The case for R for AI developers

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In the realm of real-time applications, Large Language Models (LLMs) have long dominated language-centric tasks, while tools like OpenCV have excelled in the visual domain. However, the future (maybe) lies in the fusion of LLMs and deep learning, giving birth to the revolutionary concept of Large Action Models (LAMs). Imagine a world where AI not only comprehends language but mimics human actions on technology interfaces. For example, the Rabbit r1 device presented at CES 2024, driven by an AI operating system and LAM, brings this vision to life. It executes complex commands, leveraging GUIs with unprecedented ease. In this presentation, join me on a journey as a software engineer tinkering with WebRTC, Janus, and LLM/LAMs. Together, we’ll evaluate the current state of these AI technologies, unraveling the potential they hold for shaping the future of real-time applications.

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

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Technology has taken up space all over the world. From generating content with a single command on ChatGPT to getting your food served by Robots at your favorite restaurant, artificial advancements have ruled every space. Every industry is set to develop top-notch technology in every sector; finance, IT, healthcare, gaming, and banking, with competitive market standards. One of these rapidly growing industries is Mobile App Development. According to the Straits Research report, it is expected to reach USD 583.03 billion at a CAGR OF 12.8% between (2022 and 2030). It clearly shows how mobile app development has become an integral part of the digital landscape and revolutionized technology.

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Test automation is a cornerstone of software development and quality assurance in today's rapidly evolving digital landscape. Its significance cannot be overstated. Businesses can enhance efficiency, productivity, and accelerate software delivery to market through automation, streamlining testing processes effectively. This comprehensive guide addresses the best practices for test automation in 2024. It offers a detailed checklist to empower you to optimize your automation efforts and maintain a competitive edge.

The Ultimate Test Automation Guide_ Best Practices and Tips.pdf

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Many specialized tools cater to distinct stages within the software development lifecycle (SDLC). These tools target various aspects of development, delivery, and operations, each with its unique strengths. Uniting these diverse testing needs into a single continuous testing platform presents several challenges. Such a platform must seamlessly integrate with various development tools and environments, accommodate different testing methodologies, and remain flexible to adapt to organizational processes and quality standards.

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The case for R for AI developers

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