SKU: 55706368338

Modern Acrylic Paintings, Large Paintings for Living Room, Contemporary Wall Art Paintings, Hand Painted Canvas Art, Original Abstract Art

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Description

Modern Acrylic Paintings, Large Paintings for Living Room, Contemporary Wall Art Paintings, Hand Painted Canvas Art, Original Abstract ArtLarge Paintings for Living Room, Contemporary Wall Art Paintings, Modern Acrylic Paintings, Hand Painted Canvas Art, Original Abstract Art Original Hand Painted Art by Colin Ju Custom Painting Sizes: (I will complete the painting in about 11 days and send out the painting via express shipping.) The following painting sizes are large. The paintings will be rolled in tube for shipping. You will need to gallery wrapped or framed the painting at the local

Large Paintings for Living Room, Contemporary Wall Art Paintings, Modern Acrylic Paintings, Hand Painted Canvas Art, Original Abstract Art
Original Hand Painted Art by Colin Ju

Custom Painting Sizes:
(I will complete the painting in about 11 days and send out the painting via express shipping.)

The following painting sizes are large. The paintings will be rolled in tube for shipping. You will need to gallery wrapped or framed the painting at the local frame shop:
* 28x37 inch (About 71X95cm, Unframed)
* 30x40 inch (About 76X102cm, Unframed)
* 35x47 inch (About 89X119cm, Unframed)
* 40X53 inch (About 102X135cm, Unframed)
* 45X60 inch (About 114X152cm, Unframed)
* 50X67 inch (About 127X169cm, Unframed)
* 55X73 inch (About 140X186cm, Unframed)
* 60X80 inch (About 152X203cm, Unframed)
* 65X87 inch (About 165X220cm, Unframed)
* 70X93 inch (About 178X237cm, Unframed)
* 75X100 inch (About 191X254cm, Unframed)
* 80X107 inch (About 203X271cm, Unframed)
* 85X113 inch (About 216X288cm, Unframed)

The following painting size is gallery wrapped. The painting is ready to hang when you receive it:
* 28x37 inch (About 71X95cm, gallery wrapped)
* 30x40 inch (About 76X102cm, gallery wrapped)

Please feel free to contact me if you need me to custom other sizes for you.

This painting is completely hand painted and in heavy acrylic paints and textures. It's NOT a print or poster or giclee.

This is a made to order painting which is created by me. The recreation will have the same texture and brush stroke just as the original painting.

Since the painting is extra large and could not to be shipped if gallery wrapped or framed. I will roll the painting in protective tube for shipping.

I will left about 3 to 5 inches of canvas on each border so you could frame or gallery wrapped the painting at the local frame shop.

Free shipping to worldwide for unframed paintings! I will ship out the painting via Express Mail Service with Express Mail Service / DHL / EMS

This is a wonderful colorful painting direct from the artist. I use only highest quality Winsor & Newton art materials, and painted on acid free gallery museum quality canvas.

The painting is signed on the front by the artist and 100% guaranteed, I want you happy with your purchase.

Please do not hesitate to ask me any question and I'll get back to you at the earliest opportunity.

Shipping Notes
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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]
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SKU: 55706368338

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4.8 ★★★★★
Based on 19 reviews
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Product Reviews
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Verified Purchase
Jenny Holden
Houston, 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
Lake Worth, 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.
Chelsea, 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 28, 2026
B
Brahmananda Reddy
Houston, 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
Massapequa, 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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