Microsoft AI technology page Top Builders

Explore the top contributors showcasing the highest number of Microsoft AI technology page app submissions within our community.

Microsoft

Founded in 1975 by Bill Gates and Paul Allen, Microsoft has grown to become one of the world's most influential technology companies. Originally known for its operating system, MS-DOS, and later, the Windows OS, Microsoft has continuously expanded its product portfolio to include a wide range of software, hardware, and cloud-based services. The company’s mission is to empower every person and organization on the planet to achieve more. Microsoft is a key player in the development and deployment of innovative technologies, including artificial intelligence (AI), cloud computing, and enterprise software. With a strong commitment to research and development, Microsoft is at the forefront of technological advancement, providing solutions that address the needs of both consumers and businesses around the world.

Over the decades, Microsoft has also made significant acquisitions, including LinkedIn and GitHub, enhancing its influence and capabilities in professional networking, software development, and open-source communities. The company continues to push the boundaries of technology with its Azure cloud platform, AI initiatives, and hardware offerings like the Surface series of devices.

General
Company	Microsoft
Founded	April 4, 1975
Repository	https://github.com/microsoft

Start Building with Microsoft’s Products

Microsoft offers a vast ecosystem of products and services that cater to developers, businesses, and consumers alike. From the Windows operating system to the Azure cloud platform, Microsoft’s technologies power everything from personal devices to large-scale enterprise applications. Whether you're building cutting-edge AI applications, developing business solutions, or creating consumer software, Microsoft provides the tools and platforms needed to succeed. Explore the apps created with these technologies during lablab.ai hackathons to see their potential in action.

Microsoft Products

Azure

Azure is Microsoft's cloud computing platform, providing a wide range of services including virtual machines, AI, analytics, and IoT. It supports businesses in building, managing, and deploying applications at scale. Learn more about Azure

GitHub

Acquired by Microsoft, GitHub is a platform for version control and collaboration, hosting millions of software projects. It’s essential for developers looking to manage code and collaborate with teams. Visit GitHub

Office 365

Office 365 is a suite of productivity tools that includes Word, Excel, PowerPoint, and Teams, among others. It’s designed to improve collaboration and productivity across organizations. Explore Office 365

Power BI

Power BI is a business analytics tool by Microsoft that provides interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards. Discover Power BI

AutoGen

AutoGen is an advanced open-source framework by Microsoft designed for building multi-agent systems powered by large language models (LLMs). Learn more about AutoGen

Visual Studio

Visual Studio is an integrated development environment (IDE) from Microsoft used to develop computer programs, websites, web apps, web services, and mobile apps. Explore Visual Studio

By leveraging Microsoft’s diverse range of products, developers and businesses can build powerful, scalable, and innovative solutions. Check out the apps created with Microsoft’s technologies during lablab.ai hackathons to see how they are being used in real-world scenarios.

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Microsoft AI technology page Hackathon projects

Discover innovative solutions crafted with Microsoft AI technology page, developed by our community members during our engaging hackathons.

EdgeWise-Offline AI Content Moderation

EdgeWise is an AI-driven content moderation solution designed specifically for educational platforms. Leveraging advanced AI models, our tool operates entirely offline on edge devices such as IoT devices, ensuring robust performance even in environments with limited or no internet access. The architecture of EdgeWise consists of several key components: Synthetic Data Generation: We use OpenAI's Meta-Llama-3.2-80B-Instruct-Turbo model to generate synthetic training data tailored to specific content moderation categories such as spam, inappropriate content, and misleading information. Fine-Tuned Model: The generated data is used to fine-tune the Phi model. This fine-tuned model is lightweight, optimized for edge devices, and includes specialized LoRA adapters for efficient inference. Edge Deployment: The fine-tuned model is deployed locally on devices using a Streamlit-based application. This application is designed to work entirely offline, providing real-time text categorization and content filtering without relying on external APIs or cloud services. Privacy and Security: By processing all data locally, EdgeWise ensures that user information remains private and secure. The architecture is robust, cost-effective, and highly customizable, allowing it to adapt to various educational environments and needs. This combination of advanced AI, local deployment, and a focus on privacy makes EdgeWise an ideal solution for creating safe, secure, and inclusive online learning environments globally.

DefendX

DefendX is an AI-powered cybersecurity solution focused on safeguarding edge devices by detecting and mitigating security threats like unusual login activities, background-running malware, and abnormal traffic at routers. Leveraging cutting-edge technologies such as Llama, Phi3, and Streamlit, DefendX offers real-time anomaly detection and threat analysis tailored for edge environments. Llama's robust natural language processing and Phi3's advanced machine learning algorithms enable the model to efficiently recognize and respond to potential security breaches. Integrated with a user-friendly Streamlit interface, DefendX provides seamless interaction, allowing users to easily monitor and address threats while ensuring optimal device performance and data privacy.

SafeEdge- online education inclusive and enjoyable

SafeEdge is an AI-driven content moderation solution designed specifically for educational platforms. Leveraging advanced AI models, our tool operates entirely offline on edge devices such as tablets, laptops, and IoT devices, ensuring robust performance even in environments with limited or no internet access. The architecture of SafeEdge consists of several key components: Synthetic Data Generation: We use OpenAI's Meta-Llama-3.1-70B-Instruct-Turbo model to generate synthetic training data tailored to specific content moderation categories such as spam, inappropriate content, and misleading information. Fine-Tuned Model: The generated data is used to fine-tune the Phi-3-mini-4k-instruct model. This fine-tuned model is lightweight, optimized for edge devices, and includes specialized LoRA adapters for efficient inference. Edge Deployment: The fine-tuned model is deployed locally on devices using a Streamlit-based application. This application is designed to work entirely offline, providing real-time text categorization and content filtering without relying on external APIs or cloud services. Privacy and Security: By processing all data locally, SafeEdge ensures that user information remains private and secure. The architecture is robust, cost-effective, and highly customizable, allowing it to adapt to various educational environments and needs. This combination of advanced AI, local deployment, and a focus on privacy makes SafeEdge an ideal solution for creating safe, secure, and inclusive online learning environments globally.

AI Power Cybersecurity 4 Next Gen EndPoint Detect

Team Qerberos is committed to revolutionizing the cybersecurity landscape by leveraging cutting-edge LLMs and other AI algorithms to address complex challenges in the field. Our strategy centers on creating solutions that not only advance security protocols but also integrate seamlessly into existing infrastructures. For this hackathon, Team Qerberos have developed prototype LLM models designed for synthetic data creations and deployment across various endpoints—including smartphones, laptops, servers, and IoT devices. This model operates without disrupting current production processes, providing robust, real-time detection of malicious activities with exceptional accuracy. Team Qerberos’ approach ensures that organizations can enhance their security posture while maintaining operational efficiency.