Features
AI Players
PlayerZero’s AI Player serves as your knowledge partner, helping you navigate complex codebases, debug issues, and analyze code changes through natural conversation.
What Is an AI Player?
PlayerZero’s AI Players acts as your knowledge-rich agents, designed to help you understand, investigate, and solve problems across your codebase and systems. Whether you’re debugging a tricky issue, exploring architecture, or researching historical changes, the AI Player provides fast, context-aware insights. To use it, navigate to any project in PlayerZero and start a conversation through the free‑form prompt interface.Starting Points
Free‑Form Input
Free‑Form Input
- Access Point: Available on the homepage and within any project view.
- Use Case: Ideal for open-ended questions, quick lookups, or initiating deep investigations.
- Behavior: AI builds context from your selected project and branch, then continues to expand the conversation as you refine your queries.
Debug Report
Debug Report
- Access Point: From any session replay or Debug Report timeline.
- Use Case: Perfect when you need to investigate user-reported issues with full session, DOM, and trace context.
- Behavior: Clicking “Find in My Code” or “Debug” launches a chat that’s pre-loaded with session data, related logs, and linked code paths for faster root-cause analysis.
Log Error
Log Error
- Access Point: From any error log, stack trace, or telemetry panel.
- Use Case: For digging into specific errors or performance alerts without starting a fresh session view.
- Behavior: AI automatically correlates the log or stack trace with relevant files, recent commits, and known historical issues, so you can jump straight to a potential fix.
PR Summary
PR Summary
- Access Point: Directly from Pull Request pages in PlayerZero.
- Use Case: Quickly understand what a PR changes, potential risks, and how it may impact customers.
- Behavior: AI generates a natural-language summary, identifies impacted areas, and lets you ask follow-up questions about specific functions, modules, or risks.
Modes of Operation
Fast Mode
Fast Mode
- Purpose: Instant responses for straightforward questions.
- Best For: Simple code lookups, lightweight explanations, or quick verifications.
- Ideal When: You need an answer now and don’t need detailed reasoning.
Normal Mode
Normal Mode
- Purpose: Balanced speed and detail, with transparent reasoning.
- Best For: Most investigation and debugging work.
- Special Feature: Toggle “View Thought Process” to see how the AI connects files, context, and logic.
DeepThink / Deep Research Mode
DeepThink / Deep Research Mode
- Purpose: Comprehensive exploration of your codebase and systems.
- Best For: Complex investigations (e.g., “How does authentication work across all services?”).
- Behavior: The AI systematically analyzes files, dependencies, and historical changes to produce a detailed, end‑to‑end understanding.
Context & Navigation Features
Repo and Branch Selection
Repo and Branch Selection
- Point the agent at specific Repositories branches at any time during your chat.
- Switch between Git branches at any time during your chat.
- The AI keeps your investigation context, analyzing how code and logic differ between versions.
Follow‑Up Questions
Follow‑Up Questions
- Build deeper understanding by layering questions.
- The AI remembers your conversation history and evolves responses as you refine your investigation.
Fork Conversation
Fork Conversation
- Split into parallel threads to explore multiple topics (e.g., one thread for performance bottlenecks, another for API behaviors).
- Keeps your original investigation intact while exploring side questions.
Context Workspace
Context Workspace
- A persistent workspace tracks:
- Files you’ve viewed
- Code patterns analyzed
- Problems under investigation
- The AI uses this workspace to maintain continuity across your session.
Semantic Code Search
Semantic Code Search
Ask about functionality in plain English and the AI will:
- Search your entire repository with semantic understanding (not just keywords).
- Map relationships across files.
- Suggest related areas of code you may not have considered.
Model Training Dataset
Code
Code
- Full repository structures and files
- Branch-specific code and diffs
- Semantic embeddings of functions, classes, and modules
- Cross-file dependency mapping
- Historical changes and commit context
Tickets
Tickets
- Pull from support tickets, bug reports, feature requests, and customer issues
- Surface historical context for recurring or related problems
- Correlate reported behavior with relevant code or changes
Telemetry
Telemetry
Leverage session and performance data, including:
- User session traces
- Error logs and stack traces
- Network requests and console logs
- Performance metrics and usage trends
Actionable Outputs
Diagrams
Diagrams
Generate visualizations like:
- Mermaid diagrams
- Network and flow charts
- System architecture diagrams
- Process workflows for debugging or onboarding
Documents
Documents
Automatically create:
- Technical documentation
- Investigation summaries
- Troubleshooting guides
- Structured code analysis reports
Code Snippets
Code Snippets
Write and deliver:
- Example code snippets and test implementations
- Fix and refactor suggestions
- Executable stubs based on your actual code patterns
Tickets
Tickets
Generate structured content to support issue tracking:
- Bug write‑ups and root cause summaries
- Feature requirement outlines
- Problem descriptions for direct ticket creation