Gemma 4 is here: Google just dropped its most capable open model
Google released Gemma 4 under Apache 2.0 with four model sizes, multimodal support, and edge deployment. Here's what developers need to know.
Insights zu KI-Agenten, RAG-Systemen und intelligenter Automatisierung.
Praxisnahe Artikel über KI in Unternehmen. Ich schreibe aus echten Projekten: was funktioniert, was nicht, und warum. Technische Deep-Dives, Architekturentscheidungen, Lessons Learned aus der DSGVO-konformen KI-Integration. Kein Buzzword-Bingo, sondern das, was ich selbst gebraucht hätte.
Google released Gemma 4 under Apache 2.0 with four model sizes, multimodal support, and edge deployment. Here's what developers need to know.
PageIndex hit 98.7% accuracy on FinanceBench while traditional vector RAG sits at 60-80%. We break down the architecture differences, benchmark numbers, and a practical decision framework for choosing the right retrieval approach.
A step-by-step guide to migrating Figma designs to production-ready React code using design tokens, Storybook, Tailwind, and the Figma MCP server. No paid tools required.
What it takes to run LLM agents reliably in production: architecture patterns, evaluation frameworks, and observability tooling — from deployment path to failure patterns.
Skills provide knowledge. MCP provides connectivity. Plugins provide distribution. Learn how the three layers of the AI agent stack work together in 2026.
KI-Agenten scheitern selten an der Technologie — sondern an Architektur, Monitoring und fehlenden Guardrails. So machen Sie es besser.
Most AI agent projects don't fail because of the technology — they fail because of architecture, monitoring, and missing guardrails. Here's how to do better.
Ein RAG-System fuer die Demo ist einfach. Eines fuer die Produktion ist eine ganz andere Herausforderung. Erfahren Sie, worauf es ankommt.
Building a RAG system that works in a demo is easy. Building one that works in production is an entirely different challenge. Here's what you need to know.
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