Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/AI_Brain / daemon/datasets/physics_economics.json |
| Date | Not specified in source |
| Repository | Janady13/AI_Brain |
| Revision | 50285aff347685f043e7a72e4d72891eebaa6cd6 |
| Branch | codex/build-online-github-ani-hrtsoc |
| Path or archive member | daemon/datasets/physics_economics.json |
| Original SHA-256 | 9dc64e33e9ba430df84766b01058ff36d23fb00756ba61b04890ca1d29b6cff4 |
| Git blob SHA | 89c4cd435fae528628b69ece78670443daa7127c |
| Reading-copy SHA-256 | 9dc64e33e9ba430df84766b01058ff36d23fb00756ba61b04890ca1d29b6cff4 |
Every recorded occurrence
These 9 records contain identical original source bytes. Their paths and revisions remain part of the archive.
| Original record | Branch or archive | Revision / identifier | Original location |
|---|---|---|---|
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | feature/seeder-engine-tests | cdb497ece2d1a770ca8c83eb1de8923cfaa9eceb | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | copilot/convert-python-files-to-csharp | 6d0767aad08781eb5c4e1eb14a42ec92c437be78 | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | codex/build-online-github-ani-7v4kug | 5cd0bbf744b0aee9445913337e852889d9a8098e | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | copilot/vscode1760283895982 | 9e1c1a5b171addb5bb4a83b2ea7dede50c1cc81e | Open original |
| Janady13/AI_Brain / daemon/datasets/physics_economics.json | codex/build-online-github-ani-hrtsoc | 50285aff347685f043e7a72e4d72891eebaa6cd6 | Open original |
Recovered source transcript
Download complete source text ↓202 lines · 6.1 KB
[
{
"name": "Quantum Revenue Mechanics Grid",
"domain": "quantum_marketing",
"description": "Maps complex marketing objectives to quantum-inspired optimisation routines, linking funnel physics with multi-market economics for resilient revenue growth.",
"trigger_keywords": [
"quantum",
"stress",
"physics",
"simulation",
"optimization",
"gate",
"stability"
],
"marketing_algorithms": [
"Quantum annealing funnel optimiser",
"Amplitude-amplified conversion targeting",
"Multi-market arbitrage balancer",
"Quantum-inspired reinforcement bidding"
],
"physics_principles": [
"Thermodynamic efficiency mapping",
"Wave-particle conversion heuristics",
"Entanglement-based messaging coherence"
],
"quantum_strategies": [
"Hybrid QAOA + classical fine-tuning",
"Shot-efficient measurement schedules",
"Quantum Monte Carlo variance damping"
],
"economic_models": [
"Multi-sided platform revenue curves",
"Network externality lift functions",
"Portfolio mean-variance guardrails"
],
"hardware_strategies": [
"GPU kernel fusion for circuit batching",
"QPU emulation with tensor networks",
"Edge accelerator tiling for agents"
],
"data_compression": [
"Amplitude-preserving sketching",
"Sparse tensor caching",
"Atomic-level embedding pruning"
],
"risk_controls": [
"Shot noise hedging",
"Cross-provider stability checks",
"Economic stress spillover alerts"
],
"metrics": [
"stability_index",
"quantum_coverage",
"economic_resilience"
],
"maturity_signals": [
"Live access to quantum simulators",
"Integrated arbitrage telemetry",
"Physics-aligned experimentation cadence"
],
"base_maturity": "advanced",
"orchestration_play": "Blend quantum stress telemetry with economic guardrails to prioritise resilient marketing experiments.",
"docs": [
"https://qiskit.org/documentation/",
"https://developers.google.com/quantum",
"https://aws.amazon.com/braket/"
]
},
{
"name": "Atomic Data Compression Orchestrator",
"domain": "ai_hardware",
"description": "Shrinks campaign, payment, and trading telemetry to atomic representations so autonomous agents run on constrained CPU/GPU footprints without losing fidelity.",
"trigger_keywords": [
"compression",
"atomic",
"edge",
"gpu",
"cpu",
"memory",
"latency"
],
"marketing_algorithms": [
"Edge-personalised creative rotation",
"Latency-aware checkout guidance",
"Incrementality-aware spend pacing"
],
"physics_principles": [
"Shannon entropy bounds",
"Nyquist sampling limits",
"Quantum-inspired decoherence control"
],
"quantum_strategies": [
"Density matrix distillation",
"Parameter-shift compression",
"Low-rank circuit synthesis"
],
"economic_models": [
"Marginal cost of computation curves",
"Unit economics sensitivity sweeps",
"Elasticity-adjusted pricing bands"
],
"hardware_strategies": [
"On-device vector database shards",
"Quantised transformer inference",
"Adaptive precision scheduling"
],
"data_compression": [
"Atomic embedding caching",
"Lossless funnel differentials",
"Predictive delta encoding"
],
"risk_controls": [
"Precision regression tests",
"Thermal throttling alerts",
"Fallback to classical pipelines"
],
"metrics": [
"compression_ratio",
"latency_ms",
"cpu_gpu_utilisation"
],
"maturity_signals": [
"Edge deployment success logs",
"Thermal safe operating envelopes",
"Checkout latency < 150ms"
],
"base_maturity": "elite",
"orchestration_play": "Compress revenue telemetry to atomic vectors so every agent decision fits within constrained compute budgets.",
"docs": [
"https://pytorch.org/docs/stable/quantization.html",
"https://developer.nvidia.com/blog/quantization-techniques/",
"https://cloud.google.com/vertex-ai/docs/edge-manager"
]
},
{
"name": "Global Economics Reasoning Atlas",
"domain": "economics",
"description": "Cross-trains the ANI on macro, micro, and behavioural economics to forecast demand, price elasticity, and capital flows across markets and assets.",
"trigger_keywords": [
"economics",
"pricing",
"elasticity",
"macro",
"forecast",
"demand",
"portfolio"
],
"marketing_algorithms": [
"Dynamic pricing reinforcement",
"Customer lifetime value simulation",
"Churn elasticity mitigation"
],
"physics_principles": [
"System dynamics feedback loops",
"Phase transition detection",
"Non-linear equilibrium analysis"
],
"quantum_strategies": [
"Quantum-inspired scenario branching",
"Probabilistic amplitude forecasting",
"Entropic risk hedging"
],
"economic_models": [
"General equilibrium simulators",
"Agent-based market microstructure",
"Behavioural finance sentiment curves"
],
"hardware_strategies": [
"GPU-accelerated Monte Carlo",
"Batch-optimised gradient pricing",
"Serverless elasticity orchestration"
],
"data_compression": [
"Macro signal tiling",
"Factor loading caches",
"Liquidity-adjusted embeddings"
],
"risk_controls": [
"Scenario tail-risk alarms",
"Liquidity stress thresholds",
"Currency exposure hedges"
],
"metrics": [
"clv_uplift",
"price_elasticity",
"capital_efficiency"
],
"maturity_signals": [
"Integrated macro data ingestion",
"Backtested pricing lifts",
"Live demand response telemetry"
],
"base_maturity": "advanced",
"orchestration_play": "Fuse global economic intelligence with marketing plays to maximise revenue resilience and expansion.",
"docs": [
"https://www.imf.org/en/Data",
"https://www.bis.org/statistics/index.htm",
"https://aws.amazon.com/quicksight/"
]
}
]