Hats
SKU: 66305735171

Hats

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Description

HatsYou have an invitation at 5 p. m. at the Mad Hatter's garden. The table is ready, the cookies taste like buttery heaven, and the tea is strong and spicy. "More sugar?" asks the Mad Hatter, giving you a bizarre look. "Yes, plea" "Time's up!" he yells, interrupting you. Sugar, cookies, and millions of hats fly everywhere. Tea spills all over the tablecloth as he proceeds with a huge smile on his face. "It's time to play a game." In Hats, two to four

You have an invitation at 5 p.m. at the Mad Hatter's garden. The table is ready, the cookies taste like buttery heaven, and the tea is strong and spicy. "More sugar?" asks the Mad Hatter, giving you a bizarre look.

"Yes, plea..."

"Time's up!" he yells, interrupting you. Sugar, cookies, and millions of hats fly everywhere. Tea spills all over the tablecloth as he proceeds with a huge smile on his face. "It's time to play a game."

---

In Hats, two to four players compete to acquire the most outstanding hats by exchanging cards in hand with cards on the tea table board. Each card exchange influences how each hat is scored. Naturally, at the end of the game, the player with the highest score will be declared the maddest!

To play, everyone draws nine cards. You take your turn by performing one of the following two actions:

Exchange hats - Play a single card from your hand face up and exchange it with one of the same type (color) from the tea table board or exchange it for a card of any type but of a lower value. Add the card exchanged from the tea table board to your collection by placing it face up in front of you.
Create a black hat - Play a single card face down in front of you to add it to your collection as a black hat. Each black hat in a player's collection at game's end is worth 1 point.
Optional action: At any time during your turn, you may discard a single card of your choice to draw a new one from the draw deck. In a four-player game, players will exchange cards with their teammates.

END GAME SCORING

Hat Collection: Players earn points for the hat cards in their collection based on the position of the matching type on the tea table board. If two or more hat cards on the tea table board are of the same type, find the hat card of that type with the lowest position on the tea table board and keep it face up, while turning all other cards of that type face down.

Favorite Hat: Each player reveals the final card in their hand as their "favorite hat" type. Players gain points equal to the sum of all cards in their collection that match the type of their "favorite hat" minus the value of the final card in their hand.

The Last Cookie: There is only one cookie left at the table. Players compete for the cookie by having the most different types of hat cards in their collection. Black hats count as a type. During the game, pass the cookie to the player who has the most different types of hat cards. The chocolate chip cookie is worth five points at the end of the game.

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SKU: 66305735171

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Lake Worth, 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.
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Reviewed in the United States on July 25, 2025
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Jiewen Wang
Chelsea, 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.
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Reviewed in the United States on July 2, 2025
N
Nader
Whiting, 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
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Reviewed in the United States on December 31, 2025
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noam barkay
New York, US
★★★★★ 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
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Ryan Meyer
Omaha, US
★★★★★ 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

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