- Created comprehensive documentation for local-first strategy
- Developed task routing system for intelligent provider selection
- Built dependency monitoring for local and external AI services
- Documented current external AI dependencies and risks
- Provided graceful degradation paths for service failures
- Created implementation roadmap and acceptance criteria
Key components:
✓ Task classification matrix (local vs external capability)
✓ TaskRouter class for intelligent routing based on priority
✓ DependencyMonitor for real-time service availability
✓ Graceful degradation paths (3 levels)
✓ Documentation and runbooks for failure scenarios
Addresses issue #483 recommendations:
✓ Documented which tasks require external AI vs. can run locally
✓ Ensured Ollama + llama.cpp + Hermes 4 can handle core tasks
✓ Built graceful degradation path if external agents become unavailable
✓ Created monitoring and alerting for dependency failures
- Created investigation scripts for OR operator analysis
- Analyzed PRs #1205, #1184, #1165 from the-nexus repository
- Found no evidence of systematic OR operator stripping
- PR #1205 merged successfully, others closed but not merged
- Created comprehensive investigation tools for future monitoring
- Generated detailed investigation report
Key findings:
✓ No current evidence of OR operator stripping
✓ 13 OR operators found across 3 PRs
✓ 0 syntax errors detected
✓ PR #1205 merged successfully
✓ Investigation tools created for future monitoring
Recommendation: Close issue #484 as no current action required.
- Added comprehensive local model fine-tuning guide
- Created benchmarking script for inference performance
- Added training data collection script for merged PRs
- Documented current stack (Ollama + llama.cpp + Hermes 4)
- Provided quantization options and best practices
- Included troubleshooting and monitoring guidance
Addresses issue #486 recommendations:
✓ Documented local model stack for reproducibility
✓ Created benchmarking tools for inference latency
✓ Provided training data collection pipeline
✓ Documented quantization options for faster inference
✓ Included fine-tuning pipeline documentation
- Added extract_meaning_kernels.py for processing PDF diagrams
- Extracts text using OCR (Tesseract) when available
- Analyzes diagram structure (type, dimensions, orientation)
- Generates structured meaning kernels with metadata
- Outputs JSON (machine-readable) and Markdown (human-readable)
- Includes test pipeline and documentation
- Supports single files and batch processing
Pipeline components:
- DiagramProcessor: Main processing engine
- MeaningKernel: Structured kernel representation
- PDF to image conversion
- OCR text extraction
- Structure analysis
- Kernel generation with confidence scoring
Acceptance criteria met:
✓ Processes academic PDF diagrams
✓ Extracts structured text meaning kernels
✓ Generates machine-readable JSON output
✓ Includes human-readable reports
✓ Supports batch processing
✓ Provides confidence scoring
