Addy Osmani – The Ai-Assisted Developer Workflow - Build Faster and Smarter Today, JSNation US 2024
AI Summary
Summary of AI Transforming Software Engineering Talk by Addie Osmani
- Introduction:
- Addie Osmani, an engineering lead at Google, discusses how AI is transforming software engineering.
- AI agents can reduce the gap from idea to execution, especially in coding and product development.
- AI Agents in Coding:
- AI agents can autonomously complete tasks and make decisions based on data.
- They can understand tasks, plan steps, execute actions, and adapt to the environment.
- Real-World Examples:
- Taxi AI: Protects GitHub repository branches by autonomously creating branch protection rules.
- Agent.exe: An open-source CLI tool that can navigate to Google Flights and perform searches.
- AI for Code Generation:
- Traditional prompt-response AI vs. agents that can plan, generate code, run tests, detect bugs, and fix issues iteratively.
- AI becomes more of a collaborator in the software engineering lifecycle.
- AI Agent Stack:
- The AI agent stack is large and includes model serving, storage, vector databases, and various tools and libraries.
- Scalable services with standardized APIs are expected to emerge as the field matures.
- Automating Tedious Tasks:
- AI can help with tasks like migrations, augmenting workflows, and automating code generation.
- Tools and Services:
- VZer (from Vercel): An AI interface builder that generates code based on user descriptions and offers a live preview.
- Bolt.new (from StackBlitz): Provides AI models with control over the entire development environment, including file system and package management.
- Co-pilot: An AI auto-completion and code generation tool for editors, with support for different third-party models.
- Cursor: A fork of VS Code built for AI integration with full codebase awareness and multimodal flexibility.
- Klein (formerly Claude Dev): A VS Code extension with agentic coding abilities, file structure analysis, and browser interaction capabilities.
- Guidelines for Coding with Agents:
- Precisely define requirements for desired outputs.
- Choose the right model for the task to optimize performance and cost.
- Plan for token limits to avoid interruptions.
- Frequent testing is essential to catch unintended modifications.
- Conclusion:
- AI agents offer opportunities for more efficient coding, leading to faster idea execution.
- As workflows with AI agents mature, a significant shift in software development approaches is anticipated.
Detailed Instructions and Tips:
- VZer Pro Tips:
- Be specific in prompts.
- Include additional documentation for third-party libraries.
- Use CLI integration for easy component installation.
- Bolt Pro Tips:
- Perform targeted edits and lock files to prevent unwanted changes.
- Regularly save progress to GitHub or download checkpoints.
- Use dummy data for backend development initially.
- Bolt can be run locally for privacy-sensitive projects.
- Cursor Pro Tips:
- Use cursor ignore files or cursor rules to refine interactions.
- Check out the cursor directory and cursor rules in the community for prompts.
- Break up large tasks into smaller, more manageable ones.
- Klein Pro Tips:
- To reduce costs, use open router or local models.
- Use custom instructions to improve performance.
- Break up large tasks into smaller ones for better control.
Real-World Application Examples:
- VZer:
- Created a Google Slides-like presentation from a JSON file.
- Recreated a design from a screenshot and turned it into an interactive component.
- Bolt:
- Scaffolds apps, showing file generation and providing rollback options.
- Integrated with Netlify for easy deployment.
- Added third-party authentication and CRUD functionality to a movies app.
- Cursor:
- Built a movies application with features like favorites, browsing, and dark mode.
- Handled errors by updating Next.js configuration settings.
- Klein:
- Generated a movies app with login and authentication flow.
- Supported end-to-end testing by launching a browser and interacting with UI elements.