Uploaded April 2026 | Updated September 2026, 3 weeks ago
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To tell an AI coding agent what to build, you must remove all ambiguity. But removing ambiguity is literally what writing code is. So did the complexity actually disappear, or did it just move?
Working effectively with AI agents turns out to look a lot like being a good engineer taking requirements from a product owner: you don't write the code yourself, but you set constraints, probe for gaps, and iterate fast when something's off.
In this live demo I'll show the techniques that actually close that gap. Things like grilling your own spec before the agent sees it, and using visualization to catch misunderstandings before they become code. I'll do it with pi, the open-source coding agent harness I think every developer should be using right now, and make the case for why a tool you shape to fit your thinking beats one you adapt yourself to. You'll leave with concrete techniques you can use yourself, and a different mental model for working with AI.
Please subscribe to our YouTube channel @ youtube.com/@DevoxxForever
Subscribe to LinkedIn @ linkedin.com/company/voxxed-days-amsterdam
Follow us on Twitter @ twitter.com/voxxedamsterdam
To tell an AI coding agent what to build, you must remove all ambiguity. But removing ambiguity is literally what writing code is. So did the complexity actually disappear, or did it just move?
Working effectively with AI agents turns out to look a lot like being a good engineer taking requirements from a product owner: you don't write the code yourself, but you set constraints, probe for gaps, and iterate fast when something's off.
In this live demo I'll show the techniques that actually close that gap. Things like grilling your own spec before the agent sees it, and using visualization to catch misunderstandings before they become code. I'll do it with pi, the open-source coding agent harness I think every developer should be using right now, and make the case for why a tool you shape to fit your thinking beats one you adapt yourself to. You'll leave with concrete techniques you can use yourself, and a different mental model for working with AI.
![[VDBUH2026] Alexander Chatzizacharias: How to build your own fun and absurd pair programmer](https://i.ytimg.com/vi/jnPtYh-n0LM/mqdefault.jpg "[VDBUH2026] Alexander Chatzizacharias: How to build your own fun and absurd pair programmer
Tired of AI assistants that are always so boring and soulless? Alexander was. So, he decided to build his own. An AI assistant with personality, flair, and a healthy dose of sarcasm. Imagine a pair programmer that offers sarcastic feedback, makes absurd suggestions, and threatens to blow up your code when it disagrees with your changes. And when things get too quiet, it might even challenge you to a game of tic-tac-toe.
This session is for anyone who believes the best way to learn new technologies is by playfully breaking them. If you’re curious about LLMs and agents beyond the typical use cases, this talk is for you. You’ll leave with practical insights into building your own agentic LLM workflows using Spring Boot, vector databases, and locally running models. Alexander will talk about Retrieval-Augmented Generation (RAG) flows that feed LLMs the right context, multi-vector search for fast context retrieval, and Model Context Protocol (MCP) integrations that let the assistant directly meddle with your file system.
Come to learn, chuckle, and get inspired to create your own dysfunctional digital sidekick.")
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Modern data platforms demand scalability, flexibility, and interoperability—capabilities that traditional architectures often fail to deliver. The lakehouse paradigm bridges this gap by combining the strengths of data lakes and data warehouses into a unified, performant system.
This talk explores how open source technologies like Apache Spark, Delta Lake, and Apache Iceberg enable scalable lakehouse architectures. It also highlights the strategic value of contributing back to these ecosystems—driving innovation, improving reliability, and shaping the future of data engineering. Attendees will leave with practical insights on building resilient data platforms and leveraging open source as a force multiplier.")
