Breaktime Tech Talks

Ep47: Langchain4j GraphRAG Friction + MCP Authorization Headaches

 
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 misaligned with how enterprises actually structure identity and access. The article I discuss breaks down both the architectural intentions and the practical enterprise concerns—token handling, overhead, and developer friction.
If you’re working at the intersection of GenAI infrastructure and enterprise systems, this one’s for you.
 

Ep46: Quarkus Neo4j Application + Development Lessons Learned

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 from an article on developer philosophy, touching on code rewrites, estimation challenges, and the importance of automation and edge cases.
Whether just curious or writing code, we all learn and face similar development challenges!

Ep45: Exploring Java Frameworks + Launch of Spring AI 1.0

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 – Lessons learned on managing updates in stages for cleaner version control and project maintenance.
🤖 Spring AI 1.0 Release – Highlights from the official launch, including an AWS blog post on architecture insights, real-world examples, and key resources for getting started with AI in Java.
Whether you're deep into Java development or just exploring the intersection of frameworks and AI, there's something here for you!

Ep44: Updates in Java tools + MCP and Security

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 to understand.
Next, we take a look at Jackson Jr, a lightweight version of the popular Jackson library. If you're working in resource-constrained environments or want faster startup times, this stripped-down data processor might be just what your project needs.
Finally, we revisit Model Context Protocol (MCP) security, following up on concerns raised in Episode 42. I share two recent articles that highlight current security limitations in MCP and practical tips for developers looking to build safely with it today, even before full support matures.
Whether you're optimizing your Java stack or exploring AI protocols, there’s something in this episode for you.

Ep43: MCP Java SDK + Naive RAG vs GraphRAG

In this episode, we dive into the latest upgrades in Neo4j tooling, along with recent bug fixes and enhancements in the LLM Knowledge Graph Builder. We also explore a new preview feature for Java object mapping in the Neo4j Java driver and check out the MCP Java SDK. Next, we highlight the new "Using Neo4j with Java" course on GraphAcademy and unpack a compelling Weaviate article on RAG vs. GraphRAG, featuring Microsoft’s GraphRAG methodology.Whether you're a Java dev, graph enthusiast, or AI-curious, there's something in here for you!

Ep42: RAG and Java + AI-generated content and MCP

Star Wars Day is nearly here, and this episode is stacked with tech goodness to celebrate! I’m diving into highlights from the Neo4j ecosystem, starting with an early look at the Using Neo4j with Java course—perfect for getting started with the Java driver in a framework-less setup.
Also in this episode:
⚙️ Behind-the-scenes of APOC + Pinecone integration
✨ Part 2 of my Intro to Retrieval Augmented Generation series
🎥 My recent guest spot on Neo4j Live, discussing the Developer’s Guide to Building a Knowledge Graph
🤖 A fascinating series on an AI content experiment from Mark Heckler
📚 Michael Hunger’s must-read blog on the Model Context Protocol (MCP)
May the Fourth be with you!

Ep41: Spring AI M7, NODES, RAG + Neo4j, Quarkus, and Intelligent Apps

In this episode, we unpack a busy week of updates, learning, and cool tech! From Spring AI’s milestone 7 release with simplified Pinecone configuration to some tricky wifi, I walk through recent changes and adventures. Plus, NODES 2025 is officially announced, and there’s hints of our upcoming GraphAcademy Java course. I also talk about the first part of my new blog series on Retrieval Augmented Generation and highlight a fantastic article on Neo4j, Quarkus, and intelligent applications.

Ep40: Recent AI learnings + More than a vector database

Fresh from the Arc of AI conference, I’m unpacking the biggest insights that stuck with me—ranging from the extremes of AI’s capabilities to the deeper implications for how we build and maintain our tech systems. I’ll also share a new blog post and code repo I published on loading data into Pinecone, some next-gen tools I’m eyeing, and thoughts on a great article from the Redis blog about why vector databases aren’t enough. Navigate the evolving landscape of LLMs, generative AI, and modern infrastructure with me in this episode.

Ep39: Why embedding models should match + Advice for starting a blog

In this podcast episode, hear about my hands-on experience (code repository on Github) understanding the importance of using the same embedding models for both creating and searching vector embeddings in databases and how mismatched models can lead to poor search results.
I also pull highlights from an article with advice for those interested in blogging, and how it particularly relates to my own approach to tech blogging.

Ep38: Spring AI Debugging + How to Contribute to Open Source

In this episode, I continue my journey with vector databases, integrating Pinecone, Neo4j, and Spring AI. While making some progress, I also encountered hurdles, such as evolving APIs and the unique architecture of vector stores.
Next, I share insights from an article on contributing to open-source projects, how it can accelerate your career and enhance both your technical and soft skills. From picking the right project to building credibility within the community, it's a series of steps that gets better with time and practice!