SKU: 34632884746
friedman dirty shirley pedal manual

friedman dirty shirley pedal manual Friedman IR-D Dual Tube Preamp & DI Pedal

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Description

friedman dirty shirley pedal manual Friedman IR-D Dual Tube Preamp & DI PedalMaker: Friedman Amplification Model: IR D Condition: New Description: The Friedman IR D dual channel tube preamp is an entire Friedman rig in a compact pedalboard friendly package. IR D boasts two fully independent identical foot switchable channels which are based on Friedmans legendary Dirty Shirley Twin Sister design. This Dave modded JTM45 circuit delivers the signature British tone for which Friedman is known for. Powered by two high voltage

Maker: Friedman Amplification

Model: IR-D

Condition: New

 

Description:

The Friedman IR-D dual-channel tube preamp is an entire Friedman rig in a compact pedalboard-friendly package. IR-D boasts two fully independent identical foot-switchable channels which are based on Friedman’s legendary Dirty Shirley/Twin Sister design. This Dave modded JTM45 circuit delivers the signature British tone for which Friedman is known for.

Powered by two high-voltage 12AX7 preamp tubes, each channel delivers the  harmonic richness and touch response of Dave Friedman’s finest designs. DSP IR cabinet/power amp simulation sends a studio-quality mic’d-up tone to your DAW or Front-of-House. The IR-D provides easy-to-use controls without complex menus and 100-page manuals.

IR-D isn’t simulating a Friedman tube preamp. It is one. While many available preamp pedals utilize tubes to “warm up” their digital or solid-state circuit, two glowing hot 12AX7 preamp tubes, running at the proper high voltage, are this pedal’s beating heart. A genuine preamp in every way, IR-D delivers all the tone, response, gain characteristics, and harmonic complexity you expect from a Friedman design.

All great tube amps need a great speaker cabinet, and whether sending your signal straight to the board or silently playing through the onboard headphone output, IR-D gives you a selection of Dave Friedman’s favorite IRs. Select the IR per channel using the 3-way mini toggle switches or load your own quickly and easily via IR-D’s editing software. You can even disengage the onboard IRs entirely to use IR-D’s tube preamp with another IR loader.

IR-D FEATURES:

  • All-tube HIGH-VOLTAGE preamp - DI

  • 2 x 12AX7 preamp tubes

  • Two independent identical channels + boost

    • Based on Friedman’s Legendary Dirty Shirley/Twin Sister design

  • MIDI programmable features:

    • Channel select / boost

    • IR Select

    • Thump and Presence – three-way switch per channel

    • Effects Loop On/Off

    • Boost Lock

  • Power amp simulation with software editable Presence and Thump (per channel)

  • Low latency Impulse Response cabinet simulation (bypassable)

  • Three position CAB/IR select switch per channel (programmable via software)

  • 13 free Friedman IRs or load your own

  • Boost Volume and Gain controls (per channel)

  • Volume and Gain controls (per channel)

  • Full EQ consisting of Bass, Middle, Treble (per channel)

  • 2-way Bright/Tight switch (per channel)

  • 3-way Structure switch (per channel)

  • Three multi-function foot switches

  • Ultra-transparent series FX loop (bypassable)

  • TRS balanced output

  • Headphone output

  • MIDI 5 pin standard connector and USB MIDI

  • USB software editor - powered by SYNERGY

  • Limited 3-year Warranty

  • Universal 12V Power Supply Included

  • 6 Foot USB2.0 A Male to B Male cable Included

  • Dimensions (Including Knobs): 5.5” (D) x 6.5” (W) x 2.3 (H)

  • Weight: 1.5 lbs.
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    SKU: 34632884746

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    J
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    Jenny Holden
    Omaha, 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.
    WAS THIS REVIEW HELPFUL?YesReportShare
    Reviewed in the United States on February 17, 2026
    D
    Verified Purchase
    David Escobar
    Belleville, 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.
    Birmingham, 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
    Natrona Heights, 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
    Belleville, 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

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