WELWYN WH5-1RJI Resistor, Axial Leaded, 1 ohm, 10 W, 160 V, � 5%, WH Series, Wirewound
SKU: 24341689585

WELWYN WH5-1RJI Resistor, Axial Leaded, 1 ohm, 10 W, 160 V, � 5%, WH Series, Wirewound

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Description

WELWYN WH5-1RJI Resistor, Axial Leaded, 1 ohm, 10 W, 160 V, � 5%, WH Series, WirewoundThis is a Resistor, Axial Leaded, 1 ohm, 10 W, 160 V, 5%, WH Series, Wirewound product from WELWYN with the model number WH5 1RJIProduct details Temperature Coefficient 50ppm C Power Rating 10W Resistor Element Material Wirewound Resistance 1ohm Resistance Tolerance 5% Product Range WH Series Resistor Terminals Axial Leaded Voltage Rating 160V Other details Brand WELWYN Part Number WH5 1RJI Quantity Each Technical Data Sheet EN All product and company

This is a Resistor, Axial Leaded, 1 ohm, 10 W, 160 V, � 5%, WH Series, Wirewound product from WELWYN with the model number WH5-1RJI

Product details

Temperature Coefficient � 50ppm/�C
Power Rating 10W
Resistor Element Material Wirewound
Resistance 1ohm
Resistance Tolerance � 5%
Product Range WH Series
Resistor Terminals Axial Leaded
Voltage Rating 160V

Other details

Brand WELWYN
Part Number WH5-1RJI
Quantity Each
Technical Data Sheet EN

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SKU: 24341689585

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David Escobar
Pawtucket, 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
San Leandro, 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
Dallas, 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
Grantham, 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Already outdated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
Louisville, 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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