Introducing Moto: Latent Motion Token as the Bridging Language... for Robot Manipulation. Motion prior learned from videos can be seamlessly transferred to robot manipulation. Code and model released! Yi CHEN Yuying Ge Yixiao Ge Mingyu Ding Ying Shanshow more

✨ Any static 3D assets ➡️ 4D dynamic worlds.... Introducing CHORD, a universal framework for generating scene-level 4D dynamic motion from any static 3D inputs. It generalizes surprisingly well across a wide range of objects 🤯 and can even be used to learn robotics manipulation policy 🤖! Project page: Dive deeper in a 🧵: 1/nshow more

Building robots that can effectively operate alongside human workers... is difficult. 🛠️ Advances in open-source physics, open foundation models, and frameworks are helping accelerate physical #AI deployment. ✔️ Newton Physics Engine, an open-source GPU-powered simulation built on OpenUSD, speeds up robot learning for advanced manipulation and mobility. ✔️ NVIDIA Cosmos Reason, an open reasoning vision language model, gives robots the ability to think like humans using prior knowledge, common sense and physics ✔️NVIDIA Isaac GR00T N1.6, an open robot foundation model, enables humanoids to understand ambiguous instructions Leading robotics developers including Agility Robotics, Lightwheel, Mentee Robotics, UniversalRobots, and Wandelbots are adopting simulation technologies and libraries to accelerate physical AI development and deployment. Omniverse Ambassador Dylan Tobin built an AI chatbot trained on Isaac Sim workflows, helping devs navigate Omniverse faster. Read the full blog 👉show more

ZACHXBT FLAGS POSSIBLE INSIDER ACTIVITY ON $RAVE On-chain investigator... ZachXBT has raised concerns about alleged insider manipulation of the RaveDAO token, posting that insiders appear to control more than 90% of $RAVE's supply. In his post, ZachXBT pointed to what he described as pump-and-dump activity originating from Binance, @bitgetglobal, and Gate.io, and called on Binance's He Yi and Bitget CEO Gracy Chen @Bitget to investigate and offboard those involved. He also offered a $10,000 bounty for whistleblowers who come forward with evidence related to the alleged scheme. Bitget CEO Gracy Chen publicly responded shortly after, confirming the exchange had opened an investigation into $RAVE. $RAVE is up over 10,000% in the past 30 days, with the token rallying another 44% on Saturday.show more

𝗥𝗼𝗯𝗼𝘁𝘀 𝗱𝗼𝗻’𝘁 𝗻𝗲𝗲𝗱 𝗺𝗼𝗿𝗲 𝗱𝗲𝗺𝗼𝗻𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀. 𝗧𝗵𝗲𝘆 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗹𝗲𝗮𝗿𝗻... 𝗳𝗿𝗼𝗺 𝗳𝗮𝗶𝗹𝘂𝗿𝗲 — 𝗮𝗳𝘁𝗲𝗿 𝘄𝗮𝘁𝗰𝗵𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝘀. Most robot learning systems assume failure is the end of learning. In our new work, we study whether robots can improve after deployment by learning from their own failures, without any human intervention, teleoperation, or corrective labels. The key idea is simple: human videos contain structure about how the world works. We use them to learn cross-embodiment representations of action, dynamics, and value, enabling a shared predictive space between human behavior and robot experience. This allows a new learning loop: 👉 pretrain on human videos 👉 deploy robot policy 👉 observe failures 👉 reinterpret failures using human priors 👉 improve autonomously We evaluate this across 7 real-world manipulation tasks, showing: 📈 40% → 81% success rate 🏆 Strong improvements over π0.6 RECAP and RISE ✔️ Zero human intervention during post-deployment improvement 🧬 Generalizes across robot embodiments and policy backbones A key finding is that explicit failure repair significantly outperforms failure reweighting, yielding substantially larger gains under identical data conditions (+25 pts vs +5 pts on the same π0.5 base policy). Overall, the results suggest a shift in how we think about robot learning: Human videos are not only for pretraining policies. They can provide the structure needed for continual self-improvement after deployment. 📄 Paper: 🌐 Project: I am grateful for working with the fantastic leads Hanzhi Chen and Anran Zhang, and our collaborators Simon Schaefer, Kejia Chen, Shi Chen, Daniel Cremers. Special thanks to Stefan Leutenegger for co-advising this project with me. ETH Zürich TU München Microsoft Check out Hanzhi's 🧵 for more detailsshow more

Introducing VL-JEPA: Vision-Language Joint Embedding Predictive Architecture for streaming,... live action recognition, retrieval, VQA, and classification tasks with better performance and higher efficiency than large VLMs. • VL-JEPA is the first non-generative model that can perform general-domain vision-language tasks in real-time, built on a joint embedding predictive architecture. • We demonstrate in controlled experiments that VL-JEPA, trained with latent space embedding prediction, outperforms VLMs that rely on data space token prediction. • We show that VL-JEPA delivers significant efficiency gains over VLMs for online video streaming applications, thanks to its non-autoregressive design and native support for selective decoding. • We highlight that our VL-JEPA model, with an unified model architecture, can effectively handle a wide range of classification, retrieval, and VQA tasks at the same time. by Delong Chen (陈德龙) Mustafa Shukor Théo Moutakanni Willy Jade Lei Yu Tejaswi Kasarla Allen Bolourchi Yann LeCun Pascale Fungshow more

🦿Xpeng showed a humanoid robot called IRON whose movement... looked so human that the team literally cut it open on stage to prove it is a machine. IRON uses a bionic body with a flexible spine, synthetic muscles, and soft skin so joints and torso can twist smoothly like a person. The system has 82 degrees of freedom in total with 22 in each hand for fine finger control. Compute runs on 3 custom AI chips rated at 2,250 TOPS (Tera Operations Per Second), which is far above typical laptop neural accelerators, so it can handle vision and motion planning on the robot. The AI stack focuses on turning camera input directly into body movement without routing through text, which reduces lag and makes the gait look natural. Xpeng staged the cut-open demo at AI Day in Guangzhou this week, addressing rumors that a performer was inside by exposing internal actuators, wiring, and cooling. Company materials also mention a large physical-world model and a multi-brain control setup for dialogue, perception, and locomotion, hinting at a path from stage demos to service work. Production is targeted for 2026, so near-term tasks will be limited, but the hardware shows a serious step toward human-scale manipulation.show more

The Hidden Language of Diffusion Models paper page: tackle... the challenge of understanding concept representations in text-to-image models by decomposing an input text prompt into a small set of interpretable elements. This is achieved by learning a pseudo-token that is a sparse weighted combination of tokens from the model's vocabulary, with the objective of reconstructing the images generated for the given concept. Applied over the state-of-the-art Stable Diffusion model, this decomposition reveals non-trivial and surprising structures in the representations of concepts. For example, we find that some concepts such as "a president" or "a composer" are dominated by specific instances (e.g., "Obama", "Biden") and their interpolations. Other concepts, such as "happiness" combine associated terms that can be concrete ("family", "laughter") or abstract ("friendship", "emotion"). In addition to peering into the inner workings of Stable Diffusion, our method also enables applications such as single-image decomposition to tokens, bias detection and mitigation, and semantic image manipulationshow more

Kling 2.6 Motion Control is absolutely insane 🤯 Take... any reference video and transfer the exact motion onto an AI character: full-body sync, facial expressions, hand gestures, everything. All with just a few clicks. Perfect for e-comm brands and agencies creating AI video ads that don't look like AI. Here's the problem: AI-generated video ads still look robotic. The movements are stiff, the expressions are flat. Your audience clocks it as AI instantly and keeps scrolling. Kling 2.6 Motion Control fixes it: → Start with any reference clip (stock footage, existing UGC, motion reference) → Upload to Kling → Map the exact movement onto any AI character → Full-body motion, hand gestures, facial expressions—all transferred → Generate up to 30 seconds of video No stiff AI movements, no uncanny valley, no instant "skip this ad" reaction. What this unlocks: - Use one winning UGC motion → swap in different AI creators - Pull reference clips from anywhere → generate branded variations - Create dynamic AI video ads with real human movement - Test multiple "creators" without filming anyone new I recorded a quick walkthrough showing how to do this step-by-step. Want access? > Comment "KLING" > Like this post And I'll send it over (must be following so I can DM)show more

You pretrained a robot policy on millions of camera... frames. Now you want to add a force sensor. Do you really have to retrain on everything from scratch? MuSe adds a force-torque sensor to a frozen vision-only policy using a tiny amount of contact data. - lifts contact-rich task success (peg insertion 60% to 87%, vase wiping 33% to 77%) - fuse the new sensor both early (shared token space) and late (cross-attention), train the policy as a world model that predicts future video, future force, and actions together, and replay old vision-only data with the force input masked to prevent forgetting. - adding new sensor modalities to pre-trained World Model can be cheap & improve performance significantlyshow more

🛠️ What if a robot could invent its own... tools. And teach itself how to use them? That’s exactly what VLMgineer does: a new framework that lets Vision Language Models (VLMs) design physical tools and the actions to use them, entirely on their own. No templates. No human demonstrations. Just raw, AI-driven creativity. Why it matters ✅ Co-designs tools and actions together using VLMs, ensuring tight coupling between form and function ✅ Uses VLM-guided evolution (not random search) to refine designs intelligently ✅ Outperforms human-designed tools by +64.7% in task success across 12 RoboToolBench challenges ✅ Produces better-than-everyday tools for real manipulation tasks—measured in success rate and elegance It builds on the emerging trend of large-model-guided evolutionary design (like Eureka and AlphaEvolve) and brings it into physical robotics. It opens the door to general-purpose, automated hardware design, no strong priors needed. Code & paper: —- Weekly robotics and AI insights. Subscribe free:show more

Today may be the ImageNet moment for robotics. RT-X:... the largest open-source robot dataset ever compiled, across 33 institutes, 22 robot hardware, 527 skills, and 1M episodes. Why is robotics lagging so far behind NLP, vision, and other AI domains? Data scarcity is the main culprit to blame, among other difficulties. Unlike text, images, and videos, you cannot download mass amounts of onboard robot control data from the internet. They simply don't exist in the wild. 11 yrs ago, ImageNet kicked off the deep learning revolution. 3-4 yrs ago, internet-scale data fueled the first GPTs and Diffusions that define this era of foundation models. I think 2023 is finally the year for robotics to scale up. Robot foundation models like VIMA ( my team's work at NVIDIA) and RT-1/2 ( Google DeepMind's effort) are extremely data hungry. While massively parallel simulations like NVIDIA IsaacGym & Omniverse can alleviate the problem to some extent, it's still not quite enough to bridge the gap to the messy, physical world. This new dataset is not just a technical contribution. I also see it as a commendable effort to overcome institutional bureaucracies and unite researchers from around the world to tackle a grand challenge together. Robotics will be the final holy grail that we capture in AI. We are not there yet, but ascending in the right gradient direction. RT-X website: Launch blog:show more

Robot Utility Models (RUMs) enable basic tasks – door... opening, drawer opening, object reorientation, etc. – at ~90% accuracy without ANY finetuning (i.e. zero-shot) in unseen new environments. Fully open source!!! models, data, code & hw. We think this is super exciting, why?👇 1. Unlocks many practical home utility tasks that often involve these basic tasks as part of an action chain. “Go get me a fork” involves opening the kitchen door and then opening the cutlery drawer. 2. This works well **zero-shot in unseen and new** environments, which is practically a huge deal. Turn the robot on, and get going. 3. The recipe for building a new model is fairly generic, and we think with a bit more refinement this can be a general recipe to build many more Utility models. More details and access 👇show more

South Korea's local governments are deploying around 7,000 AI-robot... dolls to seniors and dementia patients. The $1,800 robot doll by Hyodal can hold full conversations to tackle loneliness and remind users to take medication. Dystopian, yes, but the data is fascinating: 1. Studies (with over 9,000 users) found that depression levels reduced from 5.73 to 3.14, and medicine intake improved from 2.69 to 2.87. 2. The doll comes with a companion app and web monitoring platform for caretakers to monitor remotely. 3. Safety features are installed to alert when no movement has been detected for a certain period, essentially always watching the user. 4. The doll also offers touch interaction, 24-hour voice reminders, check-ins, voice messages, a health coach, quizzes, exercise, music, and more. 5. Caregivers have access to the app, allowing them to send/receive voice messages, make group announcements, and monitor motion detection. I'd definitely have some privacy and data collection concerns here before handing this off to my family, but the product actually seems really cool. Will be interesting to watch the data to see if this idea has legs. Keep in mind, SK has a rapidly aging population and one of the world's lowest birth rates, so it makes sense for the local governments to be early adopters here.show more

We have released Seedance 2.0. Due to the 2500-character... limit, please translate the following prompts into Chinese before use. [Technical Specs] Generate a 10-second, 16:9, 720p cinematic video. Smooth continuous camera motion with no cuts. The overall pacing is fast and tightly compressed, with rapid escalation from start to finish. Audio evolves quickly from a high-performance engine idle into intricate mechanical shifting and clicks, culminating in a soft electronic chime and the distinct sound of a "mwah" blowing kiss. [Global Constraints] Only the evolving mechanical character appears; no other humans or characters. All transformations must follow physical logic and maintain structural continuity. No object should pass through or intersect with other solid objects. Every robotic component must originate from visible parts of the Porsche 911 (doors, hood, wheels, chassis) through unfolding, splitting, or reconfiguration. [Scene Setup — 0:00–0:01] A sleek, metallic silver Porsche 911 sits on a rain-slicked futuristic city street at night, neon lights reflecting off its polished surface. The camera starts at a low-angle front-quarter view and begins a fast, smooth tracking-arc towards the side. [Rapid Transformation Initiation — 0:01–0:03] Transformation triggers instantly. The car’s suspension drops, and the frame begins to fracture into a complex grid of panels. The doors swing open and begin to segment into articulated arm structures. The front hood splits down the center, folding inward to reveal a glowing internal core. The headlights flicker and start to reorient as the "eyes." [Accelerated Feminine Reconfiguration — 0:03–0:07] The mechanical action is dense, overlapping, and fluid, emphasizing graceful but powerful motion. Lower Body: The rear wheels and wheel arches split and rotate downward, reassembling into slender, high-heeled mechanical legs. Torso: The roof and rear engine cover slide and compress, forming a sleek, curvaceous hourglass torso that retains the car’s aerodynamic lines. Arms & Hands: The side mirrors and door panels unfold into delicate but strong hands and fingers. Head: The front bumper and emblem area segment and rise, folding into a feminine-shaped head with a sleek metallic "helmet" visor. [Logical Transformation Constraints — No Spontaneous Appearance] The robot’s "skin" is composed of the car's outer silver panels. The internal frame and wiring emerge from the engine and undercarriage. No parts appear out of thin air; every joint is a reconfigured automotive component. [Transformation Completion — 0:07–0:08.5] The robot stands tall and elegant. The silver panels lock into place with a satisfying "click," revealing glowing blue LED accents in the seams. The silhouette is clearly feminine, humanoid, and sophisticated, reflecting the premium design of the original vehicle. [Final Hero Ending — 0:08.5–0:10] As the robot stabilizes, the camera performs a rapid, smooth zoom-in (Dolly-In) directly to her face. The robot tilts its head slightly, and the optic sensors (eyes) brighten. It brings its mechanical hand to its metallic lips and performs a graceful blowing kiss (fly-kiss) gesture toward the camera. The video ends with a close-up of the face, capturing the reflection of neon lights in its visor just as the kiss is released. [Cinematography Notes] Continuous Motion: No cuts or fades; the camera must transition from the car-tracking shot to the face-zoom seamlessly. Material Consistency: The robot must maintain the exact metallic silver paint, texture, and reflections of the Porsche. Energy: The transformation should feel high-energy and "force-driven," while the final gesture is soft and charismatic.show more

𝗣𝗼𝗽𝘂𝗹𝗮𝗿 𝗼𝗽𝗶𝗻𝗶𝗼𝗻: "𝗝𝘂𝘀𝘁 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗺𝗼𝗿𝗲 𝘀𝗶𝗺𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗱𝗮𝘁𝗮." After working... with many 𝗿𝗼𝗯𝗼𝘁 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 teams who've fallen into the simulation trap, here's what I've learned: Simulation teaches your robot to be really, really good at simulation. Unlike blind locomotion policies that can get away with sim-to-real transfer because they rely mainly on proprioception and contact forces, 𝘃𝗶𝘀𝗶𝗼𝗻-𝗴𝘂𝗶𝗱𝗲𝗱 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗲𝘅𝘁𝗿𝗲𝗺𝗲𝗹𝘆 𝘀𝗲𝗻𝘀𝗶𝘁𝗶𝘃𝗲 𝘁𝗼 𝘃𝗶𝘀𝘂𝗮𝗹 𝗱𝗼𝗺𝗮𝗶𝗻 𝗴𝗮𝗽. The subtle differences accumulate: - Simulated friction vs real surface textures - Perfect lighting vs shadows, reflections, glare - Ideal object geometries vs manufacturing tolerances - Instantaneous sensor readings vs real-world noise and latency - Clean backgrounds vs cluttered, dynamic environments 𝗧𝗵𝗲 𝗰𝗹𝗮𝘀𝘀𝗶𝗰 𝗽𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻: Week 1: "Our model works perfectly in sim!" Week 2: "Let's collect some real data to fine-tune." Week 3: "The real data completely contradicts what the sim taught..." Week 4: "Okay, let's collect way more real data." Month 2: "We basically need to retrain from scratch." 𝗧𝗵𝗲 𝗽𝗮𝗶𝗻𝗳𝘂𝗹 𝘁𝗿𝘂𝘁𝗵: There's no shortcut to real-world data collection for vision-based manipulation. Simulation is amazing for debugging, prototyping, safety testing, and of course to supplement your real data. But it's not a substitute for understanding how your robot actually behaves in the actual environment. 𝗪𝗵𝗮𝘁 𝘄𝗼𝗿𝗸𝘀: Use simulation strategically - for exploring edge cases, testing safety boundaries, and rapid iteration. But build your production models on real data from real environments. The teams that succeed treat simulation as a powerful tool, not a magic solution. This is why Neuracore focuses on making real-world data collection so much easier and faster. Because the physics of your actual environment can't be simulated away. 𝗪𝗼𝗿𝗹𝗱 𝗺𝗼𝗱𝗲𝗹𝘀, 𝘆𝗼𝘂 𝘀𝗮𝘆? 𝗪𝗲𝗹𝗹, 𝗽𝗲𝗿𝗵𝗮𝗽𝘀 𝗺𝗼𝗿𝗲 𝗼𝗻 𝘁𝗵𝗮𝘁 𝗶𝗻 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗽𝗼𝘀𝘁! 𝗪𝗵𝗮𝘁'𝘀 𝗯𝗲𝗲𝗻 𝘆𝗼𝘂𝗿 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝘀𝗶𝗺-𝘁𝗼-𝗿𝗲𝗮𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗲𝗿? 𝗛𝗮𝘀 𝗶𝘁 𝘄𝗼𝗿𝗸𝗲𝗱 𝗮𝘀 𝘄𝗲𝗹𝗹 𝗮𝘀 𝗲𝘅𝗽𝗲𝗰𝘁𝗲𝗱?show more

AI Is Moving Beyond “Generating Videos” — Toward “Generating... Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:show more

🚨 RWA START-UP EXPLODES 1,200% AT LAUNCH — A... PROUD MOMENT FOR US Our incubatee T-RIZE’s debut on Kraken was nothing short of extraordinary — peaking at a 1200% — and we’re deeply honored to have been a proud partner from the start. The $RIZE token is now live and trading, marking a major milestone in real-world asset tokenization. With a $300M deal for Canada’s 960-unit Project Champfleury and a $2B pipeline in motion, T-RIZE is setting the new standard for RWA platforms. Built on Rizenet — a proprietary blockchain optimized for decentralized machine learning and AI-driven insights — it’s pushing the space forward in real time. We’re proud to have contributed across strategy, marketing, partnerships, and the incredible shows that brought this to life. Watch the official demo + explore the platform: Disclaimer: We do not provide financial or investment advice of any kind. Always do your own research, as cryptocurrency prices can be extremely volatile. This project is part of our incubation program.show more

🟩STAT, is one of your columnists, Adam Feuerstein, colluding... with hedge funds?⁉️ ➡️In this post I'll do a cursory review of Mr. Feuerstein's possible collusive activities with hedge funds that are purportedly engaged in illegal share price manipulation. A May 2, 2016 article entitled “Is Adam Feuerstein the most feared man in biotech?” in relevant part, states as follows: Adam Feuerstein (Adam Feuerstein ✡️ ) often targets lower profile “small and medium-sized drug companies…” Further, Adam Feuerstein “isn’t shy about stating — without evidence — that companies are intentionally spinning data or hyping anecdotes to goose their stock.” (emphasis added) The article insinuates that Mr. Feuerstein’s articles move the market. The article: ➡️Let’s look at one such company Mr. Feuerstein has targeted and the statements he made, without evidence. Northwest Biotherapeutics, Inc. Symbol: $NWBO Mr. Feuerstein has been writing about $NWBO for over a decade. More recently Mr. Feuerstein released an article and a rash of tweets about $NWBO’s May 10, 2022 release of top-line data. Mr. Feuerstein’s article and tweets can best be summed up in his own words: “20+ years of investigation and a $1B clinical trial that failed to show a benefit for GBM patients.” See Image 1. Yet, a peer reviewed journal article from 73 authors stated the opposite: “In this study, adding DCVax-L to SOC resulted in clinically meaningful and statistically significant extension of survival for patients with both nGBM and rGBM compared with contemporaneous, matched external controls who received SOC alone.” (emphasis added) The peer reviewed journal article: In fact, before the May 10th topline data presentation occurred Mr. Feuerstein stated: “The NYAS symposium talk (now by Dr. Mulholland) will not contain any new data/results from the DCVax phase 3 clinical trial.” See Image 2. The topline data presentation can be found here: The presentation, despite Mr. Feuerstein's statement to the contrary, presented new data. So, what do we have here? $NWBO is about to release their topline data for a nearly 2-decade trial and Mr. Feuerstein is first falsely stating that no new data will be released and secondly, once the data is released, Mr. Feuerstein falsely claims the trial failed. It appears Mr. Feuerstein is trying to get people to not watch the presentation for themselves so he can then put his own spin on the topline data. ➡️What else occurred on May 9th and May 10th other than Mr. Feuerstein’s false and/or misleading article and tweets? We have the largest and one of the largest illegal share price manipulation days on record according to the $NWBO spoofing lawsuit found here: May 9, 2022 ☑️74 spoofing episodes ☑️Baiting Orders: 632,901 “The Baiting Orders successfully induced the entry of sell orders from other market participants, artificially driving down the price of NWBO shares by -2.623% on average.” May 10, 2022 ☑️100 spoofing episodes ☑️Baiting Orders: 2,883,387 “The Baiting Orders successfully induced the entry of sell orders from other market participants, artificially driving down the price of NWBO shares by -11.77% on average.” “Defendants spoofed the market for NWBO shares on both OTC Link LLC and NYSE ARCA Global OTC that day, driving down the price of NWBO shares from a high of $1.73 to a low of $0.3862. This decline of 78% in the price on a day with positive news about the Company was caused, at least in part, by Defendants’ relentless and brazen manipulation of the market for NWBO shares.” ➡️Were Mr. Feuerstein’s article and social media posts designed to give cover to illegal share price manipulation? They were certainly used as cover. The From the defendant market makers’ filing March 20, 2023: “NWBO also omits that on May 10, 2022—a day on which NWBO alleges “the market learned excellent news” about an NWBO clinical trial, and yet its share price suffered a “staggering decline . . . caused by Defendants’ relentless and brazen manipulation,” ¶ 64—an industry commentator published an analysis of NWBO’s trial data, writing that the results of the trial were “the antithesis of what’s required from any effective cancer treatment,” and actually showed that NWBO’s drug “perform[ed] worse than a placebo.”14” “14 Burck Decl. Ex. 5, Adam Feuerstein, It took years, but the failure of Northwest Bio’s brain cancer vaccine is now in the open, STAT News (May 10, 2022), The Court may take judicial notice of press coverage. See supra n.3.” See Image 3. ➡️Is this an isolated incidence of Mr. Feuerstein's article being used as cover for possible illegal share price manipulation or was the timing coincidence? No. A very quick review of Mr. Feuerstein’s articles show he seems to go out of his way to offer cover for allegations of illegal trading by hedge funds. ☑️Mr. Feuerstein calls the alleged $NWBO share price manipulation “conspiracy theories”. [1] ☑️Mr. Feuerstein, in referencing the allegations of naked shorting by hedge funds, states the allegations are “fantastical” and that there is a “non-existent hedge fund wolfpack”. [1] ☑️Here Mr. Feuerstein spends an entire article offering cover for the potential $NWBO shorts. [2] ☑️Here is another article where Mr. Feuerstein offers additional reasons for the “deep plunge in the value of Northwest Bio shares…”[3] ➡️Mr. Feuerstein went on to call $NWBO’s spoofing lawsuit "nonsense". See: Yet, a federal Judge in the Southern District of New York stated the trading in $NWBO stock bears “all these indica of spoofing.” [4] On reason put forth by $NWBO and their lead attorney, Laura Posner, for the extensive illegal share price manipulation is for the purpose of a naked short covering scheme: "And like here, the plaintiff alleged that defendants sought to benefit from their spoofing by obtaining shares at below-market prices in order to cover short positions established through a related alleged scheme of naked short selling." (Emphasis added) [5] ➡️Then we get into Mr. Feuerstein's unusual behavior Here is Mr. Feuerstein leaving a creepy voicemail with a $NWBO retail investor. See Image/video 4. Or how about emailing a university because a real doctor dares question Mr. Feuerstein's false narratives? See: There is more alleged questionable behavior, but you get the picture. ➡️Which begs the question, STAT, have you looked into this alleged behavior? Rick Berke Linda Pizzuti Henry @angusmacaulay alissa ambrose Torie Bosch lclcl Gideon Gil @lisonjoseph Alexander Bois-Spinelli 🏳️‍🌈 Jason Ukman Elaine Chen Allison DeAngelis Matthew Herper pharmalot Eric Boodman Angus Rohan Chen Olivia Goldhill Bob Herman Casey Ross @brittwhitmore STAT [1] [2] [3] [4] [5]show more

📥Please read: My heart is broken… 😭our dreams and... hard work have been shattered.💔 After five years of effort and study, I graduated from university with a degree in Mechatronics Engineering, despite difficult financial circumstances. Before the war, I worked on graduation projects for university students in exchange for a small amount of money to cover my basic needs. I dreamed of working for a company abroad, as I believed in my abilities and knew I could be creative in my field. After graduation, I completed English and German language studies within two years. Learning English was easy for me because my entire five-year university education was in English. My last project was a line-following robot that detects and follows a black track using sensors. But today… instead of celebrating success, we find ourselves in tents, crying from hunger, trying only to survive, and struggling to stop the tears of starving children. Please donate here: ( PAYPAL🔗( We call on you to support us… so we can live. Will 🦥 Menaker ★ ★Nate D Hernandez★ Stop the Nonsense Tim Walker The Trillbilliesshow more

THIS IS THE CRAZIEST STORY IN CRYPTO HISTORY!!!🤯 A... man drained $110 MILLION from a crypto exchange in 20 minutes. Then used the stolen tokens to vote himself amnesty. He beat every federal charge in court. But still went to prison because of what the FBI found on his laptop. In October 2022, Avraham Eisenberg identified a flaw in Mango Markets, a decentralized exchange on Solana. Not a code bug, an economic design flaw. Here's what he did. He deposited $5 million, split it across two wallets, used one wallet to sell 483 million futures contracts, used the other to buy them all. Both sides of the same trade. Zero market risk. Maximum leverage. Then he went to the spot market. He aggressively bought the MNGO token on three exchanges with such thin liquidity that his buying pressure pumped the price 1,300% in 20 minutes. The price oracle fed that inflated price back to Mango Markets. The smart contract recalculated his portfolio value. Suddenly his position was worth hundreds of millions. He borrowed $110 million in Bitcoin, Ethereum, and stablecoins against the fake collateral, withdrew everything, then dumped his tokens and crashed the price back down. The platform was instantly insolvent. Every user's funds were gone. Then he went on Twitter, under his real name, and called it a "highly profitable trading strategy." He said, "all of our actions were legal open market actions, using the protocol as designed." The Mango DAO held a governance vote on whether to let him keep $47 million as a "bug bounty." It passed. 9.46% voted yes. 0.33% voted no. Over half the yes votes came from just two developer wallets. And Eisenberg himself voted for his own amnesty using the tokens he had just stolen. Then he fled to Israel. The FBI found his search history: "Elements of fraud," "When market manipulation becomes a crime," "Statute of limitations market manipulation," "Extradition rules from Israel," "FBI surveillance." He also used a fake Ukrainian identity to set up some of his trading accounts. So much for "transparent open market actions." In December 2022, he flew to Puerto Rico. The FBI was waiting. Arrested at the airport. Laptop and phones seized. In April 2024, a federal jury convicted him on every count. Commodities fraud. Market manipulation. Wire fraud. The first ever criminal conviction for open-market manipulation in crypto. Then his lawyers filed a Rule 29 motion. And the judge threw out everything. The commodities charges, vacated. Wrong jurisdiction. Eisenberg was in Puerto Rico. The trades happened on Solana. The government's entire case for being in New York was that a third-party vendor had employees in Manhattan who monitored accounts. The judge said that's not enough. The wire fraud charge, full acquittal. The judge ruled that Mango Markets had no terms of service, no rules, no prohibition against what he did. The smart contract executed exactly as coded. The oracle reported the real market price. And you can't commit fraud against a protocol that never told you what the rules were. He beat the biggest crypto fraud case in history. But here's the twist nobody saw coming. When the FBI seized his devices at the airport, they were looking for evidence of market manipulation. Instead, they found child abuse material on his laptop. The "plain view" doctrine. If agents executing a valid search warrant for one crime find evidence of another crime, it's fully admissible. He pleaded guilty. 52 months in federal prison. He outsmarted a $110 million exchange. Outsmarted the DOJ. Outsmarted the SEC. Outsmarted the CFTC. But he couldn't outsmart the contents of his own hard drive. The feds came for the $110 million. They stayed for what they found on the laptop.show more