Uploaded April 2026 | Updated September 2026, 2 weeks ago
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Join the maintainers of LangChain4j for a fast-paced tour of its latest features and how to use them in real-world apps. We’ll demo an AI researcher agent for drug discovery while covering concepts like guardrails, observability, multi-LLM orchestration, agents, and testing for non-deterministic systems. We’ll also explore advanced RAG, basic RAG’s less clumsy big brother, to give your models access to data from documents, databases, and the web. By the end of this talk, you'll be ready to take your AI-powered proof-of-concept to production.
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
Join the maintainers of LangChain4j for a fast-paced tour of its latest features and how to use them in real-world apps. We’ll demo an AI researcher agent for drug discovery while covering concepts like guardrails, observability, multi-LLM orchestration, agents, and testing for non-deterministic systems. We’ll also explore advanced RAG, basic RAG’s less clumsy big brother, to give your models access to data from documents, databases, and the web. By the end of this talk, you'll be ready to take your AI-powered proof-of-concept to production.
![[VDBUH2026] Magda Miu & Alin Miu - Engineering Leadership in the Age of AI](https://i.ytimg.com/vi/DAgVdXO4NFM/mqdefault.jpg "[VDBUH2026] Magda Miu & Alin Miu - Engineering Leadership in the Age of AI
Engineering management isn’t a promotion, it’s a career shift from solving technical problems to navigating human complexity. In the AI era, this shift becomes even more challenging as leaders must make high-stakes decisions under uncertainty and guide teams through rapid change.
This talk is a condensed “management lab” for senior engineers and new managers, offering practical frameworks for building psychological safety, running effective feedback systems, coaching for autonomy, and making AI-aware decisions.
You will leave with actionable tools, not theory, to help you lead with clarity, confidence, and strategic impact from day one.")
![[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us](https://i.ytimg.com/vi/G58PbxBXC8c/mqdefault.jpg "[VDBUH2026] Abdel Sghiouar - Optimizing LLM Inference for the Rest of Us
Not every organization operates with the hyperscale resources of Anthropic, Google, or OpenAI. For the majority of businesses integrating Large Language Models (LLMs) into their critical paths, the high costs and scarcity of GPU/TPU accelerators present a significant challenge. Striking the balance between performance, availability, scalability, and cost-efficiency is a must.
While Kubernetes is a ubiquitous runtime for modern workloads, deploying LLM inference effectively demands a specialized approach. This session dives deep into practical strategies for optimizing your Kubernetes clusters and LLM Inference workloads to run efficiently and cost effectively. We will explore:
– Container and Model Optimization
– Accelerator Management
– Data & Storage
– Network & Load Balancing
– Observability
Attendees will leave with practical techniques for maximizing cost/performance for LLM inference for their AI-powered applications on Kubernetes.")
