Breaktime Tech Talks

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...

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 advis...

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 Protoc...

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...

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 inciden...

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...

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 wit...

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...

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...

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 effect...

  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...

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 ....

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. 🔧 Co...

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 wi...

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. G...

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 Augmen...

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...

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 vec...

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 o...

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. Fro...

In this episode, I discuss my challenges exploring vector databases for an upcoming demo. From what is a vector database to integration issues, hear how I tried a few different approaches with limited success and discover the surprising one with the most promise. I also explore Microsoft's "Lazy Graph RAG" approach, which seems to trade one challenge for another but could be valuable in certain ca...

This week, I explored the Javalin Java framework and project decisions I'm trying to make. I also answer: What exactly does a Developer Advocate/Evangelist/Devrel do? Finally, I reflect on an article on balancing digital consumption with actual productivity.

In this episode, there are two topics I'm looking forward to diving deeper into: vector databases and AI agents. I'm particularly interested in understanding how vector databases work, how they work with data, and their role in AI applications. Then I share my thoughts on Anthropic’s article about Building AI Agents , which discusses their varying definitions—from simple workflows to fully autonom...

In this podcast episode, hear my process of preparing for an upcoming conference with insights on how outlines enhance presentations and blog posts, as well as code and architecture. Also discuss how constant improvement is key with an online course as an example. Finally, I highlight a historical read on cryptography , and share reflections from Grace Hopper's 1982 lecture on data, hardware, and...

In this episode, I share recent blog posts. The first is about tackling challenges with GitHub repositories , offering solutions on syncing forks and using git rebasing, diffs, and pruning. Next, I created examples with Cypher DSL , providing beginner-friendly details to help users get started. Additionally, I started prepping for the Devnexus conference , where I'll be delivering my first keynote...