Build a fully local RAG system on a laptop using quantized models, compact embeddings, and file based indexes.
Author Archive | Vinod Chugani
7 Regression Tests Every AI Agent Should Pass Before Deploy
In this article, you will learn seven concrete regression tests for catching the orchestration-layer failure modes that matter most before deploying an AI agent to production.
7 Async Patterns for Running Agents Concurrently in Python
In this article, you will learn seven async patterns for running AI agents concurrently in Python, what each pattern is suited for, and the production-level pitfalls to watch out for with each.
Identifying Token Costs Hiding in Your Agentic Loop
Learn how context accumulation, retry loops, and static prompts silently inflate token costs in agentic AI systems.
7 Chunking Strategies That Decide Whether Your RAG Works
Discover 7 chunking strategies for RAG pipelines and learn which approach fits your document type and query needs.
Ollama vs. LM Studio vs. llama.cpp: Which Local AI Runtime Should You Use in 2026?
Compare Ollama, LM Studio, and llama.cpp across five key dimensions to find the right local AI runtime for your workflow.
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.