Squid Game is back—and this time, the knives are out. In the thrilling Season 3 premiere, Player 456 is spiraling and a brutal round of hide-and-seek forces players to kill or be killed. Hosts Phil Yu and Kiera Please break down Gi-hun’s descent into vengeance, Guard 011’s daring betrayal of the Game, and the shocking moment players are forced to choose between murdering their friends… or dying. Then, Carlos Juico and Gavin Ruta from the Jumpers Jump podcast join us to unpack their wild theories for the season. Plus, Phil and Kiera face off in a high-stakes round of “Hot Sweet Potato.” SPOILER ALERT! Make sure you watch Squid Game Season 3 Episode 1 before listening on. Play one last time. IG - @SquidGameNetflix X (f.k.a. Twitter) - @SquidGame Check out more from Phil Yu @angryasianman , Kiera Please @kieraplease and the Jumpers Jump podcast Listen to more from Netflix Podcasts . Squid Game: The Official Podcast is produced by Netflix and The Mash-Up Americans.…
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Ep38: Spring AI Debugging + How to Contribute to Open Source
Manage episode 472593289 series 3579839
Inhalt bereitgestellt von jmhreif. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von jmhreif oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
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!
53 Episoden
TeilenManage episode 472593289 series 3579839
Inhalt bereitgestellt von jmhreif. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von jmhreif oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.
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!
53 Episoden
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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…
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…
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…
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.…
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.…
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Breaktime Tech Talks
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!…
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.…
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!…
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!…
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.…
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!…
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!…
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 .…
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.…
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.…
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