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Description
emporio armani dress Easy-to-Wear Spring 2020 Emporio Armani Runway Look 59 Grey Silk Organ – Shrimpton CoutureThe twin of this dress walked the runway for the Spring 2020 Emporio Armani collection for the Look 59. It was made under the creative direction of Giorgio Armani. The collection was entitled "Air" and the pieces were soft. The show opened under a projection of a lightly clouded sky, and when the evening pieces walked the runway, it changed to a star filled moonscape. Appropriate given the silvers and greys of the final grouping of dresses. The
- The twin of this dress walked the runway for the Spring 2020 Emporio Armani collection for the Look 59.
- It was made under the creative direction of Giorgio Armani.
- The collection was entitled "Air" and the pieces were soft. The show opened under a projection of a lightly clouded sky, and when the evening pieces walked the runway, it changed to a star filled moonscape. Appropriate given the silvers and greys of the final grouping of dresses. The sequins and paillettes on the dress catch the light beautifully and you can see that in the reference video.
- This was shown as a strapless piece on the runway. The straps that it was sent to the shops with are set inside the upper edge of the bodice so that they could easily be tucked in if you preferred it that way.
- The dress is made out of a textured silk organza that is beautifully light in weight but still has enough structure to hold the shape that you see.
- Onto the silk are little oval paillettes done in different shades of silver and pewter. Each is suspended from the dress by a little hole at their tops and as they progress down the dress they have a little beaded bit so that they hang off of the dress more. At the top of the dress they are so heavily overlapped that they almost stand up and off of the dress and then as they reach the hem they scatter out.
- A grey piping finishes the neckline and that is also what the straps are made out of. From there it skims over the bust and widens out around the waist for a easy to fit feel. It continues to widen out over the hips all the way to the hem.
- These were produced on a very limited basis and that is noted on the tag. It was priced at just over 6K at retail.
- The dress is fully lined in matching grey silky feeling fabric and it closes with a hidden set back zipper. It appears to have been worn very little at all.
- Tagged a vintage Armani 38
- Excellent condition
Bust: to 16.5" flat across from side seam to side seam
Waist: to 17" flat across from side seam to side seam
Hips: to 22" flat across from side seam to side seam
Total length: 55.5" from top of shoulder to hem
Modern Sizing Equivalent: XS-SML
Item# DD5360
Reference Photos/Video: Spring 2020 Emporio Armani, Look 59.
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4.0 ★★★★★
Based on 620 reviews
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Product Reviews
★★★★★ 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!
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Reviewed in the United States on April 3, 2026
★★★★★ 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
★★★★★ 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
★★★★★ 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
★★★★★ 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
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