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5 commits

Author SHA1 Message Date
rUv
2288e79bd8 feat(ai-memory-engine): Integrate persistence backends + add tests 
Integration:
- Added storageBackend input parameter parsing
- Integrated createPersistence() into main actor flow
- Load session uses new persistence with legacy fallback
- Save session uses new persistence with legacy fallback
- Added persistence result to output metadata
- Proper connection cleanup on exit

Test Suite (tests/persistence-benchmark.js):
- Hyperbolic geometry: projection, distance, Möbius add, exp/log maps
- Binary persistence: 4.8x smaller than JSON, perfect fidelity
- Session persistence: data survives across simulated runs
- Benchmarks: 2M ops/sec Euclidean, 0.4M ops/sec hyperbolic

Results:
- Binary format: 4.8x size reduction (7.06 MB → 1.46 MB)
- Serialization: 39x faster (54ms → 1.4ms)
- All hyperbolic geometry functions verified correct

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-14 05:15:53 +00:00
rUv
fbf985dc40 feat(ai-memory-engine): Add 8 vector storage backends + hyperbolic geometry 
Storage Backends:
- ApifyBinary: 4x smaller binary Float32Array storage
- PostgreSQL/Supabase/Neon: pgvector with IVFFlat indexing
- Qdrant: High-throughput cloud vector DB with filtering
- Pinecone: Serverless managed infrastructure
- Weaviate: GraphQL-based hybrid search
- LanceDB: Local embedded vector database
- Hyperbolic: Poincaré ball model for hierarchical data

Hyperbolic Geometry:
- Poincaré ball projection and distance
- Möbius addition for vector operations
- Exponential/logarithmic maps (tangent ↔ hyperbolic)
- Fréchet mean (hyperbolic centroid)
- Hyperbolic k-means clustering
- Batch distance computation (SIMD-friendly)
- Parallel transport and geodesic interpolation

Input Schema:
- storageBackend selector (8 options)
- Connection strings for Postgres/Qdrant/Pinecone/Weaviate
- Curvature parameter for hyperbolic embeddings
- Hyperbolic clustering toggle

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-14 04:12:06 +00:00
rUv
936c55f703 docs(agentic-synth): Add benchmark results and comparisons 
- Add performance badges: 10K records in 53ms, 150x faster
- Detailed benchmark table: 1ms (100) to 53ms (10K records)
- Per-data-type benchmarks: Bloomberg 12ms, Medical 15ms
- Comparison vs traditional tools: Faker.js ~800ms, Python ~1200ms
- Add AI Memory Engine integration section with code example

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-14 02:42:51 +00:00
rUv
e7b36d1ac2 feat(ai-memory-engine): Add Neural Trader System integration 
- Add integrate_trading action for AI trading signal integration
- Add Trading Integration UI section in input_schema.json
- Support live mode (call Neural Trader actor) and simulated mode
- Store trading signals as searchable memories
- Add trading history search via semantic similarity
- Support multiple strategies: ensemble, neural_momentum, lstm_prediction, transformer_attention, reinforcement
- Fix apifyToken variable scope issue

Build 1.0.26 deployed to Apify.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-13 19:25:20 +00:00
rUv
c1f89de337 feat(apify): Add AI Synthetic Data Generator with MCP & Actor Integration 
- Add agentic-synth actor with TRM/SONA self-learning
- Integrate 13 popular Apify scrapers for data grounding
- Add 6 use case templates (lead-intelligence, competitor-monitor, etc.)
- Include MCP server for AI agent integration
- Add comprehensive README with tutorials and SEO optimization
- Support generate/integrate/template modes
- Add webhook and embedding generation support

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
 2025-12-13 16:27:54 +00:00