Exploring Four AI Native Development Patterns: Transforming Developer Roles

Join me as we explore the four patterns of AI-native development, a journey that's transforming our workflow from producer to manager, intent-based coding, discovery, and knowledge capture, revolutionizing the way we develop software with AI.

1. The speaker is an industry advisor with expertise in DevOps, helping the AI native dev community curate content.
2. They are discussing four patterns of AI native development and how technology has advanced from LLMs to agents, impacting the development workflow.
3. AI native development changes tasks, replacing, enhancing, or introducing new ones, leading to a shift in roles for developers from producers to managers.
4. With AI generating code, review time increases, cognitive load goes up, and new ways of reviewing code are needed, like using summaries instead of diffs.
5. Multiple files review can be broken down into steps, making it easier to manage and reducing cognitive load.
6. Reviewing diagrams instead of text can help spot errors more easily and is a potential trend in AI native development.
7. There's an increase in longer-running agents, which require managing, and new approaches like auto-accepting commits based on risk or impact.
8. Checkpoints have emerged for long-running agents to avoid re-reviewing entire iterations.
9. Constraints can be set for AI, preventing it from touching certain files and defining permissions.
10. The second pattern is moving from caring about actual implementation to specifying intent, using tools like markdown files and GitHub task-oriented approaches.
11. Intent-based coding focuses on defining tasks instead of chatting or text completion, building a plan and generating code accordingly.
12. Specification-centric tools focus on functional, technical, security requirements and workflows, moving away from directly viewing code.
13. Pattern three involves discovering the right intent by exploring ideas and working problems, using tools like Lavable or Bolt for rapid prototyping.
14. This discovery process includes creating multiple iterations to choose the best one and can be applied to customer interactions for tailored product design.
15. The final pattern is turning learned content into knowledge, capturing insights from production issues, incident responses, and code lessons.
16. AI can help in tracking features and decisions as part of a feature memory, reducing onboarding time, and improving overall coding solutions.
17. AI native development roles include producers becoming managers, QA professionals, architects, product owners, and data engineers.
18. Good senior developers already perform tasks beyond just coding, and AI enhances these roles instead of only speeding up typing.
19. The AI Native Dev landscape provides a curated list of 300+ tools at [aianativedev.io](http://aianativedev.io).
20. Subscribing to the AI Native Dev newsletter focuses on intersections between coding, software engineering, and AI.
21. The four AI native dev patterns include management, intent specification, discovery, and knowledge creation.
22. AI allows developers to become managers, QA professionals, architects, product owners, and data engineers.
23. AI native development tools focus on functional, technical, security requirements, and workflows instead of directly viewing code.
24. AI aids in tracking features, decision-making, reducing onboarding time, improving coding solutions, and managing knowledge.

Source: AI Engineer via YouTube

❓ What do you think? What does it mean to truly integrate AI into our development workflow, and how do we adapt our roles as developers to become AI-native professionals? Feel free to share your thoughts in the comments!