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14k Yellow Gold Enameled 3 Leaf Clover PendantIntroducing our beautiful '14k Yellow Gold Enameled 3 Leaf Clover Pendant'. Made with high quality 14k yellow gold, this pendant is both elegant and durable. With a weight of 1. 13 GM and a finish that is polished to perfection, this piece is sure to turn heads. The length of the item is 22 mm, with a width of 15 mm, and is solidly manufactured through a casting process. The pendant features a stunning enameled 3 leaf clover with a vibrant green
Introducing our beautiful '14k Yellow Gold Enameled 3 Leaf Clover Pendant'. Made with high-quality 14k yellow gold, this pendant is both elegant and durable. With a weight of 1.13 GM and a finish that is polished to perfection, this piece is sure to turn heads. The length of the item is 22 mm, with a width of 15 mm, and is solidly manufactured through a casting process. The pendant features a stunning enameled 3 leaf clover with a vibrant green accent color. While the charm/element is 22 mm in length and 15 mm in width, the textured back enhances the design's intricate details. This product is perfect for those who appreciate quality craftsmanship and fine jewelry. Sold by unit, this themed pendant/charm is not recommended for engraving. The primary color of this masterpiece is yellow – a must-have for all jewelry enthusiasts. Upgrade your collection with our '14k Yellow Gold Enameled 3 Leaf Clover Pendant'!Precious Metal Type: 14k Yellow Gold
Weight: 1.13 GM
Material: Primary - Purity:14k
Finish:Polished
Length of Item:22 mm
Material: Accents:Enamel
Material: Accent Color 1:Green
Charm/Element Length:22 mm
Charm/Element Width:15 mm
Feature:Solid
Manufacturing Process:Casted
Material: Primary:Gold
Item Weight U/M:gm
Width of Item:15 mm
Engravable:Not Recommended
Product Type:Jewelry
Jewelry Type:Pendants & Charms
Sold By Unit:Each
Pendant/Charm Type:Themed
Profile Type:Textured Back
Material: Primary - Color:Yellow
Product ID: K1721
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4.8 ★★★★★
Based on 22 reviews
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Product Reviews
★★★★★ 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
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Reviewed in the United States on December 31, 2025
★★★★★ 5
Excellent book to truly understand LLM design patterns
Format: Paperback
I just finished reviewing Ken Huang's pocket book on LLM Design Patterns, and WOW what an amazing resource! This book is excellent if you want to truly understand how to create and enhance intelligent AI language models, all that in your pocket!
Ken makes the difficult things seem surprisingly easy, and that's the real MAGIC.
- How to prepare your data for training by making it extremely clean.
Developing the brains: the practical aspects of training, optimizing, and maintaining your models.
- Learn amazing prompting techniques (such as Chain-of-Thought and Tree-of-Thoughts) to improve your AI's reasoning and problem-solving abilities.
Learn everything there is to know about RAGs so that your LLM can incorporate outside expertise.
- It also delves into creating "agentic" AI that is capable of action and planning (not only simple plan and execute but also enhanced techniques like ReWoo!)
Really, this feels like a useful toolkit, so Ken thank you for that resource
Thanks, Idan Habler
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Reviewed in the United States on June 9, 2025
★★★★★ 3
A Broad Overview, But Light on Modern Fine-Tuning
Format: Paperback
I'm currently really interested in fine-tuning LLMs and recently completed my first LoRA-based fine-tuning on a quantized model. I came to this book looking for more detail on fine-tuning. While it touches on the topic, I found the content didn’t quite align with the current state of the field in 2025. Techniques like LoRA, QLoRA, and PEFT weren’t really covered, and the material leaned more toward what I think are older or lower level approaches. That made it harder to connect with what I’m actually working on.
That said, when I shifted to other chapters — like the sections on prompt engineering techniques such as Chain of Thought (CoT) and Tree of Thought (ToT) — I found more value. These sections were clearer, and I picked up a few practical insights, like using few-shot examples that walk through the CoT reasoning process. That’s not something I’ve tried before, and I can see how it might help smaller models that struggle with any type of reasoning tasks.
Overall, the book feels more like a broad overview of all LLM concepts. For someone exploring many topics across the LLM ecosystem, it offers a wide-ranging introduction. But for readers like me who are actively trying to learn and apply techniques like fine-tuning and quantization, it may leave you wanting up-to-date guidance.
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Reviewed in the United States on August 10, 2025
★★★★★ 5
Great foundation read for security!
Format: Paperback
This book is a great read! It builds a strong foundation and I would highly recommend it for builders who are interetsed in building on LLMs and ensuring everything is secure. Security is super important and this book does it justice!
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Reviewed in the United States on June 27, 2025
★★★★★ 5
Loved it
Format: Paperback
I’ve easily read dozens of tech books. I liked this one a lot. Sure, there were boring parts, but most of it was engaging, especially on dry subjects. I previously read “How AI Works” and found this more informative and way more enjoyable. I got through the 700 pages in about 5 weeks while also learning about probability and linear algebra from other books and online sources. I’d love to read something more advanced by the author, maybe getting into more modern applications. I feel more comfortable with the subject and feel I am now ready to conquer more advanced texts. I initially picked this up to give me some background before reading “How to Build a LLM (from scratch)”. I’ve ordered an intermediary Deep Learning with Python book as well, but wouldn’t mind a more advanced theory book to accompany these books. I’ll definitely be rereading sections of this book to further familiarize myself with topics like backpropagation. Highly recommend if you’re looking for a gentle, but broad introduction to the topic.
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Reviewed in the United States on November 14, 2025