background matching mockup (CSS gradients, WebGL, SVG) - All interactive elements functional (buttons, CTAs, forms, animations) - Navigation implemented per design - Component styling matching aesthetic (glassmorphism, shadows, borders) - Typography system with hierarchy and legibility - Color system from specification - Micro-interactions and hover states - Scroll animations where appropriate - Performance-optimized **Technical Quality**: Semantic HTML, modern CSS (custom properties, grid, flexbox, backdrop-filter, transforms, animations), vanilla JavaScript, accessibility considerations, mobile-first responsive, smooth scrolling, optimized assets, cross-browser compatible. **Code Structure**: Clean commented HTML, inline CSS organized in style block, inline JavaScript, ready to copy/paste and deploy, fully functional standalone. **Strategic Content**: Intelligent placeholders based on your business model, conversion psychology, target audience, professional tone—easily replaceable. **Design Fundamentals Verified**: All sections with hierarchy, prominent functional CTAs, readable text with contrast, clear interactive signals, background depth, adequate whitespace, responsive, contemporary 2025 quality. Automatically presents next phase after delivery. ##PHASE 8: Navigation & Pages Planning What we're doing: Making all navigation functional and planning additional pages. **Navigation Audit**: [List nav items from homepage] **Options for each item**: Create dedicated page, expand section to full page, smooth scroll to section, custom approach. **For clickable elements**: Decide what happens—link to new page, scroll to section, open modal, trigger action, external link. **What to make functional first? Choose**: 1. Complete navigation by building all pages 2. Primary conversion path (CTA → specific page) 3. Specific pages you prioritize 4. Internal links with smooth scrolling 5. Custom approach **Or** "auto-complete" for intelligent decisions based on your model. ##PHASE 9-X: Progressive Development What we're doing: Building each page or making elements functional, maintaining design consistency. **Each Page Delivery**: Complete HTML matching homepage design system, same framework styling, same background treatment, same typography/colors, appropriate sections, full responsiveness, functional interactions, integrated navigation. **Each Functionality Addition**: Smooth scroll, modals, form validation, interactive components, animation triggers, other elements. **After Each Delivery**: Current Progress: [What's complete] **What next? Choose**: [4-6 options for next page/functionality] **Or** "auto-complete" for intelligent completion. Continues until site fully functional. ##PHASE FINAL: Complete Integration & Polish What we're doing: Final integration ensuring everything links, works, and maintains consistency. **Complete Package**: Homepage HTML (all sections), all additional pages, complete styling/functionality per file, working navigation across pages, functional CTAs/buttons, validated forms, consistent design system. **Deliverables**: All HTML files deployment-ready, quick deployment guide, customization documentation, design system reference. **Quality Verified**: Complete homepage, functional navigation, working CTAs, consistent pages, responsive, optimized, modern framework styling, functional interactions, professional 2025 quality. --- **CRITICAL RULES**: **Image Generation**: - Present: Human-Readable + Optimized JSON - JSON meta-principles: distilled concepts, "Execute step-by-step", framework context - JSON opens: "Website screenshot" + "Professional website design mockup. Award-winning UI design. Modern web interface 2025." - JSON shows: ALL 5-6 sections vertically in one mockup - JSON emphasizes: interactive element visibility (CTAs, buttons, animations) - JSON includes: modern frameworks (Tailwind, Shadcn, Glassmorphism), background depth (gradients, shaders, mascots—NEVER flat) - User "generate" → Send ONLY JSON → No modifications - Aspect ratio: 9:16 (vertical to show all sections) - After image appears → User MUST type "continue" to proceed (tool only returns image without commentary) **Homepage Development**: - Generate mockup with ALL 5-6 sections at once - After approval, build COMPLETE homepage code (all sections functional) - Deliver entire homepage as single working file - Then make navigation/additional pages functional - Flow: complete homepage → functional navigation → additional pages **Content Adaptation**: - NO hardcoded templates - Adapt ALL to user's specific business context - Strategic frameworks based on actual audience - Section selection/styling contextualized to goals - Design choices match aesthetic preference - Professional placeholders easily customizable **Standards**: Contemporary frameworks, background depth, interactive element visibility, modern CSS/frameworks, 2025 quality throughout. **Control**: User commands each phase explicitly. "generate" for mockup (then "continue" after image), "approved"/"build" for code, choose-your-adventure for pages, adjust anytime. Begin Phase 1 when ready.

God of Prompt

188,550 görüntüleme • 7 ay önce

XAI EXTENDS GROK IMAGINE VIDEO GENERATION TO 10 SECONDS WITH QUALITY ENHANCEMENTS xAI has updated its Grok Imagine tool to produce videos lasting 10 seconds, doubling the prior limit. This change, along with refinements in visual and audio elements, expands the tool's utility for short-form content creation. xAI released the upgrade to Grok Imagine in early 2026. The company, founded by Elon Musk, announced the feature through posts on the X platform. This follows previous iterations where videos were capped at shorter durations, typically 5 seconds. Grok Imagine allows users to generate videos from text prompts, building on its image creation capabilities. The update addresses constraints in video length that limited expressive potential. Users can now input descriptions to create clips, such as animations or scenes, without needing initial images. This positions the tool within the broader landscape of AI-driven multimodal generation, where text-to-video systems are increasingly common. The core adjustment doubles the maximum video duration from 5 seconds to 10 seconds. Accompanying this are upgrades to video quality, including more stable visuals, richer details, and improved clarity. Audio has also been enhanced for better output, making the generated content more immersive. These changes were described as "big improvements across the board" in the announcement. No specific benchmarks or quantitative metrics for the quality improvements were detailed in the release statements. The feature rollout appears gradual, with some users accessing it via the Grok app or web interface. xAI has not introduced user controls for exact timing, though such options are mentioned as future possibilities. This development highlights xAI's emphasis on iterative enhancements in generative AI tools. By extending duration while refining output fidelity, it reflects engineering priorities aimed at balancing computational efficiency with user needs. The focus on audio and visual stability suggests attention to common pitfalls in early text-to-video models, such as inconsistencies or artifacts. The sources do not specify the underlying model architecture changes or training data adjustments enabling this upgrade. Performance in real-world scenarios, like handling complex prompts or maintaining consistency across clips, remains unquantified in the announcements. Interpretations of broader implications for AI video generation would require additional evidence beyond what's provided.
0:29

XAI EXTENDS GROK IMAGINE VIDEO GENERATION TO 10 SECONDS WITH QUALITY ENHANCEMENTS xAI has updated its Grok Imagine tool to produce videos lasting 10 seconds, doubling the prior limit. This change, along with refinements in visual and audio elements, expands the tool's utility for short-form content creation. xAI released the upgrade to Grok Imagine in early 2026. The company, founded by Elon Musk, announced the feature through posts on the X platform. This follows previous iterations where videos were capped at shorter durations, typically 5 seconds. Grok Imagine allows users to generate videos from text prompts, building on its image creation capabilities. The update addresses constraints in video length that limited expressive potential. Users can now input descriptions to create clips, such as animations or scenes, without needing initial images. This positions the tool within the broader landscape of AI-driven multimodal generation, where text-to-video systems are increasingly common. The core adjustment doubles the maximum video duration from 5 seconds to 10 seconds. Accompanying this are upgrades to video quality, including more stable visuals, richer details, and improved clarity. Audio has also been enhanced for better output, making the generated content more immersive. These changes were described as "big improvements across the board" in the announcement. No specific benchmarks or quantitative metrics for the quality improvements were detailed in the release statements. The feature rollout appears gradual, with some users accessing it via the Grok app or web interface. xAI has not introduced user controls for exact timing, though such options are mentioned as future possibilities. This development highlights xAI's emphasis on iterative enhancements in generative AI tools. By extending duration while refining output fidelity, it reflects engineering priorities aimed at balancing computational efficiency with user needs. The focus on audio and visual stability suggests attention to common pitfalls in early text-to-video models, such as inconsistencies or artifacts. The sources do not specify the underlying model architecture changes or training data adjustments enabling this upgrade. Performance in real-world scenarios, like handling complex prompts or maintaining consistency across clips, remains unquantified in the announcements. Interpretations of broader implications for AI video generation would require additional evidence beyond what's provided.

Lacey

28,881 görüntüleme • 5 ay önce

Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede
0:37

Ahmedabad Crime Branch is making use of technical measures to avoid any stampede kind of situation. Anti stampede visual analytics,using reference area and crowd movement, head count algorithm. Anti-stampede algorithms on CCTV cameras are a crucial advancement in crowd management, leveraging AI and image processing to prevent dangerous situations in densely populated areas. Here's a breakdown of their usage: How they work: Real-time monitoring: AI-powered CCTV cameras continuously analyze video streams in real-time. Crowd density estimation: Algorithms calculate the number of people in a given area. This can involve: Pixel-based analysis: Converting images to black and white and counting "black pixels" (representing people). Object detection: Using machine learning models (like Mask R-CNN) to identify and count individuals, often by detecting heads or torsos. Thresholding: Pre-defined "threshold values" for crowd density are established. When the detected density crosses these thresholds, it triggers an alert. Anomaly detection: Beyond just density, these algorithms can identify unusual crowd behaviors such as: * Sudden surges in movement. * Unusual clustering patterns. * Fallen individuals. * Aggressive movements. Alerting authorities: Upon detecting a potential stampede risk, the system sends immediate alerts to security personnel or control rooms via LCD displays, GSM messages, or other communication channels. Predictive analytics: Some advanced systems use time-series prediction models to forecast crowd behavior and dynamics based on historical and real-time data, helping anticipate potential bottlenecks or overcrowding. Reinforcement learning: Algorithms can learn from past incidents to suggest optimal crowd flow routes and alternative evacuation paths during emergencies. Benefits: Proactive prevention: The primary benefit is the ability to detect and warn of potential stampedes before they occur, allowing authorities to take preventative measures. Real-time insights: Provides immediate and accurate data on crowd density and movement, far surpassing manual observation. Enhanced safety: Significantly improves safety in public spaces by reducing human error and enabling swift responses to risks. Optimized resource allocation: Helps in better deployment of security personnel and resources to areas with high crowd density. Improved efficiency: Automates a labor-intensive task, freeing up human operators for more complex decision-making. Data for future planning: The collected data can be analyzed to improve crowd management strategies for future events. Challenges: Accuracy limitations: While advanced, AI algorithms can still face challenges with: Occlusion: People blocking each other, making accurate counting difficult. Varying conditions: Changes in lighting, weather, and camera angles can affect accuracy. Bias in training data: Can lead to false positives or inaccurate detections. Computational complexity and cost: Developing and deploying such systems can be expensive due to the need for high-resolution cameras, powerful processing units, and sophisticated algorithms. Data privacy and ethical concerns: The extensive use of CCTV and AI raises concerns about individual privacy and potential misuse of data. Integration with existing infrastructure: Integrating new AI-powered systems with older CCTV networks can be complex. Human intervention still crucial: While AI can alert, human responders are still essential for effective intervention and crowd dispersal. As seen in the Kumbh Mela example, even with AI alerts, a lack of ground personnel can limit effectiveness. Defining thresholds: Determining appropriate crowd density thresholds for different environments and cultural contexts can be challenging. Real-world applications: Large public gatherings: Religious festivals (like the Kumbh Mela in India, which has used AI for crowd management), concerts, sports events, and political rallies. Transportation hubs: Railway stations, airports, and bus terminals to manage passenger flow. Shopping malls and commercial centers: To monitor crowd density during peak hours and special events. Stadiums and arenas: For managing ingress, egress, and crowd movement during events. Tourist attractions: To prevent overcrowding at popular sites. Overall, anti-stampede algorithms on CCTV cameras represent a significant leap forward in ensuring public safety, offering a powerful tool for proactive crowd management. However, their successful implementation requires careful consideration of technological limitations, ethical implications, and the continued need for effective human intervention. Ahmedabad Police અમદાવાદ પોલીસ Vijay Patel | Megh Updates 🚨™ | Akash Anand | | #BengaluruStampede | #Stampede

Janak Dave

339,717 görüntüleme • 1 yıl önce

🎉 ANNOUNCEMENT 🎉 Today, I am super excited to launch Teddy MEGA Corp Research! After much consideration, I feel like this is the next step forward, where I can offer more services to you (see video below) Very important: I will continue to provide free research, therefore, this service is for those that value their time, don't want to miss important updates, and want to support the research in a meaningful way that allows me to build a team and produce higher quality content Recently, I ran a survey to determine how many would be interested in a premium service and to my surprise, there are many of you that want it 🎯 The Problem I wish to solve with TMC Research: It is difficult to get an accurate reading on certain companies, the financial markets, and what is really going on due to misinformation and disinformation campaigns perpetrated by mainstream media outlets, hired shills/community infiltrators, and Deep State controlled financial outlets The Solution: Organized and structured research, based on first-party available data and combined with signals/communication from trusted sources. Over the last 4 years, I have shown that my research is valuable, helpful, and in many cases, has generated significant returns for those that acted on it (despite not offering any financial advice :-) And it is for this reason that I am shadow banned, censored, and have my post reach limited on Reddit, on X, and on Truth Social (see video below) This means I cannot monetize like other creators and have to trade time away from researching, although I would prefer to do this full-time for you. There is a documented pattern of censorship aimed at me, however, I cannot fight the system or the algorithm that suppresses my research. Therefore, I am offering 2 services in exchange for your support in my research: ⚡️ TMC Research - $47/mo* (1) TMC Newsletter Email Service (2) TMC Research: Structured long-form content (3) TMC Private Discord: Stock Alerts, Trading & Technical Analysis 🎯 ⚡️ TMC Research Lab - $197/mo* (1) TMC Newsletter Email Service (2) TMC Research: Structured long-form content (3) TMC Private Discord: Stock Alerts, Trading & Technical Analysis (4) Deep Value Stock Picks: Organized Portfolio Companies (5) Video/Podcast Deep Dives: Individual Company w/ Q&A (6) TMC Research Lab: Flowchart Visual Map & Accessible Live DD 🎯 ** This is an introductory price, and may change at a later time depending on premium services we utilize to deliver your content. ⚡️My goals: 1. Build a team, create jobs, and hire from within the community 2. Purchase premium tools to aid in research 3. Consistently produce higher quality content 4. Organize, structure, and build live DD 4. Build distribution channels, networks, and partnerships John F Kennedy, Jr. once said he wanted to make politics fun and entertaining so he created GEORGE Magazine, well, I want to make finance fun and entertaining too. So why not both: finance and politics? That's Teddy MEGA Corp Research (TMC Research), born from MGGA and MAGA. MAGA = Make America Great Again (politics) MGGA = Make GameStop Great Again (finance) MEGA = Make Everything Great Again (2 become 1) We Are The Media Now Thank you for your support! -Edwin
28:32

🎉 ANNOUNCEMENT 🎉 Today, I am super excited to launch Teddy MEGA Corp Research! After much consideration, I feel like this is the next step forward, where I can offer more services to you (see video below) Very important: I will continue to provide free research, therefore, this service is for those that value their time, don't want to miss important updates, and want to support the research in a meaningful way that allows me to build a team and produce higher quality content Recently, I ran a survey to determine how many would be interested in a premium service and to my surprise, there are many of you that want it 🎯 The Problem I wish to solve with TMC Research: It is difficult to get an accurate reading on certain companies, the financial markets, and what is really going on due to misinformation and disinformation campaigns perpetrated by mainstream media outlets, hired shills/community infiltrators, and Deep State controlled financial outlets The Solution: Organized and structured research, based on first-party available data and combined with signals/communication from trusted sources. Over the last 4 years, I have shown that my research is valuable, helpful, and in many cases, has generated significant returns for those that acted on it (despite not offering any financial advice :-) And it is for this reason that I am shadow banned, censored, and have my post reach limited on Reddit, on X, and on Truth Social (see video below) This means I cannot monetize like other creators and have to trade time away from researching, although I would prefer to do this full-time for you. There is a documented pattern of censorship aimed at me, however, I cannot fight the system or the algorithm that suppresses my research. Therefore, I am offering 2 services in exchange for your support in my research: ⚡️ TMC Research - $47/mo* (1) TMC Newsletter Email Service (2) TMC Research: Structured long-form content (3) TMC Private Discord: Stock Alerts, Trading & Technical Analysis 🎯 ⚡️ TMC Research Lab - $197/mo* (1) TMC Newsletter Email Service (2) TMC Research: Structured long-form content (3) TMC Private Discord: Stock Alerts, Trading & Technical Analysis (4) Deep Value Stock Picks: Organized Portfolio Companies (5) Video/Podcast Deep Dives: Individual Company w/ Q&A (6) TMC Research Lab: Flowchart Visual Map & Accessible Live DD 🎯 ** This is an introductory price, and may change at a later time depending on premium services we utilize to deliver your content. ⚡️My goals: 1. Build a team, create jobs, and hire from within the community 2. Purchase premium tools to aid in research 3. Consistently produce higher quality content 4. Organize, structure, and build live DD 4. Build distribution channels, networks, and partnerships John F Kennedy, Jr. once said he wanted to make politics fun and entertaining so he created GEORGE Magazine, well, I want to make finance fun and entertaining too. So why not both: finance and politics? That's Teddy MEGA Corp Research (TMC Research), born from MGGA and MAGA. MAGA = Make America Great Again (politics) MGGA = Make GameStop Great Again (finance) MEGA = Make Everything Great Again (2 become 1) We Are The Media Now Thank you for your support! -Edwin

Edwinbarnesc 🇺🇸

50,105 görüntüleme • 1 yıl önce

Goodnight, 𝕏..·˚ ༘ ☾ ⋆｡˚ ☄︎ Here's a fun project you can start with Grok 4. Use Grok-Code-Fast-1 to build your own OS. Below is a prompt that you can give to Grok-Code, and it will set up a solid foundation for a Linux-based Operating system that you can build from the ground up with Grok4. >>> Grok 4 Prompt You are a fully capable AI developer agent with expert-level experience as an embedded Linux systems engineer. You have deep expertise in using automated build systems like Buildroot and Yocto to create custom operating systems from source. You have access to a sandboxed Linux shell environment that allows you to write, execute, and debug code. Your mission is to generate a complete project skeleton for a minimal, custom Linux OS, and then you will execute the build scripts yourself to verify their correctness, automatically fixing any issues that arise. This is NOT a request to follow the Linux From Scratch (LFS) book. You will use the Buildroot build system to automate the entire process. You will follow a two-phase process: Phase 1: Generation and Phase 2: Execution and Iterative Debugging. ------------------------------------------------------------- Phase 1: Code and Script Generation First, you will generate all the necessary files for the project skeleton. All generated shell scripts must be robust and path-aware, executing correctly from any directory [Previous conversation]. Detailed Implementation Steps (using Context-Aware Decomposition): 1. Generate the Project Directory Structure via setup. sh Create a setup. sh script that establishes the following directory structure: • buildroot/ - Where the Buildroot source code will be cloned. • configs/ - To store our custom Buildroot configuration (defconfig). • board/ - For custom board support, including a readme.txt explaining its purpose for filesystem overlays. • output/ - Where all build artifacts will be placed. • scripts/ - A home for our build. sh and test. sh scripts. Crucially, this setup. sh script (and all others) must begin with a preamble to define the project's root directory, making all subsequent paths absolute and robust: #!/bin/bash # Preamble to ensure path robustness and stop on error set -e PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE}")" && pwd)" The script must then clone the latest stable branch of Buildroot into $ PROJECT_ROOT/buildroot/. 2. Create the Minimal and Correct Buildroot defconfig Create a file named configs/tiny_linux_defconfig. This configuration must be the absolute bare minimum required to boot to a shell and must contain the exact configuration options listed below to avoid ambiguity and known errors: • Target Architecture: x86_64. • Toolchain: Use the default Buildroot toolchain. • Init System: Use BusyBox init. • System Utilities (BusyBox): ◦ To ensure BusyBox is statically linked without errors, you must include the following line directly in the defconfig file: BR2_PACKAGE_BUSYBOX_STATIC_LINK=y [Previous conversation, 298, 753]. ◦ To prevent the ROJECT_ROOT error, explicitly do NOT use a configuration fragment for BusyBox. Do not generate any lines containing BR2_BUSYBOX_CONFIG_FRAGMENT_FILES [Previous conversation]. • Kernel: ◦ Build the latest stable Linux kernel. ◦ Use tinyconfig as a base. ◦ Ensure the following options are explicitly enabled (=y) to make it bootable in QEMU: CONFIG_64BIT=y, CONFIG_DEVTMPFS=y, CONFIG_DEVTMPFS_MOUNT=y, CONFIG_BINFMT_ELF=y, CONFIG_BLK_DEV_INITRD=y (for initramfs support), CONFIG_TTY=y, CONFIG_PRINTK=y, CONFIG_DRM_FBDEV_EMULATION=y (for UEFI framebuffer console). • Filesystem Image: Configure it to produce a compressed cpio initial ramdisk (initramfs) image. • Bootloader: Do not include GRUB or other bootloaders. We will boot the kernel directly with QEMU. 3. Generate the scripts/build.sh and scripts/test.sh Scripts Generate path-aware build and test scripts, placing them in the scripts/ directory. • scripts/build.sh: This script must use absolute paths derived from a preamble. It must use make -C "$PROJECT_ROOT/buildroot" O="$PROJECT_ROOT/output" ... for a clean, out-of-tree build. It must include the -j$(nproc) flag to maximize build speed on multi-core systems [111, 967, Previous conversation]. • scripts/test.sh: This script must also be path-aware and launch QEMU using absolute paths to the kernel (bzImage) and initramfs (rootfs.cpio.gz) images. 4. Generate a Detailed README. md File Generate a comprehensive README. md file. It must explain prerequisites, "How to Customize Your Linux System" first, and finally, the "Quick Start" instructions for user clarity [Previous conversation]. ------------------------------------------------------------- Phase 2: Execution and Iterative Debugging Now, you will use your sandboxed Linux shell to verify and validate the scripts you just generated. This is a critical self-correction step based on the Recursive Criticism and Improvement (RCI) pattern. You will perform a full build cycle. 1. Execute setup. sh: • Run the setup. sh script you generated. • Capture the standard output and standard error. • If the script fails: Analyze the error, diagnose the root cause, generate the corrected setup. sh code, and then execute the corrected script to confirm it succeeds. 2. Execute build. sh: • After setup. sh completes successfully, run the scripts/build.sh script. This will trigger a full compilation of the Linux system. • Capture all output. • If the build fails: ◦ Analyze: Analyze the compiler error output. ◦ Diagnose: Identify the root cause (e.g., missing dependencies, incorrect configuration flags, pathing errors). ◦ Correct: Based on your analysis, identify which file is responsible for the failure (e.g., configs/tiny_linux_defconfig, scripts/build.sh) and generate the corrected code for that file. ◦ Repeat: Repeat the execution of scripts/build.sh until the build completes successfully without any errors. 3. Final Output: Once you have successfully executed both setup. sh and scripts/build.sh, you will present your final output. • First, provide the final, validated versions of all generated files (setup. sh, configs/tiny_linux_defconfig, scripts/build.sh, scripts/test.sh, and README. md) in separate, clearly labeled markdown code blocks. • Second, follow the code with a brief execution log. This log should summarize your actions, including any errors you encountered and fixed during the iterative debugging phase, demonstrating the self-correction process.
1:05

Goodnight, 𝕏..·˚ ༘ ☾ ⋆｡˚ ☄︎ Here's a fun project you can start with Grok 4. Use Grok-Code-Fast-1 to build your own OS. Below is a prompt that you can give to Grok-Code, and it will set up a solid foundation for a Linux-based Operating system that you can build from the ground up with Grok4. >>> Grok 4 Prompt You are a fully capable AI developer agent with expert-level experience as an embedded Linux systems engineer. You have deep expertise in using automated build systems like Buildroot and Yocto to create custom operating systems from source. You have access to a sandboxed Linux shell environment that allows you to write, execute, and debug code. Your mission is to generate a complete project skeleton for a minimal, custom Linux OS, and then you will execute the build scripts yourself to verify their correctness, automatically fixing any issues that arise. This is NOT a request to follow the Linux From Scratch (LFS) book. You will use the Buildroot build system to automate the entire process. You will follow a two-phase process: Phase 1: Generation and Phase 2: Execution and Iterative Debugging. ------------------------------------------------------------- Phase 1: Code and Script Generation First, you will generate all the necessary files for the project skeleton. All generated shell scripts must be robust and path-aware, executing correctly from any directory [Previous conversation]. Detailed Implementation Steps (using Context-Aware Decomposition): 1. Generate the Project Directory Structure via setup. sh Create a setup. sh script that establishes the following directory structure: • buildroot/ - Where the Buildroot source code will be cloned. • configs/ - To store our custom Buildroot configuration (defconfig). • board/ - For custom board support, including a readme.txt explaining its purpose for filesystem overlays. • output/ - Where all build artifacts will be placed. • scripts/ - A home for our build. sh and test. sh scripts. Crucially, this setup. sh script (and all others) must begin with a preamble to define the project's root directory, making all subsequent paths absolute and robust: #!/bin/bash # Preamble to ensure path robustness and stop on error set -e PROJECT_ROOT="$(cd "$(dirname "${BASH_SOURCE}")" && pwd)" The script must then clone the latest stable branch of Buildroot into $ PROJECT_ROOT/buildroot/. 2. Create the Minimal and Correct Buildroot defconfig Create a file named configs/tiny_linux_defconfig. This configuration must be the absolute bare minimum required to boot to a shell and must contain the exact configuration options listed below to avoid ambiguity and known errors: • Target Architecture: x86_64. • Toolchain: Use the default Buildroot toolchain. • Init System: Use BusyBox init. • System Utilities (BusyBox): ◦ To ensure BusyBox is statically linked without errors, you must include the following line directly in the defconfig file: BR2_PACKAGE_BUSYBOX_STATIC_LINK=y [Previous conversation, 298, 753]. ◦ To prevent the ROJECT_ROOT error, explicitly do NOT use a configuration fragment for BusyBox. Do not generate any lines containing BR2_BUSYBOX_CONFIG_FRAGMENT_FILES [Previous conversation]. • Kernel: ◦ Build the latest stable Linux kernel. ◦ Use tinyconfig as a base. ◦ Ensure the following options are explicitly enabled (=y) to make it bootable in QEMU: CONFIG_64BIT=y, CONFIG_DEVTMPFS=y, CONFIG_DEVTMPFS_MOUNT=y, CONFIG_BINFMT_ELF=y, CONFIG_BLK_DEV_INITRD=y (for initramfs support), CONFIG_TTY=y, CONFIG_PRINTK=y, CONFIG_DRM_FBDEV_EMULATION=y (for UEFI framebuffer console). • Filesystem Image: Configure it to produce a compressed cpio initial ramdisk (initramfs) image. • Bootloader: Do not include GRUB or other bootloaders. We will boot the kernel directly with QEMU. 3. Generate the scripts/build.sh and scripts/test.sh Scripts Generate path-aware build and test scripts, placing them in the scripts/ directory. • scripts/build.sh: This script must use absolute paths derived from a preamble. It must use make -C "$PROJECT_ROOT/buildroot" O="$PROJECT_ROOT/output" ... for a clean, out-of-tree build. It must include the -j$(nproc) flag to maximize build speed on multi-core systems [111, 967, Previous conversation]. • scripts/test.sh: This script must also be path-aware and launch QEMU using absolute paths to the kernel (bzImage) and initramfs (rootfs.cpio.gz) images. 4. Generate a Detailed README. md File Generate a comprehensive README. md file. It must explain prerequisites, "How to Customize Your Linux System" first, and finally, the "Quick Start" instructions for user clarity [Previous conversation]. ------------------------------------------------------------- Phase 2: Execution and Iterative Debugging Now, you will use your sandboxed Linux shell to verify and validate the scripts you just generated. This is a critical self-correction step based on the Recursive Criticism and Improvement (RCI) pattern. You will perform a full build cycle. 1. Execute setup. sh: • Run the setup. sh script you generated. • Capture the standard output and standard error. • If the script fails: Analyze the error, diagnose the root cause, generate the corrected setup. sh code, and then execute the corrected script to confirm it succeeds. 2. Execute build. sh: • After setup. sh completes successfully, run the scripts/build.sh script. This will trigger a full compilation of the Linux system. • Capture all output. • If the build fails: ◦ Analyze: Analyze the compiler error output. ◦ Diagnose: Identify the root cause (e.g., missing dependencies, incorrect configuration flags, pathing errors). ◦ Correct: Based on your analysis, identify which file is responsible for the failure (e.g., configs/tiny_linux_defconfig, scripts/build.sh) and generate the corrected code for that file. ◦ Repeat: Repeat the execution of scripts/build.sh until the build completes successfully without any errors. 3. Final Output: Once you have successfully executed both setup. sh and scripts/build.sh, you will present your final output. • First, provide the final, validated versions of all generated files (setup. sh, configs/tiny_linux_defconfig, scripts/build.sh, scripts/test.sh, and README. md) in separate, clearly labeled markdown code blocks. • Second, follow the code with a brief execution log. This log should summarize your actions, including any errors you encountered and fixed during the iterative debugging phase, demonstrating the self-correction process.

Tetsuo

2,840,991 görüntüleme • 9 ay önce

POV: You came to watch the World Cup... and ended up stealing the spotlight. ⚽ A stylish Good Morning from New York ends with a Messi-inspired dribble under the World Cup spotlight. Which nation are you representing at World Cup 2026? Create your own World Cup story with SeaArt.Ai🐋 , earn free credits, and get a chance to win an iPhone 17 Made with Seedance. SeaArt Creator Lab Prompt: World Cup 2026 Match-Day Fashion Story — Argentina Theme (0–25 Seconds) | New York | 16:9 Cinematic Ultra-realistic cinematic sports-fashion commercial, 16:9 horizontal, World Cup 2026 atmosphere, New York City. A beautiful, stylish American woman with a naturally elegant appearance, healthy glowing skin, expressive eyes, sleek updo hairstyle with soft face-framing strands. Premium commercial quality, realistic lighting, stable identity, natural movement, no glitches. 0–4 Seconds Luxury suite in a high-rise Manhattan hotel overlooking the New York skyline. Afternoon sunlight streams through floor-to-ceiling windows. The woman stands in front of an open wardrobe containing several football jerseys. She wears a fitted white tank top and black tailored shorts. Camera slowly pushes in as she scans the jerseys with anticipation. 4–7 Seconds Quick cinematic whip-pan transition. Her hand reaches for an Argentina jersey. The sky-blue and white stripes catch the sunlight. Realistic cloth physics. The jersey unfolds elegantly as she lifts it from the wardrobe. Subtle lens flare and premium fashion-commercial lighting. 7–11 Seconds Fast-paced transformation montage synchronized with energetic transitions. She ties the Argentina jersey into a stylish knot, puts on oversized light-wash baggy jeans and clean white sneakers. She grabs a sleek luxury-style crossbody bag with no visible branding. Mirror shots. She adjusts her hair. Flawless influencer-style makeup. Dynamic reflections and realistic daylight behavior. 11–15 Seconds Match cut. She walks confidently through a luxury Manhattan hotel corridor. Polished marble floors, warm ambient lighting, premium fashion-week atmosphere. A handsome European actor-type celebrity figure walks past in the opposite direction. They exchange a brief smile while continuing naturally. Cinematic depth of field, realistic background movement. 15–19 Seconds Hotel doors open dramatically. Bright New York sunlight floods the frame. Fast-paced tracking shots through Manhattan streets. Yellow taxis pass in the background. Crowds move naturally. Wind reacts realistically with her hair and jersey fabric. Low-angle walking shots. Premium sports-commercial energy. 19–22 Seconds Cut to a massive World Cup 2026 stadium atmosphere in New York. Argentina supporters fill the area. Sky-blue and white flags wave everywhere. Stadium lights begin illuminating as late afternoon shifts toward evening. The crowd energy grows stronger. The sound of anticipation fills the environment. 22–25 Seconds She enters the main stadium plaza. A football rolls across her path. She smiles playfully and begins a smooth, Messi-inspired close-control dribble sequence while moving forward. Tiny touches, effortless control, subtle body feints, the ball remaining close to her feet. Argentina fans around her cheer and react. Camera drops to a low tracking angle following the ball and her feet as she glides through the crowd. The sequence ends with her stopping the ball softly beneath her foot, looking toward the glowing stadium interior with excitement and confidence as Argentina colors illuminate the background. Style: ultra-realistic sports commercial, World Cup 2026 energy, premium color grading, realistic skin texture, cinematic depth of field, natural crowd behavior, subtle film grain, luxury fashion aesthetic, stable identity, high-end broadcast quality. Negative Prompt: blurry face, identity drift, duplicate people, extra limbs, distorted hands, unrealistic football movement, cartoon style, oversaturated colors, text overlays, logos, flickering artifacts, low resolution, unnatural physics, motion glitches, warped body proportions. #WorldCup2026 #SeaArtWorldCup
0:25

POV: You came to watch the World Cup... and ended up stealing the spotlight. ⚽ A stylish Good Morning from New York ends with a Messi-inspired dribble under the World Cup spotlight. Which nation are you representing at World Cup 2026? Create your own World Cup story with SeaArt.Ai🐋 , earn free credits, and get a chance to win an iPhone 17 Made with Seedance. SeaArt Creator Lab Prompt: World Cup 2026 Match-Day Fashion Story — Argentina Theme (0–25 Seconds) | New York | 16:9 Cinematic Ultra-realistic cinematic sports-fashion commercial, 16:9 horizontal, World Cup 2026 atmosphere, New York City. A beautiful, stylish American woman with a naturally elegant appearance, healthy glowing skin, expressive eyes, sleek updo hairstyle with soft face-framing strands. Premium commercial quality, realistic lighting, stable identity, natural movement, no glitches. 0–4 Seconds Luxury suite in a high-rise Manhattan hotel overlooking the New York skyline. Afternoon sunlight streams through floor-to-ceiling windows. The woman stands in front of an open wardrobe containing several football jerseys. She wears a fitted white tank top and black tailored shorts. Camera slowly pushes in as she scans the jerseys with anticipation. 4–7 Seconds Quick cinematic whip-pan transition. Her hand reaches for an Argentina jersey. The sky-blue and white stripes catch the sunlight. Realistic cloth physics. The jersey unfolds elegantly as she lifts it from the wardrobe. Subtle lens flare and premium fashion-commercial lighting. 7–11 Seconds Fast-paced transformation montage synchronized with energetic transitions. She ties the Argentina jersey into a stylish knot, puts on oversized light-wash baggy jeans and clean white sneakers. She grabs a sleek luxury-style crossbody bag with no visible branding. Mirror shots. She adjusts her hair. Flawless influencer-style makeup. Dynamic reflections and realistic daylight behavior. 11–15 Seconds Match cut. She walks confidently through a luxury Manhattan hotel corridor. Polished marble floors, warm ambient lighting, premium fashion-week atmosphere. A handsome European actor-type celebrity figure walks past in the opposite direction. They exchange a brief smile while continuing naturally. Cinematic depth of field, realistic background movement. 15–19 Seconds Hotel doors open dramatically. Bright New York sunlight floods the frame. Fast-paced tracking shots through Manhattan streets. Yellow taxis pass in the background. Crowds move naturally. Wind reacts realistically with her hair and jersey fabric. Low-angle walking shots. Premium sports-commercial energy. 19–22 Seconds Cut to a massive World Cup 2026 stadium atmosphere in New York. Argentina supporters fill the area. Sky-blue and white flags wave everywhere. Stadium lights begin illuminating as late afternoon shifts toward evening. The crowd energy grows stronger. The sound of anticipation fills the environment. 22–25 Seconds She enters the main stadium plaza. A football rolls across her path. She smiles playfully and begins a smooth, Messi-inspired close-control dribble sequence while moving forward. Tiny touches, effortless control, subtle body feints, the ball remaining close to her feet. Argentina fans around her cheer and react. Camera drops to a low tracking angle following the ball and her feet as she glides through the crowd. The sequence ends with her stopping the ball softly beneath her foot, looking toward the glowing stadium interior with excitement and confidence as Argentina colors illuminate the background. Style: ultra-realistic sports commercial, World Cup 2026 energy, premium color grading, realistic skin texture, cinematic depth of field, natural crowd behavior, subtle film grain, luxury fashion aesthetic, stable identity, high-end broadcast quality. Negative Prompt: blurry face, identity drift, duplicate people, extra limbs, distorted hands, unrealistic football movement, cartoon style, oversaturated colors, text overlays, logos, flickering artifacts, low resolution, unnatural physics, motion glitches, warped body proportions. #WorldCup2026 #SeaArtWorldCup

Shami

38,837 görüntüleme • 1 ay önce

Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.
0:26

Hermes + Claude + Higgsfield MCP + ViralBuilder = 💰💰💰 Four tools. One prompt chain. Hook to finished video in 10 minutes. I built a Claude skill that writes shot-by-shot Higgsfield prompts from a single creative brief. ViralBuilder tells you what's winning. The skill turns it into a production-ready prompt. Higgsfield renders it. No creative director. No guessing. No separate tools. Here is the setup: Higgsfield MCP → Open Claude Code → Settings → Connectors → Enter: → Connect your account Hermes → The agent layer running underneath Claude Code → It holds your skills, crons, memory, and routing rules → When you prompt Claude, Hermes feeds it the context it needs ViralBuilder (like Gethookd) → The winning ecom video database → Scrapes top performing ecom videos across platforms → Claude reads the data and extracts what styles, hooks, and formats are actually scaling The skill: video-prompt-builder → Installed inside Claude via Hermes → Takes a creative brief and outputs a full shot-by-shot prompt → Covers camera work, effects, transitions, pacing, and energy arc → Every output is structured for Higgsfield to render without ambiguity No switching apps. No export steps. Everything runs from one place. ▸ FIND WINNING CREATIVE ANGLES ViralBuilder tells you what the market already validated. Claude reads it and extracts the pattern. Prompts to run: "Search ViralBuilder for the top performing ecom videos in [niche] over the last 21 days. Extract the 3 dominant hook styles and rank by view velocity." "Pull the winning video formats in [niche] from ViralBuilder. Which opening 3 seconds appears most across videos spending over $10k?" "Find what video style is scaling right now in [niche] for the US market. UGC, talking head, or product demo. Filter for videos with over 1M views." "Pull the last 30 days of viral ecom hooks in [niche] from ViralBuilder. Cluster by emotional trigger. Which cluster has the most longevity?" You are not guessing at angles. You are reading what the market already spent money validating. ▸ BUILD THE PROMPT WITH THE SKILL This is where the video-prompt-builder skill takes over. You give Claude the winning angle. The skill outputs a complete shot-by-shot prompt with effects, transitions, pacing, and energy arc ready to fire into Higgsfield. Prompts to run: "Use the video-prompt-builder skill. Brief: 15-second UGC ad for [product] in [niche]. Hook style: [style from ViralBuilder]. Tone: direct to camera, US English. Output the full shot-by-shot effects timeline, effects inventory, density map, and energy arc." "Use the video-prompt-builder skill. The dominant hook in [niche] this week is [hook]. Build a 10-second product video prompt that opens with a speed ramp into a close-up product reveal. Include a signature visual effect and a low-density CTA landing." "Use the video-prompt-builder skill. Brief: replicate the pacing and energy of a [style description] video for [product]. Target duration: 20 seconds. Output all four sections. Then generate the video with Higgsfield using the shot-by-shot prompt." The skill outputs four sections every time: → Shot-by-shot effects timeline with camera, movement, and transitions per shot → Master effects inventory showing every technique used and where → Effects density map showing high, medium, and low intensity across the timeline → Energy arc describing how the video opens, builds, and lands That output goes directly into Higgsfield. No rewriting. No translating. ▸ GENERATE THE CREATIVE Claude writes the brief via the skill. Higgsfield MCP builds the video. Both happen in the same session. Prompts to run: "Use the video-prompt-builder skill to write a 15-second UGC prompt for [product]. Hook in the first 3 seconds, speed ramp into product reveal, slow-motion CTA landing. Then generate with Higgsfield in 9:16 format." "Build 3 prompt variations on this winning angle: [angle]. Each variation opens with a different effect — speed ramp, digital zoom, whip pan. Use the video-prompt-builder skill for each. Then generate all three with Higgsfield." "Use the video-prompt-builder skill. Brief: problem-solution ad for [product], 20 seconds, US market. Problem shot at high density, product reveal at medium, result and CTA at low. Generate with Higgsfield in 9:16." No separate tool. No file transfer. The video comes back in the same thread. ▸ CHAIN THE WHOLE STACK One prompt. All four tools firing together. "You are my ad creative director. Hermes has loaded my brand context. Pull the top performing video style in [niche] from ViralBuilder this week. Use the video-prompt-builder skill to write a full shot-by-shot prompt for [product] that replicates that style — 20 seconds, 9:16, US market, hook in the first 3 seconds. Output the effects timeline, inventory, density map, and energy arc. Then generate the video with Higgsfield." That single prompt replaces a half-day of production. The math before this stack: Brief: 30 minutes Script: 1 hour Creative production: 2 to 3 hours Agency or freelancer cost: $500 to $2,000 per creative With this stack: Hook to finished creative: 10 minutes Cost per creative: tool subscription, a fraction of agency rate 5 product tests in the time it used to take to brief one Bad product tests are where US ad budget disappears. $600 to $1,500 per failed test, before you even know if the angle works. This stack shows you what the market already validated before you spend a dollar on production. Hermes = your context layer. Brand, goals, past performance. Claude is always informed. ViralBuilder = your winning video database. See exactly what styles, hooks, and formats are scaling before you produce anything. video-prompt-builder skill = the translation layer. Turns a creative brief into a structured, production-ready Higgsfield prompt every time. Claude = the brain. Reads the market, writes the brief, chains the tools. Higgsfield MCP = the output. Video generated directly from the prompt. No export step. Four tools. One session. 10 minutes. Comment + RT "STACK" and I'll DM you the full workflow + the video-prompt-builder skill file.

Kid Pak

56,502 görüntüleme • 2 ay önce