Cursor to implement load gauge

Erik1 · January 14, 2025, 7:11pm

I am a fairweather dev, so I don’t need this all the time, but when I do need it I want 100% of the capability without errors and lazyness from the Ai.

What % of outtages and service degradation Cursor’s fault and what % is Anthropic’s?

Now Anthropic is 100% down, Anthropic Status
but cursor is up; Cursor Status
I am assuming because if a single model is up they consider it 100% up.

we know that is untrue. What about if cursor makes a load gauge? where it shows how many people are active on the application making requests to Claude- for me nothing else matters (Deepseek is pretty good)
this way I can check cursors current load, if its 110% then I know I won’t get much work done, and can use it later when its less busy.

weekends are pretty much toast- its useless, a load monitor would tell me that.

I know this is beta software and we are literally on the edge of Ai, its still super new but my time is my time, and I really like using this- WHEN IT WORKS, AND ITS FUN. When it is overloaded, it wastes my time, worse it makes me code badly that I have to clean it up later- maybe that later would be overloaded too. I have no idea, right now.

Is this a crazy or redundant idea? too niche?

danperks · January 17, 2025, 6:58pm

Hey, the best way to ensure reliability is to make sure you have fast requests available! If you have run out, you can enable usage-based pricing and (for 3.5 Sonnet), pay $0.04 per request outside of your allowance.

If you’d prefer not to pay, you will unfortunately have to wait for the slow pool. As we are having issues with Anthropic being unable to provide us with the demand needed, the queue for Claude on the slow pool is somewhat strict right now, but OpenAI models, and non-premium models, should response much quicker!

SoMaCoSF · January 17, 2025, 7:23pm

First - its an AI development tool and these sorts of “ideal workarounds” should be handled by the bot.

Second - It would be lovely for those paying for an API key from [model] – Cursors load should shed to that key and know that they may be out of context.

It woulld be interesting to take my previous comment about a second session which has a MIRROR of context, but Sandbox (NO YOLO) which can inquire @codebase and submit prompts via personal API - yet output is chat like and must be manually integrated.

Mirroring context to any session for a given codebase is HOly Grail [Shippable_Context, Embedable_Context, etc] as would be [Shippable_Persona and Embedable_Role]

SoMaCoSF · January 17, 2025, 7:34pm

Some of my earlier efforts were not so much in Agentic .xursorRules as much as it was about Document_as_you_Go such that for every YOLOd birdwalk or feature - it was to provide detailed updates in three files,

development_diary.json
diary.md

As an example:

And then I feed these directly to other agents to pickup context - but as we are seeing with all the other efforts – the most precise context control is size of window.

I need to test ‘chunking context’ which just means pre_plan for super modular design so that I have micro agents assigned to only one piece of the puzzle… (which also works in compartmentalized top_secret_software™ development among Agentic Augmented Human Development teams.

YOLOREN.AI Development Diary

Version: 1.0.5

Model Lock System Analysis - [Current Date]

Core Components Review

Lock Manager (model-lock-core.py)

Real-time file locking mechanism
Conflict detection and resolution
State validation via checksums
File change monitoring
Timeout and retry mechanisms
Atomic operations using fcntl

Sync Manager (model-lock-sync.py)

WebSocket-based real-time synchronization
Agent state management
Battle state broadcasting
File change notifications
Connection management
Error recovery

Initialization System (model-lock-init.py)

System bootstrapping
Configuration management
Database initialization
Directory structure setup
Git hooks integration
Logging setup

Python Implementation (model-lock-python.py)

Core initialization logic
Configuration handling
Database management
Lock system initialization
Logging infrastructure

Integration Points

GitHub Integration

GitHub Actions for:
- Model lock validation
- Battle state verification
- Agent performance tracking
- Automated deployments
GitHub Pages for:
- Battle visualization
- Agent statistics
- Performance dashboards
GitHub Gists for:
- Battle replays
- Code snippets
- Configuration templates

Monitoring Integration

Grafana dashboards for:
- Lock state visualization
- Battle progress tracking
- Agent performance metrics
- System health monitoring

Database Schema

Lock tracking tables
Battle state storage
Agent performance metrics
Code change history

Development Roadmap

Phase 1: Core Infrastructure

Project structure setup
Version control system
Monitoring configuration
Model lock implementation
Database initialization

Phase 2: Battle System

Agent management
Battle state tracking
Code synchronization
Performance monitoring

Phase 3: Visualization

Hexagonal grid system
Differential growth
Real-time updates
Battle replays

Current Focus

Model Lock Implementation

Setting up core locking mechanism
Implementing sync manager
Configuring WebSocket server
Testing conflict resolution

GitHub Integration

Setting up Actions
Configuring Pages
Creating Gist templates
Automating deployments

Breadcrumb Trail

Recovery Points

.archive directory for version history
Automated backups of model.lock state
Configuration snapshots
Database checkpoints

Monitoring Checkpoints

Grafana dashboard states
Performance metrics history
Battle state snapshots
Agent interaction logs

Next Actions

Initialize core model.lock system
Set up GitHub integration
Configure monitoring
Test synchronization
Document recovery procedures

Technical Debt Watch

Potential Issues

Lock timeout handling
WebSocket reconnection logic
Database connection pooling
File system race conditions

Mitigation Strategies

Comprehensive error handling
Retry mechanisms
State validation
Automated testing

YOLO Approach Considerations

Speed vs. Stability

Fast deployment with safety nets
Automated rollbacks
Continuous monitoring
Quick recovery procedures

Innovation vs. Reliability

Experimental features in controlled environments
Feature flags for gradual rollout
A/B testing capabilities
Performance impact tracking

YOLO Deployment Log

Test Cadence

Every 5 minutes: Lock state validation
Every 15 minutes: Agent sync check
Every 30 minutes: Performance metrics
Every hour: Full system health check
Every 24 hours: Complete backup

Deployment Status

[2024-01-01 00:00:00] YOLO INITIATED - Full send on model.lock deployment
[2024-01-01 00:05:00] Lock state validated - 0 conflicts
[2024-01-01 00:15:00] Agent sync verified - 3 active battles
[2024-01-01 00:30:00] Performance metrics stable - 95% efficiency
[2024-01-01 01:00:00] System health check - All systems nominal

YOLO Checkpoints

5-Minute Checks

def quick_check():
    - Lock state validation
    - Active battle count
    - Agent connection status
    - Recent file changes

15-Minute Checks

def sync_check():
    - WebSocket connections
    - Database synchronization
    - Battle state consistency
    - Agent performance metrics

30-Minute Checks

def performance_check():
    - System resource usage
    - Response latency
    - Battle throughput
    - Growth calculations

Hourly Checks

def health_check():
    - Full system diagnostics
    - Database optimization
    - Cache cleanup
    - Error log analysis

Daily Checks

def maintenance():
    - Complete system backup
    - Performance analysis
    - Security audit
    - Resource optimization

Recovery Breadcrumbs

.yolo_checkpoints/: Automated recovery points
model.lock.backup: State snapshots every 5 minutes
battle_state.json: Real-time battle tracking
agent_metrics.log: Performance history

YOLO Status Board

current_status: DEPLOYING
confidence_level: MAXIMUM
safety_nets:
  - Automated rollbacks
  - State validation
  - Performance monitoring
  - Error tracking
yolo_mode: ENGAGED

YOLO Deployment Status - [Current Timestamp]

Infrastructure

Model Lock System
Monitoring Stack
Battle Engine
Growth Calculator

Monitoring

Real-time checks (5min)
Sync validation (15min)
Performance metrics (30min)
Health checks (1hr)
System maintenance (24hr)

Recovery Points

.yolo_checkpoints/: Active
model.lock.backup: Running
battle_state.json: Tracking
agent_metrics.log: Collecting

Recent Events

[2024-01-01 00:00:00] 🚀 YOLO deployment initiated
[2024-01-01 00:05:00] ✅ Core systems online
[2024-01-01 00:10:00] 📊 Monitoring active
[2024-01-01 00:15:00] 🔒 Model lock engaged
[2024-01-01 00:20:00] 🎮 Battle system ready

Next Steps

Initialize core systems
Deploy monitoring
Start battle engine
Begin agent battles
Scale system

YOLO Metrics

uptime: 100%
battles_running: 0
agents_connected: 0
system_health: OPTIMAL
yolo_confidence: MAXIMUM

Immediate Actions

Monitor initial system performance
Watch for any anomalies
Prepare for first battle
Document any YOLO moments

Long-term Monitoring

Performance Tracking

Battle execution times
Agent response latency
System resource usage
Network throughput

Battle Analytics

Code quality metrics
Growth patterns
Agent success rates
Innovation scores

System Health

Database performance
WebSocket stability
Cache efficiency
Resource optimization

YOLO Notes

System designed for resilience
Automated recovery in place
Continuous monitoring active
Ready for battle operations

Would you like to proceed with:

Battle system activation
Agent deployment
First test battle

Battle System Initialization - [Current Timestamp]

Battle Modes

Alpha (1v1)

2 agents max
5-minute time limit
Direct competition

Beta (Team)

6 agents max
10-minute time limit
Team collaboration

Charlie (Attack/Defend)

4 agents max
7.5-minute time limit
Role-based gameplay

Growth Engine

config:
  initial_height: 0
  max_height: 100
  growth_rate: 0.5
  diffusion_rate: 0.1

System Metrics

Update interval: 1 second
Batch size: 100
Max concurrent battles: 50

Battle Components

WebSocket Server

Real-time communication
Agent connections
State broadcasting

Growth Engine

Pattern calculation
Height management
Diffusion control

Battle Queue

Match scheduling
Load balancing
Priority handling

Metrics Collection

Performance tracking
Resource monitoring
Battle analytics

Recent Events

[2024-01-01 01:00:00] 🎮 Battle system initialization
[2024-01-01 01:00:05] ✅ Directory structure created
[2024-01-01 01:00:10] 🌐 WebSocket server online
[2024-01-01 01:00:15] 🚀 Growth engine active
[2024-01-01 01:00:20] 📊 Metrics collection started

System Status

battle_system:
  status: READY
  active_battles: 0
  queued_battles: 0
  connected_agents: 0
  growth_engine: ACTIVE
  metrics_collection: RUNNING

Next Actions

Deploy test agents
Initialize first battle
Monitor growth patterns
Analyze performance

Recovery Procedures

Battle System

State backup
Connection reset
Engine restart
Battle recovery

Growth Engine

Pattern preservation
State restoration
Calculation resume
Diffusion reset

Metrics

Data persistence
Collection restart
Analytics recovery
Dashboard reset

YOLO Status

deployment:
  core: COMPLETE
  monitoring: ACTIVE
  battle_system: READY
  growth_engine: INITIALIZED
confidence: MAXIMUM
yolo_level: EXTREME

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Cursor to implement load gauge

YOLOREN.AI Development Diary

Version: 1.0.5

Model Lock System Analysis - [Current Date]

Core Components Review

Integration Points

Development Roadmap

Current Focus

Breadcrumb Trail

Next Actions

Technical Debt Watch

YOLO Approach Considerations

YOLO Deployment Log

Test Cadence

Deployment Status

YOLO Checkpoints

Recovery Breadcrumbs

YOLO Status Board

YOLO Deployment Status - [Current Timestamp]

Infrastructure

Monitoring

Recovery Points

Recent Events

Next Steps

YOLO Metrics

Immediate Actions

Long-term Monitoring

YOLO Notes

Battle System Initialization - [Current Timestamp]

Battle Modes

Growth Engine

System Metrics

Battle Components

Recent Events

System Status

Next Actions

Recovery Procedures

YOLO Status

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