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Complete Guide to Claude Agent Skills: Part 1 – Introduction and Fundamentals
Building Production-Ready Claude Skills: A Complete Guide to Extending AI Capabilities with Custom Workflows
Vibe Coding in 2026: The Truth About AI-Powered Development and What the Data Really Shows
Repository Intelligence: Microsoft’s AI Revolution That Understands Your Entire Codebase, Not Just Lines of Code
Production Operations and Distributed Deployment: Monitoring, Versioning, and Maintaining Edge AI at Scale
Advanced Optimization Patterns: Concurrent Multi-Model Inference and Resource Management on Edge Hardware
Multi-Language Edge Inference Servers: Building REST APIs for Real-Time Object Detection
Deploying to NVIDIA Jetson with TensorRT: Production-Grade Inference Optimization
YOLOv8 Implementation and Quantization: From Training to Edge Deployment
Real-Time Object Detection on Edge Devices: Building Production-Ready CNNs for On-Device Visual Analysis 
Discover Claude Agent Skills, Anthropic’s revolutionary approach to extending AI capabilities through modular, filesystem-based resources. This comprehensive guide covers the fundamentals of Agent Skills architecture, progressive disclosure patterns, and cross-platform portability.
Learn how to build production-ready Claude Skills that extend AI capabilities through modular, filesystem-based components. This comprehensive guide covers architecture principles, implementation in Node.js, Python, and C#, API integration, and real-world enterprise use cases.
Examine the heated debate around vibe coding and AI-assisted development through hard data and real-world case studies. This analytical deep-dive separates marketing hype from measurable productivity gains.
Explore how Microsoft’s repository intelligence transforms AI coding assistants from simple autocomplete tools into context-aware development partners that understand your entire codebase, relationships, and history.
Comprehensive production operations guide for distributed edge AI deployments. Covers Prometheus/Jaeger monitoring integration, data drift detection with statistical analysis, model versioning and registry management, canary deployment with automated rollback, OTA update orchestration, and fleet management patterns for 100+ edge devices.
Advanced optimization patterns for production edge AI deployments. Covers memory-aware multi-model scheduling, GPU resource pooling with priority queuing, adaptive batching for throughput optimization, KV cache management for transformers, and SLA enforcement achieving 50-70% latency reduction through intelligent resource coordination.
Comprehensive guide to building production-ready multi-language inference servers for edge AI. Covers Node.js/Express and C#/ASP.NET Core implementations, camera integration for live streams, asynchronous request handling, error recovery mechanisms, and load testing achieving 15-22ms latency with 30+ concurrent requests on Jetson platforms.
Production deployment guide for YOLOv8 on NVIDIA Jetson platforms. Covers JetPack setup, TensorRT engine compilation with FP16/INT8 precision, calibration procedures, efficient inference implementation, performance tuning strategies, thermal management, and platform-specific benchmarks across Jetson Nano, Xavier NX, and Orin families.
Comprehensive implementation guide for training and quantizing YOLOv8 models for edge deployment. Covers PTQ and QAT workflows, model export to ONNX/TensorRT/TFLite formats, rigorous validation methodologies, and performance benchmarking demonstrating 4x compression and 1.5-2.75x speedup with sub-2% accuracy degradation.
Comprehensive guide to deploying production-ready CNNs on edge devices for real-time object detection. Covers architecture fundamentals, YOLOv8 vs YOLO26 comparison, quantization techniques achieving 4x compression, and hardware platform selection including NVIDIA Jetson, Raspberry Pi + Coral TPU, and Intel OpenVINO solutions.
