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At TECUNIQUE, we're a stable and steadily growing Indian software services company with over 14 years of industry experience. Specializing in offshore software development and quality assurance services, we've built a reputation for delivering unique and effective solutions to start-ups, software development companies, enterprises, and digital agencies. We pride ourselves on our commitment to excellence and innovation. By blending insightful business domain knowledge with exceptional technical prowess, we craft tailor-made solutions that meet the unique needs of our clients. Our dedicated teams are adept in specific technologies, ensuring seamless integration of skills and delivering reliable, scalable, and high-quality software solutions aligned with our clients' preferences. Bespoke Dedicated Teams: Crafted to meet your specific needs and technology preferences, our dedicated teams are committed to delivering top-notch software solutions. Offshore Software Development: Accelerate your software development and scale up quickly with our 12+ years of expertise in offshore development. Quality Assurance Services: Ensure the quality of your software products with our dedicated teams of experienced QA professionals. IT Staff Augmentation: Overcome skill gaps with our client-centric software team, offering staff augmentation services. Expert Software Services: Unlock our capabilities in custom software development, product development, and quality assurance. Mission and Vision: Our mission at TECUNIQUE is to be the catalyst for our clients' success in the dynamic domain of software development. Rooted in our core values of respect, authenticity, and responsibility, we strive to ease the software outsourcing experience, reducing both time and cost to market for our clients. We envision ourselves as the leading Indian software services company, renowned for our unwavering commitment to excellence and innovation. www.tecunique.com

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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.

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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.

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Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

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