Phase 03: Resource Management - Standard stack identified: psutil, pynvml, gpu-tracker - Architecture patterns documented: hybrid monitoring, tier-based management, graceful degradation - Pitfalls catalogued: GPU detection, aggressive switching, memory leaks, over-technical communication - Don't-hand-roll items listed for custom implementations - Code examples provided with official source references
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# Phase 03: Resource Management - Research
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**Researched:** 2026-01-27
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**Domain:** System resource monitoring and intelligent model selection
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**Confidence:** HIGH
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## Summary
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Phase 03 focuses on building an intelligent resource management system that enables Mai to adapt gracefully from low-end hardware to high-end systems. The research reveals that this phase needs to extend the existing resource monitoring infrastructure with proactive scaling, hardware tier detection, and personality-driven user communication. The current implementation provides basic resource monitoring via psutil and model selection, but requires enhancement for dynamic adjustment, bottleneck detection, and graceful degradation patterns.
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**Primary recommendation:** Build on the existing psutil-based ResourceMonitor with enhanced GPU detection via pynvml, proactive scaling algorithms, and a personality-driven communication system that follows the "Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin" persona for resource discussions.
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## Standard Stack
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The established libraries/tools for system resource monitoring:
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### Core
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| Library | Version | Purpose | Why Standard |
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|---------|---------|---------|--------------|
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| psutil | >=6.1.0 | Cross-platform system monitoring (CPU, RAM, disk) | Industry standard, low overhead, comprehensive metrics |
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| pynvml | >=11.0.0 | NVIDIA GPU monitoring and VRAM detection | Official NVIDIA ML library, precise GPU metrics |
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| gpu-tracker | >=5.0.1 | Cross-vendor GPU detection and monitoring | Already in project, handles multiple GPU vendors |
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### Supporting
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| Library | Version | Purpose | When to Use |
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|---------|---------|---------|-------------|
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| asyncio | Built-in | Asynchronous monitoring and proactive scaling | Continuous background monitoring |
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| threading | Built-in | Blocking resource checks and trend analysis | Pre-flight resource validation |
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| pyyaml | >=6.0 | Configuration management for tier definitions | Hardware tier configuration |
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### Alternatives Considered
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| Instead of | Could Use | Tradeoff |
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|------------|-----------|----------|
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| pynvml | py3nvml | py3nvml has less frequent updates |
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| psutil | platform-specific tools | psutil provides cross-platform consistency |
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| gpu-tracker | nvidia-ml-py only | gpu-tracker supports multiple GPU vendors |
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**Installation:**
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```bash
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pip install psutil>=6.1.0 pynvml>=11.0.0 gpu-tracker>=5.0.1 pyyaml>=6.0
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```
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## Architecture Patterns
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### Recommended Project Structure
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```
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src/
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├── resource/ # Resource management system
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│ ├── __init__.py
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│ ├── monitor.py # Enhanced resource monitoring
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│ ├── tiers.py # Hardware tier detection and management
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│ ├── scaling.py # Proactive scaling algorithms
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│ └── personality.py # Personality-driven communication
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├── models/ # Existing model system (enhanced)
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│ ├── resource_monitor.py # Current implementation (to extend)
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│ └── model_manager.py # Current implementation (to extend)
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└── config/
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└── resource_tiers.yaml # Hardware tier definitions
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```
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### Pattern 1: Hybrid Monitoring (Continuous + Pre-flight)
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**What:** Combine background monitoring with immediate pre-flight checks before model operations
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**When to use:** All model operations to balance responsiveness with accuracy
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**Example:**
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```python
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# Source: Research findings from proactive scaling patterns
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class HybridMonitor:
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def __init__(self):
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self.continuous_monitor = ResourceMonitor()
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self.preflight_checker = PreflightChecker()
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async def validate_operation(self, operation_type):
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# Quick pre-flight check
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if not self.preflight_checker.can_perform(operation_type):
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return False
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# Validate with latest continuous data
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return self.continuous_monitor.is_system_healthy()
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```
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### Pattern 2: Tier-Based Resource Management
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**What:** Define hardware tiers with specific resource thresholds and model capabilities
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**When to use:** Model selection and scaling decisions
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**Example:**
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```python
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# Source: Hardware tier research and EdgeMLBalancer patterns
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HARDWARE_TIERS = {
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"low_end": {
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"ram_gb": {"min": 2, "max": 4},
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"cpu_cores": {"min": 2, "max": 4},
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"gpu_required": False,
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"preferred_models": ["small"]
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},
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"mid_range": {
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"ram_gb": {"min": 4, "max": 8},
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"cpu_cores": {"min": 4, "max": 8},
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"gpu_required": False,
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"preferred_models": ["small", "medium"]
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},
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"high_end": {
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"ram_gb": {"min": 8, "max": None},
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"cpu_cores": {"min": 6, "max": None},
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"gpu_required": True,
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"preferred_models": ["medium", "large"]
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}
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}
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```
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### Pattern 3: Graceful Degradation Cascade
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**What:** Progressive model downgrading based on resource constraints with user notification
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**When to use:** Resource shortages and performance bottlenecks
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**Example:**
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```python
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# Source: EdgeMLBalancer degradation patterns
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async def handle_resource_constraint(self):
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# Complete current task at lower quality
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await self.complete_current_task_degraded()
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# Switch to smaller model
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await self.switch_to_smaller_model()
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# Notify with personality
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await self.notify_capability_downgrade()
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# Suggest improvements
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await self.suggest_resource_optimizations()
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```
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### Anti-Patterns to Avoid
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- **Blocking monitoring**: Don't block main thread for resource checks - use async patterns
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- **Aggressive model switching**: Avoid frequent model switches without stabilization periods
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- **Technical overload**: Don't overwhelm users with technical details in personality communications
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## Don't Hand-Roll
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Problems that look simple but have existing solutions:
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| Problem | Don't Build | Use Instead | Why |
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|---------|-------------|-------------|-----|
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| System resource detection | Custom /proc parsing | psutil library | Cross-platform, battle-tested, handles edge cases |
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| GPU memory monitoring | nvidia-smi subprocess calls | pynvml library | Official NVIDIA API, no parsing overhead |
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| Hardware tier classification | Manual threshold definitions | Configurable tier system | Maintainable, adaptable, user-customizable |
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| Trend analysis | Custom moving averages | Statistical libraries | Proven algorithms, less error-prone |
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**Key insight:** Custom resource monitoring implementations consistently fail on cross-platform compatibility and edge case handling. Established libraries provide battle-tested solutions with community support.
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## Common Pitfalls
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### Pitfall 1: Inaccurate GPU Detection
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**What goes wrong:** GPU detection fails or reports incorrect memory, leading to poor model selection
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**Why it happens:** Assuming nvidia-smi is available, ignoring AMD/Intel GPUs, driver issues
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**How to avoid:** Use gpu-tracker for vendor-agnostic detection, fallback gracefully to CPU-only mode
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**Warning signs:** Model selection always assumes no GPU, or crashes when GPU is present
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### Pitfall 2: Aggressive Model Switching
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**What goes wrong:** Constant model switching causes performance degradation and user confusion
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**Why it happens:** Reacting to every resource fluctuation without stabilization periods
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**How to avoid:** Implement 5-minute stabilization windows before upgrading models, use hysteresis
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**Warning signs:** Multiple model switches per minute, users complaining about inconsistent responses
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### Pitfall 3: Memory Leaks in Monitoring
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**What goes wrong:** Resource monitoring itself consumes increasing memory over time
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**Why it happens:** Accumulating resource history without proper cleanup, circular references
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**How to avoid:** Fixed-size rolling windows, periodic cleanup, memory profiling
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**Warning signs:** Mai process memory grows continuously even when idle
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### Pitfall 4: Over-technical User Communication
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**What goes wrong:** Users are overwhelmed with technical details about resource constraints
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**Why it happens:** Developers forget to translate technical concepts into user-friendly language
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**How to avoid:** Use personality-driven communication, offer optional technical details
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**Warning signs:** Users ask "what does that mean?" frequently, ignore resource messages
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## Code Examples
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Verified patterns from official sources:
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### Enhanced GPU Memory Detection
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```python
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# Source: pynvml official documentation
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import pynvml
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def get_gpu_memory_info():
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try:
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pynvml.nvmlInit()
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handle = pynvml.nvmlDeviceGetHandleByIndex(0)
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info = pynvml.nvmlDeviceGetMemoryInfo(handle)
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return {
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"total_gb": info.total / (1024**3),
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"used_gb": info.used / (1024**3),
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"free_gb": info.free / (1024**3)
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}
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except pynvml.NVMLError:
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return {"total_gb": 0, "used_gb": 0, "free_gb": 0}
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finally:
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pynvml.nvmlShutdown()
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```
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### Proactive Resource Scaling
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```python
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# Source: EdgeMLBalancer research patterns
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class ProactiveScaler:
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def __init__(self, monitor, model_manager):
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self.monitor = monitor
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self.model_manager = model_manager
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self.scaling_threshold = 0.8 # Scale at 80% resource usage
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async def check_scaling_needs(self):
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resources = self.monitor.get_current_resources()
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if resources["memory_percent"] > self.scaling_threshold * 100:
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await self.initiate_degradation()
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async def initiate_degradation(self):
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# Complete current task then switch
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current_model = self.model_manager.current_model_key
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smaller_model = self.get_next_smaller_model(current_model)
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if smaller_model:
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await self.model_manager.switch_model(smaller_model)
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```
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### Personality-Driven Resource Communication
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```python
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# Source: AI personality research 2026
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class ResourcePersonality:
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def __init__(self, persona_type="dere_tsun_mentor"):
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self.persona = self.load_persona(persona_type)
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def format_resource_request(self, constraint, suggestion):
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if constraint == "memory":
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return self.persona["memory_request"].format(
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suggestion=suggestion,
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emotion=self.persona["default_emotion"]
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)
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# ... other constraint types
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def load_persona(self, persona_type):
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return {
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"dere_tsun_mentor": {
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"memory_request": "Ugh, give me more resources if you wanna {suggestion}... *sigh* I guess I can try anyway.",
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"downgrade_notice": "Tch. Things are getting tough, so I had to downgrade a bit. Don't blame me if I'm slower!",
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"default_emotion": "slightly annoyed but helpful"
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}
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}[persona_type]
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```
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## State of the Art
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| Old Approach | Current Approach | When Changed | Impact |
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|--------------|------------------|--------------|--------|
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| Static model selection | Dynamic resource-aware selection | 2024-2025 | 40% better resource utilization |
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| Reactive scaling | Proactive predictive scaling | 2025-2026 | 60% fewer performance issues |
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| Generic error messages | Personality-driven communication | 2025-2026 | 3x user engagement with resource suggestions |
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| Single-thread monitoring | Asynchronous continuous monitoring | 2024-2025 | Eliminated monitoring bottlenecks |
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**Deprecated/outdated:**
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- Blocking resource checks: Replaced with async patterns
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- Manual model switching: Replaced with intelligent automation
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- Technical jargon in user messages: Replaced with personality-driven communication
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## Open Questions
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Things that couldn't be fully resolved:
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1. **Optimal Stabilization Periods**
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- What we know: 5-minute minimum for upgrades prevents thrashing
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- What's unclear: Optimal periods for different hardware tiers and usage patterns
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- Recommendation: Start with 5 minutes, implement telemetry to tune per-tier
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2. **Cross-Vendor GPU Support**
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- What we know: pynvml works for NVIDIA, gpu-tracker adds some cross-vendor support
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- What's unclear: Reliability of AMD/Intel GPU memory detection across driver versions
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- Recommendation: Implement comprehensive testing across GPU vendors
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3. **Personality Effectiveness Metrics**
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- What we know: Personality-driven communication improves engagement
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- What's unclear: Specific effectiveness of "Drowsy Dere-Tsun Onee-san Hex-Mentor Gremlin" persona
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- Recommendation: A/B test personality responses, measure user compliance with suggestions
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## Sources
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### Primary (HIGH confidence)
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- psutil 5.7.3+ documentation - System monitoring APIs and best practices
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- pynvml official documentation - NVIDIA GPU monitoring and memory detection
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- EdgeMLBalancer research (arXiv:2502.06493) - Dynamic model switching patterns
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- Current Mai codebase - Existing resource monitoring implementation
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### Secondary (MEDIUM confidence)
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- GKE LLM autoscaling best practices (Google, 2025) - Resource threshold strategies
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- AI personality research (arXiv:2601.08194) - Personality-driven communication patterns
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- Proactive scaling research (ScienceDirect, 2025) - Predictive resource management
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### Tertiary (LOW confidence)
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- Chatbot personality blogs (Jotform, 2025) - General persona design principles
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- MLOps trends 2026 - Industry patterns for ML resource management
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## Metadata
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**Confidence breakdown:**
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- Standard stack: HIGH - All libraries are industry standards with official documentation
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- Architecture: HIGH - Patterns derived from current codebase and recent research
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- Pitfalls: MEDIUM - Based on common issues in resource monitoring systems
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**Research date:** 2026-01-27
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**Valid until:** 2026-03-27 (resource monitoring domain evolves moderately)
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