Are you feeling anxious about the future of AI and your job? In this GraphGeeks chat, Amy Hodler catches up with Kathryn (Kat) Erickson, co-author of Tech Confidential, live from the Open Data Science Conference (ODSC).

Kat shares how the tech industry shifted from excitement to fear, why bypassing failure with AI actually kills our creative fulfillment, and practical, "Zen" strategies to combat career anxiety through action and writing.

Key Takeaways

• Mental Toughness in Tech: Tech Confidential tools for maintaining your confidence and identity at any stage of the tech journey. https://www.techconfidential.ai/
• The Value of Failure: AI lets us skip the trial-and-error process, but we risk missing the unexpected breakthroughs and fulfillment that come from solving hard problems.
• Action Combats Fear: You can't control market shifts, but you can control your response.
• Reclaim the Explorer Mindset: AI has leveled the playing field. Instead of fearing the next 10 years, tech professionals have the power to actively build a better future.

Amy Hodler takes a moment with Paco Nathan (Senzing) at the Open Data Science Conference (ODSC) to chat about the shift happening in Graphs and AI.

From packed tutorial rooms to hallway conversations, graphs are no longer just for academics—they are driving real-world business cases. Paco breaks down his ODSC workshop on entity resolution, and combining graph algorithms with vector stores.

Key Takeaways:

• The Business Pivot: Why technical teams are shifting focus from just building knowledge graphs to solving immediate business problems.
• Graph + AI Integration: How entity resolution generates the clean data needed for advanced graph tech and downstream AI apps.
• Why ODSC is the Sweet Spot: A look at the thriving community, networking, and the future of growing graph focus.

In this Graph Chat, Amy Hodler catches up with graph expert and consultant Clair Sullivan at the Open Data Science Conference to talk about Entity Resolution, GraphRAG, and catching fraud.

Learn how concepts that used to be highly niche—like ontologies, semantics, and context graphs—are now entering mainstream tech workflows. And hear why we need to stop "vibe coding" everything and use knowledge graphs to deliver high-fidelity results.

More Info on Clair and what she does: https://github.com/cj2001

Listen to a quick hallway at the Open Data Science Conference (ODSC) with Amy Hodler and Daniell Stevens, Product Analytics at Adobe.

In this impromptu conversation, Dan shares his takeaways from the conference, including a trick for using SQL to bridge the definition gap between data science and engineering teams. We also dive into his personal history with graph path languages, how graph technology is colliding with LLMs to solve messy data problems like entity resolution, and why getting out of your day-to-day silo at events like ODSC is so critical for data practitioners.

How do we move past the ultimate graph answer: "It depends"?

Live from the Open Data Science Conference (ODSC), Amy Hodler sat down with Golven Leroy, graph researcher and data science lead at Graphable, to discuss groundbreaking core mathematics that could change how we calculate the compute cost, latency, and bounds of graphs and trees—independently of technology.

We also dive into his top takeaways from ODSC, including tracking LLM experiments with MLflow, revolutionary chip/circuit design, and open-source frameworks like Agor for visualizing enterprise AI agents.

Link to the referenced paper: https://msp.org/involve/2026/19-2/p05.xhtml

Is the future of AI fluid and semantic, or rigid and structured? In this episode of Graph Geeks in Discussion, host Amy Hodler sits down with Roie Schwaber-Cohen, Head of DevRel at Pinecone. Together, they explore a fascinating philosophical and operational debate: the intersection of vector geometry (semantic distance) and graph topology (explicit connectivity).

Tune in to discover why solving the next generation of AI challenges, from hallucinations to agentic long-term memory, requires fusing both worlds, and why we all might just need to "embrace the fuzziness."

Is data copying dead? In this episode of the GraphGeeks Podcast, host Amy Hodler sits down with Marko Budiselić ("Buda"), Co-founder & CTO at Memgraph, to discuss their major new releases: Memgraph Zero and MemGQL.

Discover how Memgraph is tackling the massive pain point of ETL pipelines by creating a federated GQL layer that queries data directly at the source—across Postgres, MySQL, Neo4j, ClickHouse, and more. Buda also shares how a centralized semantic layer is becoming essential for the future of AI agents.

Key topics covered:

• The core architecture and vision behind Memgraph Zero.
• How MemGQL acts as a federated graph query engine.
• Moving beyond context graphs into environmental graphs for AI agents.
• The shift toward agentic data modeling and mapping.

👉 Read more about the release: https://memgraph.com/docs/memgraph-zero

👉 Vote on the next data connectors in the Memgraph Community Roadmap Poll: forms.gle/2MLfWp24uwbJpsey8

In this chat, Amy Hodler catches up with William Lyon to discuss his recent return to the Neo4j Product Team and his deep dive into the world of AI agent frameworks and memory.

Wil discusses his hands-on workshop exploring a fascinating frontier: Graphs as the Memory for AI Agents. Moving beyond simple retrieval, Will explains how we can use graph technology to mirror human cognitive functions, including:

• Episodic Memory: Learning from user interactions.
• Semantic Memory: Building a canonical model of the world.
• Procedural Memory: Unlocking advanced, graph-based reasoning for agents.

Whether you're interested in the latest in GraphRAG, the evolution of Knowledge Graphs in the age of LLMs, or just want to hear about Will’s journey through the startup ecosystem, this conversation is packed with insights that have only become more vital since they were recorded.

Bryce Merkl Sasaki (Head of Marketing at gdotv) sits down with the "Gandalf of Graph Technology" himself, Paco Nathan (Senior DevRel at Senzing), at the Open Data Science Conference. Paco is a pioneer in neural networks and NLP since the 1980s, and a leading voice in the MLOps and Graph communities.

In this chat, Paco cuts through the current hype bubble to discuss how graph technologies and entity resolution are solving high-stakes, real-world problems. From interdicting $3 trillion in dark money and human trafficking to fixing systemic data disconnects in government agencies through the NIEM semantic standard, this conversation explores the profound human impact of well-engineered data.

Key Topics:

• Entity Resolution (ER): How connecting data points protects real people in the legal and financial systems.
• Semantic Standards: Using NIEM and SKOS for better context engineering
• Why the biggest bottleneck in AI isn't the math—it's designing UX & visualizations that humans can actually use.
• The AI Bubble: Paco’s perspective on the current industry jitters vs. the tangible tech that is ready for production.

What happens when two community-builders sit down at the Open Data Science Conference (ODSC)? They talk about the future of AI infrastructure and the importance of supporting the next generation of founders!

In this episode, Amy Hodler of GraphGeeks is joined by Michelle Yi, co-founder of Generationship, to dive into:

• The "Eval" Crisis: Why 95% of GenAI POCs fail and how rigorous evaluation is the cure.
• Causality & Graphs: Moving beyond "ice cream and shark attacks" to understand the why behind AI predictions.
• Spatial Reasoning: How graphs are becoming essential for robotics and multimodal AI.
• Generationship: Michelle’s mission to fund and support early-stage female+ founders building the future of AI infrastructure.

Connect with Michelle & Generationship:

🔗 https://www.generationship.ai/