SKU: 34872947638

06-09 Dodge Ram 2500 3500 Manual Trans Floor Shift Cupholder Trim Bezel Gray

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Description

06-09 Dodge Ram 2500 3500 Manual Trans Floor Shift Cupholder Trim Bezel Gray06 09 Dodge Ram 2500 3500 Manual Trans Floor Shift Cupholder Trim Bezel Gray 5KK91TRMAA Description: Dodge Ram manual transmission floor shift console cup holder trim bezel assembly, slate gray, OEM Condition: used good, light wear small scratches Removed from a 2008 Dodge Ram 4500 Slt 6. 7L Cummins Diesel G56 6 Speed Manual Transmission 4WD 188. 5In. W. B. Ext: W7 White Int: D5 Gray Approximate Fitment Year Range: 2006 2007 2008 2009 Dodge Ram 2500

06-09 Dodge Ram 2500 3500 Manual Trans Floor Shift Cupholder Trim Bezel Gray

5KK91TRMAA

Description: Dodge Ram manual transmission floor shift console cup holder trim bezel assembly, slate gray, OEM

Condition: used - good, light wear/small scratches

Removed from a 2008 Dodge Ram 4500 Slt 6.7L Cummins Diesel G56 6 Speed Manual Transmission 4WD 188.5In. W.B. / Ext: W7 White / Int: D5 Gray

Approximate Fitment Year Range: 2006 2007 2008 2009 Dodge Ram 2500 3500 4500 5500 TRUCK

Sold as: Individual

*This is a suggested year and model range for this part. Actual fitment may vary slightly; always verify fitment using part number PRIOR to purchasing a part*

All parts are USED and were removed from salvage vehicles; they have normal wear and tear associated with used items. Each part has been inspected and described as accurately as possible. Flaws are disclosed in the description and / or in the pictures; these parts are sold AS-IS, with NO warranty. It is the buyer’s responsibility to verify product fitment before purchasing.

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SKU: 34872947638

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4.3 ★★★★★
Based on 14 reviews
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Product Reviews
N
Nader
Natrona Heights, 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
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on December 31, 2025
N
noam barkay
Port Orchard, 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
R
Ryan Meyer
Draper, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 10, 2025
V
Vineeth Sai
San Leandro, US
★★★★★ 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!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 27, 2025
C
Verified Purchase
CL
Omaha, US
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 14, 2025

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