Ep75: Versioning AI Models + Define Schema for Better Knowledge Graphs

Ep77: Spring AI Memory Bug + LLMs, Skills, & Your Dev Career

A packed week of travel, debugging, writing, and reading. I share what I learned and ran into this week as a developer. Highlights: * 🗣️ Spoke at the JavaMUG [https://www.meetup.com/javamug/] (Java Metroplex User Group) meetup in Dallas — highly recommended for Dallas-area developers! * 🐛 Discovered a bug in Spring AI's Neo4j chat memory integration [https://github.com/spring-projects/spring-ai/issues/6007] with the latest Spring Boot — workaround is to use in-memory storage for now. * 📖 Working on a Java book chapter covering OOP concepts (abstraction, encapsulation, polymorphism) with a focus on practical, use-case-driven learning * Event updates: * 📅 Session from the AI Agents conference is now on YouTube [https://youtu.be/n8nDHPuoD8o?si=IbR6MxyuSSTXhMPl] * GraphRAG Fundamentals O'Reilly course [https://www.oreilly.com/live-events/graphrag-fundamentals/0642572221072/] rescheduled to June in APAC timezone * NODES CFP [https://neo4j.com/nodes/] open until June 15th * 📝 Two recommended reads: * "MCP, Skills, and Agents" [https://cra.mr/mcp-skills-and-agents] by David Cramer * "LLMs and Your Career" [https://notes.eatonphil.com/2026-01-19-llms-and-your-career.html] by Phil Eaton

Ep76: Batching in Native Cypher + Avoiding Injection Vulnerabilities

A quieter week, but still full of forward motion — from clearing the Neo4j developer blog backlog and making progress on the upcoming Java book, to lining up upcoming speaking events. Plus, two developer-focused content pieces I hope you enjoy as much as I did. This Week's Updates: * Rescheduling the GraphRAG Fundamentals training [https://www.oreilly.com/live-events/graphrag-fundamentals/0642572221072/] (likely June, APAC-friendly time zones) * Cleared a backlog of community submissions for the Neo4j Developer Blog [https://neo4j.com/blog/developer/] — open to anyone with a Medium account * Cypher/SQL injection understanding from Neo4j Definitive Guide book [https://neo4j.com/books/neo4j-the-definitive-guide/] * Writing progress on a new AI-first Java learning book, drawing on literary aspirations and music pedagogy principles for a fresh teaching approach * New speaking opportunities coming in May and June — stay tuned Content Pieces: * "Batching Like a Pro" by Gemma Lamont [https://neo4j.com/blog/developer/batching-like-a-pro/] — A detailed feature comparison of apoc.periodic.iterate vs. native Cypher CALL IN TRANSACTIONS, covering memory tracking, error handling, query planning, concurrency, entity rebinding, and retry strategies. The gap has largely closed — native Cypher is nearly on par. * NODES AI videos [https://www.youtube.com/playlist?list=PL9Hl4pk2FsvXLyavSKtnRP0ySqcrtm9qY] now on YouTube — All session recordings from Nodes AI are publicly available. Link in the show notes.

Ep75: Versioning AI Models + Define Schema for Better Knowledge Graphs

This week, I prepped for upcoming events, tweaked and strategized some existing processes, and found more data on how defining a schema can produce better knowledge graph construction. Highlights: * Prepped for two upcoming events: a Graph RAG Fundamentals training [https://www.oreilly.com/live-events/graphrag-fundamentals/0642572221072/] on O'Reilly Learning Platform and a session at a virtual AI Agents conference [https://aiagentsconference.live/]. * Updating repositories for the workshop surfaced a chain-reaction lesson: upgrading frameworks leads to data changes, which require config updates, which require prompt rewrites. * Key takeaway — don't pin your apps to latest for AI models, just as you wouldn't for Docker image tags. Tie to a specific version so updates don't cascade unexpectedly. * Also revisited my tech blogging workflow and built a template script to eliminate boilerplate setup, shaving time off the writing process without sacrificing the actual content creation. * New blog post on agents, tools, and MCP [https://jmhreif.com/blog/2026/agents-mcp-thinking/] published in the process! * On the Neo4j side, I touched on the Neo4j Educator Program and how learning patterns among new developers are shifting — happy to accept feedback from educators teaching graphs. * This week's article is Hands-on KG Relation Resolution [https://www.linkedin.com/pulse/hands-on-kg-relation-resolution-mike-dillinger-phd-bkldc/] by Mike Dillinger. It examines knowledge graph construction and why defining a narrowed schema produces cleaner, more understandable graphs. Without boundaries, LLMs and NLP processes generate overly granular, spaghetti-like structures.

Ep74: Building without a Blueprint + Scaling Graphs with Infinigraph

In this episode, I reflect on career growth in tech after speaking with a group of students, along with a few technical topics I explored this week — from Cypher optimization to scaling graph databases. 💡 Highlights * Career growth isn’t linear — most skills come from experimenting, saying yes to opportunities, and building over time rather than formal training * Project ideas come from doing — exploring tools, creating content, and solving real problems often reveal what to learn next * Cypher optimization (GraphAcademy) [https://graphacademy.neo4j.com/courses/workshop-optimization/] — hands-on practice with EXPLAIN, PROFILE, and query tuning reinforces key performance concepts * Neo4j Infinigraph [https://neo4j.com/blog/graph-database/infinigraph-is-generally-available/] — a new approach to scaling graphs by separating graph structure from large property data, improving performance and scalability (plus, NODES AI website [https://neo4j.com/nodes-ai/] and NODES AI YouTube playlist [https://www.youtube.com/playlist?list=PL9Hl4pk2FsvXLyavSKtnRP0ySqcrtm9qY]) A reminder that progress comes from building, exploring, and iterating — not waiting for the perfect plan.

Ep73: Cypher Query Tuning + Does Language Still Matter in the Age of AI?

Back from a short holiday, I caught up on a few things this week — including the inevitable yak shaving. Highlights: 📖 Book Progress: wrapped up another chapter draft. I've been finding that blocking larger chunks of dedicated time makes a real difference for focus and momentum, although getting started is still the hardest part. 🎓 Neo4j Educator Program [https://neo4j.com/graphacademy/edu-program/]: spent time refreshing slide decks, resource links, emails, and tutorials for the program. Still more to do, but happy with the progress. (Also worth knowing: the Neo4j Startup Program [https://neo4j.com/startup-program/] now offers Aura cloud database credits depending on your stage.) 🐃 Yak Shave [https://softwareengineering.stackexchange.com/questions/388092/what-exactly-is-yak-shaving] of the Week: had to clear disk space on my laptop just to run required software updates — for the second time recently. Frustrating but necessary. Content: 📚 Learning: Neo4j: The Definitive Guide [https://www.oreilly.com/library/view/neo4j-the-definitive/9781098165642/] – Ch. 5 (Query Analysis & Tuning) A fantastic chapter covering how the Cypher Query Planner works (Pipeline, Slotted, and Parallel), plus deep dives into EXPLAIN and PROFILE for query optimization. Exactly the kind of under-the-hood content I've been looking for. 💬 Reading: "Does Language Still Matter in the Age of AI? [https://foojay.io/today/does-language-still-matter-in-the-age-of-ai-yes-but-the-tradeoff-has-changed/]" — David Parry A great read on why structured, verbose languages actually perform better in AI code generation — and are easier to review. Language expertise is still very much worth developing.