🎬 Claude Sonnet 4.5 is the most INSANE AI model ever: Das müssen Sie wissen!

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Claude Sonnet 4.5 is the most INSANE AI model ever 📺 Schauen Sie sich das Video an: https://www.youtube.com/watch?v=f6DBlQGODrs ```html Claude Sonnet 4.5: Die KI, die Ihre Arbeit über Nacht erledigt…

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Donald Trump s'exprime devant les généraux américains : notre décryptage • FRANCE 24

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Donald Trump s'exprime devant les généraux américains : notre décryptage • FRANCE 24 Schauen Sie sich das Video an: https://www.youtube.com/watch?v=26_hcKPOVZk # **"General Alarm! Wie Donald Trump die US-Armee umkrempelt –…

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Maxim’s journey – Design Thinking & KI (chunk-2-2)

Der Konferenzraum „Hegel“, in dem normalerweise prozedurale Debatten oder Bobs dröhnende Verkündigungen widerhallten, war nun mit einer alarmierenden Anzahl von Haftnotizen in grellen Neonfarben zugepflastert. Timo, unser hauseigener UX/UI-Designer und…

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Maxim’s journey – Container-orchestration-scheduling (chunk-5-2)

The “Make it Fast” phase of Project Chimera was in full swing, and the AI Solutions team found themselves grappling with the formidable challenge of deploying their now type-safe and Pydantic-validated (chunk 5.1) application in a way that could handle the enterprise-scale demands Dr. Becker envisioned. The team gathered in the “Kant” conference room, its austere atmosphere oddly fitting for the complex topic at hand: Kubernetes.

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Maxim’s journey – Basic RAG Pipeline (chunk: 3.4)

The AI Solutions team had successfully navigated the initial setup of their core toolchain: Hugging Face Transformers for local models, LangChain for orchestrating LLM calls, and LlamaIndex for the fundamental task of connecting LLMs to custom data. Maxim felt a growing sense of competence. Now, it was time to combine these elements, particularly LlamaIndex and an LLM, to build their first basic Retrieval Augmented Generation (RAG) pipeline. This was the cornerstone of making Project Chimera “intelligent” about GlobalSecure’s specific insurance knowledge.

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GenAI Developers Bootcamp – Table of Contents (with Methods & Tools)

1. Introduction: Why GenAI Engineering 2025?

  • chunk: 1.0 – Intro
  • chunk: 1.1 – Trends, opportunities, challenges (Methods: AI adoption, GenAI impact; Tools: None)
  • chunk: 1.2 – Motivation, goals, story setup (Methods: Learning journey, Tools: None)
  • chunk: 1.3 – Summary
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