Best Neo4j Podcasts (2025)

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Ep51: Exploring AI Agents + Agentic GraphRAG in Java 8:39

Play Pause

10d ago8:39

8:39

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…

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Ep50: GraphRAG with Advisors + Spring AI Concepts 9:50

18d ago9:50

9:50

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, specific…

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Ep49: Moving toward AI Tools/Agents + Local RAG app with Quarkus 9:41

25d ago9:41

9:41

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: In…

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Ep48: Java GraphRAG and Langchain4j 1.0 + Thoughts on conversational interfaces 12:08

1M ago12:08

12:08

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 R…

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Ep47: Langchain4j GraphRAG Friction + MCP Authorization Headaches 7:38

1M ago7:38

7:38

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 s…

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Ep46: Quarkus Neo4j Application + Development Lessons Learned 9:21

2M ago9:21

9:21

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 Quar…

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Anna: A KVS For Any Scale 19:01

2M ago19:01

19:01

This research paper introduces Anna, a key-value store (KVS) designed for scalable performance across diverse computing environments, from single multi-core machines to globally distributed cloud deployments. Anna achieves high performance and adaptability through a partitioned, multi-master architecture utilizing wait-free execution and coordinati…

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Ep45: Exploring Java Frameworks + Launch of Spring AI 1.0 10:03

2M ago10:03

10:03

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 compar…

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Conflict-free Replicated Data Types 26:17

2M ago26:17

26:17

This academic paper introduces Conflict-free Replicated Data Types (CRDTs), which are abstract data types designed for distributed systems where data is replicated across multiple locations. CRDTs allow any replica to be modified without needing immediate coordination with other replicas, ensuring high availability and low latency. The core concept…

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Ep44: Updates in Java tools + MCP and Security 10:58

2M ago10:58

10:58

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 ful…

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CAP Twelve Years Later: How the "Rules" Have Changed 29:47

2M ago29:47

29:47

This content from InfoQ provides insights for software architects and developers through various formats like newsletters, articles, and conference information. It highlights topics in architecture, AI, data engineering, culture, methods, and DevOps. Featured pieces discuss Slack's cellular architecture, data stream processing patterns, cultivating…

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Ep43: MCP Java SDK + Naive RAG vs GraphRAG 11:18

2M ago11:18

11:18

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 unpa…

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Raft versus Paxos: An Understandable Consensus Algorithm 33:14

2M ago33:14

33:14

Raft, a consensus algorithm designed for managing a replicated log in distributed systems. It aims to be more understandable than Paxos, a widely used but complex alternative, while achieving equivalent efficiency and safety. Raft separates key consensus elements like leader election, log replication, and safety, using techniques such as problem de…

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Ep42: RAG and Java + AI-generated content and MCP 12:33

2M ago12:33

12:33

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 inte…

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Neo4j Architecture: Graph Database Internals, Performance, and Optimization 17:42

2M ago17:42

17:42

This compilation of resources offers a comprehensive examination of Neo4j's graph database architecture. It explains how Neo4j differs from relational and document-oriented databases through its native graph storage. The materials describe how nodes, relationships, and properties are stored and indexed for efficient traversal and query processing. …

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Ep41: Spring AI M7, NODES, RAG + Neo4j, Quarkus, and Intelligent Apps 11:46

3M ago11:46

11:46

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 p…

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Sentry: Error Monitoring at Scale - Design Principles Analysis 15:48

3M ago15:48

15:48

Sentry is a large-scale, open-source error monitoring platform designed for modern distributed systems. It prioritizes actionable insights by focusing on exceptions and crashes, enriching errors with contextual data, and using features such as breadcrumbs and error grouping. Sentry's architecture employs modular and decoupled components like Relay …

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Istio Service Mesh: Architecture, Security, and Traffic Management 33:58

3M ago33:58

33:58

These excerpts offer a detailed look at Istio's service mesh architecture, a critical component for managing microservices in cloud-native environments. The architecture is divided into a control plane and data plane, emphasizing security through automated mTLS and traffic management with advanced load balancing techniques. Observability is achieve…

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Ep40: Recent AI learnings + More than a vector database 10:24

3M ago10:24

10:24

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 …

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CockroachDB: SQL for Global Scale Design Principles 14:33

3M ago14:33

14:33

CockroachDB is a distributed SQL database designed for global scalability and resilience. The database achieves this through a unique architecture built on a monolithic key-value store, Raft-based replication, and hybrid logical clocks. Transaction management is optimized for global workloads using a non-blocking commit protocol and multi-region ca…

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Ep39: Why embedding models should match + Advice for starting a blog 13:36

3M ago13:36

13:36

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 bl…

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Snowflake: Revolutionizing Cloud Data Warehousing and Analytics 17:21

3M ago17:21

17:21

Snowflake, a cloud-native data warehouse, revolutionizes modern analytics through its unique architecture and capabilities. The platform separates compute and storage layers, enabling independent scaling and optimized performance. Its three-layer design encompasses cloud services, a compute layer using virtual warehouses, and a storage layer levera…

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Umar Jamil: AI Research and Models, Learning How to Learn & Technical Content - AI Portfolio Podcast 1:30:39

4M ago1:30:39

1:30:39

Machine Learning Youtuber Umar Jamil, His channel has some of the top technical explanations on technical topics related to Gen AI with over 50k followers. Follow/Connect: Umar LinkedIn: https://www.linkedin.com/in/ujamil/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 01:54 Umar's YouTube Channel 06:10 Umar's Data Scie…

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Kubernetes: Container Orchestration, Architecture, and Evolution 25:56

4M ago25:56

25:56

This collection of excerpts comprehensively examines Kubernetes, the leading container orchestration platform. It traces the historical evolution of container orchestration and highlights Kubernetes' architectural foundations, including its control plane and node components. Scalability mechanisms like horizontal pod autoscaling and cell-based arch…

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Ep38: Spring AI Debugging + How to Contribute to Open Source 13:25

4M ago13:25

13:25

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 en…

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Elasticsearch: Architecture, Applications, and Emerging Trends 18:13

4M ago18:13

18:13

This compilation of excerpts thoroughly examines Elasticsearch, focusing on its architecture, applications, and future trends. The core architecture and its integration within the Elastic Stack are highlighted, emphasizing scalability and real-time analytics. Various specialized applications are discussed, including maritime data storage, academic …

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Ep37: Vector Database Frustration + Microsoft LazyGraphRAG 12:22

4M ago12:22

12:22

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 challen…

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Maxime Labonne: LLM Scientist Roadmap, AI Scientist, LLM Course & Open Source - AI Portfolio Podcast 1:27:24

4M ago1:27:24

1:27:24

Maxime Labonne, Co-author of the LLM Engineers Handbook, creator of the LLM course on github with over 40k stars, and author of Hands on Graph Neural Networks. Follow/Connect: Maxime LinkedIn:https://www.linkedin.com/in/maxime-labonne/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 📌 00:00 – Intro 📚 01:51 – Maxime: Books & Courses …

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Ray: A Distributed Framework for Emerging AI Applications 19:40

4M ago19:40

19:40

This research paper introduces Ray, a distributed framework designed for emerging AI applications, particularly those involving reinforcement learning. It addresses the limitations of existing systems in handling the complex demands of these applications, which require continuous interaction with the environment. Ray unifies task-parallel and actor…

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Zanzibar: Google's Global Authorization System 27:21

4M ago27:21

27:21

This paper details Zanzibar, Google's globally distributed authorization system, designed to manage access control lists (ACLs) at a massive scale. Zanzibar uses a flexible data model and configuration language to handle diverse access control policies for numerous Google services, achieving high availability and low latency. The system maintains e…

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Ep36: What is a Developer Advocate + Balancing Digital Consumption 11:27

5M ago11:27

11:27

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.By jmhreif

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Google Mesa: A Geo-Replicated, Near Real-Time Data Warehouse 15:02

5M ago15:02

15:02

**Mesa** is a highly scalable, geo-replicated data warehousing system developed at Google to handle petabytes of data related to its advertising business. **Designed for near real-time data ingestion and querying**, it processes millions of updates per second and serves billions of queries daily. **Key features include strong consistency, high avai…

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Ep35: Vector Databases + Building Effective Agents 8:53

5M ago8:53

8:53

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 d…

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Time, Clocks, and the Ordering of Events in a Distributed System 13:50

5M ago13:50

13:50

This paper, "Time, Clocks, and the Ordering of Events in a Distributed System," explores the challenges of defining and managing time in distributed systems. It introduces the concept of a "happened before" relation to partially order events and presents an algorithm for creating a consistent total ordering using logical clocks. The paper then exte…

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Ep34: Outlines and Iterating + Lessons from Grace Hopper 13:21

5M ago13:21

13:21

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 Gr…

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ZooKeeper: Wait-Free Coordination for Internet-Scale Systems 26:38

5M ago26:38

26:38

This paper details the design and implementation of ZooKeeper, a high-performance coordination service for large-scale distributed systems. ZooKeeper provides a simple, wait-free API enabling developers to build various coordination primitives, such as locks and group membership, without server-side modifications. It achieves high throughput throug…

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Ep33: Cypher DSL examples + Crowd-source AI hacking 8:58

5M ago8:58

8:58

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 confer…

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TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems 17:02

5M ago17:02

17:02

This paper details TensorFlow, a large-scale machine learning system developed by Google. TensorFlow uses dataflow graphs to represent computation and manages state across diverse hardware, including CPUs, GPUs, and TPUs. It offers a flexible programming model, allowing developers to experiment with novel optimizations and training algorithms beyon…

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Tomás Hernando Kofman: Not Diamond, Gen AI Startups, AI systems and Routing - AI Portfolio Podcast 1:33:33

5M ago1:33:33

1:33:33

Tomás Hernando Kofman of Not Diamond, discussing about gen ai model routing. Follow/Connect: Tomas LinkedIn:https://www.linkedin.com/in/tomashk/ Mark Linkedin: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 01:56 Not Diamond 08:36 Router Problem 13:33 Time to Build v1 15:49 Human Categories 19:01 System Prompt 27:33 Prompt Drift 30:14…

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Ep32: Official CLI + Bread Code Framework 10:32

5M ago10:32

10:32

In this episode, I’m following up on a few things I’ve mentioned before. I dive into some updates on the Aura CLI (the official product version). I also explored the Neo4j GenAI Java library a bit more, including a connection hiccup I ran into with Neo4j Desktop. Then, I get into my experience with the Cypher DSL library and how Cypher fits into th…

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Firestore: A Serverless NoSQL Database 27:46

6M ago27:46

27:46

This paper details Google Firestore, a NoSQL serverless database built on Spanner. It highlights Firestore's ease of use, scalability, real-time query capabilities, and support for disconnected operations. The architecture, which enables multi-tenancy and efficient handling of large datasets, is explained. Performance benchmarks and practical lesso…

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Ep31: Database CLI + Containerization Refcard 9:31

6M ago9:31

9:31

In this episode, hear updates on a few projects, including Neo4j's new Aura CLI and NeoConverse. Plus, the promised blog post from last week's episode on Spring Data Neo4j entity updates. Shout-out to the JAXJUG and TampaJUG groups. If you're not involved with a tech group, there are options! Finally, a review of the DZone RefCard on Java container…

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Apache Flink: Stream and Batch Processing in a Single Engine 18:12

6M ago18:12

18:12

This research paper details Apache Flink, an open-source system unifying stream and batch data processing. Flink uses a dataflow model to handle various data processing needs, including real-time analytics and batch jobs, within a single engine. The paper explores Flink's architecture, APIs (including DataStream and DataSet APIs), and fault-toleran…

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Ep30: SDN update entity + Knowledge Graph ebook 9:06

6M ago9:06

9:06

Dive into my latest experiences with Spring Data Neo4j, specifically exploring new methods for updating nodes. After revisiting my 2023 blog post on this topic, I share insights on custom Cypher queries and projections. I also discuss the challenges of finding good examples for update operations. In addition, Neo4j announced the release of The Deve…

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Kafka: A Distributed Messaging System for Log Processing 16:52

6M ago16:52

16:52

This paper introduces Kafka, a novel distributed messaging system designed for high-throughput log processing. Kafka addresses limitations in existing messaging systems and log aggregators by offering a scalable, efficient architecture with a simple API. Key features include a pull-based consumption model, efficient storage and data transfer mechan…

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Raza Habib: Humanloop, LLM and Prompting, Gen AI Performace - AI Portfolio Podcast 1:31:00

6M ago1:31:00

1:31:00

Dr. Raza Habib, CEO of HumanLoop, discussing the complexities of large language model (LLM) deployment, monitoring, and evaluation. Follow/Connect: Raza Linkedin: https://www.linkedin.com/in/humanloop-raza/ Mark LinkedIn: https://www.linkedin.com/in/markmoyou/ Chapters: 00:00 Intro 02:19 Humanloop 06:08 Prompt Management 13:26 LLM System 17:57 AI G…

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Ep29: Neo4j GenAI plugin + How-To for Technical Content 10:16

6M ago10:16

10:16

In this episode, dive into a few exciting areas I've been exploring, including generative AI and graph technologies. I worked with Neo4j’s generative AI plugin for an experiment, and it wasn't all smooth sailing. Next, my highlights from an article in the Descript community on how to expand your educational YouTube audience—especially applied for t…

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LinkedIn: Using Set Cover to Optimize a Large-Scale Low Latency Distributed Graph 12:59

6M ago12:59

12:59

This research paper details LinkedIn's solution for optimizing low-latency graph computations within their large-scale distributed graph system. To improve performance, they implemented a modified greedy set cover algorithm to minimize the number of machines needed for processing second-degree connection queries. This optimization significantly red…

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Monolith: A Real-Time Recommendation System 20:25

6M ago20:25

20:25

This research paper details Monolith, a real-time recommendation system developed by Bytedance. Monolith addresses challenges in building scalable recommendation systems, such as sparse and dynamic data, and concept drift, by employing a collisionless embedding table and an online training architecture. Key innovations include a Cuckoo HashMap for …

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2024 Recap: Favorite highlights! 49:36

6M ago49:36

49:36

Topics: Trends/Events over the year GQL release https://neo4j.com/blog/cypher-gql-world/ GraphRAG https://neo4j.com/blog/graphrag-manifesto/ Memorable events/experiences NODES 2024, obviously :) https://neo4j.com/nodes2024/agenda/ GenAI Graph Gathering AI Eng World Fair Neo4j product releases? New Aura console LLM KG Builder https://neo4j.com/labs/…