SKU: 92229596184

Parfum N.44 – Frisch-aromatischer Unisex Duft mit alpiner Klarheit (bis zu 12 h Halt)

$44.10 $49.00
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Ships within 48 hours · Estimated delivery Aug 31 - Sep 5

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Description

Parfum N.44 – Frisch-aromatischer Unisex Duft mit alpiner Klarheit (bis zu 12 h Halt)Ein Duft wie ein klarer Gebirgsbach frisch, belebend und elegant. Parfum N. 44 kombiniert spritzige Zitrusnoten mit khler Teefrische und einer dezent holzigen Basis. Perfekt fr alle, die Leichtigkeit und pure Eleganz in einem Duft suchen ideal fr Alltag und Business. Duftpyramide: Kopfnote: Bergamotte, Mandarine Herznote: Schwarze Johannisbeere, Tee Basisnote: Galbanharz, Moschus, Pomeranze, Sandelholz Besondere Merkmale: Hoher Duftlanteil: Bis zu 50

Ein Duft wie ein klarer Gebirgsbach – frisch, belebend und elegant.

Parfum N.44 kombiniert spritzige Zitrusnoten mit kühler Teefrische und einer dezent holzigen Basis. Perfekt für alle, die Leichtigkeit und pure Eleganz in einem Duft suchen – ideal für Alltag und Business.

 

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🌿 Duftpyramide:

• Kopfnote: Bergamotte, Mandarine

• Herznote: Schwarze Johannisbeere, Tee

• Basisnote: Galbanharz, Moschus, Pomeranze, Sandelholz

 

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💎 Besondere Merkmale:

• Hoher Duftölanteil: Bis zu 50 % mehr Duftessenzen für außergewöhnliche Intensität.

• Langanhaltender Duft: Spürbar bis zu 12 Stunden – frisch, klar und edel.

• Bio-Alkohol: Sanft zur Haut, umweltfreundlich und natürlich rein.

• Ohne Wasserzusatz: Für reine, unverfälschte Duftentfaltung.

 

Erhältliche Größen: 70 ml / 30 ml / 15 ml / 3 ml

 

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💡 Anwendung & Tipps:

• Vor Gebrauch leicht schütteln, um die Duftkomponenten optimal zu aktivieren.

• Nach dem Auftragen entfalten sich zuerst die frischen Kopfnoten, gefolgt von der eleganten Herz- und Basisnote.

 

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📦 Hinweis:

 

Bei Ausverkauf kann die Lieferzeit bis zu 2 Wochen betragen

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SKU: 92229596184

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4.0 ★★★★★
Based on 19 reviews
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Product Reviews
P
Paul Pollock
Chelsea, US
★★★★★ 5
Your Blueprint for Building Smarter AI!
Format: Paperback
If you're building AI and sometimes feel a bit lost, "LLM Design Patterns" by Ken Huang is like finding the secret map you've been searching for. Ken Huang, who clearly knows his stuff (he's a renowned AI expert and works with big names like OWASP and NIST), writes in a way that just clicks, without getting bogged down in super-dense tech talk. The author even acknowledges using AI to make the language clearer for a smooth reading experience! This book covers everything you need, from getting your data squeaky clean to making AI agents that can actually think and act autonomously. For me, the parts on Retrieval-Augmented Generation (RAG) and advanced ways to 'talk' to your AI (prompting) were particularly eye-opening and immediately useful for my projects. Plus, it has handy code snippets that really help you grasp the ideas. While they're not ready for direct production copy-pasting, they illustrate the concepts perfectly for learning. It's not for absolute beginners – you'll want some basic Python and machine learning smarts to get the most out of it – but the effort is totally worth it. It even delves into making sure your AI is fair and unbiased, which was a real lightbulb moment for me. This book is crammed with actionable advice; it's less about abstract theory and more about real-world solutions you can actually use. If you're serious about building impressive AI systems professionally, this is a must-read.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 7, 2025
A
Allen Wyma
Whiting, US
★★★★★ 5
Great Resource when Integrating AI
Format: Kindle
This is a great resource when building systems that integrate with AI. It manages to cover the entire lifecycle and even tips for corporate environments!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 26, 2025
O
Om S
Whiting, US
★★★★★ 4
Title: Really Good Book for Learning LLMs
Format: Paperback, Format: Paperback
I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon. The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice. Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening. Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development. Recommended for anyone building AI systems professionally.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 25, 2025
J
Jiewen Wang
Boise, US
★★★★★ 5
a comprehensive guide at the intersection of generative AI and cybersecurity
Format: Kindle
This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity. What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike. Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 2, 2025
N
Nader
New York, US
★★★★★ 1
Light on substance and heavy on flaws
Format: Paperback
The book has a great list of topics, but fails to provide much substance any of them. Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!) Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples). Seems like the book was rushed, and the author has limited hands on experience (if any). At least we know based on the amount of flaws that it was not written by an LLM
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 31, 2025

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