AI Articles
Explore comprehensive technical guides, deep-dive benchmarks, system architecture patterns, and production breakdowns under the AI category.
Showing 1–12 of 12 ArticlesPage 1 of 1
DeepSeek-R1 & GRPO: The Open-Weights Reasoning ArchitectureAI
2026-05-156 min read
DeepSeek-R1 & GRPO: The Open-Weights Reasoning Architecture
Architecting cost-efficient reasoning pipelines with DeepSeek-R1, Group Relative Policy Optimization (GRPO), Multi-head Latent Attention (MLA), and local vLLM deployments.
Asutosh Sidhya
Building a Multi-Agent AI Framework (Part 5/5): Production Evals, Retries & TelemetryAI
2026-04-1413 min read
Building a Multi-Agent AI Framework (Part 5/5): Production Evals, Retries & Telemetry
Final step in our 5-part masterclass: Implementing exponential backoff retries, LLM-as-a-Judge evals, rate limiting, and OpenTelemetry.
Asutosh Sidhya
Building a Multi-Agent AI Framework (Part 4/5): Model Context Protocol (MCP) IntegrationAI
2026-04-0711 min read
Building a Multi-Agent AI Framework (Part 4/5): Model Context Protocol (MCP) Integration
Step 4 in our 5-part masterclass: Integrating Anthropic's Model Context Protocol (MCP) for secure tool discovery and resource streaming.
Asutosh Sidhya
Building a Multi-Agent AI Framework (Part 3/5): Multi-Agent Teams & OrchestrationAI
2026-03-3112 min read
Building a Multi-Agent AI Framework (Part 3/5): Multi-Agent Teams & Orchestration
Step 3 in our 5-part masterclass: Building Planner-Worker, Supervisor, and Handoff patterns for collaborative multi-agent swarms.
Asutosh Sidhya
Building a Multi-Agent AI Framework (Part 2/5): Hybrid Memory & Context PruningAI
2026-03-2411 min read
Building a Multi-Agent AI Framework (Part 2/5): Hybrid Memory & Context Pruning
Step 2 in our 5-part masterclass: Implementing short-term token-sliding windows, vector memory indexing, and dynamic context pruning.
Asutosh Sidhya
Building a Multi-Agent AI Framework (Part 1/5): Core Event Loop & State MachineAI
2026-03-1810 min read
Building a Multi-Agent AI Framework (Part 1/5): Core Event Loop & State Machine
Step 1 in our 5-part masterclass: Designing a deterministic state machine, agent runtime loop, and typed tool contracts in TypeScript.
Asutosh Sidhya
Building Autonomous Production Agents with LangGraph and Anthropic MCPAI
2026-03-126 min read
Building Autonomous Production Agents with LangGraph and Anthropic MCP
A comprehensive guide covering stateful cyclical agent graphs, Anthropic Model Context Protocol (MCP) integrations, human-in-the-loop checkpointing, and production tool schemas.
Asutosh Sidhya
How I Built an Autonomous AI Agent with Next.js 16AI
2026-03-048 min read
How I Built an Autonomous AI Agent with Next.js 16
A deep, production-grade guide to building autonomous goal-driven AI agents — covering tool calling, persistent memory, execution loops, and failure recovery — using TypeScript and Next.js 16 App Router.
Asutosh Sidhya
Next-Gen Agentic RAG: Hybrid Search, GraphRAG, and Self-CorrectionAI
2026-02-256 min read
Next-Gen Agentic RAG: Hybrid Search, GraphRAG, and Self-Correction
Architecting enterprise retrieval systems using LanceDB, hybrid dense-sparse vector indexing, GraphRAG knowledge graphs, and dynamic re-ranking loops.
Asutosh Sidhya
High-Performance Vector Databases: Pinecone vs Qdrant vs PgvectorAI
2026-02-188 min read
High-Performance Vector Databases: Pinecone vs Qdrant vs Pgvector
A rigorous benchmark comparison of HNSW vs IVFFlat indexing across Pinecone, Qdrant, and Pgvector — covering query latency at scale, memory overhead, metadata filtering performance, and an honest architectural recommendation for each use case.
Asutosh Sidhya
Understanding Retrieval-Augmented Generation (RAG)AI
2026-02-118 min read
Understanding Retrieval-Augmented Generation (RAG)
A practical deep dive into building production-grade RAG pipelines — covering document chunking strategies, embedding models, hybrid BM25 + dense retrieval, context window management, and the evaluation metrics that actually matter.
Asutosh Sidhya
Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Structured OutputAI
2026-02-049 min read
Advanced Prompt Engineering: Chain-of-Thought, ReAct, and Structured Output
A practitioner's guide to prompt design patterns that actually work in production — covering chain-of-thought decomposition, the ReAct pattern, few-shot formatting, and techniques for enforcing strict JSON schema adherence from any LLM.
Asutosh Sidhya