AI Engineering Articles
Articles and tutorials on AI engineering, covering machine learning, deep learning, model development, deployment pipelines, optimization, LLM usage, prompt engineering, and applied AI development patterns.
Page 1/2
AIRAGAI AgentsMCPAI Infrastructure
RAG vs Agentic RAG vs MCP: What's Really Powering the Next Generation of AI Integration
RAG vs Agentic RAG vs MCP — what's actually different, when to use each, and how Anthropic's Model Context Protocol is changing enterprise AI integration in 2025.- Published on
AISoftware EngineeringAI AgentsSDLCEngineering Leadership
AI Agent-Enabled Development: Why the SDLC Is Quietly Becoming the ADLC
The SDLC is quietly becoming the ADLC. How AI agents are reshaping software development lifecycles, and what senior engineers need to do differently in 2025 and beyond.- Published on
AISoftware EngineeringDistributed SystemsAI InfrastructureEngineering Leadership
Most Companies Don't Have an AI Strategy. They Have an OpenAI Bill.
After architecting distributed platforms serving 50M+ users and billions of transactions, I keep watching smart teams make the same expensive mistake: confusing LLM API calls with AI systems. They are not the same. Not even close.- Published on
AISoftware ArchitectureMicroservicesDistributed SystemsAI Infrastructure
Microservices vs AI-Native Architecture: Two Paradigms, One Platform
Microservices aren't dead — but AI-native architecture plays by different rules. A practical comparison of both paradigms and how to integrate them in production systems.- Published on
AIMachine LearningGPU ArchitectureCloud EngineeringInfrastructure
Why AI Needs GPUs and TPUs: The Hardware Behind Modern Machine Learning
AI is fundamentally a math problem at massive scale. Understanding why CPUs fall short, and how GPUs and TPUs changed everything, is essential knowledge for any engineer building AI-powered systems.- Published on
AI EngineeringDeveloper ToolsMulti-Agent SystemsSoftware DevelopmentDeveloper ProductivityGoogle AntigravityCursor IDE
Google Antigravity vs Cursor: The Multi-Agent Future of Software Development
A deep technical analysis by JMS Technologies into Google Antigravity vs Cursor—how multi-agent architectures, parallel planning, and deep reasoning redefine modern software development.- Published on
AI EngineeringMachine LearningBackend DevelopmentProgramming LanguagesSoftware ArchitectureRustPython
Python vs Rust: Which One Should You Learn to Grow in AI and Software Development?
A deep dive by JMS Technologies into Python vs Rust—how each language shapes careers in AI, high-performance backend engineering, and modern distributed systems in 2025.- Published on
System DesignDistributed SystemsScalabilityMachine LearningX / Twitter Architecture
How X/Twitter’s Timeline Architecture Works at Planetary Scale
A deep dive by JMS Technologies into X's (formerly Twitter) system design—analyzing fan-out on write, hybrid architecture, the 'For You' ranking algorithm, and how to scale distributed systems for millions of concurrent users.- Published on
Artificial IntelligenceLarge Language ModelsMachine LearningSoftware EngineeringTransformers
How LLMs Like ChatGPT Work: A Deep Technical Breakdown for Modern Engineering Teams
How do LLMs like ChatGPT really work? A deep technical breakdown of transformers, attention mechanisms, tokenization, RLHF, and inference — written for software engineers.- Published on