Catalytic Converter for 1999-2000 Ford Expedition 4.6L MagnaFlow 23028
SKU: 14103373132

Catalytic Converter for 1999-2000 Ford Expedition 4.6L MagnaFlow 23028

$478.35 $531.50
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Description

Catalytic Converter for 1999-2000 Ford Expedition 4.6L MagnaFlow 23028Overview: MagnaFlow HM Grade Federal EPA Compliant Direct Fit Catalytic Converter 23028 helps keep the check engine light off. DIY installation is easy with the proper tools (no cutting or welding required). With features including mandrel bent tubing to eliminate flow restrictions and stainless steel construction to resist corrosion, you can have confidence in the quality of this catalytic converter. Using state of the art 3D scanning systems in the

Overview:

MagnaFlow HM Grade Federal/EPA Compliant Direct-Fit Catalytic Converter 23028 helps keep the check engine light off. DIY installation is easy with the proper tools (no cutting or welding required). With features including mandrel-bent tubing to eliminate flow restrictions and stainless steel construction to resist corrosion, you can have confidence in the quality of this catalytic converter. Using state-of-the-art 3D scanning systems in the design process, the MagnaFlow metrology department ensures this new part to fit and function like the original equipment. This direct-fit catalytic converter is designed to fit the 1999-2000 Ford F-150 and 1999-2000 Ford Expedition.

Note : Not For Sale in California

Interchange:
Brand  Interchange Part Number

Application:

Year Make Model Submodel Engine Size
1999 - 2000 Ford F-150 4.6L/V8
1999 - 2000 Ford Expedition 4.6L/V8

Specs:

Series HM Grade
Body Material Stainless Steel
Subtitle Federal / EPA Compliant
Overall Length (In.) 32
Material Stainless Steel
Title HM Grade Direct-Fit
Substrate Material Ceramic
Air Tube Adaptable No
Type Direct-Fit
Converter Quantity 2
Finish Stainless Steel
Outlet Attachment Clamp-On
Oxygen Sensor Location Pre and Post Converter
Main Piping Diameter 2.5
Inlet Attachment Bolt-On
Heat Shield Included Yes
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SKU: 14103373132

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4.3 ★★★★★
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Verified Purchase
David Escobar
Phoenix, US
★★★★★ 1
Nothing new
Format: Audiobook
There nothing new in this book you will defiantly find this content in any leadership book
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on November 16, 2019
F
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Filipe Fernandes
Port Orchard, US
★★★★★ 5
Great book
Format: Paperback
Love the fact you put examples in python and javascript. Great book.
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Reviewed in the United States on June 10, 2025
E
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Eddwin Paz
New York, US
★★★★★ 5
proper documentation from langchain
Format: Paperback
Liked the book. But Still missing Human in the loop.
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Reviewed in the United States on April 19, 2025
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B. Black
Charlottesville, US
★★★★★ 3
Already outdated
Format: Paperback
Concepts are sound but the code in this book is already obsolete
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Reviewed in the United States on August 5, 2025
J
Joe Faith
Alexandria, US
★★★★★ 5
Unlocking Practical AI: A Developer’s Guide to Building with LLMs and LangChain
Format: Paperback
If you're a developer eager to move beyond LLM experimentation and build robust, context-aware AI applications, this book offers both inspiration and practical guidance. The authors open with a clear passion for the transformative potential of large language models (LLMs) and LangChain, framing these technologies as not just enhancements to the developer’s toolkit, but as gateways to new kinds of “thing-building” superpowers. This sense of possibility is grounded in step-by-step instruction, making the book approachable for those with Python or JavaScript backgrounds who may be new to the world of production-grade AI agents. What stands out is the book’s careful scaffolding: starting with foundational concepts like prompt-based programming and progressing to advanced capabilities such as retrieval-augmented generation, agent planning, and tool integration. Each stage is contextualized with real-world use cases, like customizing chatbots to interact with your own documents, personalizing user experiences through memory, and deploying to production with reliability and security in mind. The focus on chain-of-thought reasoning and LangGraph’s agent architecture demonstrates the authors’ awareness of the current state of AI, where context and planning are just as important as raw language ability.
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Reviewed in the United States on July 17, 2025

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