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怪俠空古力 5：幽靈寶卡卡 (顏志豪): : VS. 2020 Books from Taiwan2021TAICCA2020109Bookstart42 WIA
作者: 顏志豪 | 繪者: 曹一竹
好不容易受邀為酷必樂演唱會嘉賓的空古力，竟一時貪吃成千古恨，二話不說吃了五個胖等級的「十八層地獄草莓千層蛋糕」，因而穿不下表演服，陷入超不想面對的苦惱深淵！
同時《餓到腿軟美食雜誌》火熱熱報導著，今年讀者最想念的零食—幽靈寶卡卡，有如冰如火的清脆口感外，重點是完全零卡路里，吃再多也絕對不會發胖！讓空古力與小跟班眼睛為之一亮，精神瞬間振奮，決定出發找尋這傳說中如夢如幻的不可思議零食。
半山路上愈走愈奇怪，天色愈來愈黑，還被一位奇怪老奶奶攔截，最後空古力發揮了她的貓屎運，意外地來到幽靈寶卡卡的產地「幽靈村」，而奇怪老奶奶就是當年幽靈寶卡卡的創辦人—火龍奶奶！只是，火龍奶奶一點都不慈祥，為了讓空古力捕捉到可遇不可求的幽靈寶卡卡，根本是武打片裡流淚不眨眼的鐵血教練，簡直就像參加殘酷到不行的減肥訓練營？！！
最後空古力與小跟班能順利「瘦身」捕捉幽靈寶卡卡嗎？而幽靈村為何只剩火龍奶奶一個人，而不再發售幽靈寶卡卡呢？這其中到底有什麼不可告人的秘密？
本書特色
☆兒童文學作家顏志豪與繪者曹一竹，共同打造出熱鬧奇幻的奇幻多重宇宙冒險。
☆酷酷心腸軟空古力VS.愛哭愛跟路小跟班的可愛燒腦情感，深受小學生喜愛。
☆搭配每集主題，書中設計精美遊戲互動關卡，讓小朋友跟著主角情境一同動腦破關。
☆本集收錄「各項運動」主題中英圖鑑海報，充滿設計感的圖像激發孩子探索中英文學習的興趣！
☆透過故事中角色的發展，陪伴孩子認知人際互動、情感表達，以及身體鍛鍊的重要！
．每天運動讓身體更為健康有精神。
．思考如何與別人合作完成共同成果。
．觀察每個故事背後，是不是都有沒有被發現的原因呢？
同時《餓到腿軟美食雜誌》火熱熱報導著，今年讀者最想念的零食—幽靈寶卡卡，有如冰如火的清脆口感外，重點是完全零卡路里，吃再多也絕對不會發胖！讓空古力與小跟班眼睛為之一亮，精神瞬間振奮，決定出發找尋這傳說中如夢如幻的不可思議零食。
半山路上愈走愈奇怪，天色愈來愈黑，還被一位奇怪老奶奶攔截，最後空古力發揮了她的貓屎運，意外地來到幽靈寶卡卡的產地「幽靈村」，而奇怪老奶奶就是當年幽靈寶卡卡的創辦人—火龍奶奶！只是，火龍奶奶一點都不慈祥，為了讓空古力捕捉到可遇不可求的幽靈寶卡卡，根本是武打片裡流淚不眨眼的鐵血教練，簡直就像參加殘酷到不行的減肥訓練營？！！
最後空古力與小跟班能順利「瘦身」捕捉幽靈寶卡卡嗎？而幽靈村為何只剩火龍奶奶一個人，而不再發售幽靈寶卡卡呢？這其中到底有什麼不可告人的秘密？
本書特色
☆兒童文學作家顏志豪與繪者曹一竹，共同打造出熱鬧奇幻的奇幻多重宇宙冒險。
☆酷酷心腸軟空古力VS.愛哭愛跟路小跟班的可愛燒腦情感，深受小學生喜愛。
☆搭配每集主題，書中設計精美遊戲互動關卡，讓小朋友跟著主角情境一同動腦破關。
☆本集收錄「各項運動」主題中英圖鑑海報，充滿設計感的圖像激發孩子探索中英文學習的興趣！
☆透過故事中角色的發展，陪伴孩子認知人際互動、情感表達，以及身體鍛鍊的重要！
．每天運動讓身體更為健康有精神。
．思考如何與別人合作完成共同成果。
．觀察每個故事背後，是不是都有沒有被發現的原因呢？
作者
顏志豪
國立臺東大學兒童文學博士，現專職創作。曾獲九歌現代少兒文學獎、國語日報牧笛獎、教育部文藝創作獎、秀威青少年文學獎、吳濁流文藝獎、大墩文學獎、海洋文學獎及林君鴻兒童文學獎等獎項。
在巴巴文化的著作《夢遊》入選文策會2020 Books from Taiwan及2021波隆那插畫獎，更榮獲TAICCA特別推薦、好書大家讀2020年度最佳少年兒童讀物獎，以及109年Bookstart閱讀起步走、第42次中小學生讀物選介推薦圖書。希望自己的故事像月光，文字像星星，在黑夜時，一閃一閃亮晶晶，溫暖每個孤單的心。
繪者
曹一竹
畢業於國立臺北藝術大學美術系，現就讀國立臺東大學兒童文學研究所。曾擔任電影美術、分鏡師，專注於韓國文化研究，在臺韓兩地舉辦過數次展覽，現為插畫家及藝術創作者，也從事翻譯工作。作品曾入圍南怡島插畫獎、WIA世界插畫獎。繪本出版作品有《移動超市六七十項》、《恁久好無？安娜！》及《娃娃博物館奇案筆記》。
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4.8 ★★★★★
Based on 6 reviews
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Product Reviews
★★★★★ 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 Will become outdated quickly.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
★★★★★ 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
★★★★★ 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.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on April 11, 2026
★★★★★ 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
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