@alexanderpavel12813

MAGI is a fault-tolerant AI decision-support system inspired by Evangelion's triple-supercomputer design. Three independent nodes analyze the same input through different reasoning paradigms. A decision is released only when at least two agree and their justifications are consistent; otherwise the system escalates to a human. CASPAR (deductive): 8x NVIDIA Blackwell B200 GPUs, dual AMD EPYC CPUs, 2 TB DDR5 ECC, 100 TB NVMe RAID 10. Runs a reasoning-tuned LLM verified by symbolic solvers (Z3, OR-Tools). MELCHIOR (pattern/adaptive): 4-8x B200 GPUs, 1 TB DDR5, 50 TB NVMe plus tiered object storage. Handles anomaly detection, forecasting and continual learning (PyTorch, Kafka Streams, graph neural networks), with an optional Intel Loihi 2 neuromorphic track. BALTHASAR (probabilistic): 4x B200 GPUs for Bayesian inference and Monte Carlo simulation (PyMC, NumPyro), with optional IBM Quantum cloud access via Qiskit for experimental optimization. Consensus layer: 2-of-3 BFT-inspired quorum with coherence scoring, written in Go over gRPC with mTLS, and a tamper-evident decision log. Infrastructure: Ubuntu 24.04 LTS, Kubernetes with NVIDIA GPU Operator, Kafka ingestion, Prometheus/Grafana monitoring, HSM key management and signed model artifacts. Each node sits in a separate site with independent power and network.

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