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  • Build Securely on AWS with Proven Frameworks Icon
    Build Securely on AWS with Proven Frameworks

    Lay a foundation for success with Tested Reference Architectures developed by Fortinet’s experts. Learn more in this white paper.

    Moving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
    Download Now
  • Streamline Azure Security with Palo Alto Networks VM-Series Icon
    Streamline Azure Security with Palo Alto Networks VM-Series

    Centrally manage physical and virtualized firewalls with Panorama

    Improve your security posture and reduce incident response time. Use the VM-Series to natively analyze Azure traffic and dynamically drive policy updates based on workload changes.
    Learn more
  • 1
    Langroid

    Langroid

    Harness LLMs with Multi-Agent Programming

    Given the remarkable abilities of recent Large Language Models (LLMs), there is an unprecedented opportunity to build intelligent applications powered by this transformative technology. The top question for any enterprise is: how best to harness the power of LLMs for complex applications? For technical and practical reasons, building LLM-powered applications is not as simple as throwing a task at an LLM system and expecting it to do it. Effectively leveraging LLMs at scale requires a principled programming framework. In particular, there is often a need to maintain multiple LLM conversations, each instructed in different ways, and "responsible" for different aspects of a task.
    Downloads: 0 This Week
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  • 2
    OWL

    OWL

    Optimized Workforce Learning for General Multi-Agent Assistance

    Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation. OWL (Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation) is an advanced framework designed to enhance multi-agent collaboration, improving task automation across various domains. By utilizing dynamic agent interactions, OWL aims to streamline and optimize complex workflows, making AI collaboration more natural, efficient, and adaptable. It is built on...
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    Downloads: 0 This Week
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