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πŸ€–πŸ‘¨β€πŸ’»πŸ“ˆβ³ Learnings from two years of using AI tools for software engineering

πŸ€– AI Summary

  • AI coding assistants like πŸ€–πŸ’»πŸͺ„ GitHub Copilot for VS Code are used to scaffold entire classes πŸ“š and auto-complete logical structures.
  • ChatGPT has evolved πŸ“ˆ from a coding assistant to a sounding board for architectural discussions πŸ€”, ideation, and problem-solving.
  • The mental shift from viewing AI as a tool to a collaborator 🀝 is critical for exponential gains πŸš€.
  • Productivity gains come from thinking in parallel πŸ‘―β€β™€οΈ and delegating ✍️ subtasks to AI.
  • Engineers must act as managers πŸ§‘β€πŸ’Ό, providing precise context and detailed feedback πŸ“ to the AI.
  • A key to quality is restraint πŸ›‘ and continuous refactoring, as AI may produce over-engineered πŸ› οΈ or complex solutions.
  • The entire work experience for engineers is evolving πŸ”„ weekly.

πŸ€” Evaluation

The provided content offers a perspective 🧐 from a company that has successfully adopted AI πŸ€– tools in its workflow. It provides a practical, real-world account of AI integration, contrasting with more theoretical discussions about AI’s impact on software engineering. The piece focuses on the practical application and the cultural shift required for success, moving beyond the simple β€œproductivity boost” narrative. For a better understanding, it would be beneficial to explore perspectives πŸ—£οΈ from individuals or teams that have had negative experiences with AI integration, perhaps due to challenges with code quality, security vulnerabilities, or the β€˜black box’ nature of AI outputs. It would also be valuable to explore how AI affects junior versus senior developers πŸ§‘β€πŸ’».

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