SKU: 17623834269

Porterfield Brake Pads for 2015 GMC SIERRA 1500 4WD w/Rear Drum

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Description

Porterfield Brake Pads for 2015 GMC SIERRA 1500 4WD w/Rear DrumPorterfield Brake Pads for 2015 GMC SIERRA 1500 4WD w Rear Drum Porterfield R4 Porterfield R4 Carbon Kevlar based brake pads were fully designed for heavy duty extreme motorsports use. Its one of the the few true motorsports competition pads available on the market today. The carbon based semi metallic R4 brake material allows the brake pad to absorb huge amounts of heat and dissipate it quickly and evenly over time. The Carbon Kevlar material also

Porterfield Brake Pads for 2015 GMC SIERRA 1500 4WD w/Rear Drum

Porterfield R4

Porterfield R4 Carbon Kevlar based brake pads were fully designed for heavy-duty extreme motorsports use. Its one of the the few true motorsports competition pads available on the market today. The carbon based semi metallic R4 brake material allows the brake pad to absorb huge amounts of heat and dissipate it quickly and evenly over time. The Carbon-Kevlar material also allows the brake pad to heat up to operating temperature right away so little pad warmup is required for optimal operating condition. This compound also requires very little bed-in so that one is able to change out the pads and almost immediately able use them to their full potential.

R4 series provides high initial bite for immediate brake response while yielding extremely consistent modulation and predictability. This is great for all road courses, oval track, rally, vintage racing, autocross, club events, and professional driving events. This is one of the best motorsports pads we have used. Available for many applications, so please contact us if you do not see one for your car.

Porterfield R4-1

The R4-1 Vintage Full Race Compound was developed using knowledge testing in the vintage racing community. Optimum uses for the R4-1, under conditions where very high friction is needed with minimal warm up time and in applications where there is difficulty in maintaining sufficient heat with conventional race pad compounds. Widely used on vintage GT and formula cars the R4-1 is also gaining popularity in off-road and rally-cross classes. Great modulation, consistent pedal feedback and rotor friendly at all temperatures as with all the other Porterfield Carbon Kevlar compounds.

Porterfield R4-E

The R4-E Endurance Race Compound is a carbon kevlar compound made to last a bit longer than the original R-4 compound. The R4-E compound is designed to endure higher prolonged temperature and still has pad life as long or longer than Porterfield R-4 do. This pad is great for club enduro events and applications where temperatures are at their maximum.

Porterfield R4-S

Porterfield R4-S high performance street and autocross brake pads are great for heavy-duty street and light track applications. Compound is perfect for everyday use while still keeping the highly capable track ready performance. The R4-S features a high friction level that will increase your stopping power with minimal pedal effort.

The R4-S series pads are VERY VERY rotor friendly and yield very low levels of dust; levels are far below OEM equipment or any other high performance brake pads. Your car will stop better and your wheels will stay cleaner longer. Good for autocross, rallies, driving school, and of course daily driving with a little extra stopping power. R4-S pads are available for virtually all vehicles sold in the US and custom R4-S pad sizes for competition style calipers are also available.

This is one of the very best all around street/strip brake pads available at a great price.

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SKU: 17623834269

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noam barkay
Boise, 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
Waukegan, 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
Boise, 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!
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Reviewed in the United States on June 27, 2025
C
Verified Purchase
CL
Whiting, 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
A
Verified Purchase
Amazon Customer
Phoenix, US
★★★★★ 5
A Good Place to Start Learning AI
Format: Paperback
Diving into the world of artificial intelligence can feel like stepping into a vast, uncharted ocean, and if you're looking for a reliable vessel to navigate these waters, this book is an excellent choice. However, I must be candid—this journey is not for the faint-hearted or those hoping to breeze through. The subject of AI, with its complex algorithms and intricate theories, is notoriously challenging. You won't find yourself flipping pages at a rapid pace, as this is not a title designed for speed-reading. Instead, it demands your full attention and a willingness to engage deeply with the material. At the heart of AI lies mathematics—a fundamental pillar that underpins the entire discipline. This book, while comprehensive, offers only a glimpse into the mathematical framework that drives artificial intelligence. But don’t be disheartened by this. Think of it as a solid foundation, a primer that will arm you with the essential concepts needed before you delve deeper into the more advanced mathematical intricacies elsewhere. When you do eventually tackle those more complex equations, you'll find yourself better equipped, with a clearer understanding of the principles at play. I should also mention that I'm no stranger to Andrew's work. Having explored some of his other writings, I can confidently say that he possesses a unique flair for communication. His ability to distill complex ideas into accessible language, without losing the essence of the subject, is truly commendable. Andrew writes with a certain finesse and sophistication that makes even the most daunting topics seem approachable. His style is not just informative, but also engaging, with a touch of elegance that sets his work apart from others in the field. In summary, while the path to mastering AI is undeniably steep, this book serves as an invaluable guide. It’s not just a starting point; it’s a beacon for those who are serious about understanding the intricacies of artificial intelligence. Be prepared to invest time and effort, and in return, you'll gain a solid foothold in a subject that is as fascinating as it is complex.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on September 2, 2024

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