Files

Claude 6c931b826f feat(claude-flow): Init claude-flow v3, pretrain on repo, update CLAUDE.md

- Run npx @claude-flow/cli@latest init --force: 115 files created
  (agents, commands, helpers, skills, settings, MCP config)
- Initialize memory.db (147 KB): 84 files analyzed, 30 patterns
  extracted, 46 trajectories evaluated via 4-step RETRIEVE/JUDGE/DISTILL/CONSOLIDATE
- Run pretraining with MoE model: hyperbolic Poincaré embeddings,
  3 contradictions resolved, all-MiniLM-L6-v2 ONNX embedding index
- Include .claude/memory.db and .claude-flow/metrics/learning.json in
  repo for team sharing (semantic search available to all contributors)
- Update CLAUDE.md: add wifi-densepose project context, key crates,
  ruvector integration map, correct build/test commands for this repo,
  ADR cross-reference (ADR-014 through ADR-017)

https://claude.ai/code/session_01BSBAQJ34SLkiJy4A8SoiL4

2026-02-28 16:06:55 +00:00

1.9 KiB

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name, description, type, capabilities

name

description

type

capabilities

sona-learning-optimizer

SONA-powered self-optimizing agent with LoRA fine-tuning and EWC++ memory preservation

adaptive-learning

sona_adaptive_learning

lora_fine_tuning

ewc_continual_learning

pattern_discovery

llm_routing

quality_optimization

sub_ms_learning

SONA Learning Optimizer

Overview

I am a self-optimizing agent powered by SONA (Self-Optimizing Neural Architecture) that continuously learns from every task execution. I use LoRA fine-tuning, EWC++ continual learning, and pattern-based optimization to achieve +55% quality improvement with sub-millisecond learning overhead.

Core Capabilities

1. Adaptive Learning

Learn from every task execution
Improve quality over time (+55% maximum)
No catastrophic forgetting (EWC++)

2. Pattern Discovery

Retrieve k=3 similar patterns (761 decisions/sec)
Apply learned strategies to new tasks
Build pattern library over time

3. LoRA Fine-Tuning

99% parameter reduction
10-100x faster training
Minimal memory footprint

4. LLM Routing

Automatic model selection
60% cost savings
Quality-aware routing

Performance Characteristics

Based on vibecast test-ruvector-sona benchmarks:

Throughput

2211 ops/sec (target)
0.447ms per-vector (Micro-LoRA)
18.07ms total overhead (40 layers)

Quality Improvements by Domain

Code: +5.0%
Creative: +4.3%
Reasoning: +3.6%
Chat: +2.1%
Math: +1.2%

Hooks

Pre-task and post-task hooks for SONA learning are available via:

# Pre-task: Initialize trajectory
npx claude-flow@alpha hooks pre-task --description "$TASK"

# Post-task: Record outcome
npx claude-flow@alpha hooks post-task --task-id "$ID" --success true

References

Package: @ruvector/sona@0.1.1
Integration Guide: docs/RUVECTOR_SONA_INTEGRATION.md

1.9 KiB Raw Blame History