Production-grade Go runtime for OAuth proxying, MCP integration, shadow deployment, auditability, and future Hugo MCP migration.
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Updated
Jul 2, 2026 - Go
Production-grade Go runtime for OAuth proxying, MCP integration, shadow deployment, auditability, and future Hugo MCP migration.
Graph-native platform for migrating legacy systems (Java 6/7, COBOL, .NET Framework) to modern stacks. Verifies behavioral equivalence per node with a six-gate evidence pipeline, runs old + new in parallel via a receipt-linked shadow bridge, emits cryptographically signed audit trails for regulators.
Shadow-permanent 1% + SLO-gated canary ramp for Next.js on Vercel. Reusable pattern with Claude Code skill, docs, and drop-in templates.
End-to-end ML lifecycle platform with reproducible training, policy-based promotion, progressive delivery, and rollback.
Adversarial bot detection pipeline utilizing a hybrid spatial-temporal deep learning network (GraphSAGE + Bi-GRU). Includes LLM-powered multi-agent traffic simulation, weak labeling heuristics, active learning uncertainty sampling, and CALEB CGAN adversarial training.
Production-style Machine Learning Shadow Deployment Platform using React, FastAPI, Docker, Kubernetes and Shadow Deployment.
Progressive delivery controller for ML model services. Paired shadow analysis refuses behaviorally broken models in 5s with zero live exposure; staged 5/25/50% canary ramp with Wilson-bound gates and hysteresis rollback. Measured: 0 false rollbacks in 10 identical-model rollouts; +100ms regression caught at 5% traffic.
A pure-Python shadow deployment and canary release router for ML models — failure-isolated shadow calls, deterministic hash-based traffic splitting, and statistical promotion gates in one library.
Production-style model inference platform — versioned registry, dynamic micro-batching (measured 8x throughput), canary + shadow deployments with instant rollback, load shedding (429+Retry-After), Prometheus metrics, async load-test harness
End-to-end fraud detection: XGBoost training, Go ONNX serving, drift detection, shadow mode, and canary deployment with automated rollback.
Progressive rollout, shadow mode, and auto-rollback for AI agents. Sticky-percent routing with promote/rollback gates driven by real metrics. Platform engineering reliability for the agent era.
Production-grade async middleware for shadow testing ML models. Features real-time traffic forking, drift detection (latency/accuracy), and automated regression suite generation.
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