LUVERNE 477125-401747 - Regal 7 Polished Stainless 125 Oval W2W Steps, Select Silverado, Sierra HD
SKU: 67413708750

LUVERNE 477125-401747 - Regal 7 Polished Stainless 125 Oval W2W Steps, Select Silverado, Sierra HD

Sale price$446.38 Regular price$495.98
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Description

LUVERNE 477125-401747 - Regal 7 Polished Stainless 125 Oval W2W Steps, Select Silverado, Sierra HDLUVERNE Regal 7 oval wheel to wheel steps are the ultimate combination of deluxe style and reliable functionality. They feature extra rear step pads for convenient truck bed access, a highly rust resistant construction and a vehicle specific fit for your truck. Regal 7 wheel to wheel steps are engineered for practical access to the bed of your truck, featuring an extended length and extra rear step pads. Wheel to wheel running boards give you a solid

LUVERNE Regal 7 oval wheel-to-wheel steps are the ultimate combination of deluxe style and reliable functionality. They feature extra rear step pads for convenient truck bed access, a highly rust-resistant construction and a vehicle-specific fit for your truck. Regal 7 wheel-to-wheel steps are engineered for practical access to the bed of your truck, featuring an extended length and extra rear step pads. Wheel-to-wheel running boards give you a solid step up for easily reaching items in the truck bed, such as a truck tool box, ladder rack, 5th wheel hitch or other equipment. Regal 7 oval side steps are fitted with large, non-skid step pads. They feature a 7" width and a textured surface, providing enhanced comfort and safety when climbing in and out of your pickup truck. The step pads and matching end caps of Regal 7 wheel-to-wheel steps are angled for a sleek accent. To ensure excellent strength and lasting corrosion resistance, Regal 7 oval side steps are constructed from stainless steel. The stainless steel tubing measures 7" wide and has an elliptical shape for style and strength. All Regal 7 side steps are designed for a custom-fit installation, using vehicle-specific brackets. The brackets are built from strong carbon steel and have a durable black powder coat finish. They install using pre-existing mounting points on the truck to greatly reduce installation time and eliminate tedious modifications. This particular set of LUVERNE Regal 7 oval wheel-to-wheel steps is designed to fit specific years of the Chevy Silverado 2500 HD or 3500 HD and GMC Sierra 2500 HD or 3500 HD (see application information to verify fitment; brackets included but shipped separately).

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SKU: 67413708750

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David Escobar
Bozeman, US
★★★★★ 1
Nothing new
Format: Audiobook
There nothing new in this book you will defiantly find this content in any leadership book
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Reviewed in the United States on November 16, 2019
F
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Filipe Fernandes
Natrona Heights, 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
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Eddwin Paz
Louisville, 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
Port Orchard, 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
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Joe Faith
Chelsea, 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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