Create a comprehensive dashboard that automates your AI development journey with built-in code review tracking and workflow management.
Dashboard that automatically logs and tracks your code reviews with AI feedback scores
Automated triggers that update your progress when you complete coding tasks
Visual timeline showing your AI tool mastery journey with milestone tracking
AI-powered suggestions for next steps based on your current skill level and goals
Track improvements in your code quality over time with automated scoring
Monitor which automation hooks are active and track their performance
Free account works fine
We'll use AI to generate the dashboard code and automation logic. Either tool works perfectly for this project.
VS Code, Notepad++, or similar
You'll save the HTML/JavaScript files that AI generates. Nothing fancy needed—even a basic text editor works.
First, we'll create the main dashboard structure that will house all our tracking components. This combines the visual progress tracking from your 30-day learning plan with sections for code review metrics and workflow automation status.
Create an HTML dashboard for tracking AI development progress with these sections: 1. Header with title "My AI Development Dashboard" and current date 2. Progress Overview card showing: - Days into 30-day learning plan - Total code reviews completed - Active workflow automations count - Overall skill level (Beginner/Intermediate/Advanced) 3. Code Review Tracker section with: - Recent reviews list (date, project, AI feedback score 1-10) - Average quality score trend chart area - "Add New Review" button 4. Workflow Automation Status with: - List of active hooks (name, status, last triggered) - Performance metrics (success rate, avg response time) - "Configure Hooks" button 5. Learning Progress Timeline showing: - Week 1-4 milestones with completion status - Current focus area highlight - Next recommended action Use a dark theme with green accents. Include placeholder data to make it look realistic. Make it responsive and modern-looking with cards and clean typography.
Look for a complete HTML file with CSS styling that creates a professional dashboard layout. The AI should include realistic sample data and a dark theme that matches modern development tools.
Now we'll build the JavaScript that automatically processes and scores code reviews, just like the AI code review tutorial but integrated into our dashboard. This creates the foundation for tracking code quality improvements over time.
Add JavaScript functionality to the dashboard for automated code review tracking: 1. Code Review Processor function that: - Takes code snippet input - Simulates AI analysis (or integrates with Claude/ChatGPT API) - Generates scores for: readability, efficiency, best practices, documentation - Calculates overall score 1-10 - Stores review in localStorage with timestamp 2. Review History Manager: - Loads saved reviews from localStorage - Displays recent reviews in the dashboard - Calculates trend data (improving/declining quality) - Updates progress metrics 3. Add Review Form with: - Code input textarea - Project name field - "Analyze Code" button that triggers the processor - Results display showing detailed feedback 4. Quality Trend Visualization: - Simple chart showing score progression over time - Color coding (red=needs work, yellow=good, green=excellent) - Average score calculation Include sample review data and make the form interactive. Add proper error handling and user feedback.
The AI should provide JavaScript functions that handle code review automation, data storage, and trend analysis. Look for localStorage integration and a working form that processes code input.
Building on the Claude Code Hooks tutorial, we'll create a system that monitors and manages your development workflow automations. This tracks which automations are running and how they're performing.
Create a workflow automation management system: 1. Hook Registry that tracks: - Git commit hooks (pre-commit, post-commit) - Code formatting automations - Test runner triggers - Documentation generators - Each with: name, status (active/inactive), last run time, success rate 2. Hook Status Monitor: - Simulates checking hook health - Updates dashboard with current status - Shows performance metrics (runs per day, failure rate) - Highlights hooks that need attention 3. Hook Configuration Panel: - Toggle hooks on/off - Edit hook settings (mock interface) - Add new workflow automation - Delete existing hooks 4. Activity Feed: - Recent hook executions log - Success/failure indicators - Execution time tracking - Error messages for failed runs 5. Performance Dashboard: - Overall automation success rate - Time saved through automation - Most/least reliable hooks - Recommendations for improvement Include realistic sample hook data and make the interface interactive with proper state management.
Expect JavaScript that simulates workflow automation management with data persistence, status monitoring, and an interactive configuration interface.
Implementing the structured 30-day learning plan from our tutorial, we'll create a visual progress tracker that shows your AI tool mastery journey and suggests next steps based on your current progress.
Create a comprehensive learning progress tracking system: 1. 30-Day Learning Plan Structure: - Week 1: AI Basics (prompting, safety, tools setup) - Week 2: Code Integration (code review, debugging, assistance) - Week 3: Automation (workflows, hooks, custom instructions) - Week 4: Advanced Usage (custom GPTs, complex projects) - Each week has 5-7 specific milestones 2. Progress Visualization: - Timeline view with completed/pending milestones - Progress bars for each week - Current day highlighting - Achievement badges for completed sections 3. Milestone Tracking System: - Checkbox interface to mark milestones complete - Automatic progress calculation - Streak tracking (consecutive days of progress) - Estimated completion date 4. Smart Recommendations Engine: - Analyzes current progress and focus areas - Suggests next logical steps - Identifies knowledge gaps - Recommends practice projects 5. Skill Level Assessment: - Calculates overall skill level based on completed milestones - Shows strengths and areas for improvement - Progress comparison (where you started vs now) Make it interactive with localStorage to save progress between sessions.
Look for a detailed learning plan implementation with interactive progress tracking, milestone management, and intelligent recommendations based on user progress.
Finally, we'll connect all the components together and add the finishing touches that make this a cohesive, professional dashboard for tracking your AI development journey.
Complete the dashboard integration and add professional polish:
1. Data Synchronization:
- Connect code review scores to overall progress calculation
- Link workflow automation success to learning milestones
- Update dashboard overview metrics in real-time
- Cross-reference activities with learning plan progress
2. Dashboard Overview Cards:
- Total development score (combination of all metrics)
- Days active streak
- Code quality trend (improving/stable/declining)
- Automation efficiency percentage
- Next milestone reminder
3. Interactive Features:
- Smooth animations for progress updates
- Hover effects and tooltips for detailed information
- Quick action buttons ("Start Today's Task", "Review Recent Code")
- Collapsible sections for better organization
4. Data Export/Import:
- Export progress data as JSON
- Import existing data to restore dashboard
- Backup reminder system
5. Professional Polish:
- Responsive design for mobile/desktop
- Loading states and smooth transitions
- Error handling with user-friendly messages
- Clean, consistent styling throughout
- Performance optimization for smooth operation
Add final touches like keyboard shortcuts, dark/light theme toggle, and print-friendly styles.The AI should deliver a fully integrated dashboard with smooth interactions, data synchronization between components, and professional styling that ties everything together into a cohesive tool.
If your progress resets when you close the browser, the localStorage isn't working properly.
Check that you're opening the HTML file with a proper web server (like VS Code Live Server) rather than just double-clicking it. Some browsers restrict localStorage on file:// URLs.
If the cards are overlapping or the layout looks messy, the CSS grid might need adjustment.
Ask the AI to "fix responsive layout issues and ensure proper spacing on mobile and desktop" with your current code attached.
The simulated code analysis might give inconsistent scores for similar code.
This is expected with the mock analysis. For real scoring, you'd integrate with Claude or ChatGPT's API. The tutorial focuses on the dashboard structure, not actual AI integration.
Combined AI code review concepts with progress tracking to create a system that monitors code quality improvements over time.
Built a system to monitor and manage Claude Code Hooks, tracking their performance and providing a centralized control panel.
Implemented the 30-day learning plan as an interactive tracking system with milestones, progress visualization, and smart recommendations.
Learned to prompt AI for building interconnected systems where multiple components share data and update each other dynamically.
Replace the simulated code review with actual Claude or ChatGPT API calls for real AI feedback on your code.
This would make the code quality scores much more accurate and useful for actual development work.
Build charts showing code quality trends, productivity metrics, and learning velocity over longer time periods.
Could include weekly/monthly reports, skill area breakdowns, and comparison with other developers.
Connect the dashboard to your actual Git repositories to automatically track commits, pull requests, and code changes.
This would eliminate manual entry and provide real data about your development activity and progress.
Expand it into a team tool where multiple developers can track their progress and compare learning paths.
Add features like leaderboards, team challenges, and collaborative code review sessions.