SKU: 18437891871

Torq Locker TL-HP500 for Honda Pioneer by Torq Masters

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Description

Torq Locker TL-HP500 for Honda Pioneer by Torq MastersFree Shipping to the Lower 48 states. The TORQ Locker is the most advanced, strongest, highest quality automatic locking differential on the planet, proven in the most extreme vehicles from hard core Rock Crawling, racing the King of the Hammers, Rock Bouncing, Sand Dunes and Mud Drags. To back this all up, Torq Masters Industries offers an unprecedented 4 year warranty on the TORQ Locker with no tire size, horsepower limits or loopholes. Torq Masters

Free Shipping to the Lower 48 states.

The TORQ Locker is the most advanced, strongest, highest quality automatic locking differential on the planet, proven in the most extreme vehicles from hard-core Rock Crawling, racing the King of the Hammers, Rock Bouncing, Sand Dunes and Mud Drags. To back this all up, Torq-Masters Industries offers an unprecedented 4 year warranty on the TORQ Locker with no tire size, horsepower limits or loopholes. Torq-Masters Industries is committed to building the highest quality locking differentials possible. To be the best, we build with the best, TORQ Locker is 100 percent Made in the USA with 100 percent USA raw materials.        


Keep it Simple, no air-lines to leak, compressors to fail, cables to adjust or electrical gremlins, the TORQ Locker gives you piece of mind as a reliable, mechanical, automatic locker that can handle any terrain. The innovative design features of the TORQ Locker have three game-changing benefits;

  • Creates true 4WD by replacing stock spider gears in the differential.
  • Easiest Locker on the market to install, no spacers, no dowel pins, no cables, air or electrical lines.
  • The machined Key-Way design of the Cam gears can't shear, ever.

To build the TORQ Locker, Torq-Masters Industries demands the highest quality control standards, protocol and materials. The TORQ Locker is CNC Machined from USA made 9310 steel alloy for maximum durability and long life using their proprietary manufacturing processes in an ISO 9001 certified facility in Rochester NY. Locker components are then heat treated with proprietary procedures in a TS16949 certified facility with 100 percent quality audits utilizing electron microscopy testing. Quality control of this caliber is very rare in the Aftermarket Off-Road Industry, but Torq-Masters goes the extra mile to build the best, because you deserve the best.


Torq-Masters team of engineers make every effort to ensure that the model fitment listed is accurate. However, due to vehicle manufacturers differential options, using available inventory, prior vehicle owner modifications, etc. it is the customers responsibility to confirm that the model TORQ Locker they order fits their differential. All TORQ Lockers are designed for installation in an open differential.

Fitment:

  • 2015-2022 Honda Pioneer 500, every model
  • 2021 + Honda Pioneer 520, every model
  • 2014-2022  Honda Pioneer 700, every model
    • The 700 comes with a factory locking option external to the differential. In case of failure, or for a more robust differential the TORQ Locker can be installed.
  • 2009-2013  Honda 700 MUV Big Red, every model
    • The 700 comes with a factory locking option external to the differential. In case of failure, or for a more robust differential the TORQ Locker can be installed.

Click here for Installation Instructions 

 DM 12-8-22

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SKU: 18437891871

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4.9 ★★★★★
Based on 21 reviews
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Verified Purchase
Jenny Holden
Battle Creek, US
★★★★★ 1
Not useful
Format: Paperback
This book has a few pieces of good advice, but its buried under mountains of weird and amateur level musings. Example: Paul Singman advocates for eliminating ETL entirely. How? Just reprogram the applications to which you may or may not have the source code to handle your data processing. He calls Intention Data Transfer 🥴 Thanks for the advice Paul, I'll get right on that.
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Reviewed in the United States on February 17, 2026
D
Verified Purchase
David Escobar
Boise, US
★★★★★ 5
Good starting point. But can't find the code.
Format: Kindle
Reading chapter 3. It was so far so good, but can't find the code in the repo. "All the related code can be found in the repository under project/hooks-notification." And in the repo I see no project folder. Please help!
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 3, 2026
W
Verified Purchase
WU.
Houston, US
★★★★★ 4
Good overview of the leading Agentic Framework. Will become outdated quickly.
Format: Paperback
3.5 Stars rounded up. Not a bad place to start if you need to get up to speed fast with Claude Code, understand its vast feature set, how it works under the hood, best practices, and the various agent primitives and how to get the most out of them. Agentic frameworks (Claude Code in particular) are quickly becoming table stakes for anyone working in tech, so it's best to start now. I appreciated the author's ability to flesh out areas where Anthropic's documentation is lacking in depth and nuance, and for some not already working with Claude in their own repos, the fact that he provides "toy" repos where one can experiment with the tools without fear of consequence. Where the book falls short is that most of the stuff in here is already covered pretty well already in Anthropic's docs, or even better so in their free "Skilljar" courses. What's more, some areas are given a bit of a shallow treatment, while others are a bit better done. So it's a bit inconsistent in that sense. Also, I can see how this book will quickly lose its currency in a few months at the pace things are going. Ultimately, for me, the price of this book was a bit rich for my liking given the criticisms above. Still, I feel like I got valuable info that rounded up what I already knew from working with this agentic framework. Recommended.
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Reviewed in the United States on May 28, 2026
B
Brahmananda Reddy
Chelsea, US
★★★★★ 5
Practical AI Engineering Beyond Prompts — One of the Better Books on Agentic Coding
Format: Paperback
This book is not another “AI coding hype” book. A lot of books talk about agents at a very high level. This one actually explains how things work when you try to use them inside real development workflows. That was the biggest difference for me. What I liked most was the focus on context engineering, memory, MCP, hooks, subagents, and workflow orchestration instead of just “prompt better.” The author spends time explaining why long-running agent systems fail, how context grows over time, and why most AI coding setups become messy without structure. The examples also feel practical — The HookHub project, Next.js setup, GitHub workflows, Claude memory files, and MCP integrations make it easier to connect theory with actual implementation. From my retail domain experience perspective, I could immediately connect this to forecasting and pricing workflows. For example: * agents helping analysts generate specs before model development * automated code review for promo forecasting pipelines * isolated subagents for pricing, promotions, assortment * persistent memory for business rules across teams * MCP integrations to pull context from internal systems safely The section around context isolation and subagents especially stood out because that is very similar to how enterprise forecasting teams already operate in reality. Different teams own different decision spaces. One thing I appreciated: the author does not oversell AI. There is a strong focus on constraints, context pollution, hallucinations, performance degradation, and workflow reliability. That makes the book feel grounded instead of marketing-heavy. This is not for complete beginners though. If someone has never worked with Git, APIs, coding agents, or LLM workflows, parts of the book may feel overwhelming early on. The author clearly says this is not beginner-level content. Overall, probably one of the more practical books I have read recently on agentic coding systems. Good for: * software engineers * AI engineers * enterprise architecture teams * technical product teams * analytics leaders trying to operationalize AI development workflows Especially useful if your organization is trying to move from “AI demos” into actual production workflows.
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Reviewed in the United States on May 20, 2026
U
UA
Los Angeles, US
★★★★★ 5
A Good Reality Check on How AI Agents Actually Work in Enterprise Systems
Format: Paperback
Most AI books stop at prompts. This one goes deeper into how agent systems actually behave once you try to use them inside large workflows with memory, tools, permissions, automation, and multiple agents working together. That part felt very relevant for healthcare and enterprise environments. The book does a good job explaining why context engineering matters and how poor context handling creates hallucinations, inconsistent outputs, and degraded performance over time. Honestly, that is one of the biggest problems organizations underestimate right now. In healthcare workflows, context matters a lot: * prior interactions * business rules * auditability * escalation logic * safety constraints * tool permissions * workflow boundaries The sections on persistent memory, scoped context, subagents, and structured workflows connected strongly to that reality. I work in enterprise analytics, and while reading this book I kept thinking about use cases like: * pharmacy workflow automation * prior authorization support systems * coding assistants for healthcare engineering teams * AI copilots for operational analytics * agent-based escalation systems * claims and workflow orchestration The MCP chapters were also useful because they explain integration challenges clearly instead of treating tooling as magic. What made this book stand out for me was the balance between implementation and architecture. The author explains: * why long contexts fail * how context poisoning happens * why isolation matters * when parallel agents help * when they actually create more complexity That level of honesty is missing in many AI books right now. Another thing: the examples are not overly academic — The Next.js project setup, GitHub automation, Claude desktop workflows, memory systems, hooks, and subagents make the learning process feel practical and hands-on. One limitation: this book assumes technical background. Someone completely new to coding agents, LLMs, Git, or development workflows may struggle in the first few chapters. But for engineers, AI teams, enterprise architects, and technical leaders trying to understand where agentic coding is actually going, this book is worth reading. Especially for organizations trying to operationalize AI safely instead of just experimenting with chatbots.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 20, 2026

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