SKU: 88176983851

SP 1074533005 AFAM Aluminiumritzel hinten 15104 - 428 passend fuer SUZUKI RM 85

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SP 1074533005 AFAM Aluminiumritzel hinten 15104 - 428 passend fuer SUZUKI RM 85SP 1074533005 AFAM Aluminiumritzel hinten 15104 428 passend fuer SUZUKI RM 85 2015 AFAM Alu Kettenrder werden aus einer leichten und widerstandsfhigen Legierung hergestellt. Die Fertigungsqualitt und przision seiner Ritzel macht AFAM zur weltweit anerkannten Referenzmarke in Sachen Motorrad Antriebskomponenten. Ultraleichtes Alu Kettenrad Aluminium 7075 T6 Hochwertige Qualitt und erstklassiges Finish Geringeres Gewicht gegenber Stahl M E R K M A L

SP 1074533005 AFAM Aluminiumritzel hinten 15104 - 428 passend fuer SUZUKI RM 85 2015

AFAM Alu-Kettenräder werden aus einer leichten und widerstandsfähigen Legierung hergestellt. Die Fertigungsqualität und -präzision seiner Ritzel macht AFAM zur weltweit anerkannten Referenzmarke in Sachen Motorrad-Antriebskomponenten.

  • Ultraleichtes Alu-Kettenrad
  • Aluminium 7075 T6
  • Hochwertige Qualität und erstklassiges Finish
  • Geringeres Gewicht gegenüber Stahl

 

- M E R K M A L -

Kettenrad Version: Ultra-light

Kettenteilung: 428

Kettenrad Zahnanzahl: 50

Kettenrad Typ: 15104

Material: Aluminium

passend für:

SUZUKI RM 80 STD WHEELS 17/14 1983
SUZUKI RM 80 STD WHEELS 17/14 1989
SUZUKI RM 80 STD WHEELS 17/14 1990
SUZUKI RM 80 STD WHEELS 17/14 1991
SUZUKI RM 80 STD WHEELS 17/14 1992
SUZUKI RM 80 STD WHEELS 17/14 1993
SUZUKI RM 80 STD WHEELS 17/14 1994
SUZUKI RM 80 STD WHEELS 17/14 1995
SUZUKI RM 80 STD WHEELS 17/14 1996
SUZUKI RM 80 STD WHEELS 17/14 1997
SUZUKI RM 80 STD WHEELS 17/14 1998
SUZUKI RM 80 STD WHEELS 17/14 1999
SUZUKI RM 80 STD WHEELS 17/14 2000
SUZUKI RM 85 BIG WHEELS 19/16 2002
SUZUKI RM 85 BIG WHEELS 19/16 2003
SUZUKI RM 85 BIG WHEELS 19/16 2004
SUZUKI RM 85 BIG WHEELS 19/16 2005
SUZUKI RM 85 BIG WHEELS 19/16 2006
SUZUKI RM 85 BIG WHEELS 19/16 2007
SUZUKI RM 85 BIG WHEELS 19/16 2008
SUZUKI RM 85 BIG WHEELS 19/16 2009
SUZUKI RM 85 BIG WHEELS 19/16 2010
SUZUKI RM 85 BIG WHEELS 19/16 2011
SUZUKI RM 85 BIG WHEELS 19/16 2012
SUZUKI RM 85 BIG WHEELS 19/16 2013
SUZUKI RM 85 BIG WHEELS 19/16 2014
SUZUKI RM 85 BIG WHEELS 19/16 2015
SUZUKI RM 85 BIG WHEELS 19/16 2016
SUZUKI RM 85 BIG WHEELS 19/16 2017
SUZUKI RM 85 BIG WHEELS 19/16 2018
SUZUKI RM 85 STD WHEELS 17/14 2002
SUZUKI RM 85 STD WHEELS 17/14 2003
SUZUKI RM 85 STD WHEELS 17/14 2004
SUZUKI RM 85 STD WHEELS 17/14 2005
SUZUKI RM 85 STD WHEELS 17/14 2006
SUZUKI RM 85 STD WHEELS 17/14 2007
SUZUKI RM 85 STD WHEELS 17/14 2008
SUZUKI RM 85 STD WHEELS 17/14 2009
SUZUKI RM 85 STD WHEELS 17/14 2010
SUZUKI RM 85 STD WHEELS 17/14 2011
SUZUKI RM 85 STD WHEELS 17/14 2012
SUZUKI RM 85 STD WHEELS 17/14 2013
SUZUKI RM 85 STD WHEELS 17/14 2014
SUZUKI RM 85 STD WHEELS 17/14 2015
SUZUKI RM 85 STD WHEELS 17/14 2016
SUZUKI RM 85 STD WHEELS 17/14 2017
SUZUKI RM 85 STD WHEELS 17/14 2018
YAMAHA YZ 80 1993
YAMAHA YZ 80 1994
YAMAHA YZ 80 1995
YAMAHA YZ 80 1996
YAMAHA YZ 80 1997
YAMAHA YZ 80 1998
YAMAHA YZ 80 1999
YAMAHA YZ 80 2000
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YAMAHA YZ 80 LW BIG WHEELS 19/16 1994
YAMAHA YZ 80 LW BIG WHEELS 19/16 1995
YAMAHA YZ 80 LW BIG WHEELS 19/16 1996
YAMAHA YZ 80 LW BIG WHEELS 19/16 1997
YAMAHA YZ 80 LW BIG WHEELS 19/16 1998
YAMAHA YZ 80 LW BIG WHEELS 19/16 1999
YAMAHA YZ 80 LW BIG WHEELS 19/16 2000
YAMAHA YZ 80 LW BIG WHEELS 19/16 2001
YAMAHA YZ 85 (CB04) 2002
YAMAHA YZ 85 (CB04) 2003
YAMAHA YZ 85 (CB07) 2004
YAMAHA YZ 85 (CB09) 2005
YAMAHA YZ 85 (CB09) 2006
YAMAHA YZ 85 (CB09) 2007
YAMAHA YZ 85 (CB09) 2008
YAMAHA YZ 85 (CB09) 2009
YAMAHA YZ 85 (CB09) 2010
YAMAHA YZ 85 (CB09) 2011
YAMAHA YZ 85 (CB09) 2012
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB05) 2002
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB05) 2003
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB08) 2004
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2005
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2006
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2007
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2008
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2009
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2010
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2011
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2012
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2013
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2014
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2015
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2016
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2017
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2018
SUZUKI RM 80 BIG WHEELS 21/18 1983
SUZUKI RM 85 STD WHEELS 17/14 2019
SUZUKI RM 85 STD WHEELS 17/14 2020
SUZUKI RM 85 STD WHEELS 17/14 2021
SUZUKI RM 85 BIG WHEELS 19/16 2019
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2019
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2021
SUZUKI RM 85 BIG WHEELS 19/16 2020
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2020
SUZUKI RM 85 BIG WHEELS 19/16 2021
SUZUKI RM 85 BIG WHEELS 19/16 2022
SUZUKI RM 85 STD WHEELS 17/14 2022
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2022
SUZUKI RM 80 STD WHEELS 17/14 1980
SUZUKI RM 80 STD WHEELS 17/14 1981
SUZUKI RM 80 STD WHEELS 17/14 1982
SUZUKI RM 80 STD WHEELS 17/14 1984
SUZUKI RM 80 STD WHEELS 17/14 1985
SUZUKI RM 80 STD WHEELS 17/14 1986
SUZUKI RM 80 STD WHEELS 17/14 1987
SUZUKI RM 80 STD WHEELS 17/14 1988
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2023
YAMAHA YZ 85 2024
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2024
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2025
YAMAHA YZ 85 LW BIG WHEELS 19/16 (CB10) 2026
YAMAHA YZ 85 2026
YAMAHA YZ 85 2025

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4.6 ★★★★★
Based on 15 reviews
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Product Reviews
B
Brahmananda Reddy
Massapequa, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
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
C
Christopher West
Louisville, US
★★★★★ 5
Great book! Practical and for developers that already use AI!
Format: Paperback
I purchased "Agentic Coding" by Claude Code due to my desire for an alternative to generic "Prompt Template" type resources related to AI-based development. This book accomplishes just that. As opposed to merely viewing Claude Code as a "magic box", the author has explained how to utilize it in conjunction with other actual development processes. The authors' emphasis on "context engineering" (i.e., structuring data/information; managing knowledge in a project; guiding an AI agent to produce consistent results vs. producing random/unknown results) represents the strongest component of the book. It should be noted that the book appears to be intended primarily for experienced developers with prior experience in software development and/or familiarity with AI-based development tools. Should you be familiar with Git, the command-line interface, and/or modern development processes, you may find this resource very helpful. Conversely, I did appreciate the fact that there were no novice-oriented descriptions provided throughout the book. The aspect of the book that I found most valuable, however, is the extremely pragmatic nature of the material contained within. The examples illustrated through developing/maintaining CLAUDE.md files; utilizing Claude Code in combination with GitHub Workflows; employing MCP Servers; and creating multi-agent or sub-agent workflows all seemed to reflect a clear focus on "real world usage" rather than theoretical constructs. In addition, each chapter builds upon previous chapters in such a manner as to provide a logical progression through which the reader can easily understand and ultimately implement the concepts learned. I also appreciated that the author included guidance on responsible utilization of the tool(s), as well as maintaining control over what changes are made by the agent. While numerous books regarding AI focus solely on what AI tools can accomplish, this book addresses both how to utilize these tools effectively in a real codebase, as well as responsibility and safety considerations. In summary, this is not a book for individuals completely inexperienced in either programming or generative AI. However, if you are currently experimenting with tools such as Claude, Cursor, GitHub Actions, or MCP, this is likely one of the more useful and practical books available on the subject. Recommended for software engineers seeking to transition from simply "prompting an AI" into establishing a repeatable/professional workflow process surrounding agentic coding.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 11, 2026
P
Paul Pollock
Alexandria, US
★★★★★ 4
⭐⭐⭐⭐ (so far)
Format: Paperback
I'm maybe a third of the way through this and already rethinking how I talk to coding agents. The reframe from "prompt engineering" to "context engineering" sounds like semantics until Marco walks you through why context poisoning, context clash, the Goldilocks zone for system prompts. That chapter alone reorganized something in my head. I keep going back to the line about garbage in, garbage out being the real reason agentic systems underperform. The hands-on stuff lands well too. Building the HookHub project from scratch, wiring up Playwright MCP, watching Claude generate a CLAUDE.md file and then not automatically loading a memory file you just created — that moment where you expect magic and get silence instead? That's the kind of honest teaching I appreciate. It made the "why" behind memory hierarchies click.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 12, 2026
J
Jonathan Reeves
Belleville, US
★★★★★ 5
Essential Reading for Developers Serious About Agentic AI Workflows with Claude Code
Format: Paperback
Agentic Coding with Claude Code is easily one of the most practical and forward-thinking AI development books I’ve read. Instead of treating Claude Code like a simple chatbot, this book shows how to turn it into a true agentic development platform capable of handling real-world engineering workflows. What I appreciated most was how actionable the content is. The explanations around slash commands, hooks, persistent memory files, and MCP servers are incredibly clear and immediately useful. The author does an excellent job balancing foundational concepts with hands-on implementation, making advanced topics like multi-agent orchestration and hierarchical delegation approachable for experienced developers. The chapters on MCP and context engineering were especially valuable. Most AI books stay at the surface level, but this one dives deep into structured context sharing, workflow automation, and scalable AI-assisted development practices that actually matter in production environments. I also liked that the book focuses heavily on maintainability and control. It doesn’t just show flashy demos—it teaches how to safely integrate AI agents into existing terminal and IDE workflows while enforcing coding standards and keeping projects organized. The examples using Claude Code with Next.js projects were practical and helped connect the concepts to real software engineering scenarios. The sections on subagents, planning workflows, and reusable automation patterns opened my eyes to entirely new ways of approaching AI pair programming and development productivity. If you are a developer, AI engineer, or technical lead looking to move beyond basic prompt engineering and build reliable, scalable AI-assisted workflows, this book is absolutely worth reading. Highly recommended for anyone serious about modern agentic coding and AI-powered software development.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 9, 2026

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