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Description
Catalytic Converter for California Grade CARB Compliant Universal Catalytic Converter MagnaFlow 338015Overview: MagnaFlow California Grade CARB Compliant Universal Catalytic Converter 338015 helps keep the check engine light off. Get the right fit for a wide array of vehicles thanks to the adaptable Universal design (cutting and welding is required). With highly corrosion resistant, stainless steel construction, you can have confidence in the quality of this catalytic converter. This direct fit catalytic converter is designed to fit the 1980 1995 Ford
Overview:
MagnaFlow California Grade CARB Compliant Universal Catalytic Converter 338015 helps keep the check engine light off. Get the right fit for a wide array of vehicles thanks to the adaptable Universal design (cutting and welding is required). With highly corrosion resistant, stainless steel construction, you can have confidence in the quality of this catalytic converter.
This direct-fit catalytic converter is designed to fit the 1980-1995 Ford Mustang, 1981-1989 Dodge Diplomat, and additional vehicles. This replacement part is designed to meet or exceed California Air Resource Board requirements, and has undergone additional testing to be certified by CARB for legal use in CA/CARB restricted states for the listed vehicles.
| Brand | Interchange Part Number |
|---|
Application:
| Year | Make | Model | Submodel | Engine Size |
|---|---|---|---|---|
| 1982 - 1985 | Mercury | Capri | 5L/V8 | |
| 1982 | Dodge | 400 | 2.2L/L4 | |
| 1980 - 1983 | Ford | Mustang | 2.3L/L4 | |
| 1980 | American Motors | AMX | 4.2L/L6 | |
| 1980 - 1983 | American Motors | Concord | 4.2L/L6 | |
| 1981 - 1983 | American Motors | Eagle | 4.2L/L6 | |
| 1981 - 1983 | American Motors | Spirit | 4.2L/L6 | |
| 1980 - 1983 | Mercury | Capri | 2.3L/L4 | |
| 1982 | Plymouth | Reliant | 2.2L/L4 | |
| 1980 - 1983 | Chrysler | Cordoba | 5.2L/V8 | |
| 1983 | Chrysler | Fifth Avenue | 3.7L/L6 | |
| 1983 - 1989 | Chrysler | Fifth Avenue | 5.2L/V8 | |
| 1981 - 1983 | Chrysler | Imperial | 5.2L/V8 | |
| 1980 - 1981 | Chrysler | LeBaron | 5.2L/V8 | |
| 1980 - 1982 | Chrysler | New Yorker | 5.2L/V8 | |
| 1979 - 1981 | Chrysler | Newport | 5.2L/V8 | |
| 1980 | Chrysler | Town & Country | 5.2L/V8 | |
| 1981 - 1989 | Dodge | Diplomat | 5.2L/V8 | |
| 1981 - 1983 | Dodge | Mirada | 5.2L/V8 | |
| 1979 - 1981 | Dodge | St. Regis | 5.2L/V8 | |
| 1981 - 1982 | Ford | Fairmont | 4.2L/V8 | |
| 1981 | Ford | Granada | 4.2L/V8 | |
| 1983 | Ford | LTD | 2.3L/L4 | |
| 1981 | Ford | Mustang | 4.2L/V8 | |
| 1982 - 1995 | Ford | Mustang | 5L/V8 | |
| 1993 - 1995 | Mazda | RX-7 | 1.3L/R2 | |
| 1981 | Mercury | Capri | 4.2L/V8 | |
| 1981 - 1982 | Mercury | Zephyr | 4.2L/V8 | |
| 1980 - 1989 | Plymouth | Gran Fury | 5.2L/V8 |
Specs:
| Heat Shield Material | Aluminized Steel |
|---|---|
| Outlet Orientation | Straight |
| Outlet Diameter | 2.25 |
| Finish | Stainless Steel |
| Body Type | Clam Shell |
| Substrate Material | Ceramic |
| Heat Shield Included | Yes |
| Body Material | Stainless Steel |
| Sensor Port Count | 0 |
| Overall Length (In.) | 16 |
| Inlet Attachment | Weld-On |
| Outlet Quantity | 1 |
| Material | Stainless Steel |
| Inlet Quantity | 1 |
| Outlet Attachment | Weld-On |
| Heat Shield Attached | Yes |
| Oxygen Sensor Location | None |
| Body Width (In.) | 5.5 |
| Inlet Orientation | Straight |
| Inlet Diameter | 2.25 |
| Body Shape | Oval |
| Series | California |
| Body Length (In.) | 12 |
| Subtitle | California / CARB Compliant |
| Body Height (In.) | 3.75 |
| Air Tube Count | 1 |
| Title | California Universal |
| Type | Universal |
| Air Tube Adaptable | Yes |
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- Free Standard Shipping on $100+ Orders to the USA.
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Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
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4.7 ★★★★★
Based on 28 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
WAS THIS REVIEW HELPFUL?YesReportShare
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
WAS THIS REVIEW HELPFUL?YesReportShare
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.
WAS THIS REVIEW HELPFUL?YesReportShare
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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 14, 2025
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