SKU: 84315790130

B Stock Candy Apple Red Telecaster Style Body

Sale price$141.30 Regular price$157.00
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Ships within 48 hours · Estimated delivery Aug 15 - Aug 20

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Description

B Stock Candy Apple Red Telecaster Style BodyAfter months of extensive research and careful selection, we have partnered with an exceptional manufacturer to bring you top notch necks and bodies. Throughout this rigorous process, we have meticulously reviewed numerous samples in an array of captivating colors. Each sample boasts flawless quality, completely free from any defects. However, we faced a dilemma regarding what to do with these remarkable pieces. That's when we came up with a solution

After months of extensive research and careful selection, we have partnered with an exceptional manufacturer to bring you top-notch necks and bodies.

Throughout this rigorous process, we have meticulously reviewed numerous samples in an array of captivating colors. Each sample boasts flawless quality, completely free from any defects. However, we faced a dilemma regarding what to do with these remarkable pieces.

That's when we came up with a solution – a reverse auction.

Damages/Defects:

This body has a slightly mis-routed neck pocket, it;s not completely straight. As you can see from the images, it's also had mounting holes drilled for a bridge, pickguard & contorl plate, and feruules installed (badly) on the rear. 

Here's how it works:

We have assigned a fixed price to each individual body, and every 24 hours, we will reduce the price by £10 until it finds its new owner.

Please note, we have a limited number of these exceptional bodies available for auction. If you're seeking a thrilling new guitar project and a remarkable deal, this opportunity is tailor-made for you.

Remember, time is of the essence. Act swiftly to secure the body that has captured your interest, as someone else might just beat you to it!

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
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Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 84315790130

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4.3 ★★★★★
Based on 18 reviews
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A
Allen Wyma
Natrona Heights, 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
Phoenix, 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
Massapequa, 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
Lake Worth, 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
N
noam barkay
Fort Morgan, 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
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 9, 2025

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