✅VW CRAFTER CR35 2.0 DIESEL 2017 INTERCOOLER PIPE 2E0145762A 03K906051 Warranty
SKU: 55302371846

✅VW CRAFTER CR35 2.0 DIESEL 2017 INTERCOOLER PIPE 2E0145762A 03K906051 Warranty

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Description

✅VW CRAFTER CR35 2.0 DIESEL 2017 INTERCOOLER PIPE 2E0145762A 03K906051 WarrantyPlease ask price for supply and fitting service Description: INTERCOOLER PIPE 2E0145762A 03K906051 Vehicle details: VW CRAFTER CR35 2. 0 DIESEL 2017 224519 mileage Vehicle Identification No. WV1ZZZ2EZG6051596 Model Crafter 35 panel van CR Date of production 22. 07. 2016 Year 2016 Sales type 2ED1I5 Engine Code CSLC Transmission Code MWB Axle drive 6S Equipment AR Roof color B4 Exterior color Paint Code B4 B9A Please check pictures carefully and ensure

Please ask price for supply and fitting service

Description:

INTERCOOLER PIPE 2E0145762A 03K906051 

Vehicle details:

✅VW CRAFTER CR35 2.0 DIESEL 2017 

 224519 mileage

 Vehicle Identification No.

WV1ZZZ2EZG6051596
Model
Crafter 35 panel van CR
Date of production
22.07.2016
Year
2016
Sales type
2ED1I5
Engine Code
CSLC
Transmission Code
MWB
Axle drive
6S
Equipment
AR
Roof color
B4
Exterior color / Paint Code
B4 / B9A

Please check pictures carefully and ensure your part is the same. 

TERMS:

 

All ECU's we are selling are removed from a vehicle without any reprogramming or resetting. PLEASE MAKE SURE YOU WILL BE ABLE TO INSTALL, RESET OR PROGRAM IT ON YOUR OWN, IF NEEDED.

 

Engines

 

Engine need to be fitted by qualified mechanics for any warranty to be valid. We recommend a new timing belt / cam belt kit, as this is one of the most common reasons for engine failure. Any accessories that are left on engine are free of charge and warranty on them does not apply, warranty only apply for bare engine. We ship our engines and gearboxes with no oil. Please refill the engine/gearbox with manufacturers recommended oil grade before attempting to start the engine

 

Warranty and Returns

Please Note: Unwanted orders will be collected subject to a 25% charge depending on your order (to cover the carrier's charges) this also includes failed deliveries after 3 attempts and refused orders at the point of delivery. This does not apply to products which are faulty - these will be collected/replaced free of charge.

All items come with a 30 days warranty from the date of purchase. Warranty relates to the supplied part(s) only, it expressly excludes any consequential loss & fitting/ labour charges. If an item arrive faulty, damaged or was lost in the post, we will send you a replacement. If a replacement is not available, then a full refund will be issued. If you buy the item by mistake, you change your mind or don't check part number, you are welcome to send the item back at your expense. If any unauthorized alterations are found to have been made to a part then any return will be refused. Refunds are issued within 1-2 working days of part returned.

Please note Delivery includes a main land delivery to Scotland, Northern Ireland any Islands will be for extra charge. Please request delivery price to your location. Thanks.

Authorised Treatment Facility (ATF) Waste Permit No.
EPR/WE0582AC/A001

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
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: 55302371846

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4.5 ★★★★★
Based on 1652 reviews
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Product Reviews
O
Om S
Charlottesville, 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
Boise, 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
Omaha, 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
Cuba, 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
Dallas, 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

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