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AI 개발 현실 체크 포스트의 내용 보강 및 날짜 수정. 블로그 자동화 과정에서의 경험과 깨달음을 추가하여 포스트의 깊이를 더욱 향상시켰습니다.
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‎_posts/2025-06-10-ai-dev-reality-diary-en.md‎

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---
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layout: post
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title: "AI Development Reality Check: Lessons from Blog Automation"
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date: 2025-06-10 08:30:00 +0900
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categories: [Development, DevDiary]
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tags: [AI Development, GitHub Pages, Automation, Development Experience, Junior Developer]
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author: "Kevin Park"
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excerpt: "Real stories from building a GitHub Pages blog with AI over the past few days. The light and shadow of AI development"
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image: "/assets/images/posts/ai-dev-reality-diary/hero.png"
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lang: en
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---
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# AI Development Reality Check: Lessons from Blog Automation
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![AI Development Reality](/assets/images/posts/ai-dev-reality-diary/hero.png)
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*The reality of AI development: coexistence of convenience and complexity*
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## 📝 Today's Problem
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For the past few days, I've been completely immersed in creating a GitHub Pages blog and working on AI prompt automation.
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Since AI was generating code so smoothly, I thought "Wow, this is really easy!" But when I actually tried to run it, problems kept popping up everywhere.
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## 💡 Problem-Solving Process
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### AI's Strengths: Rapid Prototyping
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```yaml
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# Jekyll configuration generated by AI in 1 minute
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plugins:
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- jekyll-feed
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- jekyll-sitemap
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- jekyll-seo-tag
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collections:
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posts:
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output: true
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permalink: /:year/:month/:day/:title/
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```
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**What was great**: AI creates basic structures and templates really quickly
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### AI's Limitations: Detailed Debugging
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```bash
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# In reality, these errors kept occurring
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Error: Liquid syntax error: Unknown tag 'mermaid'
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Error: Github Pages build failed
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```
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**The problem**: When AI-generated code doesn't work in the actual environment, finding what went wrong is ultimately the developer's responsibility
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## 🎯 Key Insights
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### 1. AI + Existing Knowledge: An Essential Combination
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- AI generates code, but **guiding it in the right direction** is the developer's role
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- Existing knowledge to **validate and modify** the generated output is essential
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### 2. The New Dilemma for Junior Developers
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```javascript
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// Old junior developer's goal
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const juniorGoal = "Step by step from Hello World";
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// Current junior developer's reality
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const currentReality = "This much with AI should be basic, right?";
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```
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**Paradoxical situation**:
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- Learning has become easier, but expectations have risen to 5-6 years of experience level
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- AI tool utilization skills are now additionally required
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### 3. Core Skills in AI Development
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- **Prompt Engineering**: Accurately conveying requirements to AI
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- **Output Validation**: Identifying problems in generated code
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- **Iterative Improvement**: Progressively enhancing quality together with AI
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## 📈 Conclusion
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AI development is **an innovation in tools**, not **a replacement for development knowledge**.
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In fact, you need existing development knowledge to properly utilize AI and to judge "this isn't right" when AI makes mistakes.
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**Essential competencies for new developers**:
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- Traditional development knowledge (fundamentals)
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- AI tool utilization skills (new fundamentals)
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- Insight to connect these two domains
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It's quite ironic, but this is reality. Developers in the AI era need to know even more.

‎_posts/2025-06-10-ai-dev-reality-diary-ja.md‎

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---
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layout: post
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title: "AI開発の現実チェック：ブログ自動化で気づいたこと"
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date: 2025-06-10 08:30:00 +0900
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categories: [Development, DevDiary]
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tags: [AI開発, GitHubPages, 自動化, 開発経験, 新人開発者]
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author: "Kevin Park"
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excerpt: "数日間GitHubページブログをAIで作成した際の現実的な話。AI開発の明と暗"
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image: "/assets/images/posts/ai-dev-reality-diary/hero.png"
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lang: ja
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---
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# AI開発の現実チェック：ブログ自動化で気づいたこと
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![AI Development Reality](/assets/images/posts/ai-dev-reality-diary/hero.png)
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*AI開発の現実的な姿：便利さと複雑さの共存*
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## 📝 今日の問題
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数日間、GitHubページでブログを作成し、AI プロンプト作業による自動化に完全にはまっていた。
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AIがコードをすらすら生成してくれるので「すごい、これは本当に簡単だ！」と思ったが、実際に動かしてみると、あちこちで動かない部分が続出した。
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## 💡 解決プロセス
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### AIの長所：迅速なプロトタイピング
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```yaml
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# AIが1分で生成したJekyll設定
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plugins:
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- jekyll-feed
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- jekyll-sitemap
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- jekyll-seo-tag
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collections:
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posts:
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output: true
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permalink: /:year/:month/:day/:title/
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```
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**良かった点**：基本構造やテンプレートは本当に早く作成してくれる
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### AIの限界：詳細なデバッグ
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```bash
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# 実際にはこのようなエラーが継続的に発生
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Error: Liquid syntax error: Unknown tag 'mermaid'
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Error: Github Pages build failed
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```
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**問題点**：AIが生成したコードが実際の環境で動作しない時、どこが間違っているかを見つけ出すのは結局開発者の仕事
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## 🎯 気づいたこと
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### 1. AI + 既存知識の必須組み合わせ
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- AIはコードを生成してくれるが、**正しい方向に導く**のは開発者の役割
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- 生成された結果物を**検証し修正**できる既存知識が必須
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### 2. 新人開発者の新しいジレンマ
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```javascript
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// 以前の新人の目標
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const juniorGoal = "Hello Worldから着実に";
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// 現在の新人の現実
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const currentReality = "AIでこの程度は基本でしょう？";
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```
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**逆説的状況**：
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- 学習はより簡単になったが、期待値は5-6年目レベルに上昇
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- AIツール活用能力まで追加で要求される
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### 3. AI開発の核心スキル
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- **プロンプトエンジニアリング**：AIに正確な要求事項を伝達
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- **結果物検証**：生成されたコードの問題点を把握
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- **段階的改善**：AIと共に反復的に完成度を高める
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## 📈 結論
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AI開発は**ツールの革新**であり、**開発知識の代替**ではない。
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むしろ既存の開発知識があってこそAIを適切に活用でき、AIが間違った時に「これは違う」と判断できる。
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**新しい開発者の必須能力**：
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- 従来の開発知識（基礎）
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- AIツール活用能力（新しい基礎）
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- この二つの領域を繋ぐ洞察力
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とてもアイロニカルだが、これが現実である。AI時代の開発者はより多くのことを知らなければならない。

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