Breaktime Tech Talks

jmhreif
  • 5.0（2則評分）
  • 科技
  • 每週更新

A bite-sized tech podcast for busy developers where we’ll briefly cover technical topics, new snippets, and more in short time blocks. Your host, Jennifer Reif, is an avid developer and problem-solver with special interest in data, learning, and all things technology.

  1. 3 天前

    Ep55: Demystifying MCP + Future of Vibe Coding and RAG

    Explore the latest challenge with Neo4j vector indexes, demystify Model Context Protocol (MCP), and hear insights on vibe coding and Retrieval-Augmented Generation (RAG). What's Inside: Confusion around Neo4j vector indexes - models and dimensions Why knowing the embedding model matters for vector similarity search The limitations of current Neo4j vector index metadata What is Model Context Protocol (MCP) and why it matters for generative AI Real-world analogies for understanding MCP (microservices, snack choices, Docker containers) The power of MCP servers for secure, modular data access Article highlight: “From Gimmick to Game Changer – Vibe Coding Myths Debunked” How AI coding tools and generative AI are lowering barriers for developers and business users Risk mitigation vs. risk avoidance in adopting new technologies YouTube livestream: “RAG Was Fine, Until It Wasn’t” – lessons from Neo4j Graph Academy’s evolution The importance of focusing on goals over syntax in development Links & Resources: Neo4j vector index documentation Neo4j MCP server information From Gimmick to Game Changer – Vibe Coding Myths Debunked (article by Michael Hunger) RAG Was Fine, Until It Wasn’t (YouTube livestream) Thanks for listening! If you enjoyed this episode, please subscribe, share, and leave a review. Happy coding!

    13 分鐘

  2. 8月29日

    Ep54: Spring AI Integrations + Real-World RAG Challenges

    Hear my latest hands-on experiences and lessons learned from the world of AI, graph databases, and developer tooling. What’s Inside: The difference between sparse and dense vectors, and how Neo4j handles them in real-world scenarios. First impressions and practical tips on integrating Spring AI MCP with Neo4j’s MCP servers—including what worked, what didn’t, and how to piece together documentation from multiple sources. Working with Pinecone and Neo4j for vector RAG (Retrieval-Augmented Generation) and graph RAG, plus the challenges of mapping results back to Java entities. Reflections on the limitations of keyword search versus the power of contextual, conversational AI queries—using a book recommendation system demo. Highlights from the article “Your RAG Pipeline is Lying with Confidence—Here’s How I Gave It a Brain with Neo4j”, including strategies for smarter chunking, avoiding semantic drift, and improving retrieval accuracy. Links & Resources: Neo4j MCP Cypher server repository Spring AI MCP client Your RAG Pipeline is Lying with Confidence Jennifer’s Goodreads demo app Thanks for listening! If you enjoyed this episode, please subscribe, share, and leave a review. Happy coding!

    13 分鐘

  3. 8月1日

    Ep53: Language Models for Data Tasks + MCP Journey Begins

    In this episode of Breaktime Tech Talks, I delve into my recent experiences with Model Context Protocol (MCP) and Large Language Models, specifically Claude. First, I share my experiment using an LLM to clean up flat files. Then, my journey with MCP began integrating a Neo4j MCP server with Claude, highlighting the practical benefits and challenges faced with an anecdote on one particular incident where the LLM blended facts. It's also crucial to have clean data sets, but this is rather challenging. To round us out, I summarize an article about the recently released Neo4j data modeling MCP server and its functionality. Join me as I navigates these intriguing tech explorations and sift out the practical takeaways.   00:00 Introduction to Breaktime Tech Talks 00:48 Exploring Large Language Models for Flat File Cleanup 03:01 Diving into MCP Exploration 05:02 Challenges with Large Language Models 08:33 Data Set Challenges and Solutions 10:05 Highlight: Neo4j Data Modeling MCP Server 12:11 Conclusion and Future Directions

    13 分鐘

  4. 7月25日

    Ep52: Enhancing AI with Spring Advisors + GraphRAG Python Adventures

    In this episode of Breaktime Tech Talks, I share insights from my recent work, including a successful GraphRAG workshop and breakthroughs in utilizing Spring AI advisors for vector search and generative AI - check out code in my Github repository for QuestionAnswerAdvisor branch and custom advisors branch. I discuss my methods for integrating default and custom advisors, including coding details and implementation challenges. I also cover my exploration of Neo4j's GraphRAG Python package, highlighting its components and the learning curve. I give updates on my upcoming projects, advocacy activities, and my experience with new developer tools like Claude code. Finally, I share a great resource on everything you need to know about GraphRAG.   00:00 Introduction to Breaktime Tech Talks 00:37 GraphRAG Workshop and Python Learning 01:27 Spring AI Advisors and Custom Implementations 06:32 GraphRAG Python Package Insights 08:42 Developer Advocacy Updates 10:15 Exploring AI Tools and Learning Approaches 11:39 GraphRAG.com Resource Overview 12:53 Conclusion and Upcoming Projects

    13 分鐘

  5. 7月4日

    Ep51: Exploring AI Agents + Agentic GraphRAG in Java

    In this episode, I delve into the world of agents, discussing my experience with Spring AI tool calling. I share my approach to vector search and graph retrieval tools, address JSON deserialization, and avoid manual boilerplate - the code of which is all available in a Github repository branch. Plus, 1.0 updates to the main branch of the repository using traditional/manual GraphRAG. I wrap up with a recent content piece by Christoffer Bergman from Neo4j, which explores agentic AI frameworks with Java and Neo4j and the differences between traditional and agentic GraphRAG approaches. P.S. Don't forget to leave your feedback/suggestions for BTT in this form!   00:00 Introduction to Breaktime Tech Talks 00:54 Exploring Spring AI Tool Calling 01:20 Understanding Agentic Frameworks 02:13 Hands-On with Vector Search and Graph Retrieval 02:36 Challenges and Solutions in Tool Functionality 04:02 Updates and Future Plans 05:01 Agentic AI with Java and Neo4j 08:06 Conclusion and Recap

    9 分鐘

  6. 6月27日

    Ep50: GraphRAG with Advisors + Spring AI Concepts

    It's the 50th episode of Breaktime Tech Talks! And to celebrate, I launched a podcast feedback form for you, my listeners. In this 50th episode, follow my latest explorations into Spring AI and GraphRAG. I delve into my attempts to streamline the manual GraphRAG process using Spring AI advisors and tools, sharing the challenges I'm facing, specifically with context parsing from one advisor to the next. I also update the Spring AI starter kit to the 1.0 GA release and recap my Neo4j developer certification livestream. To wrap up, I highlight the Spring AI documentation's AI Concepts page that beautifully blends a blog-post style with key project information.

    10 分鐘

  7. 6月20日

    Ep49: Moving toward AI Tools/Agents + Local RAG app with Quarkus

    This week, I simplified my Langchain4j project with improved prompt variable injection. Then hear my perspective on the role of tools vs. agents in AI workflows—looking at how structured processes differ from autonomous systems, especially in the context of Java frameworks and GraphRAG. Get an inside scoop on how I use different AI coding tools: IntelliJ IDEA for in-flow coding, VS Code with agent mode for problem-solving, and ChatGPT for summarizing and refining content. Lastly, hear highlights from an article on building a local RAG app with Quarkus—clear diagrams and step-by-step breakdown of ingestion vs. retrieval workflows.

    10 分鐘

  8. 6月13日

    Ep48: Java GraphRAG and Langchain4j 1.0 + Thoughts on conversational interfaces

    This week, there were quite a few things I learned: Common steps for implementing GraphRAG in Java using Spring AI and Langchain4j, highlighting key differences in setup and customization. Study prep updates and help on the Neo4j Developer Certification for June! Celebrate Langchain4j’s 1.0 release. Two thought-provoking articles—one on enhancing RAG with graphs, and another analyzing the effectiveness of voice-based interfaces. For a high-level review of steps for GraphRAG in Java, upcoming step-by-step help for prepping to take the Neo4j certification, Langchain4j GA news, and keeping up on tech content, this episode has you covered!

    12 分鐘
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5
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簡介

A bite-sized tech podcast for busy developers where we’ll briefly cover technical topics, new snippets, and more in short time blocks. Your host, Jennifer Reif, is an avid developer and problem-solver with special interest in data, learning, and all things technology.

資訊

  • 創作者
    jmhreif
  • 活躍年代
    2024年 - 2025年
  • 集數
    55
  • 年齡分級
    兒少適宜
  • 版權
    © Copyright 2024 All rights reserved.
  • 節目網站
    Breaktime Tech Talks

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