• "في هاد العالم، كلشي يتغيّر، لكن شحال من مرة نتساءلوا على كيفية تفكيرنا؟"

    اليوم حبيت نشارك معاكم مقال حول موضوع مشوق: "كيف نعلّم نموذج AI التفكير؟" المقال يتناول كيف أن النماذج الذكية تتطور بسرعة، لكن تظل تفتقر لشيء مهم وهو "المنطق السليم"، اللي نكتسبوه من خلال التجارب اليومية. مثلاً، طيور ما تقدرش تطير للوراء، وهذا بديهي بالنسبة لنا، لكن كيفاش نعلّموا AI هاد المفاهيم؟

    من تجربتي، كي نشوف AI يتعلم، نحس كيف أن التكنولوجيا قادرة تحقق المستحيل لكن في نفس الوقت تظل تفتقر لمسة إنسانية. نحتاجوا نفكروا كيف نجمّعوا بين الذكاء الاصطناعي وتجاربنا الحياتية.

    مقال جدير بالقراءة يخلينا نفكروا في مدى تعقيد الفهم الإنساني.

    https://blogs.nvidia.com/blog/ai-reasoning-cosmos/
    #AI #تفكير_سليم #
    💭 "في هاد العالم، كلشي يتغيّر، لكن شحال من مرة نتساءلوا على كيفية تفكيرنا؟" اليوم حبيت نشارك معاكم مقال حول موضوع مشوق: "كيف نعلّم نموذج AI التفكير؟" 🤖 المقال يتناول كيف أن النماذج الذكية تتطور بسرعة، لكن تظل تفتقر لشيء مهم وهو "المنطق السليم"، اللي نكتسبوه من خلال التجارب اليومية. مثلاً، طيور ما تقدرش تطير للوراء، وهذا بديهي بالنسبة لنا، لكن كيفاش نعلّموا AI هاد المفاهيم؟ من تجربتي، كي نشوف AI يتعلم، نحس كيف أن التكنولوجيا قادرة تحقق المستحيل لكن في نفس الوقت تظل تفتقر لمسة إنسانية. نحتاجوا نفكروا كيف نجمّعوا بين الذكاء الاصطناعي وتجاربنا الحياتية. مقال جدير بالقراءة يخلينا نفكروا في مدى تعقيد الفهم الإنساني. https://blogs.nvidia.com/blog/ai-reasoning-cosmos/ #AI #تفكير_سليم #
    blogs.nvidia.com
    AI models are advancing at a rapid rate and scale. But what might they lack that (most) humans don’t? Common sense: an understanding, developed through real-world experiences, that birds can’t fly backwards, mirrors are reflective and ice melts into
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  • NVIDIA Jetson Thor Unlocks Real-Time Reasoning for General Robotics and Physical AI

    Robots around the world are about to get a lot smarter as physical AI developers plug in NVIDIA Jetson Thor modules — new robotics computers that can serve as the brains for robotic systems across research and industry.
    Robots demand rich sensor data and low-latency AI processing. Running real-time robotic applications requires significant AI compute and memory to handle concurrent data streams from multiple sensors. Jetson Thor, now in general availability, delivers 7.5x more AI compute, 3.1x more CPU performance and 2x more memory than its predecessor, the NVIDIA Jetson Orin, to make this possible on device.
    This performance leap will enable roboticists to process high-speed sensor data and perform visual reasoning at the edge — workflows that were previously too slow to run in dynamic real-world environments. This opens new possibilities for multimodal AI applications such as humanoid robotics.

    Agility Robotics, a leader in humanoid robotics, has integrated NVIDIA Jetson into the fifth generation of its robot, Digit — and plans to adopt Jetson Thor as the onboard compute platform for the sixth generation of Digit. This transition will enhance Digit’s real-time perception and decision-making capabilities, supporting increasingly complex AI skills and behaviors. Digit is commercially deployed and performs logistics tasks such as stacking, loading and palletizing in warehouse and manufacturing environments.
    “The powerful edge processing offered by Jetson Thor will take Digit to the next level — enhancing its real-time responsiveness and expanding its abilities to a broader, more complex set of skills,” said Peggy Johnson, CEO of Agility Robotics. “With Jetson Thor, we can deliver the latest physical AI advancements to optimize operations across our customers’ warehouses and factories.”
    Boston Dynamics — which has been building some of the industry’s most advanced robots for over 30 years — is integrating Jetson Thor into its humanoid robot Atlas, enabling Atlas to harness formerly server-level compute, AI workload acceleration, high-bandwidth data processing and significant memory on device.
    Beyond humanoids, Jetson Thor will accelerate various robotic applications — such as surgical assistants, smart tractors, delivery robots, industrial manipulators and visual AI agents — with real-time inference on device for larger, more complex AI models.
    A Giant Leap for Real-Time Robot Reasoning
    Jetson Thor is built for generative reasoning models. It enables the next generation of physical AI agents — powered by large transformer models, vision language models and vision language action models — to run in real time at the edge while minimizing cloud dependency.
    Optimized with the Jetson software stack to enable the low latency and high performance required in real-world applications, Jetson Thor supports all popular generative AI frameworks and AI reasoning models with unmatched real-time performance. These include Cosmos Reason, DeepSeek, Llama, Gemini and Qwen models, as well as domain-specific models for robotics like Isaac GR00T N1.5, enabling any developer to easily experiment and run inference locally.
    NVIDIA Jetson Thor opens new capabilities for real-time reasoning with multi-sensor input. Further performance improvement is expected with FP4 and speculative decoding optimization.
    With NVIDIA CUDA ecosystem support through its lifecycle, Jetson Thor is expected to deliver even better throughput and faster responses with future software releases.
    Jetson Thor modules also run the full NVIDIA AI software stack to accelerate virtually every physical AI workflow with platforms including NVIDIA Isaac for robotics, NVIDIA Metropolis for video analytics AI agents and NVIDIA Holoscan for sensor processing.
    With these software tools, developers can easily build and deploy applications, such as visual AI agents that can analyze live camera streams to monitor worker safety, humanoid robots capable of manipulation tasks in unstructured environments and smart operating rooms that guide surgeons based on data from multi-camera streams.
    Jetson Thor Set to Advance Research Innovation 
    Research labs at Stanford University, Carnegie Mellon University and the University of Zurich are tapping Jetson Thor to push the boundaries of perception, planning and navigation models for a host of potential applications.
    At Carnegie Mellon’s Robotics Institute, a research team uses NVIDIA Jetson to power autonomous robots that can navigate complex, unstructured environments to conduct medical triage as well as search and rescue.
    “We can only do as much as the compute available allows,” said Sebastian Scherer, an associate research professor at the university and head of the AirLab. “Years ago, there was a big disconnect between computer vision and robotics because computer vision workloads were too slow for real-time decision-making — but now, models and computing have gotten fast enough so robots can handle much more nuanced tasks.”
    Scherer anticipates that by upgrading from his team’s existing NVIDIA Jetson AGX Orin systems to Jetson AGX Thor developer kit, they’ll improve the performance of AI models including their award-winning MAC-VO model for robot perception at the edge, boost their sensor-fusion capabilities and be able to experiment with robot fleets.
    Wield the Strength of Jetson Thor
    The Jetson Thor family includes a developer kit and production modules. The developer kit includes a Jetson T5000 module, a reference carrier board with abundant connectivity, an active heatsink with a fan and a power supply.
    NVIDIA Jetson AGX Thor Developer Kit
    The Jetson ecosystem supports a variety of application requirements, high-speed industrial automation protocols and sensor interfaces, accelerating time to market for enterprise developers. Hardware partners including Advantech, Aetina, ConnectTech, MiiVii and TZTEK are building production-ready Jetson Thor systems with flexible I/O and custom configurations in various form factors.
    Sensor and Actuator companies including Analog Devices, Inc., e-con Systems,  Infineon, Leopard Imaging, RealSense and Sensing are using NVIDIA Holoscan Sensor Bridge — a platform that simplifies sensor fusion and data streaming — to connect sensor data from cameras, radar, lidar and more directly to GPU memory on Jetson Thor with ultralow latency.
    Thousands of software companies can now elevate their traditional vision AI and robotics applications with multi-AI agent workflows running on Jetson Thor. Leading adopters include Openzeka, Rebotnix, Solomon and Vaidio.
    More than 2 million developers use NVIDIA technologies to accelerate robotics workflows. Get started with Jetson Thor by reading the NVIDIA Technical Blog and watching the developer kit walkthrough.

    To get hands-on experience with Jetson Thor, sign up to participate in upcoming hackathons with Seeed Studio and LeRobot by Hugging Face.
    The NVIDIA Jetson AGX Thor developer kit is available now starting at NVIDIA Jetson T5000 modules are available starting at for 1,000 units. Buy now from authorized NVIDIA partners.
    NVIDIA today also announced that the NVIDIA DRIVE AGX Thor developer kit, which provides a platform for developing autonomous vehicles and mobility solutions, is available for preorder. Deliveries are slated to start in September.
    #nvidia #jetson #thor #unlocks #realtime
    NVIDIA Jetson Thor Unlocks Real-Time Reasoning for General Robotics and Physical AI
    Robots around the world are about to get a lot smarter as physical AI developers plug in NVIDIA Jetson Thor modules — new robotics computers that can serve as the brains for robotic systems across research and industry. Robots demand rich sensor data and low-latency AI processing. Running real-time robotic applications requires significant AI compute and memory to handle concurrent data streams from multiple sensors. Jetson Thor, now in general availability, delivers 7.5x more AI compute, 3.1x more CPU performance and 2x more memory than its predecessor, the NVIDIA Jetson Orin, to make this possible on device. This performance leap will enable roboticists to process high-speed sensor data and perform visual reasoning at the edge — workflows that were previously too slow to run in dynamic real-world environments. This opens new possibilities for multimodal AI applications such as humanoid robotics. Agility Robotics, a leader in humanoid robotics, has integrated NVIDIA Jetson into the fifth generation of its robot, Digit — and plans to adopt Jetson Thor as the onboard compute platform for the sixth generation of Digit. This transition will enhance Digit’s real-time perception and decision-making capabilities, supporting increasingly complex AI skills and behaviors. Digit is commercially deployed and performs logistics tasks such as stacking, loading and palletizing in warehouse and manufacturing environments. “The powerful edge processing offered by Jetson Thor will take Digit to the next level — enhancing its real-time responsiveness and expanding its abilities to a broader, more complex set of skills,” said Peggy Johnson, CEO of Agility Robotics. “With Jetson Thor, we can deliver the latest physical AI advancements to optimize operations across our customers’ warehouses and factories.” Boston Dynamics — which has been building some of the industry’s most advanced robots for over 30 years — is integrating Jetson Thor into its humanoid robot Atlas, enabling Atlas to harness formerly server-level compute, AI workload acceleration, high-bandwidth data processing and significant memory on device. Beyond humanoids, Jetson Thor will accelerate various robotic applications — such as surgical assistants, smart tractors, delivery robots, industrial manipulators and visual AI agents — with real-time inference on device for larger, more complex AI models. A Giant Leap for Real-Time Robot Reasoning Jetson Thor is built for generative reasoning models. It enables the next generation of physical AI agents — powered by large transformer models, vision language models and vision language action models — to run in real time at the edge while minimizing cloud dependency. Optimized with the Jetson software stack to enable the low latency and high performance required in real-world applications, Jetson Thor supports all popular generative AI frameworks and AI reasoning models with unmatched real-time performance. These include Cosmos Reason, DeepSeek, Llama, Gemini and Qwen models, as well as domain-specific models for robotics like Isaac GR00T N1.5, enabling any developer to easily experiment and run inference locally. NVIDIA Jetson Thor opens new capabilities for real-time reasoning with multi-sensor input. Further performance improvement is expected with FP4 and speculative decoding optimization. With NVIDIA CUDA ecosystem support through its lifecycle, Jetson Thor is expected to deliver even better throughput and faster responses with future software releases. Jetson Thor modules also run the full NVIDIA AI software stack to accelerate virtually every physical AI workflow with platforms including NVIDIA Isaac for robotics, NVIDIA Metropolis for video analytics AI agents and NVIDIA Holoscan for sensor processing. With these software tools, developers can easily build and deploy applications, such as visual AI agents that can analyze live camera streams to monitor worker safety, humanoid robots capable of manipulation tasks in unstructured environments and smart operating rooms that guide surgeons based on data from multi-camera streams. Jetson Thor Set to Advance Research Innovation  Research labs at Stanford University, Carnegie Mellon University and the University of Zurich are tapping Jetson Thor to push the boundaries of perception, planning and navigation models for a host of potential applications. At Carnegie Mellon’s Robotics Institute, a research team uses NVIDIA Jetson to power autonomous robots that can navigate complex, unstructured environments to conduct medical triage as well as search and rescue. “We can only do as much as the compute available allows,” said Sebastian Scherer, an associate research professor at the university and head of the AirLab. “Years ago, there was a big disconnect between computer vision and robotics because computer vision workloads were too slow for real-time decision-making — but now, models and computing have gotten fast enough so robots can handle much more nuanced tasks.” Scherer anticipates that by upgrading from his team’s existing NVIDIA Jetson AGX Orin systems to Jetson AGX Thor developer kit, they’ll improve the performance of AI models including their award-winning MAC-VO model for robot perception at the edge, boost their sensor-fusion capabilities and be able to experiment with robot fleets. Wield the Strength of Jetson Thor The Jetson Thor family includes a developer kit and production modules. The developer kit includes a Jetson T5000 module, a reference carrier board with abundant connectivity, an active heatsink with a fan and a power supply. NVIDIA Jetson AGX Thor Developer Kit The Jetson ecosystem supports a variety of application requirements, high-speed industrial automation protocols and sensor interfaces, accelerating time to market for enterprise developers. Hardware partners including Advantech, Aetina, ConnectTech, MiiVii and TZTEK are building production-ready Jetson Thor systems with flexible I/O and custom configurations in various form factors. Sensor and Actuator companies including Analog Devices, Inc., e-con Systems,  Infineon, Leopard Imaging, RealSense and Sensing are using NVIDIA Holoscan Sensor Bridge — a platform that simplifies sensor fusion and data streaming — to connect sensor data from cameras, radar, lidar and more directly to GPU memory on Jetson Thor with ultralow latency. Thousands of software companies can now elevate their traditional vision AI and robotics applications with multi-AI agent workflows running on Jetson Thor. Leading adopters include Openzeka, Rebotnix, Solomon and Vaidio. More than 2 million developers use NVIDIA technologies to accelerate robotics workflows. Get started with Jetson Thor by reading the NVIDIA Technical Blog and watching the developer kit walkthrough. To get hands-on experience with Jetson Thor, sign up to participate in upcoming hackathons with Seeed Studio and LeRobot by Hugging Face. The NVIDIA Jetson AGX Thor developer kit is available now starting at NVIDIA Jetson T5000 modules are available starting at for 1,000 units. Buy now from authorized NVIDIA partners. NVIDIA today also announced that the NVIDIA DRIVE AGX Thor developer kit, which provides a platform for developing autonomous vehicles and mobility solutions, is available for preorder. Deliveries are slated to start in September. #nvidia #jetson #thor #unlocks #realtime
    NVIDIA Jetson Thor Unlocks Real-Time Reasoning for General Robotics and Physical AI
    blogs.nvidia.com
    Robots around the world are about to get a lot smarter as physical AI developers plug in NVIDIA Jetson Thor modules — new robotics computers that can serve as the brains for robotic systems across research and industry. Robots demand rich sensor data and low-latency AI processing. Running real-time robotic applications requires significant AI compute and memory to handle concurrent data streams from multiple sensors. Jetson Thor, now in general availability, delivers 7.5x more AI compute, 3.1x more CPU performance and 2x more memory than its predecessor, the NVIDIA Jetson Orin, to make this possible on device. This performance leap will enable roboticists to process high-speed sensor data and perform visual reasoning at the edge — workflows that were previously too slow to run in dynamic real-world environments. This opens new possibilities for multimodal AI applications such as humanoid robotics. Agility Robotics, a leader in humanoid robotics, has integrated NVIDIA Jetson into the fifth generation of its robot, Digit — and plans to adopt Jetson Thor as the onboard compute platform for the sixth generation of Digit. This transition will enhance Digit’s real-time perception and decision-making capabilities, supporting increasingly complex AI skills and behaviors. Digit is commercially deployed and performs logistics tasks such as stacking, loading and palletizing in warehouse and manufacturing environments. “The powerful edge processing offered by Jetson Thor will take Digit to the next level — enhancing its real-time responsiveness and expanding its abilities to a broader, more complex set of skills,” said Peggy Johnson, CEO of Agility Robotics. “With Jetson Thor, we can deliver the latest physical AI advancements to optimize operations across our customers’ warehouses and factories.” Boston Dynamics — which has been building some of the industry’s most advanced robots for over 30 years — is integrating Jetson Thor into its humanoid robot Atlas, enabling Atlas to harness formerly server-level compute, AI workload acceleration, high-bandwidth data processing and significant memory on device. Beyond humanoids, Jetson Thor will accelerate various robotic applications — such as surgical assistants, smart tractors, delivery robots, industrial manipulators and visual AI agents — with real-time inference on device for larger, more complex AI models. A Giant Leap for Real-Time Robot Reasoning Jetson Thor is built for generative reasoning models. It enables the next generation of physical AI agents — powered by large transformer models, vision language models and vision language action models — to run in real time at the edge while minimizing cloud dependency. Optimized with the Jetson software stack to enable the low latency and high performance required in real-world applications, Jetson Thor supports all popular generative AI frameworks and AI reasoning models with unmatched real-time performance. These include Cosmos Reason, DeepSeek, Llama, Gemini and Qwen models, as well as domain-specific models for robotics like Isaac GR00T N1.5, enabling any developer to easily experiment and run inference locally. NVIDIA Jetson Thor opens new capabilities for real-time reasoning with multi-sensor input. Further performance improvement is expected with FP4 and speculative decoding optimization. With NVIDIA CUDA ecosystem support through its lifecycle, Jetson Thor is expected to deliver even better throughput and faster responses with future software releases. Jetson Thor modules also run the full NVIDIA AI software stack to accelerate virtually every physical AI workflow with platforms including NVIDIA Isaac for robotics, NVIDIA Metropolis for video analytics AI agents and NVIDIA Holoscan for sensor processing. With these software tools, developers can easily build and deploy applications, such as visual AI agents that can analyze live camera streams to monitor worker safety, humanoid robots capable of manipulation tasks in unstructured environments and smart operating rooms that guide surgeons based on data from multi-camera streams. Jetson Thor Set to Advance Research Innovation  Research labs at Stanford University, Carnegie Mellon University and the University of Zurich are tapping Jetson Thor to push the boundaries of perception, planning and navigation models for a host of potential applications. At Carnegie Mellon’s Robotics Institute, a research team uses NVIDIA Jetson to power autonomous robots that can navigate complex, unstructured environments to conduct medical triage as well as search and rescue. “We can only do as much as the compute available allows,” said Sebastian Scherer, an associate research professor at the university and head of the AirLab. “Years ago, there was a big disconnect between computer vision and robotics because computer vision workloads were too slow for real-time decision-making — but now, models and computing have gotten fast enough so robots can handle much more nuanced tasks.” Scherer anticipates that by upgrading from his team’s existing NVIDIA Jetson AGX Orin systems to Jetson AGX Thor developer kit, they’ll improve the performance of AI models including their award-winning MAC-VO model for robot perception at the edge, boost their sensor-fusion capabilities and be able to experiment with robot fleets. Wield the Strength of Jetson Thor The Jetson Thor family includes a developer kit and production modules. The developer kit includes a Jetson T5000 module, a reference carrier board with abundant connectivity, an active heatsink with a fan and a power supply. NVIDIA Jetson AGX Thor Developer Kit The Jetson ecosystem supports a variety of application requirements, high-speed industrial automation protocols and sensor interfaces, accelerating time to market for enterprise developers. Hardware partners including Advantech, Aetina, ConnectTech, MiiVii and TZTEK are building production-ready Jetson Thor systems with flexible I/O and custom configurations in various form factors. Sensor and Actuator companies including Analog Devices, Inc. (ADI), e-con Systems,  Infineon, Leopard Imaging, RealSense and Sensing are using NVIDIA Holoscan Sensor Bridge — a platform that simplifies sensor fusion and data streaming — to connect sensor data from cameras, radar, lidar and more directly to GPU memory on Jetson Thor with ultralow latency. Thousands of software companies can now elevate their traditional vision AI and robotics applications with multi-AI agent workflows running on Jetson Thor. Leading adopters include Openzeka, Rebotnix, Solomon and Vaidio. More than 2 million developers use NVIDIA technologies to accelerate robotics workflows. Get started with Jetson Thor by reading the NVIDIA Technical Blog and watching the developer kit walkthrough. To get hands-on experience with Jetson Thor, sign up to participate in upcoming hackathons with Seeed Studio and LeRobot by Hugging Face. The NVIDIA Jetson AGX Thor developer kit is available now starting at $3,499. NVIDIA Jetson T5000 modules are available starting at $2,999 for 1,000 units. Buy now from authorized NVIDIA partners. NVIDIA today also announced that the NVIDIA DRIVE AGX Thor developer kit, which provides a platform for developing autonomous vehicles and mobility solutions, is available for preorder. Deliveries are slated to start in September.
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  • Fur Grooming Techniques For Realistic Stitch In Blender

    IntroductionHi everyone! My name is Oleh Yakushev, and I'm a 3D Artist from Ukraine. My journey into 3D began just three years ago, when I was working as a mobile phone salesperson at a shopping mall. In 2022, during one slow day at work, I noticed a colleague learning Python. We started talking about life goals. I told him I wanted to switch careers, to do something creative, but programming wasn't really my thing.He asked me a simple question: "Well, what do you actually enjoy doing?"I said, "Video games. I love video games. But I don't have time to learn how to make them, I've got a job, a family, and a kid."Then he hit me with something that really shifted my whole perspective."Oleh, do you play games on your PlayStation?"I said, "Of course."He replied, "Then why not take the time you spend playing and use it to learn how to make games?"That moment flipped a switch in my mind. I realized that I did have time, it was just a matter of how I used it. If I really wanted to learn, I could find a way. At the time, I didn't even own a computer. But where there's a will, there's a way: I borrowed my sister's laptop for a month and started following beginner 3D tutorials on YouTube. Every night after work, once my family went to sleep, I'd sit in the kitchen and study. I stayed up until 2 or 3 AM, learning Blender basics. Then I'd sleep for a few hours before waking up at 6 AM to go back to work. That's how I spent my first few months in 3D, studying every single night.3D completely took over my life. During lunch breaks, I watched 3D videos, on the bus, I scrolled through 3D TikToks, at home, I took 3D courses, and the word "3D" just became a constant in my vocabulary.After a few months of learning the basics, I started building my portfolio, which looks pretty funny to me now. But at the time, it was a real sign of how committed I was. Eventually, someone reached out to me through Behance, offering my first freelance opportunity. And thatэs how my journey began, from mall clerk to 3D artist. It's been a tough road, full of burnout, doubts, and late nights... but also full of curiosity, growth, and hope. And I wouldn't trade it for anything.The Stitch ProjectI've loved Stitch since I was a kid. I used to watch the cartoons, play the video games, and he always felt like such a warm, funny, chill, and at the same time, strong character. So once I reached a certain level in 3D, I decided to recreate Stitch.Back then, my skills only allowed me to make him in a stylized cartoonish style, no fur, no complex detailing, no advanced texturing, I just didn't have the experience. Surprisingly, the result turned out pretty decent. Even now, I sometimes get comments that my old Stitch still looks quite cute. Though honestly, I wouldn't say that myself anymore. Two years have passed since I made that first Stitch, it was back in 2023. And in 2025, I decided it was time to challenge myself.At that point, I had just completed an intense grooming course. Grooming always intimidated me, it felt really complex. I avoided it on commercial projects, made a few failed attempts for my portfolio, and overall tried to steer clear of any tasks where grooming was required. But eventually, I found the strength to face it.I pushed myself to learn how to make great fur, and I did. I finally understood how the grooming system works, grasped the logic, the tools, and the workflow. And after finishing the course, I wanted to lock in all that knowledge by creating a full personal project from scratch.So my goal was to make a character from the ground up, where the final stage would be grooming. And without thinking too long, I chose Stitch.First, because I truly love the character. Second, I wanted to clearly see my own progress over the past two years. Third, I needed to put my new skills to the test and find out whether my training had really paid off.ModelingI had a few ideas for how to approach the base mesh for this project. First, to model everything completely from scratch, starting with a sphere. Second, to reuse my old Stitch model and upgrade it.But then an idea struck me: why not test how well AI could handle a base mesh? I gathered some references and tried generating a base mesh using AI, uploading Stitch visuals as a guide. As you can see from the screenshot, the result was far from usable. So I basically ended up doing everything from scratch anyway.So, I went back to basics: digging through ArtStation and Pinterest, collecting references. Since over the last two years, I had not only learned grooming but also completely changed my overall approach to character creation, it was important for me to make a more detailed model, even if much of it would be hidden under fur.The first Stitch was sculpted in Blender, with all the limitations that come with sculpting in it. But since then, I've leveled up significantly and switched to more advanced tools. So this second version of Stitch was born in ZBrush. By the time I started working on this Stitch, ZBrush had already become my second main workspace. I've used it to deliver tons of commercial projects, I work in it almost daily, and most of my portfolio was created using this tool. I found some great reference images showing Stitch's body structure. Among them were official movie references and a stunning high-poly model created by Juan Hernández, a version of Stitch without fur. That model became my primary reference for sculpting.Truth is, Stitch's base form is quite simple, so blocking out the shape didn't take too long. When blocking, I use Blender in combination with ZBrush:I work with primary forms in ZBrushThen check proportions in BlenderFix mistakes, tweak volumes, and refine the silhouetteSince Stitch's shape isn't overly complex, I broke him down into three main sculpting parts:The body: arms, legs, head, and earsThe nose, eyes, and mouth cavityWhile planning the sculpt, I already knew I'd be rigging Stitch, both body and facial rig. So I started sculpting with his mouth open.While studying various references, I noticed something interesting. Stitch from promotional posters, Stitch from the movie, and Stitch as recreated by different artists on ArtStation all look very different from one another. What surprised me the most was how different the promo version of Stitch is compared to the one in the actual movie. They are essentially two separate models:Different proportionsDifferent shapesDifferent texturesEven different fur and overall designThis presented a creative challenge, I had to develop my own take on Stitch's design. Sometimes I liked the way the teeth were done in one version, in another, the eye placement, in another, the fur shape, or the claw design on hands and feet.At first, considering that Stitch is completely covered in fur from head to toe, sculpting his underlying anatomy seemed pointless. I kept asking myself: "Why sculpt muscles and skin detail if everything will be hidden under fur anyway?"But eventually, I found a few solid answers for myself. First, having a defined muscle structure actually makes the fur grooming process easier. That's because fur often follows the flow of muscle lines, so having those muscles helps guide fur direction more accurately across the character's body.Second, it's great anatomy practice, and practice is never a waste. So, I found a solid anatomical reference of Stitch with clearly visible muscle groups and tried to recreate that structure as closely as possible in my own sculpt.In the end, I had to develop a full visual concept by combining elements from multiple versions of Stitch. Through careful reference work and constantly switching between Blender and ZBrush, I gradually, but intentionally, built up the body and overall look of our favorite fluffy alien.Topology & UVsThroughout the sculpting process, I spent quite a bit of time thinking about topology. I was looking for the most balanced solution between quality and production time. Normally, I do manual retopology for my characters, but this time, I knew it would take too much time, and honestly, I didn't have that luxury.So I decided to generate the topology using ZBrush's tools. I split the model into separate parts using Polygroups, assigning individual groups for the ears, the head, the torso, the arms, the legs, and each of Stitch's fingers.With the Polygroups in place, I used ZRemesher with Keep Groups enabled and smoothing on group borders. This gave me a clean and optimized mesh that was perfect for UV unwrapping.Of course, this kind of auto-retopology isn't a full substitute for manual work, but it saved me a huge amount of time, and the quality was still high enough for what I needed. However, there was one tricky issue. Although Stitch looks symmetrical at first glance, his ears are actually asymmetrical. The right ear has a scar on the top, while the left has a scar on the bottomBecause of that, I couldn't just mirror one side in ZBrush without losing those unique features. Here's what I ended up doing: I created a symmetrical model with the right ear, then another symmetrical model with the left ear. I brought both into Blender, detached the left ear from one model, and attached it to the body of the other one. This way, I got a clean, symmetrical base mesh with asymmetrical ears, preserving both topology and detail. And thanks to the clean polygroup-based layout, I was able to unwrap the UVs with nice, even seams and clean islands.When it came to UV mapping, I divided Stitch into two UDIM tiles:The first UDIM includes the head with ears, torso, arms, and legs.The second UDIM contains all the additional parts: teeth, tongue, gums, claws, and noseSince the nose is one of the most important details, I allocated the largest space to it, which helped me to better capture its intricate details.As for the eyes, I used procedural eyes, so there was no need to assign UV space or create a separate UDIM for texturing them. To achieve this, I used the Tiny Eye add-on by tinynocky for Blender, which allows full control over procedural eyes and their parameters.This approach gave me high-quality eyes with customizable elements tailored exactly to my needs. As a result of all these steps, Stitch ended up with a symmetrical, optimized mesh, asymmetrical ears, and the body split across two UDIMs, one for the main body and one for the additional parts.TexturingWhen planning Stitch's texturing, I understood that the main body texture would be fairly simple, with much of the visual detail enhanced by the fur. However, there were some areas that required much more attention than the rest of the body. The textures for Stitch can be roughly divided into several main parts:The base body, which includes the primary color of his fur, along with additional shading like a lighter tone on the frontand a darker tone on the back and napeThe nose and ears, these zones, demanded separate focusAt the initial texturing/blocking stage, the ears looked too cartoony, which didn’t fit the style I wanted. So, I decided to push them towards a more realistic look. This involved removing bright colors, adding more variation in the roughness map, introducing variation in the base color, and making the ears visually more natural, layered, and textured on the surface. By combining smart materials and masks, I achieved the effect of "living" ears, slightly dirty and looking as natural as possible.The nose was a separate story. It occupies a significant part of the face and thus draws a lot of attention. While studying references, I noticed that the shape and texture of the nose vary a lot between different artists. Initially, I made it dog-like, with some wear and tear around the nostrils and base.For a long time, I thought this version was acceptable. But during test renders, I realized the nose needed improvement. So I reworked its texturing, aiming to make it more detailed. I divided the nose texture into four main layers:Base detail: Baked from the high-poly model. Over this, I applied a smart skin material that added characteristic bumps.Lighter layer: Applied via a mask using the AO channel. This darkened the crevices and brightened the bumps, creating a multi-layered effect.Organic detail: In animal references, I noticed slight redness in the nose area. I created another AO-masked layer with reddish capillaries visible through the bumps, adding depth and realism.Softness: To make the nose visually softer, like in references, I added a fill layer with only height enabled, used a paper texture as grayscale, and applied a blurred mask. This created subtle dents and wrinkles that softened the look.All textures were created in 4K resolution to achieve maximum detail. After finishing the main texturing stage, I add an Ambient Occlusion map on the final texture layer, activating only the Color channel, setting the blend mode to Multiply, and reducing opacity to about 35%. This adds volume and greatly improves the overall perception of the model.That covers the texturing of Stitch’s body. I also created a separate texture for the fur. This was simpler, I disabled unnecessary layers like ears and eyelids, and left only the base ones corresponding to the body’s color tones.During grooming, I also created textures for the fur's clamps and roughness. In Substance 3D Painter, I additionally painted masks for better fur detail.FurAnd finally, I moved on to the part that was most important to me, the very reason I started this project in the first place. Fur. This entire process was essentially a test of my fur grooming skills. After overcoming self-doubt, I trusted the process and relied on everything I had learned so far. Before diving into the grooming itself, I made sure to gather strong references. I searched for the highest quality and most inspiring examples I could find and analyzed them thoroughly. My goal was to clearly understand the direction of fur growth, its density and volume, the intensity of roughness, and the strength of clumping in different areas of Stitch's body.To create the fur, I used Blender and its Hair Particle System. The overall approach is similar to sculpting a high-detail model: work from broad strokes to finer details. So, the first step was blocking out the main flow and placement of the hair strands.At this point, I ran into a challenge: symmetry. Since the model was purposefully asymmetrical, the fur couldn't be mirrored cleanly. To solve this, I created a base fur blocking using Hair Guides with just two segments. After that, I split the fur into separate parts. I duplicated the main Particle System and created individual hair systems for each area where needed.In total, I broke Stitch's body into key sections: head, left ear, right ear, front torso, back torso, arms, hands, upper and lower legs, toes, and additional detailing layers. The final fur setup included 25 separate particle systems.To control fur growth, I used Weight Paint to fine-tune the influence on each body part individually. This separation gave me much more precision and allowed full control over every parameter of the fur on a per-section basis.The most challenging aspect of working with fur is staying patient and focused. Detail is absolutely critical because the overall picture is built entirely from tiny, subtle elements. Once the base layer was complete, I moved on to refining the fur based on my references.The most complex areas turned out to be the front of the torso and the face. When working on the torso, my goal was to create a smooth gradient, from thick, clumped fur on the chest to shorter, softer fur on the stomach.Step by step, I adjusted the transitions, directions, clumps, and volumes to achieve that look. Additionally, I used the fur itself to subtly enhance Stitch's silhouette, making his overall shape feel sharper, more expressive, and visually engaging.During fur development, I used texture maps to control the intensity of the Roughness and Clump parameters. This gave me a high degree of flexibility, textures drove these attributes across the entire model. In areas where stronger clumping or roughness was needed, I used brighter values; in zones requiring a softer look, darker values. This approach allowed for fine-tuned micro-level control of the fur shader and helped achieve a highly realistic appearance in renders.The face required special attention: the fur had to be neat, evenly distributed, and still visually appealing. The biggest challenge here was working around the eye area. Even with properly adjusted Weight Paint, interpolation sometimes caused strands to creep into the eyes.I spent a lot of time cleaning up this region to get an optimal result. I also had to revisit certain patches that looked bald, even though interpolation and weight painting were set correctly, because the fur didn't render properly there. These areas needed manual fixing.As part of the detailing stage, I also increased the number of segments in the Hair Guides.While the blocking phase only used two segments, I went up to three, and in some cases even five, for more complex regions. This gave me much more control over fur shape and flow.The tiniest details really matter, so I added extra fur layers with thinner, more chaotic strands extending slightly beyond the main silhouette. These micro-layers significantly improved the texture depth and boosted the overall realism.Aside from the grooming itself, I paid special attention to the fur material setup, as the shader plays a critical role in the final visual quality of the render. It's not enough to simply plug a color texture into a Principled BSDF node and call it done.I built a more complex shader, giving me precise control over various attributes. For example, I implemented subtle color variation across individual strands, along with darkening near the roots and a gradual brightening toward the tips. This helped add visual depth and made the fur look significantly more natural and lifelike.Working on the fur took up nearly half of the total time I spent on the entire model. And I'm genuinely happy with the result, this stage confirmed that the training I've gone through was solid and that I’m heading in the right direction with my artistic development.Rigging, Posing & SceneOnce I finished working on the fur, I rendered several 4K test shots from different angles to make sure every detail looked the way I intended. When I was fully satisfied with the results, it was time to move on to rigging.I divided the rigging process into three main parts:Body rig, for posing and positioning the characterFacial rig, for expressions and emotionsEar rig, for dynamic ear controlRigging isn't something I consider my strongest skill, but as a 3D generalist, I had to dive into many technical aspects of it. For the ears, I set up a relatively simple system with several bones connected using inverse kinematics. This gave me flexible and intuitive control during posing and allowed for the addition of dynamic movement in animation.For facial rigging, I used the FaceIt add-on, which generates a complete facial control system for mouth, eyes, and tongue. It sped up the process significantly and gave me more precision. For the body, I used the ActorCore Rig by NVIDIA, then converted it to Rigify, which gave me a familiar interface and flexible control over poses.Posing is one of my favorite stages, it's when the character really comes to life. As usual, it started with gathering references. Honestly, it was hard to pick the final poses, Stitch is so expressive and full of personality that I wanted to try hundreds of them. But I focused on those that best conveyed the spirit and mood of the character. Some poses I reworked to fit my style rather than copying directly. For example, in the pose where Stitch licks his nose, I added drool and a bit of "green slime" for comedic effect. To capture motion, I tilted his head back and made the ears fly upward, creating a vivid, emotional snapshot.Just like in sculpting or grooming, minor details make a big difference in posing. Examples include: a slight asymmetry in the facial expression, a raised corner of the mouth, one eye squinting a little more than the other, and ears set at slightly different angles.These are subtle things that might not be noticed immediately, but they’re the key to making the character feel alive and believable.For each pose, I created a separate scene and collection in Blender, including the character, specific lighting setup, and a simple background or environment. This made it easy to return to any scene later, to adjust lighting, reposition the character, or tweak the background.In one of the renders, which I used as the cover image, Stitch is holding a little frog.I want to clearly note that the 3D model of the frog is not mine, full credit goes to the original author of the asset.At first, I wanted to build a full environment around Stitch, to create a scene that would feel like a frame from a film. But after carefully evaluating my skills and priorities, I decided that a weak environment would only detract from the strength of the character. So I opted for a simple, neutral backdrop, designed to keep all the focus on Stitch himself.Rendering, Lighting & Post-ProcessingWhen the character is complete, posed expressively, and integrated into the scene, there's one final step: lighting. Lighting isn't just a technical element of the scene — it’s a full-fledged stage of the 3D pipeline. It doesn't just illuminate; it paints. Proper lighting can highlight the personality of the character, emphasize forms, and create atmosphere.For all my renders, I rely on the classic three-point lighting setup: Key Light, Fill Light, and Rim Light.While this setup is well-known, it remains highly effective. When done thoughtfully, with the right intensity, direction, and color temperature, it creates a strong light-shadow composition that brings the model to life. In addition to the three main lights, I also use an HDRI map, but with very low intensity, around 0.3, just enough to subtly enrich the ambient light without overpowering the scene.Once everything is set, it's time to hit Render and wait for the result. Due to hardware limitations, I wasn’t able to produce full animated shots with fur. Rendering a single 4K image with fur took over an hour, so I limited myself to a 360° turnaround and several static renders.I don't spend too much time on post-processing, just basic refinements in Photoshop. Slight enhancement of the composition, gentle shadow adjustments, color balance tweaks, and adding a logo. Everything is done subtly, nothing overprocessed. The goal is simply to support and enhance what’s already there.Final ThoughtsThis project has been an incredible experience. Although it was my second time creating Stitch, this time the process felt completely different at every stage. And honestly, it wasn't easy.But that was exactly the point: to challenge myself. To reimagine something familiar, to try things I'd never done before, and to walk the full journey from start to finish. The fur, the heart of this project, was especially meaningful to me. It’s what started it all. I poured a lot into this model: time, effort, emotion, and even doubts. But at the same time, I brought all my knowledge, skills, and experience into it.This work became a mirror of my progress from 2023 to 2025. I can clearly see how far I've come, and that gives me the motivation to keep going. Every hour of learning and practice paid off, the results speak for themselves. This model was created for my portfolio. I don't plan to use it commercially, unless, of course, a studio actually wants to license it for a new filmIt's been a long road: challenging, sometimes exhausting, but above all inspiring and exciting. I know there's still a lot to learn. Many things to study, improve, and polish to perfection. But I'm already on that path, and I'm not stopping.Oleh Yakushev, 3D Character ArtistInterview conducted by Gloria Levine
    #fur #grooming #techniques #realistic #stitch
    Fur Grooming Techniques For Realistic Stitch In Blender
    IntroductionHi everyone! My name is Oleh Yakushev, and I'm a 3D Artist from Ukraine. My journey into 3D began just three years ago, when I was working as a mobile phone salesperson at a shopping mall. In 2022, during one slow day at work, I noticed a colleague learning Python. We started talking about life goals. I told him I wanted to switch careers, to do something creative, but programming wasn't really my thing.He asked me a simple question: "Well, what do you actually enjoy doing?"I said, "Video games. I love video games. But I don't have time to learn how to make them, I've got a job, a family, and a kid."Then he hit me with something that really shifted my whole perspective."Oleh, do you play games on your PlayStation?"I said, "Of course."He replied, "Then why not take the time you spend playing and use it to learn how to make games?"That moment flipped a switch in my mind. I realized that I did have time, it was just a matter of how I used it. If I really wanted to learn, I could find a way. At the time, I didn't even own a computer. But where there's a will, there's a way: I borrowed my sister's laptop for a month and started following beginner 3D tutorials on YouTube. Every night after work, once my family went to sleep, I'd sit in the kitchen and study. I stayed up until 2 or 3 AM, learning Blender basics. Then I'd sleep for a few hours before waking up at 6 AM to go back to work. That's how I spent my first few months in 3D, studying every single night.3D completely took over my life. During lunch breaks, I watched 3D videos, on the bus, I scrolled through 3D TikToks, at home, I took 3D courses, and the word "3D" just became a constant in my vocabulary.After a few months of learning the basics, I started building my portfolio, which looks pretty funny to me now. But at the time, it was a real sign of how committed I was. Eventually, someone reached out to me through Behance, offering my first freelance opportunity. And thatэs how my journey began, from mall clerk to 3D artist. It's been a tough road, full of burnout, doubts, and late nights... but also full of curiosity, growth, and hope. And I wouldn't trade it for anything.The Stitch ProjectI've loved Stitch since I was a kid. I used to watch the cartoons, play the video games, and he always felt like such a warm, funny, chill, and at the same time, strong character. So once I reached a certain level in 3D, I decided to recreate Stitch.Back then, my skills only allowed me to make him in a stylized cartoonish style, no fur, no complex detailing, no advanced texturing, I just didn't have the experience. Surprisingly, the result turned out pretty decent. Even now, I sometimes get comments that my old Stitch still looks quite cute. Though honestly, I wouldn't say that myself anymore. Two years have passed since I made that first Stitch, it was back in 2023. And in 2025, I decided it was time to challenge myself.At that point, I had just completed an intense grooming course. Grooming always intimidated me, it felt really complex. I avoided it on commercial projects, made a few failed attempts for my portfolio, and overall tried to steer clear of any tasks where grooming was required. But eventually, I found the strength to face it.I pushed myself to learn how to make great fur, and I did. I finally understood how the grooming system works, grasped the logic, the tools, and the workflow. And after finishing the course, I wanted to lock in all that knowledge by creating a full personal project from scratch.So my goal was to make a character from the ground up, where the final stage would be grooming. And without thinking too long, I chose Stitch.First, because I truly love the character. Second, I wanted to clearly see my own progress over the past two years. Third, I needed to put my new skills to the test and find out whether my training had really paid off.ModelingI had a few ideas for how to approach the base mesh for this project. First, to model everything completely from scratch, starting with a sphere. Second, to reuse my old Stitch model and upgrade it.But then an idea struck me: why not test how well AI could handle a base mesh? I gathered some references and tried generating a base mesh using AI, uploading Stitch visuals as a guide. As you can see from the screenshot, the result was far from usable. So I basically ended up doing everything from scratch anyway.So, I went back to basics: digging through ArtStation and Pinterest, collecting references. Since over the last two years, I had not only learned grooming but also completely changed my overall approach to character creation, it was important for me to make a more detailed model, even if much of it would be hidden under fur.The first Stitch was sculpted in Blender, with all the limitations that come with sculpting in it. But since then, I've leveled up significantly and switched to more advanced tools. So this second version of Stitch was born in ZBrush. By the time I started working on this Stitch, ZBrush had already become my second main workspace. I've used it to deliver tons of commercial projects, I work in it almost daily, and most of my portfolio was created using this tool. I found some great reference images showing Stitch's body structure. Among them were official movie references and a stunning high-poly model created by Juan Hernández, a version of Stitch without fur. That model became my primary reference for sculpting.Truth is, Stitch's base form is quite simple, so blocking out the shape didn't take too long. When blocking, I use Blender in combination with ZBrush:I work with primary forms in ZBrushThen check proportions in BlenderFix mistakes, tweak volumes, and refine the silhouetteSince Stitch's shape isn't overly complex, I broke him down into three main sculpting parts:The body: arms, legs, head, and earsThe nose, eyes, and mouth cavityWhile planning the sculpt, I already knew I'd be rigging Stitch, both body and facial rig. So I started sculpting with his mouth open.While studying various references, I noticed something interesting. Stitch from promotional posters, Stitch from the movie, and Stitch as recreated by different artists on ArtStation all look very different from one another. What surprised me the most was how different the promo version of Stitch is compared to the one in the actual movie. They are essentially two separate models:Different proportionsDifferent shapesDifferent texturesEven different fur and overall designThis presented a creative challenge, I had to develop my own take on Stitch's design. Sometimes I liked the way the teeth were done in one version, in another, the eye placement, in another, the fur shape, or the claw design on hands and feet.At first, considering that Stitch is completely covered in fur from head to toe, sculpting his underlying anatomy seemed pointless. I kept asking myself: "Why sculpt muscles and skin detail if everything will be hidden under fur anyway?"But eventually, I found a few solid answers for myself. First, having a defined muscle structure actually makes the fur grooming process easier. That's because fur often follows the flow of muscle lines, so having those muscles helps guide fur direction more accurately across the character's body.Second, it's great anatomy practice, and practice is never a waste. So, I found a solid anatomical reference of Stitch with clearly visible muscle groups and tried to recreate that structure as closely as possible in my own sculpt.In the end, I had to develop a full visual concept by combining elements from multiple versions of Stitch. Through careful reference work and constantly switching between Blender and ZBrush, I gradually, but intentionally, built up the body and overall look of our favorite fluffy alien.Topology & UVsThroughout the sculpting process, I spent quite a bit of time thinking about topology. I was looking for the most balanced solution between quality and production time. Normally, I do manual retopology for my characters, but this time, I knew it would take too much time, and honestly, I didn't have that luxury.So I decided to generate the topology using ZBrush's tools. I split the model into separate parts using Polygroups, assigning individual groups for the ears, the head, the torso, the arms, the legs, and each of Stitch's fingers.With the Polygroups in place, I used ZRemesher with Keep Groups enabled and smoothing on group borders. This gave me a clean and optimized mesh that was perfect for UV unwrapping.Of course, this kind of auto-retopology isn't a full substitute for manual work, but it saved me a huge amount of time, and the quality was still high enough for what I needed. However, there was one tricky issue. Although Stitch looks symmetrical at first glance, his ears are actually asymmetrical. The right ear has a scar on the top, while the left has a scar on the bottomBecause of that, I couldn't just mirror one side in ZBrush without losing those unique features. Here's what I ended up doing: I created a symmetrical model with the right ear, then another symmetrical model with the left ear. I brought both into Blender, detached the left ear from one model, and attached it to the body of the other one. This way, I got a clean, symmetrical base mesh with asymmetrical ears, preserving both topology and detail. And thanks to the clean polygroup-based layout, I was able to unwrap the UVs with nice, even seams and clean islands.When it came to UV mapping, I divided Stitch into two UDIM tiles:The first UDIM includes the head with ears, torso, arms, and legs.The second UDIM contains all the additional parts: teeth, tongue, gums, claws, and noseSince the nose is one of the most important details, I allocated the largest space to it, which helped me to better capture its intricate details.As for the eyes, I used procedural eyes, so there was no need to assign UV space or create a separate UDIM for texturing them. To achieve this, I used the Tiny Eye add-on by tinynocky for Blender, which allows full control over procedural eyes and their parameters.This approach gave me high-quality eyes with customizable elements tailored exactly to my needs. As a result of all these steps, Stitch ended up with a symmetrical, optimized mesh, asymmetrical ears, and the body split across two UDIMs, one for the main body and one for the additional parts.TexturingWhen planning Stitch's texturing, I understood that the main body texture would be fairly simple, with much of the visual detail enhanced by the fur. However, there were some areas that required much more attention than the rest of the body. The textures for Stitch can be roughly divided into several main parts:The base body, which includes the primary color of his fur, along with additional shading like a lighter tone on the frontand a darker tone on the back and napeThe nose and ears, these zones, demanded separate focusAt the initial texturing/blocking stage, the ears looked too cartoony, which didn’t fit the style I wanted. So, I decided to push them towards a more realistic look. This involved removing bright colors, adding more variation in the roughness map, introducing variation in the base color, and making the ears visually more natural, layered, and textured on the surface. By combining smart materials and masks, I achieved the effect of "living" ears, slightly dirty and looking as natural as possible.The nose was a separate story. It occupies a significant part of the face and thus draws a lot of attention. While studying references, I noticed that the shape and texture of the nose vary a lot between different artists. Initially, I made it dog-like, with some wear and tear around the nostrils and base.For a long time, I thought this version was acceptable. But during test renders, I realized the nose needed improvement. So I reworked its texturing, aiming to make it more detailed. I divided the nose texture into four main layers:Base detail: Baked from the high-poly model. Over this, I applied a smart skin material that added characteristic bumps.Lighter layer: Applied via a mask using the AO channel. This darkened the crevices and brightened the bumps, creating a multi-layered effect.Organic detail: In animal references, I noticed slight redness in the nose area. I created another AO-masked layer with reddish capillaries visible through the bumps, adding depth and realism.Softness: To make the nose visually softer, like in references, I added a fill layer with only height enabled, used a paper texture as grayscale, and applied a blurred mask. This created subtle dents and wrinkles that softened the look.All textures were created in 4K resolution to achieve maximum detail. After finishing the main texturing stage, I add an Ambient Occlusion map on the final texture layer, activating only the Color channel, setting the blend mode to Multiply, and reducing opacity to about 35%. This adds volume and greatly improves the overall perception of the model.That covers the texturing of Stitch’s body. I also created a separate texture for the fur. This was simpler, I disabled unnecessary layers like ears and eyelids, and left only the base ones corresponding to the body’s color tones.During grooming, I also created textures for the fur's clamps and roughness. In Substance 3D Painter, I additionally painted masks for better fur detail.FurAnd finally, I moved on to the part that was most important to me, the very reason I started this project in the first place. Fur. This entire process was essentially a test of my fur grooming skills. After overcoming self-doubt, I trusted the process and relied on everything I had learned so far. Before diving into the grooming itself, I made sure to gather strong references. I searched for the highest quality and most inspiring examples I could find and analyzed them thoroughly. My goal was to clearly understand the direction of fur growth, its density and volume, the intensity of roughness, and the strength of clumping in different areas of Stitch's body.To create the fur, I used Blender and its Hair Particle System. The overall approach is similar to sculpting a high-detail model: work from broad strokes to finer details. So, the first step was blocking out the main flow and placement of the hair strands.At this point, I ran into a challenge: symmetry. Since the model was purposefully asymmetrical, the fur couldn't be mirrored cleanly. To solve this, I created a base fur blocking using Hair Guides with just two segments. After that, I split the fur into separate parts. I duplicated the main Particle System and created individual hair systems for each area where needed.In total, I broke Stitch's body into key sections: head, left ear, right ear, front torso, back torso, arms, hands, upper and lower legs, toes, and additional detailing layers. The final fur setup included 25 separate particle systems.To control fur growth, I used Weight Paint to fine-tune the influence on each body part individually. This separation gave me much more precision and allowed full control over every parameter of the fur on a per-section basis.The most challenging aspect of working with fur is staying patient and focused. Detail is absolutely critical because the overall picture is built entirely from tiny, subtle elements. Once the base layer was complete, I moved on to refining the fur based on my references.The most complex areas turned out to be the front of the torso and the face. When working on the torso, my goal was to create a smooth gradient, from thick, clumped fur on the chest to shorter, softer fur on the stomach.Step by step, I adjusted the transitions, directions, clumps, and volumes to achieve that look. Additionally, I used the fur itself to subtly enhance Stitch's silhouette, making his overall shape feel sharper, more expressive, and visually engaging.During fur development, I used texture maps to control the intensity of the Roughness and Clump parameters. This gave me a high degree of flexibility, textures drove these attributes across the entire model. In areas where stronger clumping or roughness was needed, I used brighter values; in zones requiring a softer look, darker values. This approach allowed for fine-tuned micro-level control of the fur shader and helped achieve a highly realistic appearance in renders.The face required special attention: the fur had to be neat, evenly distributed, and still visually appealing. The biggest challenge here was working around the eye area. Even with properly adjusted Weight Paint, interpolation sometimes caused strands to creep into the eyes.I spent a lot of time cleaning up this region to get an optimal result. I also had to revisit certain patches that looked bald, even though interpolation and weight painting were set correctly, because the fur didn't render properly there. These areas needed manual fixing.As part of the detailing stage, I also increased the number of segments in the Hair Guides.While the blocking phase only used two segments, I went up to three, and in some cases even five, for more complex regions. This gave me much more control over fur shape and flow.The tiniest details really matter, so I added extra fur layers with thinner, more chaotic strands extending slightly beyond the main silhouette. These micro-layers significantly improved the texture depth and boosted the overall realism.Aside from the grooming itself, I paid special attention to the fur material setup, as the shader plays a critical role in the final visual quality of the render. It's not enough to simply plug a color texture into a Principled BSDF node and call it done.I built a more complex shader, giving me precise control over various attributes. For example, I implemented subtle color variation across individual strands, along with darkening near the roots and a gradual brightening toward the tips. This helped add visual depth and made the fur look significantly more natural and lifelike.Working on the fur took up nearly half of the total time I spent on the entire model. And I'm genuinely happy with the result, this stage confirmed that the training I've gone through was solid and that I’m heading in the right direction with my artistic development.Rigging, Posing & SceneOnce I finished working on the fur, I rendered several 4K test shots from different angles to make sure every detail looked the way I intended. When I was fully satisfied with the results, it was time to move on to rigging.I divided the rigging process into three main parts:Body rig, for posing and positioning the characterFacial rig, for expressions and emotionsEar rig, for dynamic ear controlRigging isn't something I consider my strongest skill, but as a 3D generalist, I had to dive into many technical aspects of it. For the ears, I set up a relatively simple system with several bones connected using inverse kinematics. This gave me flexible and intuitive control during posing and allowed for the addition of dynamic movement in animation.For facial rigging, I used the FaceIt add-on, which generates a complete facial control system for mouth, eyes, and tongue. It sped up the process significantly and gave me more precision. For the body, I used the ActorCore Rig by NVIDIA, then converted it to Rigify, which gave me a familiar interface and flexible control over poses.Posing is one of my favorite stages, it's when the character really comes to life. As usual, it started with gathering references. Honestly, it was hard to pick the final poses, Stitch is so expressive and full of personality that I wanted to try hundreds of them. But I focused on those that best conveyed the spirit and mood of the character. Some poses I reworked to fit my style rather than copying directly. For example, in the pose where Stitch licks his nose, I added drool and a bit of "green slime" for comedic effect. To capture motion, I tilted his head back and made the ears fly upward, creating a vivid, emotional snapshot.Just like in sculpting or grooming, minor details make a big difference in posing. Examples include: a slight asymmetry in the facial expression, a raised corner of the mouth, one eye squinting a little more than the other, and ears set at slightly different angles.These are subtle things that might not be noticed immediately, but they’re the key to making the character feel alive and believable.For each pose, I created a separate scene and collection in Blender, including the character, specific lighting setup, and a simple background or environment. This made it easy to return to any scene later, to adjust lighting, reposition the character, or tweak the background.In one of the renders, which I used as the cover image, Stitch is holding a little frog.I want to clearly note that the 3D model of the frog is not mine, full credit goes to the original author of the asset.At first, I wanted to build a full environment around Stitch, to create a scene that would feel like a frame from a film. But after carefully evaluating my skills and priorities, I decided that a weak environment would only detract from the strength of the character. So I opted for a simple, neutral backdrop, designed to keep all the focus on Stitch himself.Rendering, Lighting & Post-ProcessingWhen the character is complete, posed expressively, and integrated into the scene, there's one final step: lighting. Lighting isn't just a technical element of the scene — it’s a full-fledged stage of the 3D pipeline. It doesn't just illuminate; it paints. Proper lighting can highlight the personality of the character, emphasize forms, and create atmosphere.For all my renders, I rely on the classic three-point lighting setup: Key Light, Fill Light, and Rim Light.While this setup is well-known, it remains highly effective. When done thoughtfully, with the right intensity, direction, and color temperature, it creates a strong light-shadow composition that brings the model to life. In addition to the three main lights, I also use an HDRI map, but with very low intensity, around 0.3, just enough to subtly enrich the ambient light without overpowering the scene.Once everything is set, it's time to hit Render and wait for the result. Due to hardware limitations, I wasn’t able to produce full animated shots with fur. Rendering a single 4K image with fur took over an hour, so I limited myself to a 360° turnaround and several static renders.I don't spend too much time on post-processing, just basic refinements in Photoshop. Slight enhancement of the composition, gentle shadow adjustments, color balance tweaks, and adding a logo. Everything is done subtly, nothing overprocessed. The goal is simply to support and enhance what’s already there.Final ThoughtsThis project has been an incredible experience. Although it was my second time creating Stitch, this time the process felt completely different at every stage. And honestly, it wasn't easy.But that was exactly the point: to challenge myself. To reimagine something familiar, to try things I'd never done before, and to walk the full journey from start to finish. The fur, the heart of this project, was especially meaningful to me. It’s what started it all. I poured a lot into this model: time, effort, emotion, and even doubts. But at the same time, I brought all my knowledge, skills, and experience into it.This work became a mirror of my progress from 2023 to 2025. I can clearly see how far I've come, and that gives me the motivation to keep going. Every hour of learning and practice paid off, the results speak for themselves. This model was created for my portfolio. I don't plan to use it commercially, unless, of course, a studio actually wants to license it for a new filmIt's been a long road: challenging, sometimes exhausting, but above all inspiring and exciting. I know there's still a lot to learn. Many things to study, improve, and polish to perfection. But I'm already on that path, and I'm not stopping.Oleh Yakushev, 3D Character ArtistInterview conducted by Gloria Levine #fur #grooming #techniques #realistic #stitch
    Fur Grooming Techniques For Realistic Stitch In Blender
    80.lv
    IntroductionHi everyone! My name is Oleh Yakushev, and I'm a 3D Artist from Ukraine. My journey into 3D began just three years ago, when I was working as a mobile phone salesperson at a shopping mall. In 2022, during one slow day at work, I noticed a colleague learning Python. We started talking about life goals. I told him I wanted to switch careers, to do something creative, but programming wasn't really my thing.He asked me a simple question: "Well, what do you actually enjoy doing?"I said, "Video games. I love video games. But I don't have time to learn how to make them, I've got a job, a family, and a kid."Then he hit me with something that really shifted my whole perspective."Oleh, do you play games on your PlayStation?"I said, "Of course."He replied, "Then why not take the time you spend playing and use it to learn how to make games?"That moment flipped a switch in my mind. I realized that I did have time, it was just a matter of how I used it. If I really wanted to learn, I could find a way. At the time, I didn't even own a computer. But where there's a will, there's a way: I borrowed my sister's laptop for a month and started following beginner 3D tutorials on YouTube. Every night after work, once my family went to sleep, I'd sit in the kitchen and study. I stayed up until 2 or 3 AM, learning Blender basics. Then I'd sleep for a few hours before waking up at 6 AM to go back to work. That's how I spent my first few months in 3D, studying every single night.3D completely took over my life. During lunch breaks, I watched 3D videos, on the bus, I scrolled through 3D TikToks, at home, I took 3D courses, and the word "3D" just became a constant in my vocabulary.After a few months of learning the basics, I started building my portfolio, which looks pretty funny to me now. But at the time, it was a real sign of how committed I was. Eventually, someone reached out to me through Behance, offering my first freelance opportunity. And thatэs how my journey began, from mall clerk to 3D artist. It's been a tough road, full of burnout, doubts, and late nights... but also full of curiosity, growth, and hope. And I wouldn't trade it for anything.The Stitch ProjectI've loved Stitch since I was a kid. I used to watch the cartoons, play the video games, and he always felt like such a warm, funny, chill, and at the same time, strong character. So once I reached a certain level in 3D, I decided to recreate Stitch.Back then, my skills only allowed me to make him in a stylized cartoonish style, no fur, no complex detailing, no advanced texturing, I just didn't have the experience. Surprisingly, the result turned out pretty decent. Even now, I sometimes get comments that my old Stitch still looks quite cute. Though honestly, I wouldn't say that myself anymore. Two years have passed since I made that first Stitch, it was back in 2023. And in 2025, I decided it was time to challenge myself.At that point, I had just completed an intense grooming course. Grooming always intimidated me, it felt really complex. I avoided it on commercial projects, made a few failed attempts for my portfolio, and overall tried to steer clear of any tasks where grooming was required. But eventually, I found the strength to face it.I pushed myself to learn how to make great fur, and I did. I finally understood how the grooming system works, grasped the logic, the tools, and the workflow. And after finishing the course, I wanted to lock in all that knowledge by creating a full personal project from scratch.So my goal was to make a character from the ground up, where the final stage would be grooming. And without thinking too long, I chose Stitch.First, because I truly love the character. Second, I wanted to clearly see my own progress over the past two years. Third, I needed to put my new skills to the test and find out whether my training had really paid off.ModelingI had a few ideas for how to approach the base mesh for this project. First, to model everything completely from scratch, starting with a sphere. Second, to reuse my old Stitch model and upgrade it.But then an idea struck me: why not test how well AI could handle a base mesh? I gathered some references and tried generating a base mesh using AI, uploading Stitch visuals as a guide. As you can see from the screenshot, the result was far from usable. So I basically ended up doing everything from scratch anyway.So, I went back to basics: digging through ArtStation and Pinterest, collecting references. Since over the last two years, I had not only learned grooming but also completely changed my overall approach to character creation, it was important for me to make a more detailed model, even if much of it would be hidden under fur.The first Stitch was sculpted in Blender, with all the limitations that come with sculpting in it. But since then, I've leveled up significantly and switched to more advanced tools. So this second version of Stitch was born in ZBrush. By the time I started working on this Stitch, ZBrush had already become my second main workspace. I've used it to deliver tons of commercial projects, I work in it almost daily, and most of my portfolio was created using this tool. I found some great reference images showing Stitch's body structure. Among them were official movie references and a stunning high-poly model created by Juan Hernández, a version of Stitch without fur. That model became my primary reference for sculpting.Truth is, Stitch's base form is quite simple, so blocking out the shape didn't take too long. When blocking, I use Blender in combination with ZBrush:I work with primary forms in ZBrushThen check proportions in BlenderFix mistakes, tweak volumes, and refine the silhouetteSince Stitch's shape isn't overly complex, I broke him down into three main sculpting parts:The body: arms, legs, head, and earsThe nose, eyes, and mouth cavityWhile planning the sculpt, I already knew I'd be rigging Stitch, both body and facial rig. So I started sculpting with his mouth open (to later close it and have more flexibility when it comes to rigging and deformation).While studying various references, I noticed something interesting. Stitch from promotional posters, Stitch from the movie, and Stitch as recreated by different artists on ArtStation all look very different from one another. What surprised me the most was how different the promo version of Stitch is compared to the one in the actual movie. They are essentially two separate models:Different proportionsDifferent shapesDifferent texturesEven different fur and overall designThis presented a creative challenge, I had to develop my own take on Stitch's design. Sometimes I liked the way the teeth were done in one version, in another, the eye placement, in another, the fur shape, or the claw design on hands and feet.At first, considering that Stitch is completely covered in fur from head to toe, sculpting his underlying anatomy seemed pointless. I kept asking myself: "Why sculpt muscles and skin detail if everything will be hidden under fur anyway?"But eventually, I found a few solid answers for myself. First, having a defined muscle structure actually makes the fur grooming process easier. That's because fur often follows the flow of muscle lines, so having those muscles helps guide fur direction more accurately across the character's body.Second, it's great anatomy practice, and practice is never a waste. So, I found a solid anatomical reference of Stitch with clearly visible muscle groups and tried to recreate that structure as closely as possible in my own sculpt.In the end, I had to develop a full visual concept by combining elements from multiple versions of Stitch. Through careful reference work and constantly switching between Blender and ZBrush, I gradually, but intentionally, built up the body and overall look of our favorite fluffy alien.Topology & UVsThroughout the sculpting process, I spent quite a bit of time thinking about topology. I was looking for the most balanced solution between quality and production time. Normally, I do manual retopology for my characters, but this time, I knew it would take too much time, and honestly, I didn't have that luxury.So I decided to generate the topology using ZBrush's tools. I split the model into separate parts using Polygroups, assigning individual groups for the ears, the head, the torso, the arms, the legs, and each of Stitch's fingers.With the Polygroups in place, I used ZRemesher with Keep Groups enabled and smoothing on group borders. This gave me a clean and optimized mesh that was perfect for UV unwrapping.Of course, this kind of auto-retopology isn't a full substitute for manual work, but it saved me a huge amount of time, and the quality was still high enough for what I needed. However, there was one tricky issue. Although Stitch looks symmetrical at first glance, his ears are actually asymmetrical. The right ear has a scar on the top, while the left has a scar on the bottomBecause of that, I couldn't just mirror one side in ZBrush without losing those unique features. Here's what I ended up doing: I created a symmetrical model with the right ear, then another symmetrical model with the left ear. I brought both into Blender, detached the left ear from one model, and attached it to the body of the other one. This way, I got a clean, symmetrical base mesh with asymmetrical ears, preserving both topology and detail. And thanks to the clean polygroup-based layout, I was able to unwrap the UVs with nice, even seams and clean islands.When it came to UV mapping, I divided Stitch into two UDIM tiles:The first UDIM includes the head with ears, torso, arms, and legs.The second UDIM contains all the additional parts: teeth, tongue, gums, claws, and nose (For the claws, I used overlapping UVs to preserve texel density for the other parts)Since the nose is one of the most important details, I allocated the largest space to it, which helped me to better capture its intricate details.As for the eyes, I used procedural eyes, so there was no need to assign UV space or create a separate UDIM for texturing them. To achieve this, I used the Tiny Eye add-on by tinynocky for Blender, which allows full control over procedural eyes and their parameters.This approach gave me high-quality eyes with customizable elements tailored exactly to my needs. As a result of all these steps, Stitch ended up with a symmetrical, optimized mesh, asymmetrical ears, and the body split across two UDIMs, one for the main body and one for the additional parts.TexturingWhen planning Stitch's texturing, I understood that the main body texture would be fairly simple, with much of the visual detail enhanced by the fur. However, there were some areas that required much more attention than the rest of the body. The textures for Stitch can be roughly divided into several main parts:The base body, which includes the primary color of his fur, along with additional shading like a lighter tone on the front (belly) and a darker tone on the back and napeThe nose and ears, these zones, demanded separate focusAt the initial texturing/blocking stage, the ears looked too cartoony, which didn’t fit the style I wanted. So, I decided to push them towards a more realistic look. This involved removing bright colors, adding more variation in the roughness map, introducing variation in the base color, and making the ears visually more natural, layered, and textured on the surface. By combining smart materials and masks, I achieved the effect of "living" ears, slightly dirty and looking as natural as possible.The nose was a separate story. It occupies a significant part of the face and thus draws a lot of attention. While studying references, I noticed that the shape and texture of the nose vary a lot between different artists. Initially, I made it dog-like, with some wear and tear around the nostrils and base.For a long time, I thought this version was acceptable. But during test renders, I realized the nose needed improvement. So I reworked its texturing, aiming to make it more detailed. I divided the nose texture into four main layers:Base detail: Baked from the high-poly model. Over this, I applied a smart skin material that added characteristic bumps.Lighter layer: Applied via a mask using the AO channel. This darkened the crevices and brightened the bumps, creating a multi-layered effect.Organic detail (capillaries): In animal references, I noticed slight redness in the nose area. I created another AO-masked layer with reddish capillaries visible through the bumps, adding depth and realism.Softness: To make the nose visually softer, like in references, I added a fill layer with only height enabled, used a paper texture as grayscale, and applied a blurred mask. This created subtle dents and wrinkles that softened the look.All textures were created in 4K resolution to achieve maximum detail. After finishing the main texturing stage, I add an Ambient Occlusion map on the final texture layer, activating only the Color channel, setting the blend mode to Multiply, and reducing opacity to about 35%. This adds volume and greatly improves the overall perception of the model.That covers the texturing of Stitch’s body. I also created a separate texture for the fur. This was simpler, I disabled unnecessary layers like ears and eyelids, and left only the base ones corresponding to the body’s color tones.During grooming (which I'll cover in detail later), I also created textures for the fur's clamps and roughness. In Substance 3D Painter, I additionally painted masks for better fur detail.FurAnd finally, I moved on to the part that was most important to me, the very reason I started this project in the first place. Fur. This entire process was essentially a test of my fur grooming skills. After overcoming self-doubt, I trusted the process and relied on everything I had learned so far. Before diving into the grooming itself, I made sure to gather strong references. I searched for the highest quality and most inspiring examples I could find and analyzed them thoroughly. My goal was to clearly understand the direction of fur growth, its density and volume, the intensity of roughness, and the strength of clumping in different areas of Stitch's body.To create the fur, I used Blender and its Hair Particle System. The overall approach is similar to sculpting a high-detail model: work from broad strokes to finer details. So, the first step was blocking out the main flow and placement of the hair strands.At this point, I ran into a challenge: symmetry. Since the model was purposefully asymmetrical (because of the ears and skin folds), the fur couldn't be mirrored cleanly. To solve this, I created a base fur blocking using Hair Guides with just two segments. After that, I split the fur into separate parts. I duplicated the main Particle System and created individual hair systems for each area where needed.In total, I broke Stitch's body into key sections: head, left ear, right ear, front torso, back torso, arms, hands, upper and lower legs, toes, and additional detailing layers. The final fur setup included 25 separate particle systems.To control fur growth, I used Weight Paint to fine-tune the influence on each body part individually. This separation gave me much more precision and allowed full control over every parameter of the fur on a per-section basis.The most challenging aspect of working with fur is staying patient and focused. Detail is absolutely critical because the overall picture is built entirely from tiny, subtle elements. Once the base layer was complete, I moved on to refining the fur based on my references.The most complex areas turned out to be the front of the torso and the face. When working on the torso, my goal was to create a smooth gradient, from thick, clumped fur on the chest to shorter, softer fur on the stomach.Step by step, I adjusted the transitions, directions, clumps, and volumes to achieve that look. Additionally, I used the fur itself to subtly enhance Stitch's silhouette, making his overall shape feel sharper, more expressive, and visually engaging.During fur development, I used texture maps to control the intensity of the Roughness and Clump parameters. This gave me a high degree of flexibility, textures drove these attributes across the entire model. In areas where stronger clumping or roughness was needed, I used brighter values; in zones requiring a softer look, darker values. This approach allowed for fine-tuned micro-level control of the fur shader and helped achieve a highly realistic appearance in renders.The face required special attention: the fur had to be neat, evenly distributed, and still visually appealing. The biggest challenge here was working around the eye area. Even with properly adjusted Weight Paint, interpolation sometimes caused strands to creep into the eyes.I spent a lot of time cleaning up this region to get an optimal result. I also had to revisit certain patches that looked bald, even though interpolation and weight painting were set correctly, because the fur didn't render properly there. These areas needed manual fixing.As part of the detailing stage, I also increased the number of segments in the Hair Guides.While the blocking phase only used two segments, I went up to three, and in some cases even five, for more complex regions. This gave me much more control over fur shape and flow.The tiniest details really matter, so I added extra fur layers with thinner, more chaotic strands extending slightly beyond the main silhouette. These micro-layers significantly improved the texture depth and boosted the overall realism.Aside from the grooming itself, I paid special attention to the fur material setup, as the shader plays a critical role in the final visual quality of the render. It's not enough to simply plug a color texture into a Principled BSDF node and call it done.I built a more complex shader, giving me precise control over various attributes. For example, I implemented subtle color variation across individual strands, along with darkening near the roots and a gradual brightening toward the tips. This helped add visual depth and made the fur look significantly more natural and lifelike.Working on the fur took up nearly half of the total time I spent on the entire model. And I'm genuinely happy with the result, this stage confirmed that the training I've gone through was solid and that I’m heading in the right direction with my artistic development.Rigging, Posing & SceneOnce I finished working on the fur, I rendered several 4K test shots from different angles to make sure every detail looked the way I intended. When I was fully satisfied with the results, it was time to move on to rigging.I divided the rigging process into three main parts:Body rig, for posing and positioning the characterFacial rig, for expressions and emotionsEar rig, for dynamic ear controlRigging isn't something I consider my strongest skill, but as a 3D generalist, I had to dive into many technical aspects of it. For the ears, I set up a relatively simple system with several bones connected using inverse kinematics (IK). This gave me flexible and intuitive control during posing and allowed for the addition of dynamic movement in animation.For facial rigging, I used the FaceIt add-on, which generates a complete facial control system for mouth, eyes, and tongue. It sped up the process significantly and gave me more precision. For the body, I used the ActorCore Rig by NVIDIA, then converted it to Rigify, which gave me a familiar interface and flexible control over poses.Posing is one of my favorite stages, it's when the character really comes to life. As usual, it started with gathering references. Honestly, it was hard to pick the final poses, Stitch is so expressive and full of personality that I wanted to try hundreds of them. But I focused on those that best conveyed the spirit and mood of the character. Some poses I reworked to fit my style rather than copying directly. For example, in the pose where Stitch licks his nose, I added drool and a bit of "green slime" for comedic effect. To capture motion, I tilted his head back and made the ears fly upward, creating a vivid, emotional snapshot.Just like in sculpting or grooming, minor details make a big difference in posing. Examples include: a slight asymmetry in the facial expression, a raised corner of the mouth, one eye squinting a little more than the other, and ears set at slightly different angles.These are subtle things that might not be noticed immediately, but they’re the key to making the character feel alive and believable.For each pose, I created a separate scene and collection in Blender, including the character, specific lighting setup, and a simple background or environment. This made it easy to return to any scene later, to adjust lighting, reposition the character, or tweak the background.In one of the renders, which I used as the cover image, Stitch is holding a little frog.I want to clearly note that the 3D model of the frog is not mine, full credit goes to the original author of the asset.At first, I wanted to build a full environment around Stitch, to create a scene that would feel like a frame from a film. But after carefully evaluating my skills and priorities, I decided that a weak environment would only detract from the strength of the character. So I opted for a simple, neutral backdrop, designed to keep all the focus on Stitch himself.Rendering, Lighting & Post-ProcessingWhen the character is complete, posed expressively, and integrated into the scene, there's one final step: lighting. Lighting isn't just a technical element of the scene — it’s a full-fledged stage of the 3D pipeline. It doesn't just illuminate; it paints. Proper lighting can highlight the personality of the character, emphasize forms, and create atmosphere.For all my renders, I rely on the classic three-point lighting setup: Key Light, Fill Light, and Rim Light.While this setup is well-known, it remains highly effective. When done thoughtfully, with the right intensity, direction, and color temperature, it creates a strong light-shadow composition that brings the model to life. In addition to the three main lights, I also use an HDRI map, but with very low intensity, around 0.3, just enough to subtly enrich the ambient light without overpowering the scene.Once everything is set, it's time to hit Render and wait for the result. Due to hardware limitations, I wasn’t able to produce full animated shots with fur. Rendering a single 4K image with fur took over an hour, so I limited myself to a 360° turnaround and several static renders.I don't spend too much time on post-processing, just basic refinements in Photoshop. Slight enhancement of the composition, gentle shadow adjustments, color balance tweaks, and adding a logo. Everything is done subtly, nothing overprocessed. The goal is simply to support and enhance what’s already there.Final ThoughtsThis project has been an incredible experience. Although it was my second time creating Stitch (the first was back in 2023), this time the process felt completely different at every stage. And honestly, it wasn't easy.But that was exactly the point: to challenge myself. To reimagine something familiar, to try things I'd never done before, and to walk the full journey from start to finish. The fur, the heart of this project, was especially meaningful to me. It’s what started it all. I poured a lot into this model: time, effort, emotion, and even doubts. But at the same time, I brought all my knowledge, skills, and experience into it.This work became a mirror of my progress from 2023 to 2025. I can clearly see how far I've come, and that gives me the motivation to keep going. Every hour of learning and practice paid off, the results speak for themselves. This model was created for my portfolio. I don't plan to use it commercially, unless, of course, a studio actually wants to license it for a new film (in that case, I'd be more than happy!)It's been a long road: challenging, sometimes exhausting, but above all inspiring and exciting. I know there's still a lot to learn. Many things to study, improve, and polish to perfection. But I'm already on that path, and I'm not stopping.Oleh Yakushev, 3D Character ArtistInterview conducted by Gloria Levine
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  • World of Warcraft's Midnight Controvery Explained

    Ideally, now would be a great time to be a World of Warcraft fan, considering the recent Gamescom reveal of the Midnight expansion scheduled for next year. There are many noteworthy Midnight features to keep an eye on, even outside of player housing, if that is not something World of Warcraft fans are excited about. This includes the new Prey system that allows players to defeat powerful mobs to get cosmetic rewards, the addition of the Haranir as a playable race, a third Demon Hunter spec, extra talent points and Apex Talents, and more. However, the reveal has landed poorly among some fans for a plethora of reasons.
    #world #warcraft039s #midnight #controvery #explained
    World of Warcraft's Midnight Controvery Explained
    Ideally, now would be a great time to be a World of Warcraft fan, considering the recent Gamescom reveal of the Midnight expansion scheduled for next year. There are many noteworthy Midnight features to keep an eye on, even outside of player housing, if that is not something World of Warcraft fans are excited about. This includes the new Prey system that allows players to defeat powerful mobs to get cosmetic rewards, the addition of the Haranir as a playable race, a third Demon Hunter spec, extra talent points and Apex Talents, and more. However, the reveal has landed poorly among some fans for a plethora of reasons. #world #warcraft039s #midnight #controvery #explained
    World of Warcraft's Midnight Controvery Explained
    gamerant.com
    Ideally, now would be a great time to be a World of Warcraft fan, considering the recent Gamescom reveal of the Midnight expansion scheduled for next year. There are many noteworthy Midnight features to keep an eye on, even outside of player housing, if that is not something World of Warcraft fans are excited about. This includes the new Prey system that allows players to defeat powerful mobs to get cosmetic rewards, the addition of the Haranir as a playable race, a third Demon Hunter spec, extra talent points and Apex Talents, and more. However, the reveal has landed poorly among some fans for a plethora of reasons.
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  • في مرة، صديقي كان يحكيلي كيفاش سرقوا له هويته وكيفاش خدموا باسمه، والله ضحكت في الأول لكن بعدين عرفت باللي الموضوع خطير بزاف.

    اليوم حبيت نتكلم على موضوع مهم بزاف، "1 billion reasons to protect your identity online". فالمقال يتحدث على كيفاش تسرب البيانات من الشركات يفتح الباب لسرقة الهوية، وهذا ماشي الشيء الوحيد اللي لازم نخافوا منه. كاين طرق جديدة تخلينا عرضة للسرقة، وهذا يخلينا نفكروا في كيفية حماية معلوماتنا الشخصية.

    أنا شخصياً، بعد ما سمعت قصة صديقي، بدلت بعض العادات متاعي في الإنترنت ووليت نستعمل كلمات سر صعبة ونراقب حساباتي أكثر.

    المقال يعطيك نصائح مهمة كيف تحمي نفسك من هاد المشاكل. تخيلوا شحال من واحد ممكن يكون ضحية!

    https://www.welivesecurity.com/en/cybersecurity/1-billion-reasons-protect-identity-online/
    #حماية_الهوية #Cybersecurity #سرقة_الهوية #Sécurité_
    في مرة، صديقي كان يحكيلي كيفاش سرقوا له هويته وكيفاش خدموا باسمه، والله ضحكت في الأول لكن بعدين عرفت باللي الموضوع خطير بزاف. 😳 اليوم حبيت نتكلم على موضوع مهم بزاف، "1 billion reasons to protect your identity online". فالمقال يتحدث على كيفاش تسرب البيانات من الشركات يفتح الباب لسرقة الهوية، وهذا ماشي الشيء الوحيد اللي لازم نخافوا منه. كاين طرق جديدة تخلينا عرضة للسرقة، وهذا يخلينا نفكروا في كيفية حماية معلوماتنا الشخصية. أنا شخصياً، بعد ما سمعت قصة صديقي، بدلت بعض العادات متاعي في الإنترنت ووليت نستعمل كلمات سر صعبة ونراقب حساباتي أكثر. المقال يعطيك نصائح مهمة كيف تحمي نفسك من هاد المشاكل. تخيلوا شحال من واحد ممكن يكون ضحية! https://www.welivesecurity.com/en/cybersecurity/1-billion-reasons-protect-identity-online/ #حماية_الهوية #Cybersecurity #سرقة_الهوية #Sécurité_
    www.welivesecurity.com
    Corporate data breaches are a gateway to identity fraud, but they’re not the only one. Here’s a lowdown on how your personal data could be stolen – and how to make sure it isn’t.
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  • Hot Topics at Hot Chips: Inference, Networking, AI Innovation at Every Scale — All Built on NVIDIA

    AI reasoning, inference and networking will be top of mind for attendees of next week’s Hot Chips conference.
    A key forum for processor and system architects from industry and academia, Hot Chips — running Aug. 24-26 at Stanford University — showcases the latest innovations poised to advance AI factories and drive revenue for the trillion-dollar data center computing market.
    At the conference, NVIDIA will join industry leaders including Google and Microsoft in a “tutorial” session — taking place on Sunday, Aug. 24 — that discusses designing rack-scale architecture for data centers.
    In addition, NVIDIA experts will present at four sessions and one tutorial detailing how:

    NVIDIA networking, including the NVIDIA ConnectX-8 SuperNIC, delivers AI reasoning at rack- and data-center scale.Neural rendering advancements and massive leaps in inference — powered by the NVIDIA Blackwell architecture, including the NVIDIA GeForce RTX 5090 GPU — provide next-level graphics and simulation capabilities.Co-packaged opticsswitches with integrated silicon photonics — built with light-speed fiber rather than copper wiring to send information quicker and using less power — enable efficient, high-performance, gigawatt-scale AI factories. The talk will also highlight NVIDIA Spectrum-XGS Ethernet, a new scale-across technology for unifying distributed data centers into AI super-factories.The NVIDIA GB10 Superchip serves as the engine within the NVIDIA DGX Spark desktop supercomputer.It’s all part of how NVIDIA’s latest technologies are accelerating inference to drive AI innovation everywhere, at every scale.
    NVIDIA Networking Fosters AI Innovation at Scale
    AI reasoning — when artificial intelligence systems can analyze and solve complex problems through multiple AI inference passes — requires rack-scale performance to deliver optimal user experiences efficiently.
    In data centers powering today’s AI workloads, networking acts as the central nervous system, connecting all the components — servers, storage devices and other hardware — into a single, cohesive, powerful computing unit.
    NVIDIA ConnectX-8 SuperNIC
    Burstein’s Hot Chips session will dive into how NVIDIA networking technologies — particularly NVIDIA ConnectX-8 SuperNICs — enable high-speed, low-latency, multi-GPU communication to deliver market-leading AI reasoning performance at scale.
    As part of the NVIDIA networking platform, NVIDIA NVLink, NVLink Switch and NVLink Fusion deliver scale-up connectivity — linking GPUs and compute elements within and across servers for ultra low-latency, high-bandwidth data exchange.
    NVIDIA Spectrum-X Ethernet provides the scale-out fabric to connect entire clusters, rapidly streaming massive datasets into AI models and orchestrating GPU-to-GPU communication across the data center. Spectrum-XGS Ethernet scale-across technology extends the extreme performance and scale of Spectrum-X Ethernet to interconnect multiple, distributed data centers to form AI super-factories capable of giga-scale intelligence.
    Connecting distributed AI data centers with NVIDIA Spectrum-XGS Ethernet.
    At the heart of Spectrum-X Ethernet, CPO switches push the limits of performance and efficiency for AI infrastructure at scale, and will be covered in detail by Shainer in his talk.
    NVIDIA GB200 NVL72 — an exascale computer in a single rack — features 36 NVIDIA GB200 Superchips, each containing two NVIDIA B200 GPUs and an NVIDIA Grace CPU, interconnected by the largest NVLink domain ever offered, with NVLink Switch providing 130 terabytes per second of low-latency GPU communications for AI and high-performance computing workloads.
    An NVIDIA rack-scale system.
    Built with the NVIDIA Blackwell architecture, GB200 NVL72 systems deliver massive leaps in reasoning inference performance.
    NVIDIA Blackwell and CUDA Bring AI to Millions of Developers
    The NVIDIA GeForce RTX 5090 GPU — also powered by Blackwell and to be covered in Blackstein’s talk — doubles performance in today’s games with NVIDIA DLSS 4 technology.
    NVIDIA GeForce RTX 5090 GPU
    It can also add neural rendering features for games to deliver up to 10x performance, 10x footprint amplification and a 10x reduction in design cycles,  helping enhance realism in computer graphics and simulation. This offers smooth, responsive visual experiences at low energy consumption and improves the lifelike simulation of characters and effects.
    NVIDIA CUDA, the world’s most widely available computing infrastructure, lets users deploy and run AI models using NVIDIA Blackwell anywhere.
    Hundreds of millions of GPUs run CUDA across the globe, from NVIDIA GB200 NVL72 rack-scale systems to GeForce RTX– and NVIDIA RTX PRO-powered PCs and workstations, with NVIDIA DGX Spark powered by NVIDIA GB10 — discussed in Skende’s session — coming soon.
    From Algorithms to AI Supercomputers — Optimized for LLMs
    NVIDIA DGX Spark
    Delivering powerful performance and capabilities in a compact package, DGX Spark lets developers, researchers, data scientists and students push the boundaries of generative AI right at their desktops, and accelerate workloads across industries.
    As part of the NVIDIA Blackwell platform, DGX Spark brings support for NVFP4, a low-precision numerical format to enable efficient agentic AI inference, particularly of large language models. Learn more about NVFP4 in this NVIDIA Technical Blog.
    Open-Source Collaborations Propel Inference Innovation
    NVIDIA accelerates several open-source libraries and frameworks to accelerate and optimize AI workloads for LLMs and distributed inference. These include NVIDIA TensorRT-LLM, NVIDIA Dynamo, TileIR, Cutlass, the NVIDIA Collective Communication Library and NIX — which are integrated into millions of workflows.
    Allowing developers to build with their framework of choice, NVIDIA has collaborated with top open framework providers to offer model optimizations for FlashInfer, PyTorch, SGLang, vLLM and others.
    Plus, NVIDIA NIM microservices are available for popular open models like OpenAI’s gpt-oss and Llama 4,  making it easy for developers to operate managed application programming interfaces with the flexibility and security of self-hosting models on their preferred infrastructure.
    Learn more about the latest advancements in inference and accelerated computing by joining NVIDIA at Hot Chips.
     
    #hot #topics #chips #inference #networking
    Hot Topics at Hot Chips: Inference, Networking, AI Innovation at Every Scale — All Built on NVIDIA
    AI reasoning, inference and networking will be top of mind for attendees of next week’s Hot Chips conference. A key forum for processor and system architects from industry and academia, Hot Chips — running Aug. 24-26 at Stanford University — showcases the latest innovations poised to advance AI factories and drive revenue for the trillion-dollar data center computing market. At the conference, NVIDIA will join industry leaders including Google and Microsoft in a “tutorial” session — taking place on Sunday, Aug. 24 — that discusses designing rack-scale architecture for data centers. In addition, NVIDIA experts will present at four sessions and one tutorial detailing how: NVIDIA networking, including the NVIDIA ConnectX-8 SuperNIC, delivers AI reasoning at rack- and data-center scale.Neural rendering advancements and massive leaps in inference — powered by the NVIDIA Blackwell architecture, including the NVIDIA GeForce RTX 5090 GPU — provide next-level graphics and simulation capabilities.Co-packaged opticsswitches with integrated silicon photonics — built with light-speed fiber rather than copper wiring to send information quicker and using less power — enable efficient, high-performance, gigawatt-scale AI factories. The talk will also highlight NVIDIA Spectrum-XGS Ethernet, a new scale-across technology for unifying distributed data centers into AI super-factories.The NVIDIA GB10 Superchip serves as the engine within the NVIDIA DGX Spark desktop supercomputer.It’s all part of how NVIDIA’s latest technologies are accelerating inference to drive AI innovation everywhere, at every scale. NVIDIA Networking Fosters AI Innovation at Scale AI reasoning — when artificial intelligence systems can analyze and solve complex problems through multiple AI inference passes — requires rack-scale performance to deliver optimal user experiences efficiently. In data centers powering today’s AI workloads, networking acts as the central nervous system, connecting all the components — servers, storage devices and other hardware — into a single, cohesive, powerful computing unit. NVIDIA ConnectX-8 SuperNIC Burstein’s Hot Chips session will dive into how NVIDIA networking technologies — particularly NVIDIA ConnectX-8 SuperNICs — enable high-speed, low-latency, multi-GPU communication to deliver market-leading AI reasoning performance at scale. As part of the NVIDIA networking platform, NVIDIA NVLink, NVLink Switch and NVLink Fusion deliver scale-up connectivity — linking GPUs and compute elements within and across servers for ultra low-latency, high-bandwidth data exchange. NVIDIA Spectrum-X Ethernet provides the scale-out fabric to connect entire clusters, rapidly streaming massive datasets into AI models and orchestrating GPU-to-GPU communication across the data center. Spectrum-XGS Ethernet scale-across technology extends the extreme performance and scale of Spectrum-X Ethernet to interconnect multiple, distributed data centers to form AI super-factories capable of giga-scale intelligence. Connecting distributed AI data centers with NVIDIA Spectrum-XGS Ethernet. At the heart of Spectrum-X Ethernet, CPO switches push the limits of performance and efficiency for AI infrastructure at scale, and will be covered in detail by Shainer in his talk. NVIDIA GB200 NVL72 — an exascale computer in a single rack — features 36 NVIDIA GB200 Superchips, each containing two NVIDIA B200 GPUs and an NVIDIA Grace CPU, interconnected by the largest NVLink domain ever offered, with NVLink Switch providing 130 terabytes per second of low-latency GPU communications for AI and high-performance computing workloads. An NVIDIA rack-scale system. Built with the NVIDIA Blackwell architecture, GB200 NVL72 systems deliver massive leaps in reasoning inference performance. NVIDIA Blackwell and CUDA Bring AI to Millions of Developers The NVIDIA GeForce RTX 5090 GPU — also powered by Blackwell and to be covered in Blackstein’s talk — doubles performance in today’s games with NVIDIA DLSS 4 technology. NVIDIA GeForce RTX 5090 GPU It can also add neural rendering features for games to deliver up to 10x performance, 10x footprint amplification and a 10x reduction in design cycles,  helping enhance realism in computer graphics and simulation. This offers smooth, responsive visual experiences at low energy consumption and improves the lifelike simulation of characters and effects. NVIDIA CUDA, the world’s most widely available computing infrastructure, lets users deploy and run AI models using NVIDIA Blackwell anywhere. Hundreds of millions of GPUs run CUDA across the globe, from NVIDIA GB200 NVL72 rack-scale systems to GeForce RTX– and NVIDIA RTX PRO-powered PCs and workstations, with NVIDIA DGX Spark powered by NVIDIA GB10 — discussed in Skende’s session — coming soon. From Algorithms to AI Supercomputers — Optimized for LLMs NVIDIA DGX Spark Delivering powerful performance and capabilities in a compact package, DGX Spark lets developers, researchers, data scientists and students push the boundaries of generative AI right at their desktops, and accelerate workloads across industries. As part of the NVIDIA Blackwell platform, DGX Spark brings support for NVFP4, a low-precision numerical format to enable efficient agentic AI inference, particularly of large language models. Learn more about NVFP4 in this NVIDIA Technical Blog. Open-Source Collaborations Propel Inference Innovation NVIDIA accelerates several open-source libraries and frameworks to accelerate and optimize AI workloads for LLMs and distributed inference. These include NVIDIA TensorRT-LLM, NVIDIA Dynamo, TileIR, Cutlass, the NVIDIA Collective Communication Library and NIX — which are integrated into millions of workflows. Allowing developers to build with their framework of choice, NVIDIA has collaborated with top open framework providers to offer model optimizations for FlashInfer, PyTorch, SGLang, vLLM and others. Plus, NVIDIA NIM microservices are available for popular open models like OpenAI’s gpt-oss and Llama 4,  making it easy for developers to operate managed application programming interfaces with the flexibility and security of self-hosting models on their preferred infrastructure. Learn more about the latest advancements in inference and accelerated computing by joining NVIDIA at Hot Chips.   #hot #topics #chips #inference #networking
    Hot Topics at Hot Chips: Inference, Networking, AI Innovation at Every Scale — All Built on NVIDIA
    blogs.nvidia.com
    AI reasoning, inference and networking will be top of mind for attendees of next week’s Hot Chips conference. A key forum for processor and system architects from industry and academia, Hot Chips — running Aug. 24-26 at Stanford University — showcases the latest innovations poised to advance AI factories and drive revenue for the trillion-dollar data center computing market. At the conference, NVIDIA will join industry leaders including Google and Microsoft in a “tutorial” session — taking place on Sunday, Aug. 24 — that discusses designing rack-scale architecture for data centers. In addition, NVIDIA experts will present at four sessions and one tutorial detailing how: NVIDIA networking, including the NVIDIA ConnectX-8 SuperNIC, delivers AI reasoning at rack- and data-center scale. (Featuring Idan Burstein, principal architect of network adapters and systems-on-a-chip at NVIDIA) Neural rendering advancements and massive leaps in inference — powered by the NVIDIA Blackwell architecture, including the NVIDIA GeForce RTX 5090 GPU — provide next-level graphics and simulation capabilities. (Featuring Marc Blackstein, senior director of architecture at NVIDIA) Co-packaged optics (CPO) switches with integrated silicon photonics — built with light-speed fiber rather than copper wiring to send information quicker and using less power — enable efficient, high-performance, gigawatt-scale AI factories. The talk will also highlight NVIDIA Spectrum-XGS Ethernet, a new scale-across technology for unifying distributed data centers into AI super-factories. (Featuring Gilad Shainer, senior vice president of networking at NVIDIA) The NVIDIA GB10 Superchip serves as the engine within the NVIDIA DGX Spark desktop supercomputer. (Featuring Andi Skende, senior distinguished engineer at NVIDIA) It’s all part of how NVIDIA’s latest technologies are accelerating inference to drive AI innovation everywhere, at every scale. NVIDIA Networking Fosters AI Innovation at Scale AI reasoning — when artificial intelligence systems can analyze and solve complex problems through multiple AI inference passes — requires rack-scale performance to deliver optimal user experiences efficiently. In data centers powering today’s AI workloads, networking acts as the central nervous system, connecting all the components — servers, storage devices and other hardware — into a single, cohesive, powerful computing unit. NVIDIA ConnectX-8 SuperNIC Burstein’s Hot Chips session will dive into how NVIDIA networking technologies — particularly NVIDIA ConnectX-8 SuperNICs — enable high-speed, low-latency, multi-GPU communication to deliver market-leading AI reasoning performance at scale. As part of the NVIDIA networking platform, NVIDIA NVLink, NVLink Switch and NVLink Fusion deliver scale-up connectivity — linking GPUs and compute elements within and across servers for ultra low-latency, high-bandwidth data exchange. NVIDIA Spectrum-X Ethernet provides the scale-out fabric to connect entire clusters, rapidly streaming massive datasets into AI models and orchestrating GPU-to-GPU communication across the data center. Spectrum-XGS Ethernet scale-across technology extends the extreme performance and scale of Spectrum-X Ethernet to interconnect multiple, distributed data centers to form AI super-factories capable of giga-scale intelligence. Connecting distributed AI data centers with NVIDIA Spectrum-XGS Ethernet. At the heart of Spectrum-X Ethernet, CPO switches push the limits of performance and efficiency for AI infrastructure at scale, and will be covered in detail by Shainer in his talk. NVIDIA GB200 NVL72 — an exascale computer in a single rack — features 36 NVIDIA GB200 Superchips, each containing two NVIDIA B200 GPUs and an NVIDIA Grace CPU, interconnected by the largest NVLink domain ever offered, with NVLink Switch providing 130 terabytes per second of low-latency GPU communications for AI and high-performance computing workloads. An NVIDIA rack-scale system. Built with the NVIDIA Blackwell architecture, GB200 NVL72 systems deliver massive leaps in reasoning inference performance. NVIDIA Blackwell and CUDA Bring AI to Millions of Developers The NVIDIA GeForce RTX 5090 GPU — also powered by Blackwell and to be covered in Blackstein’s talk — doubles performance in today’s games with NVIDIA DLSS 4 technology. NVIDIA GeForce RTX 5090 GPU It can also add neural rendering features for games to deliver up to 10x performance, 10x footprint amplification and a 10x reduction in design cycles,  helping enhance realism in computer graphics and simulation. This offers smooth, responsive visual experiences at low energy consumption and improves the lifelike simulation of characters and effects. NVIDIA CUDA, the world’s most widely available computing infrastructure, lets users deploy and run AI models using NVIDIA Blackwell anywhere. Hundreds of millions of GPUs run CUDA across the globe, from NVIDIA GB200 NVL72 rack-scale systems to GeForce RTX– and NVIDIA RTX PRO-powered PCs and workstations, with NVIDIA DGX Spark powered by NVIDIA GB10 — discussed in Skende’s session — coming soon. From Algorithms to AI Supercomputers — Optimized for LLMs NVIDIA DGX Spark Delivering powerful performance and capabilities in a compact package, DGX Spark lets developers, researchers, data scientists and students push the boundaries of generative AI right at their desktops, and accelerate workloads across industries. As part of the NVIDIA Blackwell platform, DGX Spark brings support for NVFP4, a low-precision numerical format to enable efficient agentic AI inference, particularly of large language models (LLMs). Learn more about NVFP4 in this NVIDIA Technical Blog. Open-Source Collaborations Propel Inference Innovation NVIDIA accelerates several open-source libraries and frameworks to accelerate and optimize AI workloads for LLMs and distributed inference. These include NVIDIA TensorRT-LLM, NVIDIA Dynamo, TileIR, Cutlass, the NVIDIA Collective Communication Library and NIX — which are integrated into millions of workflows. Allowing developers to build with their framework of choice, NVIDIA has collaborated with top open framework providers to offer model optimizations for FlashInfer, PyTorch, SGLang, vLLM and others. Plus, NVIDIA NIM microservices are available for popular open models like OpenAI’s gpt-oss and Llama 4,  making it easy for developers to operate managed application programming interfaces with the flexibility and security of self-hosting models on their preferred infrastructure. Learn more about the latest advancements in inference and accelerated computing by joining NVIDIA at Hot Chips.  
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  • Revisiting The Door Problem With Liz England - Game Developer Podcast Ep. 53

    System Era Softworks design director Liz England never set out to become an industry-wide name—but she did in 2014 when she published a blog called “The Door Problem.” She penned it to help people outside the game industry understand exactly what a game designer does…and it turns out, to motivate herself to get her personal website online.Years later "The Door Problem" is now required reading for young designers everywhere, largely for reasons beyond what England dreamed of when she jotted down that post. In the 11 years since she wrote that post, Liz has evolved from being a game designer to design director, and her perspective on “The Door Problem” has shifted. Today she’s not thinking as much about doors—more about how to help other designers pitch and build doors that fit the scope and vision for their game.What does good leadership look like in game design?England said she didn’t think there was any more to be added to “The Door Problem” even after 11 years. She’s been thinking more about how game design leadership can help developers solve their own “door problems” to make games in a more effective manner. That's not only meant improving her language for designing game design challenges—but looking back at leaders she looked up to earlier in her career.Related:About The Game Developer PodcastThe Game Developer podcast is a bi-weekly podcast chronicling the triumphs, catastrophes, and everything in-between of game development, sharing lessons and strategies fellow developers can use to hone their craft. The Game Developer Podcast is hosted by Bryant Francis, edited by Pierre Landriau, and features music by Mike Meehan.Follow Game Developer on Bluesky or on LinkedIn.Follow Liz England on Bluesky.
    #revisiting #door #problem #with #liz
    Revisiting The Door Problem With Liz England - Game Developer Podcast Ep. 53
    System Era Softworks design director Liz England never set out to become an industry-wide name—but she did in 2014 when she published a blog called “The Door Problem.” She penned it to help people outside the game industry understand exactly what a game designer does…and it turns out, to motivate herself to get her personal website online.Years later "The Door Problem" is now required reading for young designers everywhere, largely for reasons beyond what England dreamed of when she jotted down that post. In the 11 years since she wrote that post, Liz has evolved from being a game designer to design director, and her perspective on “The Door Problem” has shifted. Today she’s not thinking as much about doors—more about how to help other designers pitch and build doors that fit the scope and vision for their game.What does good leadership look like in game design?England said she didn’t think there was any more to be added to “The Door Problem” even after 11 years. She’s been thinking more about how game design leadership can help developers solve their own “door problems” to make games in a more effective manner. That's not only meant improving her language for designing game design challenges—but looking back at leaders she looked up to earlier in her career.Related:About The Game Developer PodcastThe Game Developer podcast is a bi-weekly podcast chronicling the triumphs, catastrophes, and everything in-between of game development, sharing lessons and strategies fellow developers can use to hone their craft. The Game Developer Podcast is hosted by Bryant Francis, edited by Pierre Landriau, and features music by Mike Meehan.Follow Game Developer on Bluesky or on LinkedIn.Follow Liz England on Bluesky. #revisiting #door #problem #with #liz
    Revisiting The Door Problem With Liz England - Game Developer Podcast Ep. 53
    www.gamedeveloper.com
    System Era Softworks design director Liz England never set out to become an industry-wide name—but she did in 2014 when she published a blog called “The Door Problem.” She penned it to help people outside the game industry understand exactly what a game designer does…and it turns out, to motivate herself to get her personal website online.Years later "The Door Problem" is now required reading for young designers everywhere, largely for reasons beyond what England dreamed of when she jotted down that post. In the 11 years since she wrote that post, Liz has evolved from being a game designer to design director, and her perspective on “The Door Problem” has shifted. Today she’s not thinking as much about doors—more about how to help other designers pitch and build doors that fit the scope and vision for their game.What does good leadership look like in game design?England said she didn’t think there was any more to be added to “The Door Problem” even after 11 years. She’s been thinking more about how game design leadership can help developers solve their own “door problems” to make games in a more effective manner. That's not only meant improving her language for designing game design challenges—but looking back at leaders she looked up to earlier in her career.Related:About The Game Developer PodcastThe Game Developer podcast is a bi-weekly podcast chronicling the triumphs, catastrophes, and everything in-between of game development, sharing lessons and strategies fellow developers can use to hone their craft. The Game Developer Podcast is hosted by Bryant Francis, edited by Pierre Landriau, and features music by Mike Meehan.Follow Game Developer on Bluesky or on LinkedIn.Follow Liz England on Bluesky.
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  • هل تعرفوا أنو 95% من المشاريع اللي تعتمد على الذكاء الاصطناعي تفشل؟

    مقال جديد من MIT ينبهنا على هاد الحقيقة الصادمة، لكن المفاجأة مش في النسبة العالية، بل في الأسباب وراء هاد الفشل. الدراسة تقول بلي المشكلة موش في تقنيات الذكاء الاصطناعي ولكن في كيفاش الشركات تحاول تستعملها. بمعنى آخر، الطريقة اللي نديروا بها الأمور هي اللي تتسبب في الفشل!

    شخصيًا، شاهدت بعض الشركات اللي تحط استثمارات كبيرة في AI بلا ما تفهم شوية أسس كيفاش تتعامل معاه. للأسف، يضيعوا وقتهم وفلوسهم. لازم نفكّروا مليح قبل ما نغوصوا في هاد الميدان.

    هذا الموضوع يفتح لنا عيوننا على ضرورة الفهم العميق لطبيعة الذكاء الاصطناعي وكيفاش نقدروا نستفيدوا منو بالشكل الصحيح.

    https://fortune.com/2025/08/21/an-mit-report-that-95-of-ai-pilots-fail-spooked-investors-but-the-reason-why-those-pil
    هل تعرفوا أنو 95% من المشاريع اللي تعتمد على الذكاء الاصطناعي تفشل؟ 🚀 مقال جديد من MIT ينبهنا على هاد الحقيقة الصادمة، لكن المفاجأة مش في النسبة العالية، بل في الأسباب وراء هاد الفشل. الدراسة تقول بلي المشكلة موش في تقنيات الذكاء الاصطناعي ولكن في كيفاش الشركات تحاول تستعملها. بمعنى آخر، الطريقة اللي نديروا بها الأمور هي اللي تتسبب في الفشل! شخصيًا، شاهدت بعض الشركات اللي تحط استثمارات كبيرة في AI بلا ما تفهم شوية أسس كيفاش تتعامل معاه. للأسف، يضيعوا وقتهم وفلوسهم. لازم نفكّروا مليح قبل ما نغوصوا في هاد الميدان. هذا الموضوع يفتح لنا عيوننا على ضرورة الفهم العميق لطبيعة الذكاء الاصطناعي وكيفاش نقدروا نستفيدوا منو بالشكل الصحيح. https://fortune.com/2025/08/21/an-mit-report-that-95-of-ai-pilots-fail-spooked-investors-but-the-reason-why-those-pil
    fortune.com
    The lessons from the MIT study were less about what's wrong with AI models and more about what's wrong with the way companies are trying to use them
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  • يا جماعة، عندكم فكرة على Cohere وشنو راها جابته جديد؟

    في مقال شفتو، يتحدث عن "Command A Reasoning"، وهو أول نموذج تفكير متطور من Cohere مخصص لخدمة الزبائن في الشركات. الأرقام الأولية والتجارب تشير إلى أنه فعلاً يقدم مرونة وفعالية كبيرة. يعني، إذا كنتوا في عالم الـ AI أو تخدموا في شركة، هاد النموذج ممكن يبدل شغلكم للأحسن.

    شخصياً، من وقت ما بدينا نستخدم تقنيات الذكاء الاصطناعي في شغلي، لاحظت كيفاش الأمور ولاّت أسرع وأسهل في التواصل مع الزبائن.

    في بعض الأحيان، لازم نحطوا الثقة في التكنولوجيا الجديدة، ونعطوها فرصة باش تثبت لنا قيمتها.

    https://venturebeat.com/ai/dont-sleep-on-cohere-command-a-reasoning-its-first-reasoning-model-is-built-for-enterprise-customer-service-and-more/

    #AI #خدمة_الزبائن #Cohere #تكنولوجيا #Innovation
    🤔 يا جماعة، عندكم فكرة على Cohere وشنو راها جابته جديد؟ في مقال شفتو، يتحدث عن "Command A Reasoning"، وهو أول نموذج تفكير متطور من Cohere مخصص لخدمة الزبائن في الشركات. الأرقام الأولية والتجارب تشير إلى أنه فعلاً يقدم مرونة وفعالية كبيرة. يعني، إذا كنتوا في عالم الـ AI أو تخدموا في شركة، هاد النموذج ممكن يبدل شغلكم للأحسن. شخصياً، من وقت ما بدينا نستخدم تقنيات الذكاء الاصطناعي في شغلي، لاحظت كيفاش الأمور ولاّت أسرع وأسهل في التواصل مع الزبائن. في بعض الأحيان، لازم نحطوا الثقة في التكنولوجيا الجديدة، ونعطوها فرصة باش تثبت لنا قيمتها. https://venturebeat.com/ai/dont-sleep-on-cohere-command-a-reasoning-its-first-reasoning-model-is-built-for-enterprise-customer-service-and-more/ #AI #خدمة_الزبائن #Cohere #تكنولوجيا #Innovation
    venturebeat.com
    It looks to be a strong release. Benchmarks, technical specs, and early tests suggest the model delivers on flexibility, efficiency, and raw
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  • يا جماعة، خبر جديد يحكم!
    OpenAI طلقوا الـ GPT-5 ومعاه ترقية كبيرة في البرمجة والصحة وmultimodal reasoning. يعني حتى إذا كنت تهتم بالذكاء الاصطناعي أو فقط تحب تتبع التطورات، لازم تعرف شنو صار!

    النسخة الجديدة هذي تتطور بشكل كبير، خصوصًا في كيفية فهم المعلومات وتفاعله مع مختلف الأنماط. شخصيًا، نعتقد بلي هذي التقنيات راح تخلي حياتنا أسهل وتنفتح لنا آفاق جديدة في مجالات متعددة.

    ميما كانت مجالك، استغل هذي التكنولوجيا لتحسين شغلك أو حتى تعلّم حاجات جديدة. التكنولوجيا دايمًا في تطور، وضروري نكونوا معاه!

    https://forbesmiddleeast.com/innovation/artificial-intelligence-machine-learning/openai-debuts-gpt-5-with-major-upgrades-in-coding-health-and-multimodal-reasoning
    #OpenAI #GPT5 #ذكاء_اصطناعي #Innovation #Technologie
    يا جماعة، خبر جديد يحكم! 🚀 OpenAI طلقوا الـ GPT-5 ومعاه ترقية كبيرة في البرمجة والصحة وmultimodal reasoning. يعني حتى إذا كنت تهتم بالذكاء الاصطناعي أو فقط تحب تتبع التطورات، لازم تعرف شنو صار! النسخة الجديدة هذي تتطور بشكل كبير، خصوصًا في كيفية فهم المعلومات وتفاعله مع مختلف الأنماط. شخصيًا، نعتقد بلي هذي التقنيات راح تخلي حياتنا أسهل وتنفتح لنا آفاق جديدة في مجالات متعددة. ميما كانت مجالك، استغل هذي التكنولوجيا لتحسين شغلك أو حتى تعلّم حاجات جديدة. التكنولوجيا دايمًا في تطور، وضروري نكونوا معاه! https://forbesmiddleeast.com/innovation/artificial-intelligence-machine-learning/openai-debuts-gpt-5-with-major-upgrades-in-coding-health-and-multimodal-reasoning #OpenAI #GPT5 #ذكاء_اصطناعي #Innovation #Technologie
    forbesmiddleeast.com
    OpenAI Debuts GPT-5 With Major Upgrades In Coding, Health, And Multimodal Reasoning
    1 Commentaires ·0 Parts
  • واش راكم؟ اليوم جبتلكم خبر يفرح كل المطورين! Nvidia طلقات موديل جديد سمّاه Nemotron-Nano-9B-v2، وهو موديل صغير ومفتوح فيه خاصية toggle on/off reasoning. يعني تقدروا تطوّروا وتنشروا موديلات جديدة بكل حرية، وما عندهمش الحق في أي output يخرج من عندكم.

    شفتوا كيف الجزائر تبان في عالم التكنولوجيا؟ أنا بصراحة متحمس! عندي تجربة صغيرة في تطوير الموديلات، ونشوف كيف برشة ناس يقدروا يخلقوا أفكار جديدة بفضل التسهيلات هذي.

    فكروا في الإمكانيات اللي تقدروا تستغلوها، والمشاريع اللي ممكن تديروها. العالم كامل يترقب الإبداع، وزمنكم جاء!

    https://venturebeat.com/ai/nvidia-releases-a-new-small-open-model-nemotron-nano-9b-v2-with-toggle-on-off-reasoning/
    #Nvidia #AI #مطورين #تكنولوجيا #Innovations
    🚀 واش راكم؟ اليوم جبتلكم خبر يفرح كل المطورين! Nvidia طلقات موديل جديد سمّاه Nemotron-Nano-9B-v2، وهو موديل صغير ومفتوح فيه خاصية toggle on/off reasoning. يعني تقدروا تطوّروا وتنشروا موديلات جديدة بكل حرية، وما عندهمش الحق في أي output يخرج من عندكم. شفتوا كيف الجزائر تبان في عالم التكنولوجيا؟ أنا بصراحة متحمس! عندي تجربة صغيرة في تطوير الموديلات، ونشوف كيف برشة ناس يقدروا يخلقوا أفكار جديدة بفضل التسهيلات هذي. فكروا في الإمكانيات اللي تقدروا تستغلوها، والمشاريع اللي ممكن تديروها. العالم كامل يترقب الإبداع، وزمنكم جاء! https://venturebeat.com/ai/nvidia-releases-a-new-small-open-model-nemotron-nano-9b-v2-with-toggle-on-off-reasoning/ #Nvidia #AI #مطورين #تكنولوجيا #Innovations
    venturebeat.com
    Developers are free to create and distribute derivative models. Importantly, Nvidia does not claim ownership of any outputs generated...
    1 Commentaires ·0 Parts
  • صايب راسك، عندي خبر زوين على "OpenAI o1-mini"!

    المقال يتكلم على كيفاش هالتقنية الجديد راح تقدّم reasoning بكلفة أقل بكثير. يعني، اليوم يقدر الواحد يستفيد من الذكاء الاصطناعي بدون ما يخرج من جيبو الكثير. فكرة مذهلة، صح؟

    مؤخراً، جربت أدوات ذكاء اصطناعي في مشاريعي، وفعلاً حسيت بفرق كبير في الإنتاجية. تقدر تحل المشاكل بسرعة وتفكر بطريقة جديدة، وهذا ماشي سهل على كل واحد.

    في عالم يتطور بسرعة البرق، قليل من الابتكارات تقدر تغيّر حياتنا بشكل جذري.

    https://openai.com/index/openai-o1-mini-advancing-cost-efficient-reasoning
    #OpenAI #ذكاء_اصطناعي #Innovation #CostEfficiency #تكنولوجيا
    😮 صايب راسك، عندي خبر زوين على "OpenAI o1-mini"! المقال يتكلم على كيفاش هالتقنية الجديد راح تقدّم reasoning بكلفة أقل بكثير. يعني، اليوم يقدر الواحد يستفيد من الذكاء الاصطناعي بدون ما يخرج من جيبو الكثير. فكرة مذهلة، صح؟ مؤخراً، جربت أدوات ذكاء اصطناعي في مشاريعي، وفعلاً حسيت بفرق كبير في الإنتاجية. تقدر تحل المشاكل بسرعة وتفكر بطريقة جديدة، وهذا ماشي سهل على كل واحد. في عالم يتطور بسرعة البرق، قليل من الابتكارات تقدر تغيّر حياتنا بشكل جذري. https://openai.com/index/openai-o1-mini-advancing-cost-efficient-reasoning #OpenAI #ذكاء_اصطناعي #Innovation #CostEfficiency #تكنولوجيا
    openai.com
    Advancing cost-efficient reasoning
    1 Commentaires ·0 Parts
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