SKU: 7766837184
magnetic pedals mtb

magnetic pedals mtb Fort Knox Magnetic Bike Pedals – MagLOCK Bike Pedal

Sale price$19.04 Regular price$21.15
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Description

magnetic pedals mtb Fort Knox Magnetic Bike Pedals – MagLOCK Bike PedalPLEASE NOTE THE FOLLOWING: Our pedals are only available with SPD or other 2 hole biking shoes. Please make sure you have the correct shoes to pair with our pedals. Pedals are non returnable once they have been used (unless the product is defective). Orders are normally shipped within 5 business days of your purchase date (operating business days are Monday through Friday). PRODUCT SPECIFICATIONS Set of machined aluminum pedals with MagLOCK magnetic

PLEASE NOTE THE FOLLOWING:

  • Our pedals are only available with SPD or other 2-hole biking shoes. Please make sure you have the correct shoes to pair with our pedals.
  • Pedals are non-returnable once they have been used (unless the product is defective).
  • Orders are normally shipped within 5 business days of your purchase date (operating business days are Monday through Friday).

PRODUCT SPECIFICATIONS

Set of machined aluminum pedals with MagLOCK magnetic technology. Each pedal contains 10 individual neodymium magnets, for a total of 20 magnets per set. You can use all 20 magnets, or remove magnets to reduce the strength of the magnetic connection. Each pedal can now support 70+ pounds (see "What does Attractive Force Per Pedal Mean?" in our Common Questions section for an explanation of how this works).

 Items included
  • 1 left pedal and 1 right pedal
  • 10 extra-strong rare-earth magnets per pedal, for a total of 20 magnets per set
  • 2 shoe clips (7.8 mm thick, each), and screws to attach them.
  • Instructions for use
Weight of pair of pedals, including magnets 975 g / 2.15 lbs
Weight of pair of shoe clips and screws 195 g / 0.43 lbs
Attractive force per pedal 70+ lbs*
Pedal body material Machined 6061 aluminum
Pedal body finish Red, black, or blue (anodized)
Spindle (or axle) material ChroMoly steel
Spindle (or axle) finish Black zinc plating
Spindle threads 9/16” x 20 tpi (threads per inch)
Tools required for attachment 8mm or 15mm hex wrench
Pedal dimensions 3.75” x 4.00” x 0.85” (L x W x H)

 

* The packaging for the Fort Knox pedals states that the magnetic force is 45-50 lbs, which is incorrect. We are working on updating our packaging to reflect the above information.

DISCLAIMERS

** Biking is an inherently dangerous sport. The manufacturer does not guarantee that the MagLOCK Bike Pedal will function or release as described previously. By purchasing the MagLOCK Bike Pedal you are showing that you understand these risks and release the MagLOCK Bike Pedal manufacturer from all liability for any injuries that may occur.

** DO NOT use if you are using a pacemaker.

​** Note: for International shipments, a phone number is required.  Please provide a phone number on checkout to avoid processing delays. International tariffs and customs fees are not included in the shipping price.














Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
  1. Standard Shipping : 3-10 business days
  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 7766837184

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Paul Pollock
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Reviewed in the United States on August 7, 2025
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Allen Wyma
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★★★★★ 5
Great Resource when Integrating AI
Format: Kindle
This is a great resource when building systems that integrate with AI. It manages to cover the entire lifecycle and even tips for corporate environments!
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Reviewed in the United States on August 26, 2025
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Om S
Alexandria, US
★★★★★ 4
Title: Really Good Book for Learning LLMs
Format: Paperback, Format: Paperback
I picked up this book after struggling with LLM implementation at work. Ken Huang explains things clearly without too much technical jargon. The book covers everything from data preparation to building AI agents. I especially liked the chapters on RAG and prompting techniques - they helped me improve my current projects. The code examples actually work, which is nice. Some parts are pretty advanced, so you need basic Python knowledge. I had to read a few chapters twice to fully get it. The fairness and bias detection section was eye-opening. Good practical advice throughout. Not just theory - real solutions you can use. Worth the money if you're serious about LLM development. Recommended for anyone building AI systems professionally.
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Reviewed in the United States on July 25, 2025
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Jiewen Wang
San Leandro, US
★★★★★ 5
a comprehensive guide at the intersection of generative AI and cybersecurity
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This book blends deep theoretical foundations with practical frameworks and forward-looking strategies. From adversarial risk models to actionable guidance using OWASP Top 10 for LLMs and the NIST AI RMF, it offers both technical depth and operational clarity. What makes it stand out is its balance of academic rigor and real-world CISO insights, providing a holistic perspective on securing GenAI systems. While it leans enterprise-focused, the content remains accessible to security engineers, risk managers, and policy leaders alike. Generative AI Security is a timely and essential read for anyone working to deploy GenAI responsibly—building systems with both power and integrity in today’s fast-evolving threat landscape.
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Light on substance and heavy on flaws
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The book has a great list of topics, but fails to provide much substance any of them. Most of the provided code is just comments that avoid the actual crux of the issues being discussed. (e.g. #implement the logic to validate XYZ - while the whole point of this chapter is teach how the heck we validate XYZ!) Some parts are plain wrong, for example the part on Graph based RAG is fundamentally flawed as it assumes the text embedding and the graph embedding are in the same latent space. (This is one of many more examples). Seems like the book was rushed, and the author has limited hands on experience (if any). At least we know based on the amount of flaws that it was not written by an LLM
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