Compare Ollama, LM Studio, and llama.cpp across five key dimensions to find the right local AI runtime for your workflow.
Author Archive | Vinod Chugani
5 Architectural Patterns for Persistent Memory and State in AI Agents
In this article, you will learn the five core architectural patterns for managing persistent memory and state in AI agents, and why treating them as deliberate design decisions is essential for production-grade systems.
The Current State of Agentic AI
In this article, you will learn how agentic AI architecture has evolved by mid-2026, including the shift away from orchestrated reasoning loops, the rise of multi-agent swarms, and the standardization of tool protocols through MCP.
Run a Local AI Model with Ollama in 15 Minutes
In this article, you will learn how to get a small language model running locally on your own machine in under 15 minutes using Ollama.
Deploying AI Agents to Production: Architecture, Infrastructure, and Implementation Roadmap
Understand how to choose execution models, infrastructure layers, and deployment topologies for production AI agents.
Introduction to Small Language Models: The Complete Guide for 2026
Learn when small language models outperform large models while cutting AI deployment costs by 95%.
Top 7 Small Language Models You Can Run on a Laptop
Compare seven small language models for local deployment with hardware requirements and specific use cases.
The 7 Biggest Misconceptions About AI Agents (and Why They Matter)
Learn the seven misconceptions that cause AI agent projects to fail in production environments.
Agent Evaluation: How to Test and Measure Agentic AI Performance
Learn how to evaluate AI agent performance using the Four Pillars framework: task success, tool quality, reasoning coherence, and cost efficiency.
7 Important Considerations Before Deploying Agentic AI in Production
Discover why 40% of agentic AI projects fail and how to avoid common deployment pitfalls.