Uploaded April 2026 | Updated September 2026, 2 weeks ago
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This talk explores how developers can learn smarter, not harder, by applying insights from neuroscience to coding practice. You’ll discover why learning is more than storing information. We’ll dive into the difference between rate of learning and rate of retrieval, and why being a fast learner doesn’t always make you a fast retriever. You’ll learn practical strategies to make new concepts stick.
We’ll also cover how to structure your coding sessions using the primary–recency effect. Along the way, you’ll get actionable tips for boosting focus and leveraging downtime.
Whether you’re learning a new Java framework or mastering advanced concepts, you’ll walk away with science-backed tips to learn smarter, code better, and stay ahead.
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
This talk explores how developers can learn smarter, not harder, by applying insights from neuroscience to coding practice. You’ll discover why learning is more than storing information. We’ll dive into the difference between rate of learning and rate of retrieval, and why being a fast learner doesn’t always make you a fast retriever. You’ll learn practical strategies to make new concepts stick.
We’ll also cover how to structure your coding sessions using the primary–recency effect. Along the way, you’ll get actionable tips for boosting focus and leveraging downtime.
Whether you’re learning a new Java framework or mastering advanced concepts, you’ll walk away with science-backed tips to learn smarter, code better, and stay ahead.
![[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.")
