Loading...
Personalized AI rules and profiling persistence for power users
RightNow AI's .rightnowrules feature provides hardware-aware, personalized AI assistance tailored to your CUDA development workflow and GPU architecture.
The .rightnowrules file contains custom AI guidelines that enhance every interaction - from chat conversations to code completions. It combines:
Automatic Generation
Command Palette Method:
Ctrl+Shift+P)RightNow: Generate .rightnowrules FileAuto-Suggestion:
.cu, .cuh, .cuf) in your workspace.rightnowrules existsManual Creation
Create .rightnowrules in your workspace root:
# CUDA Development Guidelines
# Hardware: RTX 4090 (Ada Lovelace) | CUDA 12.3
# Profile: Intermediate | Machine Learning | Balanced
**Key Guidelines:**
- Optimize for tensor core utilization on Ada Lovelace
- Balance memory coalescing with occupancy
- Prioritize mixed precision for AI workloads
- Target 70%+ GPU utilization for optimal performance
## My Preferences
- Use explicit memory management over automatic
- Optimize for inference latency < 10ms
- Prefer readable code over micro-optimizations
- Focus on FP16/BF16 tensor operationsRightNow AI automatically detects and optimizes for your specific GPU:
GPU Architecture Detection
Supported Architectures:
Detection Includes:
User Preference Integration
Experience Level:
beginner: Focus on correctness and learningintermediate: Balance performance and readabilityexpert: Aggressive optimizations and advanced featuresPrimary Use Case:
machine_learning: Tensor operations, mixed precisionscientific_computing: Double precision, memory bandwidthgraphics: RT cores, rasterization pipelinesgeneral: Balanced approach across domainsOptimization Focus:
performance: Maximum throughput and speedmemory: Minimize memory usage and bandwidthpower: Energy-efficient implementationsbalanced: Compromise across all factors# CUDA Development Guidelines
# Auto-generated for Windows on 1/4/2025
**Hardware:** RTX 4090 (Ada Lovelace) | CUDA 12.3
**User Profile:** Intermediate developer | machine_learning focus | balanced optimization
**Key Guidelines:**
- Optimize memory coalescing and shared memory usage
- Balance occupancy with register pressure
- Prioritize tensor operations and mixed precision when available
- Your Ada Lovelace GPU supports 3rd-gen tensor cores for AI workloads
## My Preferences
# Add your specific guidelines here:
# - Coding style (e.g., "prefer explicit memory management")
# - Performance targets (e.g., "optimize for latency < 10ms")
# - Project constraints (e.g., "limited to 4GB GPU memory")
# - Architecture preferences (e.g., "focus on Ampere features")Chat Assistant
System messages include your .rightnowrules as context for all conversations
Code Completion
Autocomplete respects your guidelines and coding preferences
Quick Edit
Ctrl+K editing follows your optimization priorities and style
.rightnowrulesRightNow AI maintains comprehensive profiling history in .rightnow/profiling/kernels.json for tracking optimization progress across sessions.
.rightnow/
└── profiling/
└── kernels.json # Persistent profiling databaseComprehensive Metrics
Core Performance Data:
Advanced Analytics:
Historical Tracking
Session Management:
Content-Based Keys:
AI Recommendations
NCU Integration:
{
"version": "1.0",
"lastUpdated": "2024-01-31T10:00:00Z",
"kernels": {
"file:///C:/projects/matmul.cu:matrixMul:a1b2c3:L45": {
"kernelName": "matrixMul",
"sourceFile": "matmul.cu",
"lineNumber": 45,
"sessions": [
{
"executionTime": 12.5,
"smEfficiency": 85.2,
"memoryThroughput": 450.8,
"occupancy": 68.4,
"l1CacheHitRate": 89.3,
"recommendations": [
"Consider increasing occupancy by reducing register usage",
"Memory access pattern is well coalesced"
],
"timestamp": 1706698800000,
"performance": "fast"
}
]
}
}
}View performance improvements over time and identify regression points
Profiling data survives editor restarts and code modifications
Share profiling data across team members for collaborative optimization
Content-based kernel IDs preserve history through code changes
Both features work automatically with minimal setup:
User Preferences (Settings → RightNow AI):
cudaExperienceLevel: Beginner, Intermediate, ExpertcudaPrimaryUseCase: ML, Scientific, Graphics, GeneralcudaOptimizationFocus: Performance, Memory, Power, BalancedFile Management:
.rightnowrules: Version control recommended for team guidelines.rightnow/profiling/: Add to .gitignore for personal profiling dataPro tip: Commit .rightnowrules to share team coding guidelines, but keep .rightnow/profiling/ local for individual optimization tracking.