SKU: 19902583432

Gwen wall sconce-Aged Brass

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Description

Gwen wall sconce-Aged BrassNote This lighting is supplied to order. Estimated delivery lead time 12 14 weeks from time of order. Dimensions 260 x 349 mm H Width Diameter 26. 04cm Height 34. 93cm Extension 9. 53cm Backplate Canopy Base 12. 07cm Top To Center 28. 58cm Shade Material Faux Silk Shade Color White Number of Lamps 2 Wattage 40w ea. Socket Type E14 Candelabra Plug In No

Note - This lighting is supplied to order. Estimated delivery lead time 12-14 weeks from time of order. Dimensions| 260 x 349 mm H

Width/Diameter 26.04cm

Height 34.93cm

Extension 9.53cm

Backplate/Canopy/Base 12.07cm

Top To Center 28.58cm

Shade Material Faux Silk

Shade Color White

Number of Lamps 2

Wattage 40w ea.

Socket Type E14 Candelabra

Plug In No

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SKU: 19902583432

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4.4 ★★★★★
Based on 11 reviews
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Product Reviews
D
Verified Purchase
David Escobar
Battle Creek, 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
Verified Purchase
Filipe Fernandes
Natrona Heights, US
★★★★★ 5
Great book
Format: Paperback
Love the fact you put examples in python and javascript. Great book.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on June 10, 2025
E
Verified Purchase
Eddwin Paz
Lowell, US
★★★★★ 5
proper documentation from langchain
Format: Paperback
Liked the book. But Still missing Human in the loop.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 19, 2025
B
Verified Purchase
B. Black
West Palm Beach, US
★★★★★ 3
Already outdated
Format: Paperback
Concepts are sound but the code in this book is already obsolete
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on August 5, 2025
J
Joe Faith
Waukegan, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on July 17, 2025

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