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Course Outline
The Cascade Conversational Interface
- Distinguishing Cascade from conventional chat panels in other integrated development environments
- Sustaining conversational context across iterative feature requests
- Navigating between explain, plan, and act modes within the Cascade environment
- Evaluating real-world conversational patterns for defect resolution and feature development
Predictive Edits and Multi-File Awareness
- Defining predictive edits and identifying automatic trigger conditions
- Managing, accepting, rejecting, and configuring edit suggestions across multiple files
- Automatically tracking dependencies among edited files
- Reverting cascading changes when predictions are inaccurate
Terminal Integration Inside the Editor
- Initiating and managing integrated terminal sessions directly within the editor
- Leveraging terminal output monitoring to refine subsequent operational steps
- Executing tests, builds, and deployments without exiting Windsurf
- Managing interactive command-line interface (CLI) prompts during automated workflows
Windsurf Indexing and Context Management
- Mechanisms for building and maintaining a real-time project index
- Differences in indexing behavior between monorepos and single repositories
- Optimizing indexing speed by excluding generated artifacts and build directories
- Rebuilding the index following significant structural modifications
Conversational Feature Building
- Describing features in plain language and reviewing Cascade-generated plans
- Reviewing proposed file lists before approving changes for government applications
- Executing generated code immediately and submitting errors back to Cascade
- Refining outcomes iteratively through conversational follow-up prompts
Custom Rules and Technology-Specific Prompting
- Developing project-specific rules for internal frameworks
- Enforcing naming conventions via Windsurf rule files
- Customizing indexing for domain-specific languages (DSLs) and non-standard file formats
- Distributing rule sets across development teams for consistency
Debugging with Cascade Assistance
- Utilizing stack traces for root-cause analysis within Cascade
- Comparing functional and defective code versions via Cascade inquiries
- Executing regression tests using the terminal watcher capabilities
- Identifying hallucinated imports or missing dependencies resulting from edits
Version Control and Review Integration
- Generating commit messages from conversational change summaries
- Drafting pull request descriptions through Cascade chat interactions
- Addressing reviewer comments with targeted file modifications
- Maintaining a clean commit history during conversational development cycles
Performance and Enterprise Deployment
- Managing large workspace indexing within memory constraints
- Optimizing startup time for repositories containing numerous files
- Understanding Windsurf data handling protocols and training opt-out provisions
- Configuring enterprise proxies and virtual private networks (VPNs) for regulated environments
Transitioning from Other Editors
- Importing keybindings and settings from VS Code or JetBrains environments
- Exporting Windsurf extensions to standard VS Code compatibility
- Implementing team migration strategies and pilot program structures for government adoption
Requirements
- Demonstrated proficiency with integrated development environments, such as Visual Studio Code or JetBrains tools.
- Working knowledge of Git version control and agile development methodologies.
- Familiarity with large language model (LLM) driven chat interfaces.
Target Audience
- Software engineers assessing Windsurf as a primary coding platform, tailored for government applications.
- Cross-functional product teams seeking to integrate native conversational artificial intelligence capabilities directly within their coding environments.
- Independent contractors aiming to optimize workflow efficiency by minimizing context switching through unified terminal and integrated development environment operations.
14 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
