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Description
Wright Tool 12-Point Flat Stem Metric Black Industrial Combination Wrench, 11mm - WT 41111 Combination WrenchWright Tool 12 Point Flat Stem Metric Black Industrial Combination Wrench, 11mm WT 41111 A combination wrench is a versatile hand tool used for tightening or loosening nuts and bolts. It typically has an open ended jaw on one side and a box ended or closed ring on the other side. The open end is useful in situations where there is limited space around the fastener, while the closed end provides a more secure grip. Features: Patented WrightGrip 2. 0
Wright Tool 12-Point Flat Stem Metric Black Industrial Combination Wrench, 11mm - WT 41111
A combination wrench is a versatile hand tool used for tightening or loosening nuts and bolts. It typically has an open-ended jaw on one side and a box-ended or closed ring on the other side. The open end is useful in situations where there is limited space around the fastener, while the closed-end provides a more secure grip.
Features:
- Patented WrightGrip 2.0 technology provides unparallel tool-to-fastener contact resulting in a superior anti-slip combination wrench
- Forged and machined for best fit and unyielding strength
- Precision heat-treated and tempered for optimal hardness, ductility and durability
- Easy to read stamp pattern in order to quickly determine opening size
- Designed, engineered and manufactured in Barberton, OH
- Meets and exceeds ASME B107 hand tool standards
Specifications:
- Material: Steel
- Finish: Black Industrial
- Point: 12
- Measurement Type: Metric
- Wrench Size: 11mm
- L Size: 165.13mm (Refer Drawing Image)
- C Size: 23.47mm (Refer Drawing Image)
- B Size: 13.42mm (Refer Drawing Image)
- D Size: 6.2mm (Refer Drawing Image)
- E Size: 7.7mm (Refer Drawing Image)
- Country of Origin: United States of America
Warnings:
- Wear safety goggles for the user and bystander
- Comfort or plastic grips do not protect against electric shock
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- Free Standard Shipping on $100+ Orders to the USA.
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4.2 ★★★★★
Based on 24 reviews
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Product Reviews
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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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