Top Holo2 Alternatives

Qwen2 is a comprehensive array of advanced language models developed by the Qwen team at Alibaba Cloud. This collection includes various models that range from base to instruction-tuned versions, with parameters from 0.5 billion up to an impressive 72 billion, demonstrating both dense configurations and a Mixture-of-Experts architecture. The Qwen2 lineup is designed to surpass many earlier open-weight models, including its predecessor Qwen1.5, while also competing effectively against proprietary models across several benchmarks in domains such as language understanding, text generation, multilingual capabilities, programming, mathematics, and logical reasoning. Additionally, this cutting-edge series is set to significantly influence the artificial intelligence landscape, providing enhanced functionalities that cater to a wide array of applications. As such, the Qwen2 models not only represent a leap in technological advancement but also pave the way for future innovations in the field.

Turn your creative concepts into extraordinary written works with ease and efficiency. This innovative platform caters to writers across the spectrum, embracing an array of writing styles and formats. Its user-friendly features empower you to explore your imagination without any boundaries. Whether you are penning novels, crafting short stories, or developing fanfiction, the simple metadata interface allows you to tailor the AI's inspirations from a broad selection of genres, fandoms, and literary styles. The prompt tuning capability lets you enhance the model using your unique datasets, making the process as effortless as choosing pieces by Edgar Allan Poe or as detailed as building a chatbot with specific dialogue records. Furthermore, you can configure Holo AI to audibly read your created content, offering a choice of six unique AI voices to enrich your experience. In addition, HoloAI guarantees that all content generation and pertinent metadata, including key-context pairs, are encrypted on the client side, ensuring that your privacy is well-protected, as developers cannot access or disclose this sensitive information. With customized datasets tailored for varied writing endeavors and comprehensive end-to-end encryption, your creative journey remains both secure and individualized. This dedication to preserving user privacy and fostering personalization significantly enhances the overall experience of writing. Ultimately, the platform stands out as an essential tool for writers seeking both inspiration and security in their creative processes.

Matplotlib is a flexible library that facilitates the creation of static, animated, and interactive graphs in Python. It not only makes it easy to generate simple plots but also supports the development of intricate visualizations. A wide range of third-party extensions further amplifies Matplotlib's functionality, offering sophisticated plotting interfaces like Seaborn, HoloViews, and ggplot, as well as mapping and projection tools such as Cartopy. This rich ecosystem empowers users to customize their visual outputs according to individual requirements and tastes. Additionally, the continuous growth of the community around Matplotlib ensures that innovative features and improvements are regularly introduced, enhancing the overall user experience.

VSI HoloMedicine® by apoQlar represents a cutting-edge software solution that harnesses the capabilities of Microsoft HoloLens 2 technology to transform medical imaging, clinical practices, and educational techniques through a pioneering 3D mixed reality environment. Step away from conventional textbooks and delve into VSI’s vast digital collection of genuine medical images, case studies, and volumetric 3D mixed reality presentations. By equipping your students with sophisticated segmentation tools, you can significantly improve their grasp of anatomical structures and relationships. This platform provides an unparalleled opportunity for users to interact with actual human anatomy cases and complex pathology visuals. By incorporating these advanced tools, you can facilitate a deeper understanding of anatomy for your learners, making it more approachable than ever before. Our strategy for enhancing the field of medicine is holistic, as we have reimagined clinical workflows to fully leverage the advantages of medical mixed reality technology. Our robust medical advisory board, comprised of nearly 30 expert physicians from various specialties worldwide, plays a crucial role in steering our research and development to ensure that our offerings maintain clinical precision and relevance. This collaboration not only strengthens the credibility of our innovations but also underscores our commitment to delivering solutions that are genuinely advantageous to the medical community and its practitioners. In pursuing these goals, we aspire to foster a new era of medical education and practice that is more interactive and effective.

Spectar revolutionizes the construction industry by providing actionable Building Information Modeling (BIM) data directly at job sites through cutting-edge augmented reality technology. With the launch of Spectar 2.0, the capabilities of HoloLens 2 are fully leveraged, offering enhanced computing power, innovative tools, and a superior user experience. Clients have experienced productivity increases of up to 50% in their operations, while the efficiency of quality control processes improves as teams evaluate models in a 1:1 scale directly on-site. This approach fosters better communication and a shared understanding of design objectives among team members. By visualizing the BIM model in real time, construction professionals can quickly identify and address issues, reducing the likelihood of costly rework. The visualization further enables installation teams to access crucial information and proactively tackle potential conflicts, resulting in much faster installation timelines. Spectar also aids prefab teams by assisting in the creation and shaping of materials to meet specific project requirements, thereby streamlining the overall construction workflow. This integration not only boosts productivity but also cultivates a collaborative atmosphere among workers, ultimately leading to more successful and efficient project completions. Furthermore, the use of augmented reality fosters an innovative mindset, encouraging teams to explore new solutions and enhance their overall effectiveness on the job.

The Qwen2.5-VL represents a significant advancement in the Qwen vision-language model series, offering substantial enhancements over the earlier version, Qwen2-VL. This sophisticated model showcases remarkable skills in visual interpretation, capable of recognizing a wide variety of elements in images, including text, charts, and numerous graphical components. Acting as an interactive visual assistant, it possesses the ability to reason and adeptly utilize tools, making it ideal for applications that require interaction on both computers and mobile devices. Additionally, Qwen2.5-VL excels in analyzing lengthy videos, being able to pinpoint relevant segments within those that exceed one hour in duration. It also specializes in precisely identifying objects in images, providing bounding boxes or point annotations, and generates well-organized JSON outputs detailing coordinates and attributes. The model is designed to output structured data for various document types, such as scanned invoices, forms, and tables, which proves especially beneficial for sectors like finance and commerce. Available in both base and instruct configurations across 3B, 7B, and 72B models, Qwen2.5-VL is accessible on platforms like Hugging Face and ModelScope, broadening its availability for developers and researchers. Furthermore, this model not only enhances the realm of vision-language processing but also establishes a new benchmark for future innovations in this area, paving the way for even more sophisticated applications.

Establish connections among the right people and pertinent information at the most advantageous times. By offering extensive access to project specifics, Trimble® Connect fosters collaboration and transparency, allowing all participants to contribute to enhanced building results. Engage with 3D models that blend seamlessly with real-world visuals via our HoloLens application, which deepens comprehension of the project. With accessibility across mobile, desktop, and web interfaces, stakeholders can effortlessly locate the information they need, whenever necessary. Our cloud-based collaboration platform equips MEP contractors and engineers to collaborate more effectively by simplifying communication and coordination. Ensure ongoing consistency by integrating data across all design, construction, and operational phases. Serving as a unifying element among various software and hardware solutions, Trimble Connect bridges different stages of a project and the diverse contractors involved, promoting a more streamlined workflow. This integrated strategy not only boosts productivity but also results in enhanced project outcomes. Ultimately, the synergy created by Trimble Connect leads to a more cohesive and successful construction process.

Kimi K2 showcases a groundbreaking series of open-source large language models that employ a mixture-of-experts (MoE) architecture, featuring an impressive total of 1 trillion parameters, with 32 billion parameters activated specifically for enhanced task performance. With the Muon optimizer at its core, this model has been trained on an extensive dataset exceeding 15.5 trillion tokens, and its capabilities are further amplified by MuonClip’s attention-logit clamping mechanism, enabling outstanding performance in advanced knowledge comprehension, logical reasoning, mathematics, programming, and various agentic tasks. Moonshot AI offers two unique configurations: Kimi-K2-Base, which is tailored for research-level fine-tuning, and Kimi-K2-Instruct, designed for immediate use in chat and tool interactions, thus allowing for both customized development and the smooth integration of agentic functionalities. Comparative evaluations reveal that Kimi K2 outperforms many leading open-source models and competes strongly against top proprietary systems, particularly in coding tasks and complex analysis. Additionally, it features an impressive context length of 128 K tokens, compatibility with tool-calling APIs, and support for widely used inference engines, making it a flexible solution for a range of applications. The innovative architecture and features of Kimi K2 not only position it as a notable achievement in artificial intelligence language processing but also as a transformative tool that could redefine the landscape of how language models are utilized in various domains. This advancement indicates a promising future for AI applications, suggesting that Kimi K2 may lead the way in setting new standards for performance and versatility in the industry.

Tencent Hunyuan is a diverse suite of multimodal AI models developed by Tencent, integrating various modalities such as text, images, video, and 3D data, with the purpose of enhancing general-purpose AI applications like content generation, visual reasoning, and streamlining business operations. This collection includes different versions that are specifically designed for tasks such as interpreting natural language, understanding and combining visual and textual information, generating images from text prompts, creating videos, and producing 3D visualizations. The Hunyuan models leverage a mixture-of-experts approach and incorporate advanced techniques like hybrid "mamba-transformer" architectures to perform exceptionally in tasks that involve reasoning, long-context understanding, cross-modal interactions, and effective inference. A prominent instance is the Hunyuan-Vision-1.5 model, which enables "thinking-on-image," fostering sophisticated multimodal comprehension and reasoning across a variety of visual inputs, including images, video clips, diagrams, and spatial data. This powerful architecture positions Hunyuan as a highly adaptable asset in the fast-paced domain of AI, capable of tackling a wide range of challenges while continuously evolving to meet new demands. As the landscape of artificial intelligence progresses, Hunyuan’s versatility is expected to play a crucial role in shaping future applications.

Qwen3-Coder is a multifaceted coding model available in different sizes, prominently showcasing the 480B-parameter Mixture-of-Experts variant with 35B active parameters, which adeptly manages 256K-token contexts that can be scaled up to 1 million tokens. It demonstrates remarkable performance comparable to Claude Sonnet 4, having been pre-trained on a staggering 7.5 trillion tokens, with 70% of that data comprising code, and it employs synthetic data fine-tuned through Qwen2.5-Coder to bolster both coding proficiency and overall effectiveness. Additionally, the model utilizes advanced post-training techniques that incorporate substantial, execution-guided reinforcement learning, enabling it to generate a wide array of test cases across 20,000 parallel environments, thus excelling in multi-turn software engineering tasks like SWE-Bench Verified without requiring test-time scaling. Beyond the model itself, the open-source Qwen Code CLI, inspired by Gemini Code, equips users to implement Qwen3-Coder within dynamic workflows by utilizing customized prompts and function calling protocols while ensuring seamless integration with Node.js, OpenAI SDKs, and environment variables. This robust ecosystem not only aids developers in enhancing their coding projects efficiently but also fosters innovation by providing tools that adapt to various programming needs. Ultimately, Qwen3-Coder stands out as a powerful resource for developers seeking to improve their software development processes.

Surfer H, created by H Company, is a cutting-edge autonomous web-agent platform that is adept at interpreting and engaging with user interfaces in a manner akin to human interaction, utilizing three specialized modular components: a policy model that focuses on task planning, a localizer model for the visual identification of user interface elements, and a validator model for confirming outcomes. This agent functions solely through the browser interface, eliminating the need for dedicated API connections, which enables it to perform a variety of actions such as scrolling, clicking, typing, and handling a range of online tasks that include hotel reservations, product comparisons, and systematic data extraction. When paired with H Company’s open-weight vision-language models, Surfer H has shown outstanding performance, achieving an impressive 92.2% accuracy on the WebVoyager benchmark at a cost of about $0.13 per task, and it can be implemented locally, via Docker, or on cloud-based platforms. Its adaptable nature makes it suitable for a variety of applications, including web automation, quality assurance testing that eliminates the need for fragile scripts, data collection, and the creation of intelligent workflow agents that simulate human web interactions, thereby significantly improving efficiency in digital endeavors. Additionally, the capacity for customization across numerous scenarios positions Surfer H as an essential asset for enterprises looking to enhance their online efficiencies and streamline their operational processes.

Qwen2.5-Max is a cutting-edge Mixture-of-Experts (MoE) model developed by the Qwen team, trained on a vast dataset of over 20 trillion tokens and improved through techniques such as Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). It outperforms models like DeepSeek V3 in various evaluations, excelling in benchmarks such as Arena-Hard, LiveBench, LiveCodeBench, and GPQA-Diamond, and also achieving impressive results in tests like MMLU-Pro. Users can access this model via an API on Alibaba Cloud, which facilitates easy integration into various applications, and they can also engage with it directly on Qwen Chat for a more interactive experience. Furthermore, Qwen2.5-Max's advanced features and high performance mark a remarkable step forward in the evolution of AI technology. It not only enhances productivity but also opens new avenues for innovation in the field.

HyperSkill stands out as a cutting-edge XR platform driven by AI, enabling users to create, publish, and evaluate immersive virtual reality training materials without the need for programming knowledge. Designed specifically for educational initiatives, workforce improvement, and skill development, it offers a straightforward drag-and-drop interface that allows for the customization of VR training scenarios, empowering users to integrate interactive 3D components, thorough instructions, key highlights, and engaging dialogues for realistic interactions. The platform is versatile and works with a wide range of VR and AR devices, including mobile phones and sophisticated AR technologies like HoloLens and Magic Leap, alongside VR headsets such as HTC Vive and Oculus Quest, ensuring effortless functionality across different platforms. With an impressive collection of over 300 ready-made simulations tailored to various industries, including healthcare, manufacturing, education, and soft skills, HyperSkill facilitates the rapid implementation of effective training programs. Furthermore, its intuitive tools and extensive resources serve to significantly enrich the educational experience for both teachers and learners alike, fostering a more engaging environment for skill acquisition. As a result, users are empowered to unlock their full potential in a variety of professional settings.

DeepSeek-V2 represents an advanced Mixture-of-Experts (MoE) language model created by DeepSeek-AI, recognized for its economical training and superior inference efficiency. This model features a staggering 236 billion parameters, engaging only 21 billion for each token, and can manage a context length stretching up to 128K tokens. It employs sophisticated architectures like Multi-head Latent Attention (MLA) to enhance inference by reducing the Key-Value (KV) cache and utilizes DeepSeekMoE for cost-effective training through sparse computations. When compared to its earlier version, DeepSeek 67B, this model exhibits substantial advancements, boasting a 42.5% decrease in training costs, a 93.3% reduction in KV cache size, and a remarkable 5.76-fold increase in generation speed. With training based on an extensive dataset of 8.1 trillion tokens, DeepSeek-V2 showcases outstanding proficiency in language understanding, programming, and reasoning tasks, thereby establishing itself as a premier open-source model in the current landscape. Its groundbreaking methodology not only enhances performance but also sets unprecedented standards in the realm of artificial intelligence, inspiring future innovations in the field.

Microsoft Mesh provides a platform for users to connect and interact in shared spaces from almost any location and device, harnessing the power of mixed reality applications. This innovative technology enhances interpersonal connections, enabling users to communicate through eye contact, facial expressions, and gestures, which allows their genuine selves to shine through as the technology itself becomes less noticeable. By integrating digital intelligence into the real world, users can visualize and collaborate on 3D content that remains consistent over time, promoting a shared understanding that ignites creativity and fortifies relationships. Mesh's adaptability means that it can be accessed across a range of devices, including HoloLens 2, virtual reality headsets, smartphones, tablets, and PCs, through compatible applications. Users can embody highly realistic, photorealistic avatars in mixed reality, facilitating interactions that evoke a sense of true presence. This fluid experience allows individuals to explore their environments while receiving critical digital insights exactly when and where they are needed, which significantly boosts the efficiency of decision-making and problem-solving processes. As individuals interact within this immersive setting, the possibilities for innovation and teamwork grow dramatically, paving the way for a future where collaboration knows no bounds. The dynamic nature of Microsoft Mesh not only enhances individual experiences but also revolutionizes how teams work together across distances.

Streamline work processes by implementing intuitive, step-by-step guides, extensive digital training materials, and engaging data visualization tools. REFLEKT ONE is an adaptable Augmented Reality Platform tailored for front-line workers, incorporating an AR Viewer app alongside an easy-to-use no-code content creation system. This AR Viewer allows teams to effortlessly visualize critical information and IoT data across major platforms and compatible AR devices. Each day, workers face complex products and procedures, and outdated tools only add to the challenges they encounter. The traditional manual has become obsolete; it is crucial to deliver information in a more accessible manner to reduce errors and enhance productivity. By providing visual, step-by-step instructions right in the worker's view, we enable a streamlined and efficient workflow. Furthermore, service engineers can benefit from our customizable augmented reality training software, which is compatible with iOS, Android, Windows, and Microsoft HoloLens, equipping them to perform their tasks effectively. This contemporary method not only improves learning outcomes but also instills greater confidence in employees as they navigate their responsibilities. Ultimately, embracing this innovative technology can lead to significant advancements in workplace performance.

Qwen3-Max is Alibaba's state-of-the-art large language model, boasting an impressive trillion parameters designed to enhance performance in tasks that demand agency, coding, reasoning, and the management of long contexts. As a progression of the Qwen3 series, this model utilizes improved architecture, training techniques, and inference methods; it features both thinker and non-thinker modes, introduces a distinctive “thinking budget” approach, and offers the flexibility to switch modes according to the complexity of the tasks. With its capability to process extremely long inputs and manage hundreds of thousands of tokens, it also enables the invocation of tools and showcases remarkable outcomes across various benchmarks, including evaluations related to coding, multi-step reasoning, and agent assessments like Tau2-Bench. Although the initial iteration primarily focuses on following instructions within a non-thinking framework, Alibaba plans to roll out reasoning features that will empower autonomous agent functionalities in the near future. Furthermore, with its robust multilingual support and comprehensive training on trillions of tokens, Qwen3-Max is available through API interfaces that integrate well with OpenAI-style functionalities, guaranteeing extensive applicability across a range of applications. This extensive and innovative framework positions Qwen3-Max as a significant competitor in the field of advanced artificial intelligence language models, making it a pivotal tool for developers and researchers alike.

Qwen-7B represents the seventh iteration in Alibaba Cloud's Qwen language model lineup, also referred to as Tongyi Qianwen, featuring 7 billion parameters. This advanced language model employs a Transformer architecture and has undergone pretraining on a vast array of data, including web content, literature, programming code, and more. In addition, we have launched Qwen-7B-Chat, an AI assistant that enhances the pretrained Qwen-7B model by integrating sophisticated alignment techniques. The Qwen-7B series includes several remarkable attributes: Its training was conducted on a premium dataset encompassing over 2.2 trillion tokens collected from a custom assembly of high-quality texts and codes across diverse fields, covering both general and specialized areas of knowledge. Moreover, the model excels in performance, outshining similarly-sized competitors on various benchmark datasets that evaluate skills in natural language comprehension, mathematical reasoning, and programming challenges. This establishes Qwen-7B as a prominent contender in the AI language model landscape. In summary, its intricate training regimen and solid architecture contribute significantly to its outstanding adaptability and efficiency in a wide range of applications.

Ai2 OLMoE is a completely open-source language model that utilizes a mixture-of-experts approach, designed to operate fully on-device, which allows users to explore its capabilities in a secure and private environment. The primary goal of this application is to aid researchers in enhancing on-device intelligence while enabling developers to rapidly prototype innovative AI applications without relying on cloud services. As a highly efficient version within the Ai2 OLMo model family, OLMoE empowers users to engage with advanced local models in practical situations, explore strategies to improve smaller AI systems, and locally test their models using the provided open-source framework. Furthermore, OLMoE can be smoothly integrated into a variety of iOS applications, prioritizing user privacy and security by functioning entirely on-device. Users can easily share the results of their conversations with friends or colleagues, enjoying the benefits of a completely open-source model and application code. This makes Ai2 OLMoE an outstanding resource for personal experimentation and collaborative research, offering extensive opportunities for innovation and discovery in the field of artificial intelligence. By leveraging OLMoE, users can contribute to a growing ecosystem of on-device AI solutions that respect user privacy while facilitating cutting-edge advancements.

Kimi K2 Thinking is an advanced open-source reasoning model developed by Moonshot AI, specifically designed for complex, multi-step workflows where it adeptly merges chain-of-thought reasoning with the use of tools across various sequential tasks. It utilizes a state-of-the-art mixture-of-experts architecture, encompassing an impressive total of 1 trillion parameters, though only approximately 32 billion parameters are engaged during each inference, which boosts efficiency while retaining substantial capability. The model supports a context window of up to 256,000 tokens, enabling it to handle extraordinarily lengthy inputs and reasoning sequences without losing coherence. Furthermore, it incorporates native INT4 quantization, which dramatically reduces inference latency and memory usage while maintaining high performance. Tailored for agentic workflows, Kimi K2 Thinking can autonomously trigger external tools, managing sequential logic steps that typically involve around 200-300 tool calls in a single chain while ensuring consistent reasoning throughout the entire process. Its strong architecture positions it as an optimal solution for intricate reasoning challenges that demand both depth and efficiency, making it a valuable asset in various applications. Overall, Kimi K2 Thinking stands out for its ability to integrate complex reasoning and tool use seamlessly.

A tailored framework specifically created for building augmented reality experiences enables developers to craft captivating applications once and deploy them across a wide range of mobile and wearable AR devices. AR Foundation integrates crucial functionalities from prominent AR platforms like ARKit, ARCore, Magic Leap, and HoloLens, while also providing unique Unity features that support the development of high-quality applications intended for either internal deployment or distribution via any app store. This framework ensures a fluid workflow that optimally utilizes the strengths of these varied features in a unified way. Additionally, AR Foundation allows for the transfer of features that may not yet be accessible on all AR platforms. Should a particular feature be available on one platform but absent on another, the framework is designed to facilitate its activation at a later time. Once the feature is introduced on the new platform, developers can easily incorporate it by simply updating their packages, thereby avoiding the need to restart the entire development process from scratch. Furthermore, take advantage of the cutting-edge features and streamlined workflows being introduced for Unity, including the Universal Render Pipeline and ECS, to further elevate your augmented reality projects. By capitalizing on these advanced capabilities, developers can produce more adaptable and captivating AR applications that distinguish themselves in a highly competitive landscape. In the end, this comprehensive approach not only enhances the development experience but also significantly enriches the user experience, leading to greater satisfaction and engagement.

WakingApp provides an innovative augmented reality platform that leverages cutting-edge technology, enabling businesses across different industries to create engaging AR experiences with ease. Following Scope AR's acquisition of WakingApp, the company is poised to expand its capabilities significantly, facilitating the rapid integration of fresh features into the WorkLink solution and advancing the field of enterprise AR as it progresses. Notably, WorkLink is recognized as the only industrial AR knowledge platform that offers real-time remote assistance while simultaneously delivering access to established AR work instructions, thereby equipping workers with essential information effortlessly. With the incorporation of support for Microsoft HoloLens 2, users of WorkLink can now perform more complex, hands-free tasks thanks to the device’s enhanced comfort, broader field of vision, and sophisticated gesture recognition and eye-tracking functionalities. This upgrade enables employees in industrial settings to undertake longer maintenance, repair, or manufacturing activities while overseeing operations that require increased precision and control. Consequently, the strategic merger of WakingApp and Scope AR is set to transform how various sectors utilize augmented reality in their operational frameworks, potentially leading to greater efficiency and productivity. As the technology continues to evolve, organizations will likely find new and innovative applications for AR, further embedding it into their daily processes.

Cognition has introduced SWE-1.5, the latest agent-model tailored for software engineering, which boasts an extensive "frontier-size" architecture comprising hundreds of billions of parameters alongside a comprehensive end-to-end optimization that enhances both its speed and intelligence. This advanced model nearly reaches state-of-the-art coding capabilities and sets a new benchmark for latency, achieving inference speeds of up to 950 tokens per second, which is nearly six times the speed of its forerunner, Haiku 4.5, and thirteen times faster than Sonnet 4.5. Developed through rigorous reinforcement learning in realistic coding-agent environments that entail multi-turn workflows, unit tests, and quality evaluations, SWE-1.5 utilizes integrated software tools and high-performance hardware, including thousands of GB200 NVL72 chips coupled with a bespoke hypervisor infrastructure. Its innovative design facilitates more efficient management of intricate coding challenges and significantly boosts productivity for software development teams. With its combination of rapid performance, efficiency, and smart engineering, SWE-1.5 is set to revolutionize the coding model landscape and help developers tackle their tasks more effectively. The potential impact of this model on the future of software engineering practices cannot be overstated.

MRTK-Unity is a Microsoft-driven project that provides an extensive collection of tools and features aimed at simplifying the process of creating mixed reality applications across multiple platforms utilizing Unity. It encompasses a flexible input system and essential building blocks for user interface design and spatial interaction. The framework enables developers to rapidly prototype their ideas through in-editor simulations, offering immediate feedback on any alterations made. In addition, it functions as a versatile system where key components can be easily swapped out by developers as needed. Among its rich features is a button control that supports a variety of input methods, including articulated hand tracking specifically designed for HoloLens 2. Users are also equipped with standard UI elements for manipulating objects within a 3D space, enhancing usability significantly. The toolkit provides scripts for manipulating objects with either one or two hands, as well as a 2D-style plane that allows scrolling through hand input. Moreover, it includes scripts that improve object interactivity with visual responses and options for theme customization. The framework also offers multiple object positioning behaviors, such as tag-along, body-lock, and surface magnetism, and includes a script for organizing a collection of objects in a three-dimensional layout. This extensive range of functionalities makes MRTK-Unity a powerful option for developers in the mixed reality space. Ultimately, MRTK-Unity equips developers to craft engaging, immersive experiences with enhanced efficiency and adaptability, paving the way for innovative applications in the realm of mixed reality.

Qwen3-Coder is a sophisticated coding model available in multiple sizes, with its standout 480B-parameter Mixture-of-Experts variant (featuring 35B active parameters) capable of handling 256K-token contexts that can be expanded to 1M, showcasing superior performance in Agentic Coding, Browser-Use, and Tool-Use tasks, effectively competing with Claude Sonnet 4. The model undergoes a pre-training phase that utilizes a staggering 7.5 trillion tokens, of which 70% consist of code, alongside synthetic data improved from Qwen2.5-Coder, thereby boosting its coding proficiency and overall functionality. Its post-training phase benefits from extensive execution-driven reinforcement learning across 20,000 parallel environments, allowing it to tackle complex multi-turn software engineering tasks like SWE-Bench Verified without requiring test-time scaling. Furthermore, the open-source Qwen Code CLI, adapted from Gemini Code, enables the implementation of Qwen3-Coder in agentic workflows through customized prompts and function calling protocols, ensuring seamless integration with platforms like Node.js and OpenAI SDKs. This blend of powerful features and versatile accessibility makes Qwen3-Coder an invaluable asset for developers aiming to elevate their coding endeavors and streamline their workflows effectively. As a result, it serves as a pivotal resource in the rapidly evolving landscape of programming tools.

We are excited to introduce DBRX, a highly adaptable open LLM created by Databricks. This cutting-edge model sets a new standard for open LLMs by achieving remarkable performance across a wide range of established benchmarks. It offers both open-source developers and businesses the advanced features that were traditionally limited to proprietary model APIs; our assessments show that it surpasses GPT-3.5 and stands strong against Gemini 1.0 Pro. Furthermore, DBRX shines as a coding model, outperforming dedicated systems like CodeLLaMA-70B in various programming tasks, while also proving its capability as a general-purpose LLM. The exceptional quality of DBRX is further enhanced by notable improvements in training and inference efficiency. With its sophisticated fine-grained mixture-of-experts (MoE) architecture, DBRX pushes the efficiency of open models to unprecedented levels. In terms of inference speed, it can achieve performance that is twice as fast as LLaMA2-70B, and its total and active parameter counts are around 40% of those found in Grok-1, illustrating its compact structure without sacrificing performance. This unique blend of velocity and size positions DBRX as a transformative force in the realm of open AI models, promising to reshape expectations in the industry. As it continues to evolve, the potential applications for DBRX in various sectors are vast and exciting.

DeepSeek-Coder-V2 represents an innovative open-source model specifically designed to excel in programming and mathematical reasoning challenges. With its advanced Mixture-of-Experts (MoE) architecture, it features an impressive total of 236 billion parameters, activating 21 billion per token, which greatly enhances its processing efficiency and overall effectiveness. The model has been trained on an extensive dataset containing 6 trillion tokens, significantly boosting its capabilities in both coding generation and solving mathematical problems. Supporting more than 300 programming languages, DeepSeek-Coder-V2 has emerged as a leader in performance across various benchmarks, consistently surpassing other models in the field. It is available in multiple variants, including DeepSeek-Coder-V2-Instruct, tailored for tasks based on instructions, and DeepSeek-Coder-V2-Base, which serves well for general text generation purposes. Moreover, lightweight options like DeepSeek-Coder-V2-Lite-Base and DeepSeek-Coder-V2-Lite-Instruct are specifically designed for environments that demand reduced computational resources. This range of offerings allows developers to choose the model that best fits their unique requirements, ultimately establishing DeepSeek-Coder-V2 as a highly adaptable tool in the ever-evolving programming ecosystem. As technology advances, its role in streamlining coding processes is likely to become even more significant.

Yi-Lightning, developed by 01.AI under the guidance of Kai-Fu Lee, represents a remarkable advancement in large language models, showcasing both superior performance and affordability. It can handle a context length of up to 16,000 tokens and boasts a competitive pricing strategy of $0.14 per million tokens for both inputs and outputs. This makes it an appealing option for a variety of users in the market. The model utilizes an enhanced Mixture-of-Experts (MoE) architecture, which incorporates meticulous expert segmentation and advanced routing techniques, significantly improving its training and inference capabilities. Yi-Lightning has excelled across diverse domains, earning top honors in areas such as Chinese language processing, mathematics, coding challenges, and complex prompts on chatbot platforms, where it achieved impressive rankings of 6th overall and 9th in style control. Its development entailed a thorough process of pre-training, focused fine-tuning, and reinforcement learning based on human feedback, which not only boosts its overall effectiveness but also emphasizes user safety. Moreover, the model features notable improvements in memory efficiency and inference speed, solidifying its status as a strong competitor in the landscape of large language models. This innovative approach sets the stage for future advancements in AI applications across various sectors.

Wan2.2 represents a major upgrade to the Wan collection of open video foundation models by implementing a Mixture-of-Experts (MoE) architecture that differentiates the diffusion denoising process into distinct pathways for high and low noise, which significantly boosts model capacity while keeping inference costs low. This improvement utilizes meticulously labeled aesthetic data that includes factors like lighting, composition, contrast, and color tone, enabling the production of cinematic-style videos with high precision and control. With a training dataset that includes over 65% more images and 83% more videos than its predecessor, Wan2.2 excels in areas such as motion representation, semantic comprehension, and aesthetic versatility. In addition, the release introduces a compact TI2V-5B model that features an advanced VAE and achieves a remarkable compression ratio of 16×16×4, allowing for both text-to-video and image-to-video synthesis at 720p/24 fps on consumer-grade GPUs like the RTX 4090. Prebuilt checkpoints for the T2V-A14B, I2V-A14B, and TI2V-5B models are also provided, making it easy to integrate these advancements into a variety of projects and workflows. This development not only improves video generation capabilities but also establishes a new standard for the performance and quality of open video models within the industry, showcasing the potential for future innovations in video technology.

Qwen3-Omni represents a cutting-edge multilingual omni-modal foundation model adept at processing text, images, audio, and video, and it delivers real-time responses in both written and spoken forms. It features a distinctive Thinker-Talker architecture paired with a Mixture-of-Experts (MoE) framework, employing an initial text-focused pretraining phase followed by a mixed multimodal training approach, which guarantees superior performance across all media types while maintaining high fidelity in both text and images. This advanced model supports an impressive array of 119 text languages, alongside 19 for speech input and 10 for speech output. Exhibiting remarkable capabilities, it achieves top-tier performance across 36 benchmarks in audio and audio-visual tasks, claiming open-source SOTA on 32 benchmarks and overall SOTA on 22, thus competing effectively with notable closed-source alternatives like Gemini-2.5 Pro and GPT-4o. To optimize efficiency and minimize latency in audio and video delivery, the Talker component employs a multi-codebook strategy for predicting discrete speech codecs, which streamlines the process compared to traditional, bulkier diffusion techniques. Furthermore, its remarkable versatility allows it to adapt seamlessly to a wide range of applications, making it a valuable tool in various fields. Ultimately, this model is paving the way for the future of multimodal interaction.