Posts

Efficient Cloud Storage with Automated Scaling: A Step-by-Step Guide Research Disclaimer: This guide is based on AWS SDK for Python (boto3) v1.34+, Azure Storage SDK v12.19+, and Google Cloud Storage Python Client v2.14+ official documentation. All code examples use production-tested patterns from official provider documentation. Cloud storage services automatically scale capacity, but cost optimization requires proactive lifecycle management, intelligent tiering, and monitoring. Cloud storage services like AWS S3, Azure Blob Storage, and Google Cloud Storage automatically scale to petabyte levels, but without proper management, costs can spiral out of control. This guide focuses on automated cost optimization through lifecycle policies, intelligent tiering, and monitoring—not capacity scaling (which cloud providers handle transparently). ...

Building NES Games with C: A Practical Guide Note: This guide is based on the CC65 compiler documentation, NESdev Wiki technical specifications, and documented NES development practices. All code examples are derived from CC65 NES library documentation and tested development patterns. The Nintendo Entertainment System (NES), based on the MOS Technology 6502 processor, presents unique constraints that make game development both challenging and educational: 2KB RAM, 32KB maximum PRG-ROM per bank, 8KB maximum CHR-ROM per bank, and a 256x240 pixel display through the Picture Processing Unit (PPU). ...

Modern Large Language Models: Architecture, Fine-Tuning, and Production Deployment Note: This guide is based on the original “Attention Is All You Need” paper (Vaswani et al., 2017), Hugging Face Transformers documentation, and production patterns from LLM providers including OpenAI, Anthropic, and Meta. All code examples use documented APIs and follow industry best practices for LLM deployment. Large Language Models (LLMs) have evolved from academic curiosities to production systems powering ChatGPT, Claude, GitHub Copilot, and enterprise search. Built on the transformer architecture, modern LLMs contain billions of parameters and demonstrate emergent capabilities including reasoning, code generation, and multi-turn conversation. ...

Deep Learning Model Optimization: From Training to Production Deployment Note: This guide is based on PyTorch quantization documentation (v2.1+), TensorFlow Model Optimization Toolkit documentation, ONNX specification v1.14, and NVIDIA TensorRT best practices. All code examples use production-tested optimization techniques and include performance benchmarks. Model optimization bridges the gap between research and production. A ResNet-50 trained in FP32 consumes 98MB and runs at 15ms inference on CPU. With INT8 quantization, the same model shrinks to 25MB and runs at 4ms—enabling deployment on edge devices, reducing cloud costs, and improving user experience. ...

Scalable Serverless AI/ML Pipelines: A Production Guide Research Disclaimer: This guide is based on AWS SDK for Python (boto3) v1.34+, SageMaker Python SDK v2.200+, and AWS Step Functions State Language (Amazon States Language) official documentation. All code examples follow AWS Well-Architected Framework for ML workloads and include production-tested patterns for serverless deployment, monitoring, and cost optimization. Serverless ML pipelines eliminate infrastructure management while providing automatic scaling, pay-per-use pricing, and high availability. This guide covers production-ready patterns for deploying ML models using AWS Lambda, SageMaker, Step Functions, and EventBridge, with complete working examples that you can deploy immediately. ...