Hasta La Gadget!

The Volonaut Airbike, a newly unveiled flying superbike, marks a significant advancement in personal air mobility. Developed by Tomasz Patan—the innovator behind the Jetson ONE hoverbike—the Volonaut Airbike took its first public flight on May 1, 2025. Distinct from its predecessor, this model features an open-air design with no surrounding frame or cockpit enclosure. This allows the rider to lean forward while flying and enjoy a full 360-degree view, enhancing both visibility and the immersive experience.

Designed for agility and minimalism, the Volonaut Airbike is constructed using advanced carbon fiber materials and parts produced through 3D printing. These choices result in a structure seven times lighter than a traditional superbike, allowing for greater ease in lift and maneuverability. The vehicle’s compact and lightweight design enables it to navigate narrow spaces, such as forest paths and valleys, as demonstrated during its inaugural flight.

A key technological feature of the Volonaut Airbike is its proprietary stabilization system, which integrates with a flight computer to allow for automatic hovering. This system ensures greater stability during flight, making the experience smoother and potentially safer.

Propulsion is achieved through a jet-powered system, and the superbike is capable of reaching speeds of up to 200 km/h (124 mph). The vehicle is designed for a single passenger and does not display any visible external propellers, contributing to its sleek and minimal aesthetic.

Additional design elements include red tail lights located at the rear, which activate when the airbike slows down, enhancing safety for other nearby aircraft. Sculpted landing stands on either side of the frame provide a stable and soft landing platform, lifting the vehicle off the ground when not in use.

The development of the Volonaut Airbike was conducted in secrecy, with no prior announcements or previews before its public unveiling. As of now, Patan has not confirmed whether the airbike will enter commercial production or become available for purchase. Nonetheless, he has expressed satisfaction in bringing another visionary concept to life, following the success of Jetson ONE. The Volonaut Airbike demonstrates the continuing evolution of personal aerial vehicles, blending innovative engineering with minimalist design.

Volonaut Airbike: Lightweight Flying Superbikes With Jet Propulsion Unveiled

, original content from Ubergizmo. Read our Copyrights and terms of use.

Xiaomi has officially entered the artificial intelligence space with the release of its first open-source reasoning model, MiMo 7B. Developed by the company’s newly established Big Model Core Team, MiMo represents a strategic shift from Xiaomi’s traditional hardware focus to advanced AI research and development.

Despite having just 7 billion parameters, MiMo has demonstrated strong performance in complex reasoning tasks, surpassing much larger models like OpenAI’s o1-mini and Alibaba’s 32B QwQ-Preview. This performance has placed Xiaomi in the spotlight for producing a highly efficient and compact model capable of rivaling more resource-intensive competitors.

Benchmark Performance and Design Strategy

MiMo 7B has outperformed its peers on benchmarks such as AIME 24-25 (a test for mathematical reasoning) and LiveCodeBench v5 (a programming challenge dataset). This success is attributed to a well-structured development process consisting of both pre-training and post-training innovations.

Pre-training Strategies Included:

• Rich Reasoning Corpus: Focused on extracting and integrating complex reasoning knowledge.
• Synthetic Data Generation: Produced approximately 200 billion tokens of expert-level reasoning data to improve training depth.
• Progressive Difficulty Training: Employed a three-phase training method with increasing levels of difficulty.
• Extensive Token Exposure: Trained across a vast 25 trillion tokens, ensuring comprehensive learning.

Post-training Enhancements:

• Test Difficulty-Driven Rewards: Introduced a novel approach for addressing reward sparsity in complex algorithmic tasks.
• Data Re-sampling Techniques: Applied to stabilize reinforcement learning processes.
• Seamless Rollout System: Increased training efficiency by 2.29 times and validation speed by 1.96 times, streamlining the reinforcement learning (RL) pipeline.

A Broader Vision for AI

The release of MiMo underscores Xiaomi’s broader ambitions in the AI sector. While previously known for consumer electronics and smart devices, Xiaomi is now positioning itself as a serious player in AI model development. By open-sourcing MiMo, Xiaomi is contributing to the collaborative AI research ecosystem, fostering innovation beyond proprietary development.

Developers and AI researchers can now access MiMo 7B and its full technical documentation via Xiaomi’s official Hugging Face repository, offering a valuable tool for further experimentation and development. This move reflects Xiaomi’s intent to build a strong presence in AI while supporting the open-source community.

MiMo 7B: Xiaomi’s Open Source AI Is A Tough New Rival To ChatGPT

, original content from Ubergizmo. Read our Copyrights and terms of use.

Xiaomi has officially entered the artificial intelligence space with the release of its first open-source reasoning model, MiMo 7B. Developed by the company’s newly established Big Model Core Team, MiMo represents a strategic shift from Xiaomi’s traditional hardware focus to advanced AI research and development.

Despite having just 7 billion parameters, MiMo has demonstrated strong performance in complex reasoning tasks, surpassing much larger models like OpenAI’s o1-mini and Alibaba’s 32B QwQ-Preview. This performance has placed Xiaomi in the spotlight for producing a highly efficient and compact model capable of rivaling more resource-intensive competitors.

Benchmark Performance and Design Strategy

MiMo 7B has outperformed its peers on benchmarks such as AIME 24-25 (a test for mathematical reasoning) and LiveCodeBench v5 (a programming challenge dataset). This success is attributed to a well-structured development process consisting of both pre-training and post-training innovations.

Pre-training Strategies Included:

• Rich Reasoning Corpus: Focused on extracting and integrating complex reasoning knowledge.
• Synthetic Data Generation: Produced approximately 200 billion tokens of expert-level reasoning data to improve training depth.
• Progressive Difficulty Training: Employed a three-phase training method with increasing levels of difficulty.
• Extensive Token Exposure: Trained across a vast 25 trillion tokens, ensuring comprehensive learning.

Post-training Enhancements:

• Test Difficulty-Driven Rewards: Introduced a novel approach for addressing reward sparsity in complex algorithmic tasks.
• Data Re-sampling Techniques: Applied to stabilize reinforcement learning processes.
• Seamless Rollout System: Increased training efficiency by 2.29 times and validation speed by 1.96 times, streamlining the reinforcement learning (RL) pipeline.

A Broader Vision for AI

The release of MiMo underscores Xiaomi’s broader ambitions in the AI sector. While previously known for consumer electronics and smart devices, Xiaomi is now positioning itself as a serious player in AI model development. By open-sourcing MiMo, Xiaomi is contributing to the collaborative AI research ecosystem, fostering innovation beyond proprietary development.

Developers and AI researchers can now access MiMo 7B and its full technical documentation via Xiaomi’s official Hugging Face repository, offering a valuable tool for further experimentation and development. This move reflects Xiaomi’s intent to build a strong presence in AI while supporting the open-source community.

MiMo 7B: Xiaomi’s Open Source AI Is A Tough New Rival To ChatGPT

, original content from Ubergizmo. Read our Copyrights and terms of use.

Xiaomi has officially entered the artificial intelligence space with the release of its first open-source reasoning model, MiMo 7B. Developed by the company’s newly established Big Model Core Team, MiMo represents a strategic shift from Xiaomi’s traditional hardware focus to advanced AI research and development.

Despite having just 7 billion parameters, MiMo has demonstrated strong performance in complex reasoning tasks, surpassing much larger models like OpenAI’s o1-mini and Alibaba’s 32B QwQ-Preview. This performance has placed Xiaomi in the spotlight for producing a highly efficient and compact model capable of rivaling more resource-intensive competitors.

Benchmark Performance and Design Strategy

MiMo 7B has outperformed its peers on benchmarks such as AIME 24-25 (a test for mathematical reasoning) and LiveCodeBench v5 (a programming challenge dataset). This success is attributed to a well-structured development process consisting of both pre-training and post-training innovations.

Pre-training Strategies Included:

• Rich Reasoning Corpus: Focused on extracting and integrating complex reasoning knowledge.
• Synthetic Data Generation: Produced approximately 200 billion tokens of expert-level reasoning data to improve training depth.
• Progressive Difficulty Training: Employed a three-phase training method with increasing levels of difficulty.
• Extensive Token Exposure: Trained across a vast 25 trillion tokens, ensuring comprehensive learning.

Post-training Enhancements:

• Test Difficulty-Driven Rewards: Introduced a novel approach for addressing reward sparsity in complex algorithmic tasks.
• Data Re-sampling Techniques: Applied to stabilize reinforcement learning processes.
• Seamless Rollout System: Increased training efficiency by 2.29 times and validation speed by 1.96 times, streamlining the reinforcement learning (RL) pipeline.

A Broader Vision for AI

The release of MiMo underscores Xiaomi’s broader ambitions in the AI sector. While previously known for consumer electronics and smart devices, Xiaomi is now positioning itself as a serious player in AI model development. By open-sourcing MiMo, Xiaomi is contributing to the collaborative AI research ecosystem, fostering innovation beyond proprietary development.

Developers and AI researchers can now access MiMo 7B and its full technical documentation via Xiaomi’s official Hugging Face repository, offering a valuable tool for further experimentation and development. This move reflects Xiaomi’s intent to build a strong presence in AI while supporting the open-source community.

MiMo 7B: Xiaomi’s Open Source AI Is A Tough New Rival To ChatGPT

, original content from Ubergizmo. Read our Copyrights and terms of use.

Xiaomi has officially entered the artificial intelligence space with the release of its first open-source reasoning model, MiMo 7B. Developed by the company’s newly established Big Model Core Team, MiMo represents a strategic shift from Xiaomi’s traditional hardware focus to advanced AI research and development.

Despite having just 7 billion parameters, MiMo has demonstrated strong performance in complex reasoning tasks, surpassing much larger models like OpenAI’s o1-mini and Alibaba’s 32B QwQ-Preview. This performance has placed Xiaomi in the spotlight for producing a highly efficient and compact model capable of rivaling more resource-intensive competitors.

Benchmark Performance and Design Strategy

MiMo 7B has outperformed its peers on benchmarks such as AIME 24-25 (a test for mathematical reasoning) and LiveCodeBench v5 (a programming challenge dataset). This success is attributed to a well-structured development process consisting of both pre-training and post-training innovations.

Pre-training Strategies Included:

• Rich Reasoning Corpus: Focused on extracting and integrating complex reasoning knowledge.
• Synthetic Data Generation: Produced approximately 200 billion tokens of expert-level reasoning data to improve training depth.
• Progressive Difficulty Training: Employed a three-phase training method with increasing levels of difficulty.
• Extensive Token Exposure: Trained across a vast 25 trillion tokens, ensuring comprehensive learning.

Post-training Enhancements:

• Test Difficulty-Driven Rewards: Introduced a novel approach for addressing reward sparsity in complex algorithmic tasks.
• Data Re-sampling Techniques: Applied to stabilize reinforcement learning processes.
• Seamless Rollout System: Increased training efficiency by 2.29 times and validation speed by 1.96 times, streamlining the reinforcement learning (RL) pipeline.

A Broader Vision for AI

The release of MiMo underscores Xiaomi’s broader ambitions in the AI sector. While previously known for consumer electronics and smart devices, Xiaomi is now positioning itself as a serious player in AI model development. By open-sourcing MiMo, Xiaomi is contributing to the collaborative AI research ecosystem, fostering innovation beyond proprietary development.

Developers and AI researchers can now access MiMo 7B and its full technical documentation via Xiaomi’s official Hugging Face repository, offering a valuable tool for further experimentation and development. This move reflects Xiaomi’s intent to build a strong presence in AI while supporting the open-source community.

MiMo 7B: Xiaomi’s Open Source AI Is A Tough New Rival To ChatGPT

, original content from Ubergizmo. Read our Copyrights and terms of use.