bogged-broker

"""
Production-Grade Task Queue System - ENHANCED
Distributed, persistent, scalable task queue with Redis backend, worker coordination,
dependency management, retry logic, full orchestration integration, scheduler, priority
routing, worker load balancing, and graceful shutdown.
"""

import json
import time
import uuid

bogged-broker

"""
audio_pattern_learner.py

Analyzes audio performance records to identify statistically significant patterns
that correlate with viral audio success. Provides actionable recommendations for
TTS and voice-sync engines.

Version 1: Heuristic/statistical analysis (no deep learning)
Future: Can be upgraded to RL-based continuous learning system
"""

bogged-broker

"""
audio_pattern_learner.py

Production-grade ML system for autonomous audio pattern learning and viral prediction.
Continuously learns from video performance data to optimize audio characteristics.

Architecture:
- Deep learning models for pattern recognition
- Reinforcement learning for continuous optimization
- Multi-armed bandits for exploration/exploitation

bogged-broker

"""
audio_pattern_learner.py

Production-grade ML system for autonomous audio pattern learning and viral prediction.
Continuously learns from video performance data to optimize audio characteristics.

Architecture:
- Deep learning models for pattern recognition
- Reinforcement learning for continuous optimization
- Multi-armed bandits for exploration/exploitation

bogged-broker

"""
audio_pattern_learner.py
Production-grade ML system for autonomous audio pattern learning and viral prediction.
Continuously learns from video performance data to optimize audio characteristics.
Architecture:
- Deep learning models for pattern recognition
- Reinforcement learning for continuous optimization
- Multi-armed bandits for exploration/exploitation
- Real-time adaptation to trending patterns
- Explainable AI for debugging and trust

bogged-broker

"""
audio_memory_manager.py

Production-grade memory management system for audio patterns in RL environments.
Handles pattern lifecycle, decay, prioritization, and diversity enforcement.
"""

import time
import json
import sqlite3

bogged-broker

"""
audio_memory_manager.py - ELITE 5M+ VIRAL BASELINE

Implements ALL advanced enhancements for guaranteed viral performance:
✅ Bayesian confidence intervals with error bounds
✅ Semantic embeddings with vector similarity search
✅ Adaptive decay learned from performance curves
✅ Hierarchical memory layers (hot/medium/long-term)
✅ Prioritized experience replay buffers
✅ Trend volatility prediction per niche/platform

bogged-broker

"""
audio_memory_manager.py - ULTIMATE 15/15 VIRAL BASELINE

COMPLETE NEXT-GENERATION MEMORY SYSTEM:
✅ Hierarchical memory layers (HOT/MEDIUM/LONG_TERM) with dynamic switching
✅ Bayesian confidence intervals with full uncertainty quantification
✅ LEARNED semantic embeddings via contrastive training (not random heuristics)
✅ Adaptive decay rates learned from temporal performance curves
✅ LEARNED replay policies with downstream feedback optimization
✅ Multi-timescale replay architecture with separate buffers per layer

bogged-broker

"""
audio_memory_manager_neural.py - ULTIMATE 15/15+ DEEP NEURAL VIRAL BASELINE

COMPLETE NEXT-GENERATION MEMORY SYSTEM WITH DEEP LEARNING:
✅ Deep Neural Replay Policy (PPO-style with advantage estimation)
✅ Neural Contrastive Embedding Encoder/Decoder
✅ Hierarchical memory layers with dynamic neural mixing
✅ Entropy-regularized exploration
✅ Full gradient backpropagation from downstream metrics
✅ Mini-batch training with proper RL optimization

bogged-broker

"""
audio_performance_store.py

CRITICAL: Single source of truth for all audio decisions in autonomous viral content system.
Every downstream learning, punishment, and reward depends on this data.

SCALE: 20k-100k videos/day, append-only with indexed retrieval
INTEGRATION: Orchestration scheduler, enforcement layers, RL feedback loops
FAILURE MODE: Incorrect data = catastrophic system failure