SKU: 36822740322

Maton Guitars EBG-808-TE Tommy Emmanuel - 17091

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Description

Maton Guitars EBG-808-TE Tommy Emmanuel - 17091Maton Guitars EBG 808 TE Tommy Emmanuel 17091 Custom Tobacco Sunburst If you've ever seen Tommy Emmanuel, C. G. P. perform you will know he is one of the world's greatest guitarists, and one of the most inspirational musicians on the planet. It takes a great guitar to meet the demands of such a world renowned player. Designed in collaboration with Tommy Emmanuel, the TE series guitars are an outstanding tribute to Tommy's musicianship and Maton's

Maton Guitars EBG-808-TE Tommy Emmanuel - 17091

Custom Tobacco Sunburst

If you've ever seen Tommy Emmanuel, C.G.P. perform you will know he is one of the world's greatest guitarists, and one of the most inspirational musicians on the planet.

It takes a great guitar to meet the demands of such a world renowned player. Designed in collaboration with Tommy Emmanuel, the TE series guitars are an outstanding tribute to Tommy's musicianship and Maton's superior craftsmanship. Built to Tommy's specifications, the TE Series provides the huge tone and performance you would expect from a guitar bearing his name.
 
Tommy looks for a guitar with superb tone, capable of the most delicate expression as well as the most ferocious attack. The neck must stay true regardless of the rigors of international travel and the guitar needs to perform at its peak, from Rome to Nashville, to Sydney!
 
The Tommy Emmanuel models feature a Mother of Pearl block inlay on the 12th Fret, engraved with "C.G.P." The acronym stands for "Certified Guitar Player", a title bestowed on Tommy by Chet Atkins and held by only 3 other guitarists in the world - John Knowles, Jerry Reed, and Steve Wariner.

The Maton EBG-808-TE bears a light aging toner on the AAA Grade Sitka Spruce top. A light Vintage Amber stain on the Queensland Maple back, sides, and neck could easily be mistaken for Mahogany.
 
A rain forest hardwood from Northern Queensland, this timber is light, strong, and visually striking. Queensland Maple, while originally used as a Mahogany "substitute", is extremely well balanced with an even response across all frequencies. If you are looking for the secret to Maton's great tone, this is a good place to start.

The proprietary Maton "808" body shape is similar, though slightly smaller that the U.S. built OM (orchestra model). However, the body depth of the 808 is substantially deeper at 4-7/8". This is significant, as moving the top and back further apart enhances the bass element within the tonal character of the voicing (bracing technique), complimenting the different wood combinations

The Maton AP5-Pro Electronics

In its 5th generation, the AP5 Pro features an internal Microphone and 6-Piezo bridge input, with controls that allow for blending of both microphone and piezo levels prior to sending the actively combined signals through the well engineered pre-amp.

The highly selective cardioid microphone system allows for much greater microphone level before feedback in live environments. This upgraded AP5-Pro microphone is direct mounted to the pre-amp casing with an adjustable arm, which permits easy final tweaking of microphone position to suit the individual artist's playing style.

Using the ‘Sweep-able Mid’ controls. Mid-range is sweep-able from 600Hz to 2.4kHz and can be cut or boosted within this range. As a general rule we start out with all settings ‘flat’ then alter them to achieve the desired tone & presence based on the venue or recording environment. For example, an open, natural sound can be achieved by setting the frequency control at 1.4khz and the boost/cut control at 5db. Alternatively, for a ‘thick’ lead sound boost your mid's by 5db and set the frequency dial at 1.7kh.

Specifications:

  • Model: EBG808TE
  • Body Shape: 808
  • Face: "AAA Select" Grade Sitka Spruce
  • Back and Sides: Queensland Maple 
  • Neck: Select Queensland Maple 
  • Fingerboard & Bridge: Streaky Ebony
  • Face Inlay/Rosette: Herringbone
  • Sound hole Diameter: 95mm  
  • Bracing: Maton Scalloped X
  • Inner Binding: Triple Pinstripe
  • Outer Binding: Ivory
  • Scale Length: 64.77cm (25.5")
  • Headstock Veneer: Queensland Walnut
  • Fretwire: Jescar FW43080
  • Number of Frets: 21
  • Fingerboard Radius: 304.8mm (12")
  • Nut Width: 44.1mm (1.736" - 1 47/64")
  • 14th Fret: Width 55mm (2.165" - 2 11/64")
  • Fingerboard Inlays: 6mm Pearl Dots "CGP" Pearl Block on 12th Fret
  • Back & Sides Finish: Vintage Amber Stain Satin
  • Top Finish: Honey Stain Satin
  • Bridge Pins: Ivory ABS
  • Machine Heads: Chrome Grover RotoMatic
  • Pick Guard: Tortoiseshell
  • End Pin/Strap: Chrome
  • Nut & Saddle: Bone
  • Case: Standard Hard Case
  • Pickup: AP5 PRO
  • Strings: Elixir Nanoweb Phosphor Bronze 12-53
  • Serial Number:  33531
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SKU: 36822740322

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4.7 ★★★★★
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B
Brahmananda Reddy
Draper, 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
Waukegan, 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.
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Reviewed in the United States on May 20, 2026
C
Christopher West
West Palm Beach, 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.
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Reviewed in the United States on April 11, 2026
P
Paul Pollock
Boise, 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.
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Reviewed in the United States on May 12, 2026
J
Jonathan Reeves
Omaha, 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.
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Reviewed in the United States on May 9, 2026

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