SKU: 75638165412

aFe Bladerunner Auto. Transmission Oil Cooler Kit 99-13 Chevrolet Silverado 1500

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Description

aFe Bladerunner Auto. Transmission Oil Cooler Kit 99-13 Chevrolet Silverado 1500BladeRunner Transmission Oil Cooler Kit GM Gas Trucks SUVs 99 13 V8 (GMT800 900) This Part Fits: Year Make Model Submodel 2002 2014 Cadillac Escalade Base 2011 2014 Cadillac Escalade Luxury 2008 2014 Cadillac Escalade Platinum 2011 2014 Cadillac Escalade Premium 2003 2008 Cadillac Escalade ESV Base 2008 Cadillac Escalade ESV Platinum 2002 2008 Cadillac Escalade EXT Base 2013 Chevrolet Avalanche Black Diamond LS 2013 Chevrolet Avalanche Black Diamond

BladeRunner Transmission Oil Cooler Kit GM Gas Trucks/SUVs 99-13 V8 (GMT800/900)

This Part Fits:

Year Make Model Submodel
2002-2014 Cadillac Escalade Base
2011-2014 Cadillac Escalade Luxury
2008-2014 Cadillac Escalade Platinum
2011-2014 Cadillac Escalade Premium
2003-2008 Cadillac Escalade ESV Base
2008 Cadillac Escalade ESV Platinum
2002-2008 Cadillac Escalade EXT Base
2013 Chevrolet Avalanche Black Diamond LS
2013 Chevrolet Avalanche Black Diamond LT
2013 Chevrolet Avalanche Black Diamond LTZ
2007-2012 Chevrolet Avalanche LS
2007-2012 Chevrolet Avalanche LT
2007-2012 Chevrolet Avalanche LTZ
2002-2005 Chevrolet Avalanche 1500 Base
2005-2006 Chevrolet Avalanche 1500 LS
2005-2006 Chevrolet Avalanche 1500 LT
2002-2003 Chevrolet Avalanche 1500 North Face
2002-2003 Chevrolet Avalanche 1500 On Road Edition
2003 Chevrolet Avalanche 1500 WBH
2004-2006 Chevrolet Avalanche 1500 Z66
2002-2006 Chevrolet Avalanche 1500 Z71
1999-2005 Chevrolet Silverado 1500 Base
1999-2006,2010-2013 Chevrolet Silverado 1500 LS
1999-2013 Chevrolet Silverado 1500 LT
2007-2013 Chevrolet Silverado 1500 LTZ
2003-2006 Chevrolet Silverado 1500 SS
2002,2004-2013 Chevrolet Silverado 1500 WT
2004-2005 Chevrolet Silverado 1500 Z71 Off-Road
2007 Chevrolet Silverado 1500 Classic LS
2007 Chevrolet Silverado 1500 Classic LT
2007 Chevrolet Silverado 1500 Classic SS
2007 Chevrolet Silverado 1500 Classic WT
2007 Chevrolet Silverado 1500 HD Classic LT
1999-2004 Chevrolet Silverado 2500 Base
1999-2004 Chevrolet Silverado 2500 LS
1999-2004 Chevrolet Silverado 2500 LT
2004 Chevrolet Silverado 2500 WT
2001-2005 Chevrolet Silverado 2500 HD Base
2001-2006 Chevrolet Silverado 2500 HD LS
2001-2013 Chevrolet Silverado 2500 HD LT
2007-2013 Chevrolet Silverado 2500 HD LTZ
2003-2013 Chevrolet Silverado 2500 HD WT
2007 Chevrolet Silverado 2500 HD Classic LS
2007 Chevrolet Silverado 2500 HD Classic LT
2007 Chevrolet Silverado 2500 HD Classic WT
2001-2005 Chevrolet Silverado 3500 Base
2001-2006 Chevrolet Silverado 3500 LS
2001-2006 Chevrolet Silverado 3500 LT
2004,2006 Chevrolet Silverado 3500 WT
2007 Chevrolet Silverado 3500 Classic LS
2007 Chevrolet Silverado 3500 Classic LT
2007 Chevrolet Silverado 3500 Classic WT
2000-2001,2006 Chevrolet Suburban 1500 Base
2000-2006 Chevrolet Suburban 1500 LS
2000-2009 Chevrolet Suburban 1500 LT
2006-2009 Chevrolet Suburban 1500 LTZ
2002-2006 Chevrolet Suburban 1500 Z71
2000-2001 Chevrolet Suburban 2500 Base
2000-2006 Chevrolet Suburban 2500 LS
2000-2006 Chevrolet Suburban 2500 LT
2000-2001,2006 Chevrolet Tahoe Base
2000-2009 Chevrolet Tahoe LS
2000-2009 Chevrolet Tahoe LT
2007-2009 Chevrolet Tahoe LTZ
2002-2006 Chevrolet Tahoe Z71
2002-2005 GMC Sierra 1500 Base
2001 GMC Sierra 1500 C3
2002-2013 GMC Sierra 1500 Denali
2002 GMC Sierra 1500 HT
1999-2003,2006-2009 GMC Sierra 1500 SL
1999-2013 GMC Sierra 1500 SLE
1999-2013 GMC Sierra 1500 SLT
2002-2009 GMC Sierra 1500 WT
2007 GMC Sierra 1500 Classic Denali
2007 GMC Sierra 1500 Classic SL
2007 GMC Sierra 1500 Classic SLE
2007 GMC Sierra 1500 Classic SLT
2007 GMC Sierra 1500 Classic WT
2007 GMC Sierra 1500 HD Classic SLE
2007 GMC Sierra 1500 HD Classic SLT
2002-2004 GMC Sierra 2500 Base
1999-2002 GMC Sierra 2500 SL
1999-2004 GMC Sierra 2500 SLE
1999-2004 GMC Sierra 2500 SLT
2003-2004 GMC Sierra 2500 WT
2002-2005 GMC Sierra 2500 HD Base
2001-2002,2006 GMC Sierra 2500 HD SL
2001-2013 GMC Sierra 2500 HD SLE
2001-2013 GMC Sierra 2500 HD SLT
2003-2013 GMC Sierra 2500 HD WT
2007 GMC Sierra 2500 HD Classic SL
2007 GMC Sierra 2500 HD Classic SLE
2007 GMC Sierra 2500 HD Classic SLT
2007 GMC Sierra 2500 HD Classic WT
2002-2005 GMC Sierra 3500 Base
2001-2002,2006 GMC Sierra 3500 SL
2001-2006 GMC Sierra 3500 SLE
2001-2006 GMC Sierra 3500 SLT
2004-2006 GMC Sierra 3500 WT
2007 GMC Sierra 3500 Classic SL
2007 GMC Sierra 3500 Classic SLE
2007 GMC Sierra 3500 Classic SLT
2007 GMC Sierra 3500 Classic WT
2007-2013 GMC Sierra 3500 HD SLE
2007-2013 GMC Sierra 3500 HD SLT
2007-2013 GMC Sierra 3500 HD WT
2001-2014 GMC Yukon Denali
2006 GMC Yukon SL
2000-2014 GMC Yukon SLE
2000-2014 GMC Yukon SLT
2001-2014 GMC Yukon XL 1500 Denali
2006 GMC Yukon XL 1500 SL
2000-2014 GMC Yukon XL 1500 SLE
2000-2014 GMC Yukon XL 1500 SLT
2000-2013 GMC Yukon XL 2500 SLE
2000-2013 GMC Yukon XL 2500 SLT
2003-2009 Hummer H2 Base
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SKU: 75638165412

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4.9 ★★★★★
Based on 10 reviews
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Product Reviews
J
Verified Purchase
Jenny Holden
Houston, US
★★★★★ 1
Not useful
Format: Paperback
This book has a few pieces of good advice, but its buried under mountains of weird and amateur level musings. Example: Paul Singman advocates for eliminating ETL entirely. How? Just reprogram the applications to which you may or may not have the source code to handle your data processing. He calls Intention Data Transfer 🥴 Thanks for the advice Paul, I'll get right on that.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on February 17, 2026
D
Verified Purchase
David Escobar
Chelsea, US
★★★★★ 5
Good starting point. But can't find the code.
Format: Kindle
Reading chapter 3. It was so far so good, but can't find the code in the repo. "All the related code can be found in the repository under project/hooks-notification." And in the repo I see no project folder. Please help!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 3, 2026
W
Verified Purchase
WU.
Lowell, US
★★★★★ 4
Good overview of the leading Agentic Framework. Will become outdated quickly.
Format: Paperback
3.5 Stars rounded up. Not a bad place to start if you need to get up to speed fast with Claude Code, understand its vast feature set, how it works under the hood, best practices, and the various agent primitives and how to get the most out of them. Agentic frameworks (Claude Code in particular) are quickly becoming table stakes for anyone working in tech, so it's best to start now. I appreciated the author's ability to flesh out areas where Anthropic's documentation is lacking in depth and nuance, and for some not already working with Claude in their own repos, the fact that he provides "toy" repos where one can experiment with the tools without fear of consequence. Where the book falls short is that most of the stuff in here is already covered pretty well already in Anthropic's docs, or even better so in their free "Skilljar" courses. What's more, some areas are given a bit of a shallow treatment, while others are a bit better done. So it's a bit inconsistent in that sense. Also, I can see how this book will quickly lose its currency in a few months at the pace things are going. Ultimately, for me, the price of this book was a bit rich for my liking given the criticisms above. Still, I feel like I got valuable info that rounded up what I already knew from working with this agentic framework. Recommended.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 28, 2026
B
Brahmananda Reddy
Boise, US
★★★★★ 5
Practical AI Engineering Beyond Prompts — One of the Better Books on Agentic Coding
Format: Paperback
This book is not another “AI coding hype” book. A lot of books talk about agents at a very high level. This one actually explains how things work when you try to use them inside real development workflows. That was the biggest difference for me. What I liked most was the focus on context engineering, memory, MCP, hooks, subagents, and workflow orchestration instead of just “prompt better.” The author spends time explaining why long-running agent systems fail, how context grows over time, and why most AI coding setups become messy without structure. The examples also feel practical — The HookHub project, Next.js setup, GitHub workflows, Claude memory files, and MCP integrations make it easier to connect theory with actual implementation. From my retail domain experience perspective, I could immediately connect this to forecasting and pricing workflows. For example: * agents helping analysts generate specs before model development * automated code review for promo forecasting pipelines * isolated subagents for pricing, promotions, assortment * persistent memory for business rules across teams * MCP integrations to pull context from internal systems safely The section around context isolation and subagents especially stood out because that is very similar to how enterprise forecasting teams already operate in reality. Different teams own different decision spaces. One thing I appreciated: the author does not oversell AI. There is a strong focus on constraints, context pollution, hallucinations, performance degradation, and workflow reliability. That makes the book feel grounded instead of marketing-heavy. This is not for complete beginners though. If someone has never worked with Git, APIs, coding agents, or LLM workflows, parts of the book may feel overwhelming early on. The author clearly says this is not beginner-level content. Overall, probably one of the more practical books I have read recently on agentic coding systems. Good for: * software engineers * AI engineers * enterprise architecture teams * technical product teams * analytics leaders trying to operationalize AI development workflows Especially useful if your organization is trying to move from “AI demos” into actual production workflows.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 20, 2026
U
UA
Charlottesville, US
★★★★★ 5
A Good Reality Check on How AI Agents Actually Work in Enterprise Systems
Format: Paperback
Most AI books stop at prompts. This one goes deeper into how agent systems actually behave once you try to use them inside large workflows with memory, tools, permissions, automation, and multiple agents working together. That part felt very relevant for healthcare and enterprise environments. The book does a good job explaining why context engineering matters and how poor context handling creates hallucinations, inconsistent outputs, and degraded performance over time. Honestly, that is one of the biggest problems organizations underestimate right now. In healthcare workflows, context matters a lot: * prior interactions * business rules * auditability * escalation logic * safety constraints * tool permissions * workflow boundaries The sections on persistent memory, scoped context, subagents, and structured workflows connected strongly to that reality. I work in enterprise analytics, and while reading this book I kept thinking about use cases like: * pharmacy workflow automation * prior authorization support systems * coding assistants for healthcare engineering teams * AI copilots for operational analytics * agent-based escalation systems * claims and workflow orchestration The MCP chapters were also useful because they explain integration challenges clearly instead of treating tooling as magic. What made this book stand out for me was the balance between implementation and architecture. The author explains: * why long contexts fail * how context poisoning happens * why isolation matters * when parallel agents help * when they actually create more complexity That level of honesty is missing in many AI books right now. Another thing: the examples are not overly academic — The Next.js project setup, GitHub automation, Claude desktop workflows, memory systems, hooks, and subagents make the learning process feel practical and hands-on. One limitation: this book assumes technical background. Someone completely new to coding agents, LLMs, Git, or development workflows may struggle in the first few chapters. But for engineers, AI teams, enterprise architects, and technical leaders trying to understand where agentic coding is actually going, this book is worth reading. Especially for organizations trying to operationalize AI safely instead of just experimenting with chatbots.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 20, 2026

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