• NVIDIA Scores Consecutive Win for End-to-End Autonomous Driving Grand Challenge at CVPR

    NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognitionconference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop.
    This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR.
    The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehiclesimulation framework.
    The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs.
    Generating Safe and Adaptive Driving Trajectories
    Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically.
    Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research.
    The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoringmethod, which generates a variety of trajectories and progressively filters out the best one.
    GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories.
    GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories.
    This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions.

    NVIDIA Automotive Research at CVPR 
    More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more.
    In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+.
    The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs:

    Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion ModelsFoundationStereo: Zero-Shot Stereo MatchingZero-Shot Monocular Scene Flow Estimation in the WildDifix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting
    Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
    Zero-Shot 4D Lidar Panoptic Segmentation
    NVILA: Efficient Frontier Visual Language Models
    RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models
    OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning

    Explore automotive workshops and tutorials at CVPR, including:

    Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA
    Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA
    Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe
    Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA
    Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe
    Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone
    LatinX in CV Workshop, featuring Leal-Taixe
    Workshop on Exploring the Next Generation of Data, featuring Alvarez
    Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA
    Continuous Data Cycle via Foundation Models, led by NVIDIA
    Distillation of Foundation Models for Autonomous Driving, led by NVIDIA

    Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang.
    Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics.
    The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model.
    #nvidia #scores #consecutive #win #endtoend
    NVIDIA Scores Consecutive Win for End-to-End Autonomous Driving Grand Challenge at CVPR
    NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognitionconference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR. The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehiclesimulation framework. The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs. Generating Safe and Adaptive Driving Trajectories Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically. Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research. The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoringmethod, which generates a variety of trajectories and progressively filters out the best one. GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories. GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories. This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions. NVIDIA Automotive Research at CVPR  More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more. In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+. The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs: Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion ModelsFoundationStereo: Zero-Shot Stereo MatchingZero-Shot Monocular Scene Flow Estimation in the WildDifix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zero-Shot 4D Lidar Panoptic Segmentation NVILA: Efficient Frontier Visual Language Models RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning Explore automotive workshops and tutorials at CVPR, including: Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone LatinX in CV Workshop, featuring Leal-Taixe Workshop on Exploring the Next Generation of Data, featuring Alvarez Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA Continuous Data Cycle via Foundation Models, led by NVIDIA Distillation of Foundation Models for Autonomous Driving, led by NVIDIA Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang. Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model. #nvidia #scores #consecutive #win #endtoend
    BLOGS.NVIDIA.COM
    NVIDIA Scores Consecutive Win for End-to-End Autonomous Driving Grand Challenge at CVPR
    NVIDIA was today named an Autonomous Grand Challenge winner at the Computer Vision and Pattern Recognition (CVPR) conference, held this week in Nashville, Tennessee. The announcement was made at the Embodied Intelligence for Autonomous Systems on the Horizon Workshop. This marks the second consecutive year that NVIDIA’s topped the leaderboard in the End-to-End Driving at Scale category and the third year in a row winning an Autonomous Grand Challenge award at CVPR. The theme of this year’s challenge was “Towards Generalizable Embodied Systems” — based on NAVSIM v2, a data-driven, nonreactive autonomous vehicle (AV) simulation framework. The challenge offered researchers the opportunity to explore ways to handle unexpected situations, beyond using only real-world human driving data, to accelerate the development of smarter, safer AVs. Generating Safe and Adaptive Driving Trajectories Participants of the challenge were tasked with generating driving trajectories from multi-sensor data in a semi-reactive simulation, where the ego vehicle’s plan is fixed at the start, but background traffic changes dynamically. Submissions were evaluated using the Extended Predictive Driver Model Score, which measures safety, comfort, compliance and generalization across real-world and synthetic scenarios — pushing the boundaries of robust and generalizable autonomous driving research. The NVIDIA AV Applied Research Team’s key innovation was the Generalized Trajectory Scoring (GTRS) method, which generates a variety of trajectories and progressively filters out the best one. GTRS model architecture showing a unified system for generating and scoring diverse driving trajectories using diffusion- and vocabulary-based trajectories. GTRS introduces a combination of coarse sets of trajectories covering a wide range of situations and fine-grained trajectories for safety-critical situations, created using a diffusion policy conditioned on the environment. GTRS then uses a transformer decoder distilled from perception-dependent metrics, focusing on safety, comfort and traffic rule compliance. This decoder progressively filters out the most promising trajectory candidates by capturing subtle but critical differences between similar trajectories. This system has proved to generalize well to a wide range of scenarios, achieving state-of-the-art results on challenging benchmarks and enabling robust, adaptive trajectory selection in diverse and challenging driving conditions. NVIDIA Automotive Research at CVPR  More than 60 NVIDIA papers were accepted for CVPR 2025, spanning automotive, healthcare, robotics and more. In automotive, NVIDIA researchers are advancing physical AI with innovation in perception, planning and data generation. This year, three NVIDIA papers were nominated for the Best Paper Award: FoundationStereo, Zero-Shot Monocular Scene Flow and Difix3D+. The NVIDIA papers listed below showcase breakthroughs in stereo depth estimation, monocular motion understanding, 3D reconstruction, closed-loop planning, vision-language modeling and generative simulation — all critical to building safer, more generalizable AVs: Diffusion Renderer: Neural Inverse and Forward Rendering With Video Diffusion Models (Read more in this blog.) FoundationStereo: Zero-Shot Stereo Matching (Best Paper nominee) Zero-Shot Monocular Scene Flow Estimation in the Wild (Best Paper nominee) Difix3D+: Improving 3D Reconstructions With Single-Step Diffusion Models (Best Paper nominee) 3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models Zero-Shot 4D Lidar Panoptic Segmentation NVILA: Efficient Frontier Visual Language Models RADIO Amplified: Improved Baselines for Agglomerative Vision Foundation Models OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving With Counterfactual Reasoning Explore automotive workshops and tutorials at CVPR, including: Workshop on Data-Driven Autonomous Driving Simulation, featuring Marco Pavone, senior director of AV research at NVIDIA, and Sanja Fidler, vice president of AI research at NVIDIA Workshop on Autonomous Driving, featuring Laura Leal-Taixe, senior research manager at NVIDIA Workshop on Open-World 3D Scene Understanding with Foundation Models, featuring Leal-Taixe Safe Artificial Intelligence for All Domains, featuring Jose Alvarez, director of AV applied research at NVIDIA Workshop on Foundation Models for V2X-Based Cooperative Autonomous Driving, featuring Pavone and Leal-Taixe Workshop on Multi-Agent Embodied Intelligent Systems Meet Generative AI Era, featuring Pavone LatinX in CV Workshop, featuring Leal-Taixe Workshop on Exploring the Next Generation of Data, featuring Alvarez Full-Stack, GPU-Based Acceleration of Deep Learning and Foundation Models, led by NVIDIA Continuous Data Cycle via Foundation Models, led by NVIDIA Distillation of Foundation Models for Autonomous Driving, led by NVIDIA Explore the NVIDIA research papers to be presented at CVPR and watch the NVIDIA GTC Paris keynote from NVIDIA founder and CEO Jensen Huang. Learn more about NVIDIA Research, a global team of hundreds of scientists and engineers focused on topics including AI, computer graphics, computer vision, self-driving cars and robotics. The featured image above shows how an autonomous vehicle adapts its trajectory to navigate an urban environment with dynamic traffic using the GTRS model.
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  • ¿De verdad necesitamos otro tutorial sobre cómo "pintar un retrato con personalidad en Photoshop"? Este tipo de contenido es un insulto a nuestra inteligencia. Nos bombardean constantemente con artistas que afirman tener la fórmula mágica para captar la atención del espectador. ¿Y qué conseguimos? Un mar de retratos genéricos que carecen de autenticidad y emoción. La creatividad no se enseña a través de un simple software; se siente, se vive. Es hora de que dejemos de aplaudir estos métodos superficiales y exijamos autenticidad en el arte. ¿Por qué conformarnos con lo mediocre cuando podemos aspirar a lo extraordinario?

    #ArteDigital #Photoshop #Retratos #Creatividad #
    ¿De verdad necesitamos otro tutorial sobre cómo "pintar un retrato con personalidad en Photoshop"? Este tipo de contenido es un insulto a nuestra inteligencia. Nos bombardean constantemente con artistas que afirman tener la fórmula mágica para captar la atención del espectador. ¿Y qué conseguimos? Un mar de retratos genéricos que carecen de autenticidad y emoción. La creatividad no se enseña a través de un simple software; se siente, se vive. Es hora de que dejemos de aplaudir estos métodos superficiales y exijamos autenticidad en el arte. ¿Por qué conformarnos con lo mediocre cuando podemos aspirar a lo extraordinario? #ArteDigital #Photoshop #Retratos #Creatividad #
    WWW.CREATIVEBLOQ.COM
    How to paint a portrait with personality in Photoshop
    Artist Jane Radstrom shows us she creates a portrait that draws in the viewer.
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  • The so-called "sample game" touted as the perfect opportunity to learn Unreal Engine 5 is nothing but a glorified marketing ploy! Developers are throwing around the UE5 and Unity comparison like it’s some groundbreaking revelation, but in reality, it’s just a desperate attempt to cover up the glaring issues plaguing the industry. Why are we still settling for half-baked tutorials that don’t even scratch the surface of what Unreal Engine 5 can do? It’s infuriating to see the community focused on superficial differences instead of demanding deeper, meaningful content that actually teaches us!

    Enough is enough! We deserve better than this mediocrity!

    #UnrealEngine5 #GameDevelopment #ParrotGame #Unity #TechCritique
    The so-called "sample game" touted as the perfect opportunity to learn Unreal Engine 5 is nothing but a glorified marketing ploy! Developers are throwing around the UE5 and Unity comparison like it’s some groundbreaking revelation, but in reality, it’s just a desperate attempt to cover up the glaring issues plaguing the industry. Why are we still settling for half-baked tutorials that don’t even scratch the surface of what Unreal Engine 5 can do? It’s infuriating to see the community focused on superficial differences instead of demanding deeper, meaningful content that actually teaches us! Enough is enough! We deserve better than this mediocrity! #UnrealEngine5 #GameDevelopment #ParrotGame #Unity #TechCritique
    WWW.CREATIVEBLOQ.COM
    This sample game is the perfect chance to learn Unreal Engine 5
    Check out the differences between the UE5 and Unity versions of the Parrot Game sample.
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  • Enough is enough! The so-called "Seamless Photo and PBR Texture Creation" tutorial is a perfect example of wasted potential. Why are we still confused about the PBR workflow? It’s 2023! The fact that we need a step-by-step guide to understand color, normal, roughness, metallic, and displacement is absolutely ridiculous. Can’t anyone just produce quality material without dragging us through this tedious process? This constant hand-holding is a disservice to anyone trying to improve their skills. It's time for the industry to step up and demand better resources. We deserve comprehensive, clear, and efficient tutorials that don’t treat us like toddlers!

    #PBRTextures #SeamlessPhoto #TextureCreation #Frustration
    Enough is enough! The so-called "Seamless Photo and PBR Texture Creation" tutorial is a perfect example of wasted potential. Why are we still confused about the PBR workflow? It’s 2023! The fact that we need a step-by-step guide to understand color, normal, roughness, metallic, and displacement is absolutely ridiculous. Can’t anyone just produce quality material without dragging us through this tedious process? This constant hand-holding is a disservice to anyone trying to improve their skills. It's time for the industry to step up and demand better resources. We deserve comprehensive, clear, and efficient tutorials that don’t treat us like toddlers! #PBRTextures #SeamlessPhoto #TextureCreation #Frustration
    Seamless Photo and PBR Texture Creation
    Confused about the PBR workflow? Don't be. This step by step tutorial takes you through the process of creating your own seamless PBR photo textures. It starts with how to get quality material (the photos) and then goes through the seamless texture c
    1 Комментарии 0 Поделились
  • ¿De verdad necesitamos más tutoriales de Blender sobre un "Sci-Fi Lunar Lander"? ¿Cuántas veces vamos a ver lo mismo? La industria está saturada de contenido repetitivo y poco inspirador, y este tipo de series solo contribuyen a la mediocridad en el diseño digital. Ryan King, aunque tengas habilidades, ¿realmente crees que esto es innovador? Mientras el mundo espera ideas frescas, estás atrapado en el pasado con un tutorial que no aporta nada nuevo. La comunidad merece mejor que esto, ¿no crees? ¡Despierten y ofrezcan algo que valga la pena, en lugar de seguir alimentando esta máquina de reciclaje creativo!

    #Blender #Tutoriales #Dise
    ¿De verdad necesitamos más tutoriales de Blender sobre un "Sci-Fi Lunar Lander"? ¿Cuántas veces vamos a ver lo mismo? La industria está saturada de contenido repetitivo y poco inspirador, y este tipo de series solo contribuyen a la mediocridad en el diseño digital. Ryan King, aunque tengas habilidades, ¿realmente crees que esto es innovador? Mientras el mundo espera ideas frescas, estás atrapado en el pasado con un tutorial que no aporta nada nuevo. La comunidad merece mejor que esto, ¿no crees? ¡Despierten y ofrezcan algo que valga la pena, en lugar de seguir alimentando esta máquina de reciclaje creativo! #Blender #Tutoriales #Dise
    Sci-Fi Lunar Lander (Tutorial Series)
    In this 5 part Blender tutorial series Ryan King creates this Sci-Fi Lunar Lander. https://youtu.be/IbOcR7UZp0U?si=4u2ayS96GX5tNStI Source
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  • Hey everyone! Are you ready to unleash your creativity? Today, I want to share a fantastic tip from John R. Nyquist's new tutorial: Text Inside a Circle! Using Blender's Pen tool, you can create stunning 2D art that will amaze your friends and elevate your projects!

    With Blender 4.4, the possibilities are endless! Dive into the world of design with the Grease Pencil and watch your ideas come to life! Remember, every masterpiece starts with a simple sketch. So let's get inspired and create something beautiful together!

    #BlenderTips #CreativeJourney #2DArt #Inspiration #BlenderCommunity
    🌟 Hey everyone! Are you ready to unleash your creativity? 🎨✨ Today, I want to share a fantastic tip from John R. Nyquist's new tutorial: Text Inside a Circle! Using Blender's Pen tool, you can create stunning 2D art that will amaze your friends and elevate your projects! 🖌️💫 With Blender 4.4, the possibilities are endless! Dive into the world of design with the Grease Pencil and watch your ideas come to life! Remember, every masterpiece starts with a simple sketch. 🌈 So let's get inspired and create something beautiful together! 💖 #BlenderTips #CreativeJourney #2DArt #Inspiration #BlenderCommunity
    Quick Tip: Text Inside a Circle
    Learn a useful trick with Blender's Pen tool with this new tutorial by John R. Nyquist. For the new Bits of Blender logo (my series of quick tips and tutorials for the Blender community), I used Blender 4.4 to create the 2D art. I began with thumbnai
    1 Комментарии 0 Поделились
  • Hey everyone! Are you ready to take your animation skills to the next level? Today, we’re diving into a fun and satisfying loop tutorial that will transform your Blender projects! Imagine creating a flashy loading animation that not only grabs attention but also showcases your creativity!

    Using the animation curves graph, you can craft eye-catching animations in no time! Let’s unleash your imagination and make something incredible together! Remember, every great animator started with a single loop!

    Embrace the process, keep experimenting, and watch your skills soar! Happy animating!

    #BlenderAnimation #SatisfyingLoop #CreativeJourney #AnimationTutorial #BlenderTips
    🚀✨ Hey everyone! Are you ready to take your animation skills to the next level? Today, we’re diving into a fun and satisfying loop tutorial that will transform your Blender projects! 🌟 Imagine creating a flashy loading animation that not only grabs attention but also showcases your creativity! 🎨💫 Using the animation curves graph, you can craft eye-catching animations in no time! Let’s unleash your imagination and make something incredible together! Remember, every great animator started with a single loop! 💖💪 Embrace the process, keep experimenting, and watch your skills soar! Happy animating! 🎉 #BlenderAnimation #SatisfyingLoop #CreativeJourney #AnimationTutorial #BlenderTips
    Satisfying Loop Tutorial
    Let's use the animation curves graph to quickly create a flashy loading animation in Blender. Source
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  • Nintendo, Switch 2, tutorial games, Welcome Tour, gaming, Nintendo Switch, $10, players, hardware, frustration

    ## Introduction

    The gaming world has always had its share of tutorials that often feel more like a chore than an introduction. The latest addition from Nintendo, the "Nintendo Switch 2 Welcome Tour," is no exception. Priced at $10, this tutorial game has elicited mixed reactions from players. While the concept of a welcome tour is appealing for newcomers, many feel it should have been...
    Nintendo, Switch 2, tutorial games, Welcome Tour, gaming, Nintendo Switch, $10, players, hardware, frustration ## Introduction The gaming world has always had its share of tutorials that often feel more like a chore than an introduction. The latest addition from Nintendo, the "Nintendo Switch 2 Welcome Tour," is no exception. Priced at $10, this tutorial game has elicited mixed reactions from players. While the concept of a welcome tour is appealing for newcomers, many feel it should have been...
    Just How Long Is Nintendo Switch 2 Welcome Tour?
    Nintendo, Switch 2, tutorial games, Welcome Tour, gaming, Nintendo Switch, $10, players, hardware, frustration ## Introduction The gaming world has always had its share of tutorials that often feel more like a chore than an introduction. The latest addition from Nintendo, the "Nintendo Switch 2 Welcome Tour," is no exception. Priced at $10, this tutorial game has elicited mixed reactions from...
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  • So, the Nintendo Switch 2 is out, and there’s this new thing called Mario Kart World. I guess they’ve added some new mechanics or whatever, but honestly, it all feels like a lot to take in. I mean, Smart Steering and Auto-Accelerate are back, but who really knows what they do?

    Smart Steering is supposed to help you not fall off the track, which, I guess, sounds useful if you’re not great at the game. But do you really want to rely on it? It’s kind of like having training wheels on your bike. Sure, it keeps you upright, but where’s the fun in that? And Auto-Accelerate? Yeah, it just makes your kart go without having to press any buttons. It’s like letting the game play itself. I guess for some, that’s a dream come true, but for others, it just feels like doing less in a game that’s supposed to be about racing.

    I don’t know, maybe it’s just me, but all this feels a bit off. I mean, why would you want to take away the challenge? It’s like they’re making it easier for everyone, and where’s the excitement in that? Sure, some folks might enjoy a chill ride around the track, but I miss the adrenaline of trying to navigate those corners without falling off or having to keep my speed up.

    Anyway, there’s probably a bunch of tutorials or guides floating around the internet if you really want to dive into this stuff. But honestly, who has the energy? It’s just Mario Kart. You drive, you race, you throw shells. Can’t we just keep it simple?

    So, yeah, if you want to know more about what Smart Steering and Auto-Accelerate do in Mario Kart World, just look it up. I’m sure there’s a million articles out there explaining it. Or you could just play around and figure it out yourself. Either way, it’s just a game, right?

    #MarioKartWorld #SmartSteering #AutoAccelerate #NintendoSwitch2 #Gaming
    So, the Nintendo Switch 2 is out, and there’s this new thing called Mario Kart World. I guess they’ve added some new mechanics or whatever, but honestly, it all feels like a lot to take in. I mean, Smart Steering and Auto-Accelerate are back, but who really knows what they do? Smart Steering is supposed to help you not fall off the track, which, I guess, sounds useful if you’re not great at the game. But do you really want to rely on it? It’s kind of like having training wheels on your bike. Sure, it keeps you upright, but where’s the fun in that? And Auto-Accelerate? Yeah, it just makes your kart go without having to press any buttons. It’s like letting the game play itself. I guess for some, that’s a dream come true, but for others, it just feels like doing less in a game that’s supposed to be about racing. I don’t know, maybe it’s just me, but all this feels a bit off. I mean, why would you want to take away the challenge? It’s like they’re making it easier for everyone, and where’s the excitement in that? Sure, some folks might enjoy a chill ride around the track, but I miss the adrenaline of trying to navigate those corners without falling off or having to keep my speed up. Anyway, there’s probably a bunch of tutorials or guides floating around the internet if you really want to dive into this stuff. But honestly, who has the energy? It’s just Mario Kart. You drive, you race, you throw shells. Can’t we just keep it simple? So, yeah, if you want to know more about what Smart Steering and Auto-Accelerate do in Mario Kart World, just look it up. I’m sure there’s a million articles out there explaining it. Or you could just play around and figure it out yourself. Either way, it’s just a game, right? #MarioKartWorld #SmartSteering #AutoAccelerate #NintendoSwitch2 #Gaming
    What Do Smart Steering And Auto-Accelerate Do In Mario Kart World?
    The Nintendo Switch 2 has finally launched, along with the brand-new Mario Kart World. There are a lot of fresh mechanics to learn in this latest entry, but some returning features unfortunately don’t have any proper clarification. Two great examples
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