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AI coding agents can drastically speed up your workflow, but without the right habits, they often lead to “context rot” and unmaintainable code. This video breaks down 10 essential lessons from a year of AI development, teaching you how to plan, test, and manage your agents like a senior engineer. Stop “vibe coding” blindly and start building reliable systems with this structured approach.
LINKS:
https://teltam.github.io/posts/using-cc.html
https://www.lighton.ai/lighton-blogs/rag-is-dead-long-live-rag-retrieval-in-the-age-of-agents
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In this video, I discuss the pros and cons of using coding agents like Claude Code and share insights from my year-long experience with them. I highlight the importance of not outsourcing your thinking, emphasizing clear planning, incremental development, and test-driven practices. Learn how to manage context, utilize Git effectively, and leverage multiple models for better results. Join me as I share essential tips to maximize the benefits of AI coding agents while minimizing common pitfalls.
00:00 The Dual Nature of Coding Agents
00:26 The Hype and the Reality of AI in Coding
01:18 Lesson 1: The Core Principle: Don’t Outsource Your Thinking
01:53 Lesson 2: Managing Context Effectively
02:35 Lesson 3: Planning Before Coding
03:38 Lesson 4: Incremental Development
04:13 Lesson 5: Treat AI as a Junior Developer
05:13 Lesson 6: Mastering Test-Driven Development
05:38 Lesson 7: Grounding Your Model
06:47 Lesson 8: Knowing When Not to Use Subagents
08:08 Lesson 9: Git as Your Safety Net
08:43 Lesson 10: Using Multiple Models for Better Results
09:34 Conclusion: The Virtuous Cycle of AI Development
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