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GCSA Insight: Critical Security Vulnerability Discovered in a Leading Robotics Manufacturer’s Product, with System-Level Privilege Compromise Verified on Physical Hardware As artificial intelligence and embodied AI technologies continue to advance, robots are evolving from conventional automated machines into intelligent systems capable of perception, decision-making, and autonomous operation. At the same time, cybersecurity risks are extending beyond software and data into the physical world. The security of robotic systems is no longer solely a matter of protecting data and devices; it can also directly affect operational safety, physical environments, and user safety. Recently, the security research team of the Global Cybersecurity Alliance (GCSA) discovered and verified a critical security vulnerability in a product developed by a leading robotics manufacturer. Through in-depth analysis and multiple rounds of testing on physical hardware, the team successfully breached the device’s system security boundaries and obtained the highest level of operating system privileges in a real-world device environment, demonstrating the vulnerability’s actual impact under specific testing conditions. This research encompassed the entire process, from identifying the security flaw and assessing its risks to validating its impact on physical hardware. The findings also highlight significant challenges facing the underlying security mechanisms of embodied AI systems. The research findings and supporting evidence have been formally submitted to the manufacturer. On November 13, 2026, the manufacturer responded by email, preliminarily confirming the existence of the vulnerability and stating that it had initiated internal remediation efforts and an assessment of the scope of affected products. It is important to emphasize that confirmation of a vulnerability does not mean the associated security risks have been eliminated. GCSA will continue to monitor the manufacturer’s assessment of the affected scope, remediation plans, and subsequent security validation, and will update its research findings as new information becomes available. For intelligent systems capable of physical interaction and movement, timely, effective, and verifiable security remediation is particularly important. In accordance with responsible vulnerability disclosure principles, GCSA is currently withholding sensitive information, including the manufacturer’s identity, specific product details, the vulnerability’s underlying mechanisms, and exploitation methods. GCSA is currently seeking guidance from relevant authorities and regulatory bodies regarding the associated security risks. Taking into account the manufacturer’s remediation progress and a comprehensive assessment of potential security implications, GCSA will carefully determine the appropriate arrangements for further disclosure. Subject to appropriate conditions and upon obtaining the consent of the relevant authorities and regulatory bodies, GCSA will publicly release a detailed security insight report, providing the industry with ongoing updates on the research findings and associated risk assessments. The findings of this research are based on specific testing environments and the evidence obtained to date. They do not imply that other product models or similar robotic systems are affected by the same security issue. This security research and validation on physical hardware represent one of GCSA’s initiatives in the field of embodied AI security. GCSA will continue to advance security research, coordinated vulnerability disclosure, and industry collaboration to promote stronger security response mechanisms across the artificial intelligence sector, enhance product security, and strengthen industry trust. 🔗 Read the full report: · Stay Connected with GCSA Follow GCSA's official social media channels for the latest news, product launches, research developments, and ecosystem updates. GCSA Official Telegram Channel: GCSA Official WeChat Account: #GCSAInsight# #GCSACybersecurity# #GCSASecVibe#

Big wins don't happen without taking risks — and this gameplay shows exactly why patience and timing matter! 🧛🔥 In this video, we follow an exciting session of Le Vampire, where every spin brings new possibilities. The game features dynamic combinations, special symbols, and bonus mechanics that keep the action unpredictable. As the session progresses, the excitement builds with several interesting combinations and a major winning moment. The highlight comes when the screen reveals an impressive TOTAL WIN of 8,771.20! What makes this session entertaining isn't just the final result, but the anticipation leading up to it. Every round creates another opportunity, keeping viewers engaged until the very end. Gaming is all about entertainment, strategy, and enjoying the experience. Sometimes, the most memorable moments come when you least expect them. Would you keep playing after a win like this, or would you call it a day? 👀

255M views on a 3D Short by Zack D. Films i tried to rebuild his style with ONE Claude prompt, then ran it on Opus 5.5 and Fable 5.2 (they did NOT make the same Short) the full prompt is below ↓ paste it into Claude Code in an empty folder and answer its 2 questions. it shows u the script and waits for ur go. u need Blender + its free MPFB add-on and a graphics card. give it a few hours (the prompt is minus one optional paid-voice line) Ask me 2 things, then wait: 1. The story in one line: a strange TRUE event where one person did one odd physical thing ("He tied 42 weather balloons to a lawn chair."). If I say "you pick", pitch 3 with one source link each and take the first unless I choose. 2. The voice. Default: Kokoro (free, runs on my computer), a calm, warm male narrator. Then tell me everything else is free: Blender (find my install, or tell me the one download), its free MPFB extension for human bodies (ask me before you install it) and into this folder a Python venv with FFmpeg (imageio-ffmpeg), kokoro-onnx + its model files (kokoro-v1.0.onnx and voices-v1.0.bin from the kokoro-onnx GitHub releases), faster-whisper (small.en), numpy and Pillow. It needs no API key or account and renders on my graphics card with EEVEE. The look: a clean, realistic 3D world, like a good game cutscene. Bright, saturated, a little filmic. - People: realistic bodies with generic faces and big readable expressions (shock, a smirk, a scream). Plain clothes, no logos. Never a real person's likeness: for a real person keep the face out of close-ups (hands, back, side, distance, straight top-down). - CALM shots only. Bodies in fast action or contact (running, jumping, fighting, falling) look stiff, so tell the story with frozen moments under a slowly moving camera, macro inserts of hands and objects, rooms, vehicles, the sky, top-down views and the aftermath. - One new picture for every caption chunk (2-4 words, about 1-1.5 s): a new framing, a new action or a camera move. Nothing ever holds still: every shot drifts, pushes in or orbits. - The shot kit: macro insert (extreme close-up of the key object or hands, very shallow depth of field), hero low angle (wide lens at waist height), god's-eye top-down with a slow rotate, tracked object, over the shoulder, smash zoom on the word that matters, whip pan between beats, glow highlight (the named object glows orange with a rim, then fades), a big 3D number lying in the scene, a thought bubble (a cloud ring showing what he hoped for), a TV in the scene replaying the event. - Light: a bright warm sun, blue sky with clouds, soft shadows. Night scenes use hard lamps and a dark sky. Wide lens up close, depth of field on every shot, motion blur on every fast camera move. - Captions: 2-4 words at a time, white bold rounded sans, about 64 px on a 1080 px-wide frame, centred, 78% down the frame, soft dark shadow, no box. Each chunk replaces the last on the same frame as its picture. - Banned: real footage, photos, downloaded textures or 3D models, logos, AI images or AI video, downloaded music, gore. 38-50 s, 100-140 words, 150-180 words a minute. The voice starts at 0.0 s. - Line 1: the most surprising physical act, in past tense, opening with "He" (or "She"). No name, date or setup. Frame 1 shows that act. - Then a cause-and-effect chain: every sentence starts with a hinge word (Then, Suddenly, So, Because, But, Later) and is ONE visible action. - The context arrives late, after "You see,". - A reveal: someone looks closer and the story turns. - The last line is the consequence. No call to action, no question. - Names are rare: use one only when it pays off. Title = "He ..." or "The Man Who ...". The cover is the first frame. Save every stage to disk so a crash never costs more than one stage. - sources.md: every fact with its link and the date you read it. Anything you can't verify stays out. - script.json: the lines and the caption chunks cut by hand (2-4 words, one picture each), each chunk with its shot: the set, the camera, what moves. - Kokoro reads the whole script in one take (speed 1.0-1.1) and pauses are squeezed to 0.35 s at most; faster-whisper times every word (pass it a 16 kHz numpy array, not a path); write chunks.json with each chunk's start and end. A picture and its caption switch together on frame round(start*30)+1. - blender/: Python scripts run headless (blender -b -P). makes one .blend with the sets, the cast, the props and every shot baked with a key on every frame. sets EEVEE with ray tracing, the AgX view with the Punchy look, motion blur, depth of field and bloom in the compositor. Bodies come from MPFB or are built by you; clothes, hair, props and sets are built in code. - Stills first: one half-size still per shot as a contact sheet. Look at every tile and fix what's off before any full render. Time one full-size frame and tell me the estimate. - Render 1080x1920 at 30 fps to PNG frames, skipping frames already on disk. - numpy only: a light music bed, a sound on every action (a whoosh on each whip pan, hits, wind, room tone), ducked under the voice, mastered to -14 LUFS on the whole stereo mix. - burn the captions in from chunks.json. Encode out/_9x16.mp4 (1080x1920, 30 fps, TV-range yuv420p) and out/_x_7x8.mp4 (1008x1152, under 10 MB, 2-pass): a crop of the 9:16 frame that keeps the subject and the caption. - Blender paths: pass absolute output paths (a relative one lands on the drive root). - MPFB headless: enable it with addon_utils.enable("bl_ext.user_default.mpfb"). Bake a body's shape keys before you pose it: the rig is fitted to the baked shape. - Clothes: cut them from the body mesh and push them 7-16 mm out along the normals; keep two rings of body faces under every hem. - Nothing coplanar: keep every resting object 1 cm off its neighbour and draw ground markings into the texture, or they flicker. - Hair: EEVEE draws hair only from a Curves object, hair type STRIP. Use a Principled BSDF on it (the Hair BSDF renders black). - Skin: keep subsurface weight near 0.2 (more turns faces black in EEVEE) and add a soft fill light from below under a noon sun. - Hands: a curled hand reads as a claw from the palm side, so shoot a grip from behind the knuckles. - Motion blur: shutter position START, a key on every frame, plus a key half a frame after each shot's last frame, so blur never reaches across a cut. - Check: run a black-frame and one-frame-flicker check on the render. Make a contact sheet (a frame every 0.5 s) and look at every tile: the act reads in the first 2 s, faces are lit, nothing clips through anything, captions stay off faces and key objects. Then step through the first 2 s, the middle and the end frame by frame. Fix, render only the changed shots again. Pitch (or take my story), then show me the script with the word count, the caption chunks with their seconds and the shot list with what the first frame shows. Build nothing until I say go. how the two runs went: one run each on the same PC, so it's one sample, not a ranking. both took under 3 hours on one shared 8 GB graphics card. the story for both: a man who tied 42 weather balloons to a lawn chair in 1982 where they split: -> Opus opened wide on the balloons over the yard. Fable opened on the man kneeling at the chair with his Jeep behind him -> Opus put 5 big 3D numbers in its scenes, Fable 2 -> Fable followed the caption spec (white text, soft shadow). Opus went ALL CAPS with a thick outline -> Fable pulled the Smithsonian's own record and ended on the chair in the museum. Opus ended on the $1,500 fine -> a few Opus shots are hazy and one opens on a blank grey frame save this for ur next Short. who did it better, Opus or Fable?

I've been doing SEO for 10 years and I've just spent 2 weeks obsessing over the design of our new SaaS blog. I think I've finally cracked it. The perfect blog design for SEO, AI search and actually turning readers into customers. (I'm rolling it out on soon and I'll share the results) Here's how you can do the same: 1. Optimise the title and metadata Use a keyword-focused H1 that matches search intent. Make the topic clear and include accurate publication and update dates. 2. Establish credibility immediately Show who wrote and reviewed the article, their credentials and first-hand experience. Give readers a reason to trust it. 3. Present your solution above the fold If someone lands with a problem your product solves, why make them scroll to discover it? Show your solution immediately with a relevant offer and CTA. 4. Lead with key takeaways Summarise the most important information at the top. Use short, scannable bullet points that help readers find answers quickly and AI systems interpret your content. 5. Keep your CTA visible Add a sticky sidebar as readers scroll. Highlight your product's key benefits and make it easy to get started. 6. Structure content for SEO and AI search Use descriptive H2s and H3s, short paragraphs, bullets and internal links. Answer questions directly so readers and search engines can follow. 7. Use tables and original visuals Include comparison tables, original screenshots, diagrams and real examples. Give readers something useful they won't find in generic AI-generated articles. 8. Add contextual conversion opportunities Don't rely on one CTA. Introduce your product naturally where it's relevant, connecting each offer to the problem you're solving. 9. Back up your claims with evidence Use original research, real data, expert insights and credible sources. Don't just tell people something works, show them the evidence. 10. Finish with a strong conversion CTA Once someone's finished reading, give them a next step. Reinforce your product's benefits, add social proof and make it easy to convert. A few extra things: Add a table of contents for navigation, FAQs for related questions and related articles to keep readers exploring through internal links. And most importantly, measure the results. Not just rankings, traffic and AI citations, but actual signups, leads and revenue. There's no point getting 1,000s of visitors to your blog if none of them become customers. And there's no point having a beautiful, high-converting blog if nobody can find it. The goal is to get discovered AND convert. I've included the full visual cheatsheet below: (Feel free to steal the design for your own site)

Join us at OCP Summit for our speaker session with Magelli Roxas, where we'll highlight the latest advances in data center liquid cooling, including high-capacity direct liquid cooling solutions engineered for the most demanding multi-hundred-kilowatt AI compute racks. 📅Monday, October 12, 2026 🕔 1:40 PM 📍 SJCC | Lower Level | LL21BC Learn more about Supermicro OCP offerings: #OCPSummit26# #Supermicro#

Game 6 Win vs Santa Monica Physical . Headhunting DB @djHeartAttak @RaymondToilolo @CoachMase55 @JamilSiegmund1 @CoachBurhop @Bigchess52087 @JUCOFFrenzy Highlight link

Obsidian 1.14 is now available with five major improvements: 1. Edit Markdown files outside your vault 2. Preview files using macOS Quick Look when the app is closed 3. Instantly add notes with Quick Capture on iOS 4. Kanban and new grouping options in Bases 5. Highlight colors

PROMPT SHARE: The biggest AI breakthrough isn’t realism. It’s presence. #AIVideo# #Marketing# #Advertising# The people, movement, noise and unpredictability give the world a sense of life beyond the camera. That’s when generation starts becoming experience. ➜ Image Prompt: Photography & Cinematography Create an ultra-realistic nighttime street portrait with the raw, spontaneous look of early-2000s nightlife photography. Direct on-camera flash illuminates the subject sharply against a much darker city background, with natural grain, slight highlight blowout, deep shadows, imperfect exposure and authentic point-and-shoot character. No fake or plastic skin, no beauty filtering, no CGI or animated appearance. Character & Pose A striking young man with rugged, high-fashion model features, lean athletic build, sharply defined jawline and cheekbones, strong brows, full lips and short dark hair styled wet and tousled with loose curls falling onto his forehead. Natural pores, subtle facial texture and realistic imperfections. He sits casually on the edge of a city curb at night, legs spread asymmetrically, one knee raised toward the camera. One forearm rests loosely across the raised knee while his other hand reaches down beside him toward a small shaggy brown dog partially entering the bottom of the frame. He looks directly into the camera with a serious, confident expression. Outfit Fitted black long-sleeve turtleneck, faded distressed gray-blue jeans with worn areas and small rips, and a black leather belt. Silver pendant necklace, small hoop earring, chunky silver cuff bracelet, dark oversized wristwatch, and a thin bracelet. Styling feels slightly rebellious, understated and naturally lived-in rather than polished. Environment A busy urban nightlife street after dark. Several casually dressed people walk behind him, cropped naturally by the frame, while parked and passing cars create scattered headlights and small pools of distant light. The pavement is gritty and imperfect with curbs, cracks and ordinary street details visible. Keep the background activity believable and incidental—the image should feel like a real moment caught outside a club or late-night gathering, not a staged fashion shoot. Wide horizontal composition, intimate close-range flash photography, authentic real-world energy.

No-look. Behind-the-back. Crosscourt behind 3 defenders. Just Joker doing Joker things... Nikola Jokić delivered this HIGHLIGHT-REEL DIME for the @nuggets in last night’s NBA preseason action 🤯