Breaktime Tech Talks

jmhreif
  • 5,0 (2)
  • TECNOLOGIA
  • SEMANAL

A bite-sized tech podcast for busy developers where we’ll briefly cover technical topics, new snippets, and more in short time blocks. Your host, Jennifer Reif, is an avid developer and problem-solver with special interest in data, learning, and all things technology.

  1. HÁ 2 H

    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 on O'Reilly Learning Platform and a session at a virtual AI Agents conference. 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 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 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.

    13 min

  2. 17 DE ABR.

    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) — hands-on practice with EXPLAIN, PROFILE, and query tuning reinforces key performance concepts Neo4j Infinigraph — a new approach to scaling graphs by separating graph structure from large property data, improving performance and scalability (plus, NODES AI website and NODES AI YouTube playlist) A reminder that progress comes from building, exploring, and iterating — not waiting for the perfect plan.

    17 min

  3. 10 DE ABR.

    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: 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 now offers Aura cloud database credits depending on your stage.) 🐃 Yak Shave 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 – 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?" — 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.

    12 min

  4. 27 DE MAR.

    Ep72: Live Coding Fails and Fixes + Why LLMs Lose the Plot

    My recap of virtual presentations, live streams, and workshop support — sharing wins, lessons from a humbling live coding session, and a fascinating article on solving long-running LLM memory problems. Highlights: Delivered a virtual meetup for the San Francisco ACM on building knowledge graphs with the Neo4j GraphRAG Python package (code repository) Helped as a TA during a Road to Nodes AI workshop covering MCP server integrations with Neo4j. Attempted a live stream refactoring Postgres to Neo4j using OGM — ran into challenges that revealed documentation gaps and learning opportunities Progress on the AI Java book with a productive working session Shared a blog post by James Dunham: "Long Running LLM Conversations Need Working Memory, Not Just More Context" — which mirrors issues encountered in a prior RPG project where LLMs lost story continuity over time Upcoming: Road to Nodes AI workshop on long-term memory & agentic workflows (free, virtual) Upcoming: NODES AI virtual conference — April 15th (free)

    11 min

  5. 20 DE MAR.

    Ep71: GraphRAG Learnings + Langchain4j Apps for Production

    This week, I share hard-won lessons from building a GraphRAG application with Neo4j in Python, plus standout tips from Lize Raes's Devoxx Belgium talk on taking Langchain4j apps to production. GraphRAG with Neo4j Built a Python GraphRAG app using the Neo4j GraphRAG package — knowledge graph construction, retrievers (vector, graph, text-to-cypher), and agentic orchestration Key lesson: don't let the LLM decide your entire data model. Providing node types, relationship types, and patterns as boundaries dramatically improves results Expect iteration — retrieval testing will send you back to refine your KG construction Github code: Neo4j GraphRAG Python package Langchain4j for Production (Lize Raes, Devoxx Belgium) Wrap RAG as an agent tool for multi-call retrieval instead of single-shot pipelines Filter available tools programmatically by domain to keep agents focused Wire sub-agents as @Tool for clean multi-agent orchestration Use immediate responses to skip the LLM summarization hop — saves tokens and latency 13-step walkthrough for production-grade agentic systems YouTube link: Level Up Your Langchain4j Apps for Production (Lize Raes, Devoxx Belgium 2025)

    15 min

  6. 13 DE MAR.

    Ep70: Devnexus Conference Recap + No-LLM Agentic Workflow

    Hear my recent experience at the Devnexus conference in Atlanta, where I delivered two sessions and connected with so many amazing people! Devnexus session 1: "Agents, Tools, and MCP, Oh My! Next Level AI Concepts for Developers" — a redesigned solo talk breaking down AI building blocks (agents, tool calls, context management, memory, and MCP) so developers can mix and match components for their own stack. Key takeaway: AI systems are much more than just the LLM — developers play a critical role in designing the surrounding architecture. Devnexus session 2: "Supercharging Applications with Java, Graphs, and a Touch of AI" (code repo 1, code repo 2) — a joint session with Erin Schnabel building an LLM-powered role-playing game using Langchain4j, Quarkus, and Neo4j. Multiple approaches: plain LLM chat, prompt engineering, and RAG with Neo4j as the vector/graph store, chunking documents while preserving structure via graph relationships. Our "Three Cs" challenge: Continuity (maintaining storyline), Context (growing context window), and Creativity (keeping the LLM on track without going off the rails). Splitting responsibilities between the LLM and a deterministic engine significantly improved results — a pattern developers should consider for complex AI apps. App redesign with an agentic architecture: dice roll, narration, suggestion, checkpoint, and recap agents — with the last three running concurrently for better performance. Markdown file (in one app) for agentic memory, enabling easy edits, rollbacks, and incremental indexing during live gameplay. Content spotlight: "No Keys, No LLM — Building a Wikidata Definition API with Embabel" — an article showcasing an agentic Java application that uses zero LLM. Embabel (a Java agentic framework) handles planning and execution with structured inputs/outputs, no external or local model required. Could the no-LLM agent pattern see broader adoption, or is it a niche experiment? New episodes will now use platform-agnostic Podfollow links. New blog post on jmhreif.com about Cypher AI procedures.

    21 min

  7. 27 DE FEV.

    Ep69: Growth through Challenge + Rise of Agentic AI

    Jennifer shares highlights from a week full of spontaneity and preparation. Highlights: The Bootiful Podcast (Coffee + Software) with Josh Long Impromptu livestream with Josh on building a Spring + Neo4j application with just 10 minutes prep Participated in an X Space panel on the rise of agentic AI with experts from AWS, Nvidia, and Brokk Final preparations for two Devnexus sessions and other activities The reality of setting boundaries as a developer advocate Content highlights: Brock AI-native coding platform and DICE knowledge graph library for Java Key Themes: Growth through unexpected challenges, maintaining quality over quantity, and leaning into spontaneous opportunities Links: The Bootiful Podcast episode Coffee + Software livestream with Josh recording X Space recording on The Rise of Agentic AI Brokk YouTube video Embabel DICE GitHub project Next Week: Devnexus in Atlanta! Visit the Neo4j booth.

    11 min

  8. 20 DE FEV.

    Ep68: Career Highlights + OGM Alternatives

    In this episode, hear my reflections on eight years as a Developer Advocate at Neo4j - learning in public, teaching before feeling “ready”, and navigating the constant balance between deep technical work and community engagement. Get updates on what I'm currently focused on: upcoming events, writing a more complex chapter of the Java book, sharpening Cypher skills, and exploring an article that challenges the default use of Object Graph Mappers (OGMs) in graph applications. Highlights 8 Years in Advocacy Learning fast by presenting and teaching Balancing deep work, travel, and ad hoc collaboration Adapting to the accelerating pace of tech and AI Current Projects Preparing for Devnexus and upcoming virtual events Contributing to Road to NODES AI workshops Writing a more advanced Java book chapter (avoiding the editing loop) Intentionally improving Cypher skills through deeper practice Rethinking OGMs Exploring the article “The Very Slowly Ticking Time Bomb, Your Graph Persistent Stack” Questioning whether OGMs add unnecessary translation layers in graph apps Considering alternatives Expanding the toolbox — no one-size-fits-all solution Events Devnexus (Atlanta, GA) San Francisco Bay ACM (Virtual Event) Road to NODES AI Workshops (Free, virtual)

    16 min
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Sobre

A bite-sized tech podcast for busy developers where we’ll briefly cover technical topics, new snippets, and more in short time blocks. Your host, Jennifer Reif, is an avid developer and problem-solver with special interest in data, learning, and all things technology.

Informações

  • Criado por
    jmhreif
  • Anos de atividade
    2024 - 2026
  • Episódios
    75
  • Classificação
    Livre
  • Copyright
    © Copyright 2024 All rights reserved.
  • Site do podcast
    Breaktime Tech Talks

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