GitHub Repository Health Telemetry
Every showcased project is verified against rigorous engineering criteria: automated CI test suites, domain-driven clean architecture boundaries, static typing, and open-source licenses.
durgesh-flagship-portfolio
Production-ReadyCI: PassingUltra-high performance flagship engineering portfolio with 3-way rendering modes (Full 60FPS WebGL, Reduced Motion, Low-Bandwidth 0 WebGL), ATS resume engine, and interactive AI Lab.
- Comprehensive 65+ automated test validation suite
- ATS-Optimized HTML resume with print stylesheet
- 3-Way Performance Engine with Zero Layout Shift
fitness-platform-architecture
Production-ReadyCI: PassingProduction Clean Architecture reference: Next.js 14, MediaPipe Edge Computer Vision kinematics, Drizzle ORM, and Stripe billing.
- Zero 404s: Fully verified public repository
- MediaPipe sub-50ms browser pose estimation
- Idempotent webhook pipeline for subscriptions
RoleRadar-AI-Job-Search-Agent-Aggregator
Production-ReadyCI: PassingIntelligent Career Platform: NLP ATS resume scoring, Google XYZ bullet rewriter, and distributed job aggregator with Redis caching.
- Deterministic taxonomy scoring across 40+ skills
- Google XYZ formula suggestion transformer
- Sub-200ms cached query responses
marketmatch-ai
Production-ReadyCI: PassingUnsupervised Customer Intelligence Platform: K-Means, GMM, DBSCAN clustering on RFM vectors with interactive PCA projection.
- Multi-algorithm clustering (K-Means silhouette: 0.507)
- 2D/3D PCA dimensionality reduction coordinates
- Automated customer persona profile generator
Autonomous-Agent-Orchestrator
Active DevelopmentCI: PassingMulti-agent workflow orchestration engine using LangChain and LangGraph for coordinated parallel research, tool invocation, and verification.
- Stateful checkpointing & reversible rollbacks
- Tool-use telemetry with token budget caps
- Integrated sandboxed execution environment
Vision-Edge-Inference
Active DevelopmentCI: PassingQuantized INT8/FP16 real-time object tracking and spatial keypoint pipeline optimized for low-power edge accelerators and WebAssembly.
- Post-training quantization reducing footprint by 74%
- SIMD vectorization for sub-25ms CPU inference
- Memory footprint under 45MB in browser heap