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Dedicated to the memory of Nick Russo. Your star was bright my friend and I wish we had more time together.A conversation with Ryan Booth, Engineering Manager at Juniper on AI development practices and related development tools.Episode DescriptionRyan Booth discusses his recent experiment building a complete application using AI assistance without writing code directly. He shares insights on managing AI development workflows, context management, testing practices, and practical tips for network engineers working with AI tools.Key Topics DiscussedBuilding applications using Claude 3.5 Sonnet through Cline (VS Code extension)Managing AI context and token limits in developmentTesting and validation strategiesFrontend vs backend development experiences with AITroubleshooting techniques when working with AITools & Technologies MentionedClaude 3.5 SonnetCline (VS Code extension)OpenRouterOllamaDeepSeek CoderLangChainLlamaIndexAnsibleRedisKey PointsBreak down development into focused tasks rather than trying to handle everything at onceMaintain proper documentation and context files in directoriesValidate and test at each step rather than waiting until the endUse Git for granular version control of AI-generated codeNotable Quotes"I learned very early on when getting into the coding stuff that you can't overload it with information. You really have to kind of start just like you would a normal project. You have to build from the foundation up.""It's network automation is managing software at the end of the day. You're writing code that you have to rely on, that you have to test, that you have to validate."ResourcesCline VS Code Extension: https://github.com/cline/clineClaude AI: https://claude.aiClaude AI Computer Use: https://www.anthropic.com/news/3-5-models-and-computer-useOpenRouter: https://openrouter.aiEpisode CreditsHost: Kirk ByersGuest: Ryan BoothRecorded December 3, 2024

SummaryIn this podcast, Kirk Byers and John Capobianco discuss the  impact of AI on network automation and engineering. They explore the significance of ChatGPT, the challenges of inference, and the concept of Retrieval-Augmented Generation (RAG). John shares insights on using LangChain for building AI applications, and the role of AI agents. The conversation emphasizes the importance of adapting to AI technologies and the potential for enhancing productivity in network engineering.TakeawaysChatGPT marked a significant turning point in AI awareness.Retrieval-Augmented Generation (RAG) enhances AI capabilities.LangChain simplifies the integration of AI with network tools.AI agents can automate complex tasks in network management.Fine-tuning models can improve AI performance in specific domains.AI can significantly reduce the time needed for project development.Chapters00:00 - Introduction to AI and Network Automation01:42 - The Impact of ChatGPT05:50 - Understanding Hallucinations and Inference09:53 - Retrieval-Augmented Generation (RAG) Explained14:42 - Building with LangChain18:37 - Exploring Models and Local LLMs22:55 - Exploring Fine-Tuning and RAG Techniques25:34 - Integrating AI with Network Data29:34 - The Rise of AI Agents34:28 - Modernizing Code39:53 - Future Directions for Network EngineersReference MaterialsSelector https://www.selector.ai/John Capobianco YouTube Video on "Multi Agent AI for Network Automation" https://www.youtube.com/watch?v=8GwSIRGae10LangChain https://www.langchain.com/LlamaIndex https://www.llamaindex.ai/Streamlit https://streamlit.io/