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Ships within 48 hours · Estimated delivery Jul 13 - Jul 18
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Description
SRAM RED XPLR X-Sync Thread Mount Chainring - 40t, 12/13-Speed, Thread Mount (For RED XPLR AXS PM Spider), 6.5mm Offset, Black/Silver, E1Designed for use with SRAM's new RED XPLR AXS power meter, these replacement rings are purpose made for use with a Thread Mount spider design. This chainring mount system allows riders to minimize weight, maximize stiffness and accuracy, and retain the ability to swap ring sizes. Thread Mount design is light, stiff, and accurate Compatible with all SRAM road Flattop chains Includes thread back up pin for chainring SRAM Thread Mount Chainring Removal
Designed for use with SRAM's new RED XPLR AXS power meter, these replacement rings are purpose made for use with a Thread Mount spider design. This chainring mount system allows riders to minimize weight, maximize stiffness and accuracy, and retain the ability to swap ring sizes.- Thread Mount design is light, stiff, and accurate
- Compatible with all SRAM road Flattop chains
- Includes thread back up pin for chainring
- SRAM Thread Mount Chainring Removal Tool required for installation (Sold separately)
- Crafted with premium aluminum
Product Specifics
- Ring Position/Type: Chain Retaining Single Ring
- Tooth Count: 40
- Drivetrain Speeds: 12
- Drivetrain Speeds: 13
- Round or Oval: Round
- Material: Aluminum
- Color: Black/Silver
- Defined Color: Black
- Defined Color: Silver
Today's Stock Status
2 Available to ship in 2-4 Business Days
UPC: 710845899621
EAN: Not available
Manufacturer Part Number: 00.6218.047.003
CR2764 212558
184898-Q0-L
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4.8 ★★★★★
Based on 758 reviews
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Product Reviews
★★★★★ 5
Great book
Format: Paperback
Great book! It is helpful as I work towards my Masters of Science in Applied Artificial Intelligence
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Reviewed in the United States on December 20, 2025
★★★★★ 5
Comprehensive Guide with Practical Insights
Format: Paperback
This is a solid book for understanding the art and science of working with LLMs and other generative AI models. I always struggled with getting the output I was looking for, and wasn't sure how best to "ask" the models for what I wanted. This book did a great job of laying out the strategies and practical guidance to craft the prompts. There were a lot of tips and tricks, but the overall understanding and framework around prompt engineering has been super useful.
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Reviewed in the United States on June 25, 2024
★★★★★ 5
A must read!
Format: Paperback
Hands down, the best book on prompt engineering and implementing LLMs. Really enjoyed Michael and James deep dive. Whether you're technical or not, this book is foundational to a deeper understanding in how to properly explore and implement LLMs.
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Reviewed in the United States on June 25, 2024
★★★★★ 4
Helpful for GPT/LangChain framework
Format: Paperback
I applaud the authors for putting forth a comprehensive introduction in a rapidly evolving space. I absorbed a lot, helpful as I was developing a prototype — using open source methods.
And that’s where I was disappointed. The LLM and examples are highly adapted to OpenAI’s GPT-x and the bulky LangChain framework, something not obvious until you dig in to the book. Sure, this may be where newbie demand was when the authors began writing. But as the open source models and OpenAI alternatives gain speed (e.g. Llama 3.1, Groq, etc.) this book may quickly need an updated and expanded version to stay relevant.
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Reviewed in the United States on August 7, 2024
★★★★★ 3
Good concepts, but uneven depth
Format: Paperback
Some helpful prompt frameworks, but parts felt repetitive and a few sections stayed too high-level for advanced users. Beginners will likely benefit more than experienced practitioners. If you’re new to prompt engineering it’s a decent starting point; if you’re already building structured prompts daily, it may feel light.
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Reviewed in the United States on January 17, 2026