Run 1 - 2025-11-09 21:56:55
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
Run 2 - 2025-11-09 21:58:46
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
Run 3 - 2025-11-09 21:59:33
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
Run 4 - 2025-11-09 22:01:17
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
Run 5 - 2025-11-09 22:02:06
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
Run 6 - 2025-11-09 22:03:58
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
Run 7 - 2025-11-09 22:04:57
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
Run 8 - 2025-11-09 22:31:43
Exploring: Framework Learning Architecture and Health Check Infrastructure Method: Systematic examination of .ravl directory structure, learning storage patterns, logging infrastructure, and state management
Discoveries:
- Dual Learning System: Found separate directories for execution_learning (infrastructure) vs loop_learning (domain logic)
- Structured Learning Storage: Framework uses both markdown (human-readable) and JSON (machine-parseable) for learning persistence
- Multi-Category Logging: Identified 1 distinct logging categories: llm
- State Persistence: Framework maintains current_state/ directory for tracking loop state across runs
- Loop Configuration System: Found 0 YAML-based loop configuration files
- Health Check Infrastructure: Discovered 14 health-check related artifacts embedded in framework
Key Insights:
1. Separation of Concerns in Learning
The framework implements a sophisticated dual-learning architecture that separates “how to execute” (solution space) from “what to accomplish” (problem space). This means:
- Syntax errors, API auth failures → execution_learning/
- Missing data, incomplete analysis → loop_learning/
- Each system can improve independently without interference
2. Hybrid Human-Machine Learning Format
Learning isn’t just data dumping - it combines:
- Markdown files for context, explanations, and human debugging
- JSON files for structured patterns that can be machine-processed
- This enables both LLM reasoning AND programmatic health checks
3. Observable Execution Through Logging
The multi-category logging system (llm) suggests the framework makes execution transparent for debugging. This is critical for health checks to diagnose failures accurately.
Significance:
These discoveries reveal that RAVL is not just a simple loop framework - it’s a learning system with self-improvement capabilities. The separation between execution and domain learning means:
- Targeted Improvement: Framework can diagnose whether failures are “I couldn’t run the code” vs “I ran it but didn’t solve the problem”
- Persistent Intelligence: Learning accumulates across runs in structured formats that both humans and LLMs can query
- Observable Execution: Rich logging infrastructure enables health checks to pinpoint exact failure modes
- Adaptive Architecture: The loop configuration system suggests multiple specialized loops can work together
Next Exploration Opportunities:
- Examine actual learning file contents to understand pattern extraction
- Analyze health check logic to see how diagnostics work
- Investigate loop configuration to understand orchestration patterns
- Map the verification criteria system and how it drives learning
