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binder cards 2-Pocket Mini Cards Binder for Pokemon Cards Fit 80 Pcs Cards – Zelda CardsExcellent and economical choice for the TCG player or collector. The full size (2. 5" x 3. 8") pages are made from a polypropylene material that is archival safe, acid free, and PVC free. Fits Pokemon TCG cards perfectly, it can hold 80 Cards with 40 removable sleeves. One card size is compatible with most of the trading cards, such as Pokemon, My Little Pony, Panini, X Men, etc. Enjoy collecting different types of cards safely! The high quality
- Excellent and economical choice for the TCG player or collector. The full-size (2.5" x 3.8") pages are made from a polypropylene material that is archival safe, acid-free, and PVC free.
- Fits Pokemon TCG cards perfectly, it can hold 80 Cards with 40 removable sleeves. One card size is compatible with most of the trading cards, such as Pokemon, My Little Pony, Panini, X-Men, etc. Enjoy collecting different types of cards safely!
- The high-quality materials and exquisite sewing techniques make it look fantastic and durable. You will never worry about the cards falling apart.
- Waterproof and durable, you’re guaranteed a long-lasting and visually-appealing album. Built to last for the long haul, this is an album that will look great on display for many years to come!
- This card binder is made of premium polyurethane leather and sturdy pages to hold your collection. Its polyurethane leather outer layer is both waterproof and stain-rejecting, so your cards will stay safe.
Convenience is key, which is why we designed this binder to be easy to use. You can fit card sleeves into the binder, and load them with your favorite cards. For display or storage, it's up to you! It's also perfect for quick access to all of your favorite cards. It's a great binder for everyday carry!
Dimensions:
Binder Size: 5.06" x 4.12" x 1.59" (inches)
Sleeves Size: 3.82" x 2.52" (inches)
Number of Pages: 40 Pages
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4.8 ★★★★★
Based on 1643 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
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