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Description
LEGO Technic Monster Jam ThunderROARus Pull-Back 42200Features: Pull back truck for kids aged 7 and up LEGO Technic Monster Jam ThunderROARus Pull Back for girls and boys who love monster truck playsets, vehicles and dinosaur toys Truck toy with pull back action Pull the truck back and then release it to recreate the thrills of the Monster Jam arena with fast action tricks and stunts Dinosaur inspired features This kids truck features authentic design details, like the scales, spikes, teeth and realistic
Features:
- Pull-back truck for kids aged 7 and up – LEGO Technic Monster Jam ThunderROARus Pull-Back for girls and boys who love monster truck playsets, vehicles and dinosaur toys
- Truck toy with pull-back action – Pull the truck back and then release it to recreate the thrills of the Monster Jam arena with fast-action tricks and stunts
- Dinosaur-inspired features – This kids’ truck features authentic design details, like the scales, spikes, teeth and realistic tyres inspired by the real Monster Jam ThunderROARus
- A treat for kids who love to build and play – This set makes a fun treat for kids, offering lots of play potential as they develop their building skills
- A great gift for kids – This LEGO monster truck makes a fun gift idea for boys and girls who love pull-back car toys
- An introduction to engineering – LEGO Technic sets feature realistic movement and mechanisms to introduce young LEGO builders to the universe of engineering
- Measurements – A 232-piece set with a truck toy model measuring over 4.5 in. (12 cm) high, 7 in. (18 cm) long and 4 in. (11 cm) wide
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4.5 ★★★★★
Based on 23 reviews
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Product Reviews
★★★★★ 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!
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Reviewed in the United States on August 26, 2025
★★★★★ 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
★★★★★ 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
★★★★★ 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