Breaktime Tech Talks

This week included reviewing abstracts for NODES developer conference returning this fall, making progress on the book, and other exciting things happening within the team.

Wrapping up a busy week of GraphRAG training and book writing with two topics worth unpacking: what we actually mean when we call AI "intelligent," and what a recent article reveals about the shifting landscape for software developers.

The hype around AI in development cuts both ways. It lowers barriers to building, but raises the stakes for intentional learning and craft. Worth thinking about wherever you are in your career.

In this episode, I sit down with Roie Schwaber-Cohen, a software engineer and developer advocate at Pinecone, to talk about smarter ways to build with AI — without burning through tokens or your patience!

What we cover:

Links & Resources:

I'm back after a couple of weeks of hiatus with a packed update. From a major book deadline to enterprise graph hackathons, summer is anything but slow.

Lots of exciting things in motion — grab the links in the show notes and happy coding!

A packed week of travel, debugging, writing, and reading. I share what I learned and ran into this week as a developer.

Highlights:

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:

Content Pieces:

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.

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.

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.

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.

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: