Breaktime Tech Talks

In this episode, hear my latest adventures in the world of Java development, focusing on integrating Langchain4j with Quarkus, tackling dependency management, and exploring the evolving landscape of generative AI in production systems. Plus, I highlight upcoming community events and must-watch videos for developers.

Highlights:

• Langchain4j + Quarkus: Read-Only Database Success & Dependency Challenges - progress on a read-o...

For the first time ever, Jennifer welcomes a guest to the show! William Lyon gives us a deep dive into the evolving world of AI agents, knowledge graphs, and the concept of memory in artificial intelligence.

Episode highlights:

• William’s career journey: from Neo4j to startups and back again
• The role of knowledge graphs in agentic memory and reasoning
• Types of memory in AI agents: episodic, procedural, and more
• How knowledge gr...

Welcome to Breaktime Tech Talks! In this episode, dive into the latest updates and challenges in the world of developer tools, AI, and graph databases. 

Episode Highlights:

• Overcoming technical hurdles with Langchain4j and Neo4j, including the new support for read-only Neo4j databases in vector indexing (Github feature pull request).
• Navigating versioning headaches and framework differences between Spring AI and Quarkus for AI...

In this episode:

• Recap of NODES 2025 and standout sessions
  • How AI and music graphs are shaping new tech (featuring Luanne Misquitta’s talk)
  • Exploring RushDB: open source tools for graph data
• Developer advocacy in the classroom: inspiring the next generation
• Updates on Spring AI, Langchain4j, and upcoming workshops
• Blog post on new Aura Fundamentals course
• Solving tough graph problems with Cypher 25

In this episode of Breaktime Tech Talks, dive into the real-world challenges and discoveries from my recent work with Langchain4j, Quarkus, and Neo4j. If you’re a developer navigating the evolving landscape of AI, vector search, and graph databases, this episode is packed with practical insights and lessons learned.

Highlights:

• Struggles with configuring hybrid search (vector + graph retrieval) in Langchain4j and Quarkus
• Pain ...

In this episode of Breaktime Tech Talks, I share an inside look into developer advocacy, discuss the highs and lows of the role, and review new features in the Cypher query language.

Highlights:

• 🔎What it’s really like to be a developer advocate: the good, the bad, and the “meh”
• 🧗🏼‍♀️Common challenges: overwhelm, travel fatigue, balancing diverse responsibilities, and learning to say “no”
• 🏢Why developer advocacy is often a ...

In this episode of Breaktime Tech Talks, we focus on frameworks, libraries, and integrations that streamline workflows and enable more powerful applications.

Key Technical Topics Covered:

• Releases! Java 25 and Langchain4j 1.5
• Spring Initializr Java version default from 17 to 21
• New blog post! Spring AI with MCP text-to-cypher
• Generating Ollama embeddings for Neo4j (Cypher vs APOC)
• Spring AI advisors (QA advisor and RAG advisor...

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 fo...

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.
• Workin...

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...

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 als...

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 Christo...

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 s...

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 Chat...

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 interface...

 

In this episode, I share some hands-on insights from building apps with Langchain4j using Quarkus and Neo4j, and compare it with Spring AI—especially around how each framework handles vector search and GraphRAG workflows. Spoiler: customization in Langchain4j feels a bit clunky.

I also dig into one article's critical take on the MCP authorization spec and why its current approach to security is mis...

In this episode, we dive into the Quarkus framework with a code repository and an article about development lessons learned. Topics covered include:

• 🔗 Building a starter application with Quarkus Neo4j and the Object Graph Mapper (OGM).
• 📝 Exploring similarities and differences between Quarkus and Spring frameworks.
• 📑 Resources for building with Quarkus and Neo4j - blog post and documentation.
• 📚 Key takeaways f...

In this episode, I have some exciting technical updates, along with insights from my recent work and learning. Topics covered include:

• 📝 Neo4j Java Driver & Object Mapping – My latest blog post and upcoming updates to the GraphAcademy Java courses.

• 🧪 Framework-less Java Apps – Experiments in building Java applications without a framework and comparing with tools like Spring and others.

• 🔧 Code Refactoring Strategy –...

In this episode, we dive into three key updates from the world of Java development and emerging tech standards:

• First, walk through a new feature in the Neo4j Java driver (v5.28.5) that enables lightweight object mapping. I’ve set up a sample code repository showcasing how to return Cypher query results directly into your Java domain objects—no full-blown OGM needed. It’s a big improvement, but with a few gotchas you’ll want ...