CrankWheel allows you to share your screen while on a call. This makes it easy to create engaging presentations. You can send a link via email or SMS to the viewer and they will be able to view it in any browser on any device. CrankWheel was designed for simplicity and can be shared with customers to facilitate business deals.
CrankWheel can be used to complement calls from insurance agents, mortgage advisors and solar advisors as well as educators and customer support specialists.
CrankWheel is easy to integrate with websites and allows users to add a Demo button to receive quick notifications. We can show you if they are paying attention. Our Chrome Extension has enabled over 50,000 users to share their screens with prospects, regardless of their technical skills or device choice. CrankWheel can be used on old browsers and obscure devices, even with poor network connections. It works on Mac, Android, iOS, Blackberries, Internet Explorer, and Android.
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Epsilon3 is the leading AI-powered procedure and resource management tool designed for teams building, testing, and operating advanced products and systems.
✔ Save Time & Money
Avoid costly delays, mistakes, and inefficiencies by automatically tracking procedures and resources.
✔ Prevent Failures
Ensure the right step is completed at the right time with conditional logic and built-in revision control.
✔ Optimize Collaboration
Real-time progress updates and role-based sign-offs keep your stakeholders on the same page.
✔ Continuously Improve
Advanced data analytics and automated reporting enable rapid iteration and data-driven decisions.
Epsilon3 is trusted by industry leaders like NASA, Blue Origin, Firefly Aerospace, Sierra Space, Redwire, Shift4, AeroVironment, Commonwealth Fusion Systems, and other commercial and government organizations.
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Qualisys Track Manager
Qualisys Track Manager (QTM) is an intuitive motion capture software that acts as the primary hub for the collection, analysis, and organization of movement data utilizing Qualisys systems, enabling accurate tracking in 2D, 3D, and 6DOF of markers, rigid bodies, and skeletal structures in real-time with minimal delay. It provides seamless integration with external devices such as force plates, EMG instruments, eye trackers, video systems, and IMUs, facilitating real-time data streaming to third-party software via its built-in server and SDK capabilities, while also managing calibration, system configuration, and trajectory modifications within a single user-friendly platform. The sophisticated motion capture algorithms and reliable inverse kinematics solver of QTM efficiently address occlusions and scenarios involving multiple actors, and the system's trajectory editor, automatic marker identification (AIM), as well as its labeling, filtering, gap-filling, and measurement features, significantly enhance the efficiency of data processing. Furthermore, the software is designed to cater to a diverse range of applications, making it an essential tool for researchers and professionals in various fields.
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MiMo-V2.6-Flash
MiMo-V2.6-Flash is Xiaomi MiMo’s efficiency-focused open-source omnimodal model for coding, automation, visual work, and agentic applications. It is designed to provide a balance between model capability, inference cost, and practical performance across a broad range of workloads. The model can perform software engineering tasks, use tools, execute multi-step workflows, and interact with computer environments. Its multimodal capabilities support applications such as frontend development, presentation design, 3D content creation, game development, and visual reasoning. MiMo-V2.6 can also use multi-view visual inputs in embodied simulation environments to reason about scenes and guide actions through feedback loops. Xiaomi trained the Flash model using reinforcement learning over roughly 750,000 trajectories spanning coding, general agents, visual tasks, and cybersecurity environments. During that training process, Xiaomi reports substantial gains in long-horizon software engineering and general workflow performance compared with the model’s earlier checkpoints. The company has open-sourced the broader MiMo-V2.6 release along with its technical report, reinforcement learning environments, and RL code to support research and reproducibility. MiMo-V2.6-Flash can be accessed through MiMo Desktop, AI Studio, MiMo Code, the MiMo API Platform, OpenRouter, and Xiaomi MiMo’s open-source distribution channels.
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