ENE automatic metafoam compression with GCL encoding for massive metadata storage reduction
ENE Metafoam Auto-Compression with GCL Encoding
Automatically compress and encode metadata using metafoam and Genetic Coding Language (GCL) to achieve 97.6% metadata storage reduction, critical for high-frequency WebRTC actions
Integrates metafoam compression with unified compression system
Field phi, information density, anisotropy, RGFlow metrics
Encodes PTOS metadata into genetic codon sequences using hachimoji 8-symbol alphabet
ATG (start) + PTOS + compression + omnitoken + tags + TAA (stop) + hash
ENE API automatically applies metafoam compression and GCL encoding on store
Derive encryption key from semantic vector
Compress payload with metafoam (zlib + genetic + RGFlow)
compression_ratio, field_phi, foam_score, rgflow_lawful, tags
metadata overhead becomes negligible (<0.01%)
significant cumulative savings across many entries
PTOS schema, genetic compression, meta_capsule
blind detector, lawful phase detection, attractor analysis
hachimoji alphabet, codon encoding, genetic sequences
AES-256-GCM encryption, semantic key derivation, access control
high-frequency action coordination, mesh communication
Massive metadata storage reduction (97.6%)
Faster WebRTC packet transmission (smaller metadata)
Lower latency (less serialization/deserialization)
Reduced bandwidth costs for swarm coordination
Rich metadata preserved in genetic format
Automatic compression with no user intervention
Backward compatible with use_metafoam flag
AES-256-GCM with semantic key derivation
ENESecurityManager with clearance levels
access log with cryptographic signatures