EBC Yellowstuff Street And Track Brake Pads (DP4848R)
SKU: 78758866440

EBC Yellowstuff Street And Track Brake Pads (DP4848R)

Sale price$117.72 Regular price$130.80
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Description

EBC Yellowstuff Street And Track Brake Pads (DP4848R)EBC Yellowstuff Brake Pads Even as our world number 1 selling pad grade, EBC Yellowstuff 3068 compound is new for 2021 and is designed where great stopping power is your goal on fast street use on modern sedans and hot hatches. 100% R90 approved. A high friction pad that works from cold and self seats after installation due to its applied brake in surface coating. Although the new Yellow grade WILL work fine under shorter duration track, trackday

EBC Yellowstuff™ Brake Pads
Even as our world number 1 selling pad grade, EBC Yellowstuff 3068 compound is new for 2021 and is designed where great stopping power is your goal on fast street use on modern sedans and hot hatches. 100% R90 approved.

A high friction pad that works from cold and self seats after installation due to its applied brake-in surface coating. Although the new Yellow grade WILL work fine under shorter duration track, trackday (lapping) use on lighter cars or drifting, it does take longer to bed in, and if track driving is your main purpose, choose Bluestuff or the new RP1 and RPX ranges or allow extra time for bedding your brakes in.

  • High friction formula improves brake effect by 15%
  • Our flagship top selling high friction brake pad for improved braking – UK made and a very high-end pad
  • Suitable for Fast streetcars, trucks and SUVs wherever a decent brake power upgrade is required.
  • Super quiet smooth braking under all conditions, fully shimmed slotted and chamfered construction
  • Excellent cold bite coupled with high-temperature fade resistance
  • Progressive braking with unparalleled pedal feel
  • Drastically reduced stopping distances compared to OE
  • Brake-In™ coating for accelerated pad bed in period
  • R90 approved thus legal for public highway use in European markets
  • For track use Bluestuff may be a better choice due to bedding in speed which is much shorter with Blue
  • Made with Nucap NRS hooks on back plates to totally eliminate the chance of pad

Fast Drivers Love it because…
EBC Yellowstuff is an aramid fibre-based brake compound with a high brake effect from cold and is possibly one of the first-ever compounds that can be used for street and on lighter cars in track driving. These pads do require some warm-up and a longer bedding procedure (read about bedding in your brakes here) but get even stronger under the heat of hard driving. These are not low dust pads, if your desire is for a low dust premium street use pad, you should consider EBC Redstuff. Please note however Redstuff is not for the track.

When considering full-race use on lighter cars, Yellowstuff has been a strong favorite. It is also the choice of 90% of the Mazda MX5 Miata competitors in the UK race series, used in drifting and on kit cars

EBC Yellowstuff pads are also used on the UK Silverstone Race Track Drivers experience cars because of their high performance and longevity. Yellowstuff also claimed a win in the Targa rally and for two years running in the Brazil Mitsubishi off Road Team Desert Challenge.

In 2022 EBC launched their new faster bedding Bluestuff grade which has even higher fade resistance than yellow and has a super-fast bed in time. If you want a super-fast bed-in at the track, go for the Blue NDX grade.

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SKU: 78758866440

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O
Om S
Birmingham, 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
J
Jiewen Wang
Whiting, 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
Charlottesville, 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
Alexandria, 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
R
Ryan Meyer
Pawtucket, 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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