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MagnaFlow 11 Ford F-150 3.7L/5.0L/6.2L SS Catback Exhaust Dual Same Side Exit w/ 3.5in SS TipsThe MagnaFlow 2011 2014 Ford F 150 Street Series Cat Back Performance Exhaust System 15461 brings out the personality of your 3. 7L, 5. 0L, 6. 2L 2011 2014 Ford F 150 with our signature resonant, powerful engine tone and dyno proven performance. Thanks to its fast flowing, mandrel bent 3in. main piping and 1 Straight Through mufflers ending in a Dual Same Side Behind Passenger Rear Tire exit with 3. 5in. Polished Welded On Double Wall Angle Cut Folded
The MagnaFlow 2011-2014 Ford F-150 Street Series Cat-Back Performance Exhaust System 15461 brings out the personality of your 3.7L, 5.0L, 6.2L 2011-2014 Ford F-150 with our signature resonant, powerful engine tone and dyno-proven performance. Thanks to its fast-flowing, mandrel bent 3in. main piping and 1 Straight-Through mufflers ending in a Dual Same Side Behind Passenger Rear Tire exit with 3.5in. Polished Welded-On Double Wall Angle Cut Folded Edge tips, this Ford F-150 exhaust kit has the performance chops to match its sporty sound and appearance. Upgrade your ride to the next level with an exhaust that has the pipes to put other Ford F-150's to shame. With this system your vehicle will be producing Aggressive exterior and Moderate interior sound levels while leaving other cars and trucks feeling envious at every stoplight. Exhaust kit 15461 features a cat-back layout that replaces your ride's stock exhaust components from near the catalytic converter on back. Extensive 3D scanning during development has resulted in a direct-fit design that you can easily install yourself. 15461 is produced using CNC precision robotic manufacturing techniques and is backed by MagnaFlow's 1-year limited finish warranty and limited lifetime construction warranty.This Part Fits:
| Year | Make | Model | Submodel |
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| 2011-2014 | Ford | F-150 | FX2 |
| 2011-2014 | Ford | F-150 | FX4 |
| 2011-2012 | Ford | F-150 | Harley-Davidson Edition |
| 2011-2014 | Ford | F-150 | King Ranch |
| 2011-2014 | Ford | F-150 | Lariat |
| 2011 | Ford | F-150 | Lariat Limited |
| 2011-2014 | Ford | F-150 | Platinum |
| 2011-2014 | Ford | F-150 | STX |
| 2011-2014 | Ford | F-150 | XL |
| 2011-2014 | Ford | F-150 | XLT |
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4.8 ★★★★★
Based on 21 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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.
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Reviewed in the United States on November 14, 2025