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🚀 Looking forward to a foundtional humanoid motion tracker? 📢Meet Any2Track — Track Any Motions under Any Disturbances! 💡Any2Track shows SOTA performance on diverse motions under multiple disturbances. Project: Codebase:

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Zhikai Zhang

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44,452 Aufrufe • vor 10 Monaten •via X (Twitter)

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Any2Track tracks diverse motions under various disturbances. The two-stage RL framework learns to track contact-rich human motions from motion capture datasets and adds adaptability to disturbances via dynamics-aware model prediction.
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Any2Track tracks diverse motions under various disturbances. The two-stage RL framework learns to track contact-rich human motions from motion capture datasets and adds adaptability to disturbances via dynamics-aware model prediction.

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Meet Any2Track, a new two-stage RL framework designed to address a core challenge for humanoid robots: reliably tracking diverse, dynamic motions in the real world. The framework consists of two key components: AnyTracker, a general motion tracker, and AnyAdapter, a module that allows the robot to adapt to real-world disturbances like uneven terrain or external forces. This approach gives robots the stability needed for practical use. The system has been successfully deployed on a Unitree G1 humanoid robot with zero-shot sim2real transfer, performing exceptionally well in various motion tracking tasks under multiple real-world disturbances. Project: Codebase:
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Meet Any2Track, a new two-stage RL framework designed to address a core challenge for humanoid robots: reliably tracking diverse, dynamic motions in the real world. The framework consists of two key components: AnyTracker, a general motion tracker, and AnyAdapter, a module that allows the robot to adapt to real-world disturbances like uneven terrain or external forces. This approach gives robots the stability needed for practical use. The system has been successfully deployed on a Unitree G1 humanoid robot with zero-shot sim2real transfer, performing exceptionally well in various motion tracking tasks under multiple real-world disturbances. Project: Codebase:

RoboHub🤖

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How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥
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How do you give a humanoid the general motion capability? Not just single motions, but all motion? Introducing SONIC, our new work on supersizing motion tracking for natural humanoid control. We argue that motion tracking is the scalable foundation task for humanoids. So we "supersized" it: 9k+ GPU hours and 100M+ motion frames. But tracking alone is not enough; we show how to make a useful control system out of it: - Universal Kinematic Planner: Enables game-like gamepad control and high-level teleoperation, just like controlling a character in a game. - VR Full-Body Teleop: Direct, real-time whole-body control by a human wearing a VR headset. - VR Keypoint Teleop: Control the upper body (hands/head) while our planner handles robust locomotion automatically. - VLA Integration: We connect this motion tracker to autonomous Visual-Language-Action (VLA) models for autonomous task execution! We use a Universal Token Space to UNIFY this command space, turning our robust tracker into a general-purpose, programmable humanoid brain. This is the generalist "System 1" for humanoids. 🚀 Project: #Humanoids #Robotics #AI #FoundationModels #NVIDIAResearch 🧠🔥

Zhengyi “Zen” Luo

62,208 Aufrufe • vor 9 Monaten

Video2Humanoid. Still trouble with bad retargeted humanoid motions? Humanoids now are Easy to track any video using our new Neural Motion Retargeting (NMR) Method. Optimization-based motion retargeting methods like IK and GMR are solving a non-convex problem frame by frame, which makes them sensitive to initialization, hard to tune, and prone to poor local minima. The result is familiar: joint discontinuities, self-collisions, and unstable foot contact. Our solution is simple but effective : instead of optimizing each frame independently, learn the mapping between human motion and robot motion as a distribution via networks. 📊Results on Unitree G1 - 0 joint jumps - 54% fewer self-collision frames - 61% fewer joint limit violations - Faster convergence for downstream control policy training NMR turns motion retargeting from a fragile optimization problem into a learned, scalable pipeline for more stable humanoid motion. please visit for more visualization results. opensource:
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Video2Humanoid. Still trouble with bad retargeted humanoid motions? Humanoids now are Easy to track any video using our new Neural Motion Retargeting (NMR) Method. Optimization-based motion retargeting methods like IK and GMR are solving a non-convex problem frame by frame, which makes them sensitive to initialization, hard to tune, and prone to poor local minima. The result is familiar: joint discontinuities, self-collisions, and unstable foot contact. Our solution is simple but effective : instead of optimizing each frame independently, learn the mapping between human motion and robot motion as a distribution via networks. 📊Results on Unitree G1 - 0 joint jumps - 54% fewer self-collision frames - 61% fewer joint limit violations - Faster convergence for downstream control policy training NMR turns motion retargeting from a fragile optimization problem into a learned, scalable pipeline for more stable humanoid motion. please visit for more visualization results. opensource:

Xiao-Xiao Long

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Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:
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Today, we are open-sourcing Hunyuan World 1.1 (WorldMirror), a universal feed-forward 3D reconstruction model. 🚀🚀🚀 While our previously released Hunyuan World 1.0 (open-sourced, lite version deployable on consumer GPUs) focused on generating 3D worlds from text or single-view images, Hunyuan World 1.1 significantly expands the input scope by unlocking video-to-3D and multi-view-to-3D world creation. Highlights: 🔹Any Input, Maximized Flexibility and Fidelity: Flexibly integrates diverse geometric priors (camera poses, intrinsics, depth maps) to resolve structural ambiguities and ensure geometrically consistent 3D outputs. 🔹Any Output, SOTA Results：This elegant architecture simultaneously generates multiple 3D representations: dense point clouds, multi-view depth maps, camera parameters, surface normals, and 3D Gaussian Splattings. 🔹Single-GPU & Fast Inference: As an all-in-one, feed-forward model, Hunyuan World 1.1 runs on a single GPU and delivers all 3D attributes in a single forward pass, within seconds. 🌐Project Page: 🔗Github： 🤗Hugging Face： ✨Demo: 📄Technical Report:

Tencent Hy

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Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.
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Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

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Humanoids finally move like humans… and can do more than copy. [Details + demos in thread 👇] A new framework, BeyondMimic, shows how to learn naturalistic whole-body control from human motion. But then goes further by composing those skills into versatile, zero-shot policies. Two key pieces make it work: •Motion tracking pipeline: robustly turns human mocap into dynamic robot motions (jump spins, sprinting, cartwheels) on real hardware •Guided diffusion policy: distills motion primitives and enables zero-shot downstream tasks with simple cost functions The result: ✅ State-of-the-art motion quality from mocap tracking ✅ Versatile task execution; waypoint navigation, joystick teleop, obstacle avoidance ✅ Stable sim-to-real transfer for diverse humanoid skills Thank you for sharing, Qiayuan Liao! 📍 Paper: Video:
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Humanoids finally move like humans… and can do more than copy. [Details + demos in thread 👇] A new framework, BeyondMimic, shows how to learn naturalistic whole-body control from human motion. But then goes further by composing those skills into versatile, zero-shot policies. Two key pieces make it work: •Motion tracking pipeline: robustly turns human mocap into dynamic robot motions (jump spins, sprinting, cartwheels) on real hardware •Guided diffusion policy: distills motion primitives and enables zero-shot downstream tasks with simple cost functions The result: ✅ State-of-the-art motion quality from mocap tracking ✅ Versatile task execution; waypoint navigation, joystick teleop, obstacle avoidance ✅ Stable sim-to-real transfer for diverse humanoid skills Thank you for sharing, Qiayuan Liao! 📍 Paper: Video:

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I would tell you about the arguments that were made for Karen Read’s parents’ phone records this AM in court, but Attorney Liza Little just mopped the floor with Hank Brennan’s argument like she did a year ago when she called out the fraud perpetrated on the court by ADA Adam Lally about “lab protocols & procedures”. Light work. Isn’t Hank Brennan an Attorney? - At the last motion hearing, Brennan failed to serve all the parties of his Rule 17 Motions seeking William and Janet Read’s Verizon records, thereby requiring another hearing (today) to argue them. - Brennan’s original Rule 17 Motion for Mr. Read’s Verizon records was premised on the fabrication that Karen Read called her father in the middle of the night after dropping John O’Keefe off at Brian and Nicole Albert’s after party. This is a provably false statement as the Verizon records—already in the Commonwealth’s possession—show that the first time Karen Read called her father that morning was at 6:32 AM. Apparently Judge Bev is unbothered when the Commonwealth lies about material facts in court and in court documents. - In addition to being based on a total fabrication, Brennan failed to produce the required accompanying affidavit with factual showing that all four elements of Lampron were met to support his Rule 17 motion. - Notably, Brennan’s Rule 17 Motion for Mr. Read’s records failed to meet its burden, or even a single part of that four prong test. TAKEAWAY? It’s a fishing expedition 🎣. But, Hank Brennan’s a good Attorney I thought? Surely he’s well versed in Rule 17 Motions and their requirements? So the real question is: Why is this seemingly seasoned Attorney filing these obviously erroneous and frivolous motions for duplicative evidence while also failing to properly serve the parties by the intended hearing on it? Given that the defense’s response filings for each of these motions were nearly 30 pages long, and these requests are clearly disingenuous as they’re duplicative and come nowhere close to meeting any burden, one has to wonder if this is a tactic by Brennan and DA Michael Morrissey’s Office to try to bleed Karen Read and her defense team dry of all resources by wasting their time and energy on useless motions? What say you? What’s the game plan here exactly? #FreeKarenRead #KarenReadTrial
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I would tell you about the arguments that were made for Karen Read’s parents’ phone records this AM in court, but Attorney Liza Little just mopped the floor with Hank Brennan’s argument like she did a year ago when she called out the fraud perpetrated on the court by ADA Adam Lally about “lab protocols & procedures”. Light work. Isn’t Hank Brennan an Attorney? - At the last motion hearing, Brennan failed to serve all the parties of his Rule 17 Motions seeking William and Janet Read’s Verizon records, thereby requiring another hearing (today) to argue them. - Brennan’s original Rule 17 Motion for Mr. Read’s Verizon records was premised on the fabrication that Karen Read called her father in the middle of the night after dropping John O’Keefe off at Brian and Nicole Albert’s after party. This is a provably false statement as the Verizon records—already in the Commonwealth’s possession—show that the first time Karen Read called her father that morning was at 6:32 AM. Apparently Judge Bev is unbothered when the Commonwealth lies about material facts in court and in court documents. - In addition to being based on a total fabrication, Brennan failed to produce the required accompanying affidavit with factual showing that all four elements of Lampron were met to support his Rule 17 motion. - Notably, Brennan’s Rule 17 Motion for Mr. Read’s records failed to meet its burden, or even a single part of that four prong test. TAKEAWAY? It’s a fishing expedition 🎣. But, Hank Brennan’s a good Attorney I thought? Surely he’s well versed in Rule 17 Motions and their requirements? So the real question is: Why is this seemingly seasoned Attorney filing these obviously erroneous and frivolous motions for duplicative evidence while also failing to properly serve the parties by the intended hearing on it? Given that the defense’s response filings for each of these motions were nearly 30 pages long, and these requests are clearly disingenuous as they’re duplicative and come nowhere close to meeting any burden, one has to wonder if this is a tactic by Brennan and DA Michael Morrissey’s Office to try to bleed Karen Read and her defense team dry of all resources by wasting their time and energy on useless motions? What say you? What’s the game plan here exactly? #FreeKarenRead #KarenReadTrial

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📢 Eyeline and Netflix latest research, 🌟Go-with-the-Track🌟, has been accepted to #SIGGRAPH2026. We propose a video generation framework that jointly conditions on multiple reference images and reference-anchored point tracks, enabling a range of content creation applications within a unified model: • Motion-preserving video restylization using multiple reference images; • Mesh- or keypoint-driven compositing and stylization; • Camera motion control for both static and dynamic scenes using multiple reference images captured from different view-points and time steps. ✊Congrats to the team: Koichi Namekata, Yash Kant, Zhizheng Liu, Ryan Burgert, Yuancheng Xu, Jordan Lin, Emmett Steven, Julien Philip, LiMa, Andrea Vedaldi, Paul Debevec, Ning Yu 📰 Paper: 🌐 Project: ⌨️ Code: *** This work is part of the ongoing research and development at Eyeline and Netflix, and we look forward to seeing its techniques and workflows adopted in future productions.

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THE LIE: Trump will sign a federal abortion ban under Project 2025. THE TRUTH: This was proven false by CNN and Trump has absolutely nothing to do with Project 2025. As reported by CNN, Donald Trump has said that he would not sign a federal ban on abortion. This is another misleading attempt to try and connect Trump to Project 2025. Here’s the facts: - Trump and his campaign completely disavowed Project 2025 multiple times. - Donald Trump called Project 2025 “seriously extreme”. - He went on to say, “I don’t know anything about it. I don’t want to know anything about it.” Here’s the official statement from the Trump campaign: “President Trump’s campaign has been very clear for over a year that Project 2025 had nothing to do with the campaign, did not speak for the campaign, and should not be associated with the campaign or the President in any way.”
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THE LIE: Trump will sign a federal abortion ban under Project 2025. THE TRUTH: This was proven false by CNN and Trump has absolutely nothing to do with Project 2025. As reported by CNN, Donald Trump has said that he would not sign a federal ban on abortion. This is another misleading attempt to try and connect Trump to Project 2025. Here’s the facts: - Trump and his campaign completely disavowed Project 2025 multiple times. - Donald Trump called Project 2025 “seriously extreme”. - He went on to say, “I don’t know anything about it. I don’t want to know anything about it.” Here’s the official statement from the Trump campaign: “President Trump’s campaign has been very clear for over a year that Project 2025 had nothing to do with the campaign, did not speak for the campaign, and should not be associated with the campaign or the President in any way.”

Media Lies

468,043 Aufrufe • vor 1 Jahr

Seamless Human Motion Composition with Blended Positional Encodings Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions.
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Seamless Human Motion Composition with Blended Positional Encodings Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a series of varying textual descriptions. In this context, we introduce FlowMDM, the first diffusion-based model that generates seamless Human Motion Compositions (HMC) without any postprocessing or redundant denoising steps. For this, we introduce the Blended Positional Encodings, a technique that leverages both absolute and relative positional encodings in the denoising chain. More specifically, global motion coherence is recovered at the absolute stage, whereas smooth and realistic transitions are built at the relative stage. As a result, we achieve state-of-the-art results in terms of accuracy, realism, and smoothness on the Babel and HumanML3D datasets. FlowMDM excels when trained with only a single description per motion sequence thanks to its Pose-Centric Cross-ATtention, which makes it robust against varying text descriptions at inference time. Finally, to address the limitations of existing HMC metrics, we propose two new metrics: the Peak Jerk and the Area Under the Jerk, to detect abrupt transitions.

AK

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In under construction real estate, the difference between people who make money and people who lose it is information asymmetry. Homebuyers, investors, NRIs, brokers advising a client on their first purchase all making multi-crore decisions on renders, rumours, and relationships with people whose commission depends entirely on the deal closing today. We built ReraTracker to end that. 2,000+ RERA registered projects across Gurgaon, Noida. NCR first and beyond later. Every project tracked from launch to completion. HRERA filings, not developer claims. Verified insights, not market rumours. Drone shots, floor plans, developer track records, price history from day one to today. Everything you need to research and decide. Without a single broker call unless you ask. If you are a homebuyer, investor, NRI, brokers, an IFA advising families on real estate or an industry player then Reratracker is now yours. Research. Track. Transact. Drop any project name in the comments. I will send you the link personally.👇 This is just Day Zero. A lot more gets built on top of this with your feedback and support. Let’s go 🚀
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In under construction real estate, the difference between people who make money and people who lose it is information asymmetry. Homebuyers, investors, NRIs, brokers advising a client on their first purchase all making multi-crore decisions on renders, rumours, and relationships with people whose commission depends entirely on the deal closing today. We built ReraTracker to end that. 2,000+ RERA registered projects across Gurgaon, Noida. NCR first and beyond later. Every project tracked from launch to completion. HRERA filings, not developer claims. Verified insights, not market rumours. Drone shots, floor plans, developer track records, price history from day one to today. Everything you need to research and decide. Without a single broker call unless you ask. If you are a homebuyer, investor, NRI, brokers, an IFA advising families on real estate or an industry player then Reratracker is now yours. Research. Track. Transact. Drop any project name in the comments. I will send you the link personally.👇 This is just Day Zero. A lot more gets built on top of this with your feedback and support. Let’s go 🚀

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WATCH: Whistleblower describes network of “Super 8(a)” entities associated with ATI CEO Firmadge Crutchfield, connected through the Susanville Indian Rancheria Corporation (SIRCO). SIRCO is a Section 17 federally chartered tribal corporation that owns several 8(a)-certified entities managed by the same executive leadership. This structure allows the use of tribal and minority contracting advantages across multiple companies, all linked to Firmadge Crutchfield, creating a unified network of federal contractors operating under SIRCO’s authority. We are calling on any & all whistleblowers to come forward with evidence of 8a SBA fraud - signal contact in bio. Kelly Loeffler
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WATCH: Whistleblower describes network of “Super 8(a)” entities associated with ATI CEO Firmadge Crutchfield, connected through the Susanville Indian Rancheria Corporation (SIRCO). SIRCO is a Section 17 federally chartered tribal corporation that owns several 8(a)-certified entities managed by the same executive leadership. This structure allows the use of tribal and minority contracting advantages across multiple companies, all linked to Firmadge Crutchfield, creating a unified network of federal contractors operating under SIRCO’s authority. We are calling on any & all whistleblowers to come forward with evidence of 8a SBA fraud - signal contact in bio. Kelly Loeffler

James O'Keefe

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Meet Sheena 🌸 and her tiny baby—both under threat of culling when a kind-hearted girl refused to just sit and watch. She hired a motor rickshaw and brought them to our shelter, giving them the safety they deserve. She didn’t wait for help to arrive at her doorstep—she became the help. Imagine if more of us did the same. Instead of excuses, if many hands came forward, we could save so many voiceless souls from misery. Let’s each play our part. ❤️ 🔗 (If you face any issue donating, please use our Linktree for multiple options.) Even £5 and a share can be the difference between life and death. 🐾
0:59

Meet Sheena 🌸 and her tiny baby—both under threat of culling when a kind-hearted girl refused to just sit and watch. She hired a motor rickshaw and brought them to our shelter, giving them the safety they deserve. She didn’t wait for help to arrive at her doorstep—she became the help. Imagine if more of us did the same. Instead of excuses, if many hands came forward, we could save so many voiceless souls from misery. Let’s each play our part. ❤️ 🔗 (If you face any issue donating, please use our Linktree for multiple options.) Even £5 and a share can be the difference between life and death. 🐾

Tahira Animal Welfare Foundation - TAWF

16,675 Aufrufe • vor 10 Monaten

‼️👩🏼‍⚖️NEW MARIE CARVALHO MOTION TO SEAL ‼️⚖️ Today, via certified mail, I received a motion from Marie Carvalho to seal last week’s court hearing. In it, she claims I shared her “medical diagnosis and history” on social media after the hearing. See her motion below. Every single detail she testified to regarding her medical history she had already publicly disclosed herself, multiple times, on her own shows. This is just a sample. She was explicitly told at the start of the hearing that it was being recorded and part of the public record. None of us were “discussing her medical history.” I simply mentioned what she said under oath, including her claim that I made fun of her do-rag. She tried spinning that into mockery of a disability. False. I had zero idea why she was wearing it, and contrary to popular belief, I don’t watch all her shitty shows. This is yet another example of Marie wasting the court’s time. I’ll be filing an opposition next week, loaded with case law. In New Hampshire, there is a strong constitutional presumption that court proceedings and records are public. The burden is on the moving party to prove that a specific and substantial interest in confidentiality outweighs that presumption. That’s a high bar. Motions to seal are for truly sensitive matters, i.e. domestic violence, minors, etc. They are not information you yourself already put out on your shows. There is no irreparable harm when the information was already public by your own hand. The judge is going to love this. Especially after you said “it’s back on” right after your petition was denied.
3:20

‼️👩🏼‍⚖️NEW MARIE CARVALHO MOTION TO SEAL ‼️⚖️ Today, via certified mail, I received a motion from Marie Carvalho to seal last week’s court hearing. In it, she claims I shared her “medical diagnosis and history” on social media after the hearing. See her motion below. Every single detail she testified to regarding her medical history she had already publicly disclosed herself, multiple times, on her own shows. This is just a sample. She was explicitly told at the start of the hearing that it was being recorded and part of the public record. None of us were “discussing her medical history.” I simply mentioned what she said under oath, including her claim that I made fun of her do-rag. She tried spinning that into mockery of a disability. False. I had zero idea why she was wearing it, and contrary to popular belief, I don’t watch all her shitty shows. This is yet another example of Marie wasting the court’s time. I’ll be filing an opposition next week, loaded with case law. In New Hampshire, there is a strong constitutional presumption that court proceedings and records are public. The burden is on the moving party to prove that a specific and substantial interest in confidentiality outweighs that presumption. That’s a high bar. Motions to seal are for truly sensitive matters, i.e. domestic violence, minors, etc. They are not information you yourself already put out on your shows. There is no irreparable harm when the information was already public by your own hand. The judge is going to love this. Especially after you said “it’s back on” right after your petition was denied.

The old M can’t come to the phone right now

13,906 Aufrufe • vor 1 Monat

Bundle Tracker - Now LIVE 🎯 After months of development and extensive testing, we're launching the most sophisticated bundle tracking system in the space. What's inside: • instant notifications when team moves tokens • unlimited wallet tracking across entire eth chain • real-time pnl calculation on team trading • precise supply retention monitoring • automated wallet relationship mapping • complete trading pattern detection How to use: /track - monitor any token /balance - check team holdings /pnl - analyze trading activity /list - manage tracked tokens Beta testers are already calling this a game-changing addition to our suite of tools. Because in a market full of noise, clear data wins. Time to put those team wallet moves under the microscope.
1:24

Bundle Tracker - Now LIVE 🎯 After months of development and extensive testing, we're launching the most sophisticated bundle tracking system in the space. What's inside: • instant notifications when team moves tokens • unlimited wallet tracking across entire eth chain • real-time pnl calculation on team trading • precise supply retention monitoring • automated wallet relationship mapping • complete trading pattern detection How to use: /track - monitor any token /balance - check team holdings /pnl - analyze trading activity /list - manage tracked tokens Beta testers are already calling this a game-changing addition to our suite of tools. Because in a market full of noise, clear data wins. Time to put those team wallet moves under the microscope.

BribeAI

108,673 Aufrufe • vor 1 Jahr