AI-TUTORIALS

Learn how to deploy and manage Large Language Models locally using Ollama and integrate them into Python applications for privacy-focused, cost-effective AI development.

Learn how to apply battle-tested SRE patterns like circuit breakers, failover chains, and dynamic latency tracking to build resilient LLM applications that survive provider outages.

A deep dive into the mechanics of distributed AI training using PyTorch, covering P2P and collective communication primitives essential for scaling large models like DeepSeek-V3 and Llama 3.

A comprehensive technical guide on optimizing LLM features for production, covering semantic caching, prompt compression, and intelligent model routing to achieve 73% cost reduction.

A comprehensive technical comparison of leading open-source LLMs in 2026, including Kimi K2.5, DeepSeek V3.2, and GLM-4.7, across coding, reasoning, and agentic benchmarks.

An exhaustive technical breakdown of Andrej Karpathy's microGPT, exploring character-level tokenization, the custom Value autograd engine, and the structural implementation of Transformer blocks without modern libraries.

Explore the shift from traditional RAG to Recursive Language Models (RLM) for processing massive documents. Learn how to build a Python-based prototype that allows LLMs to autonomously navigate and analyze 1M+ tokens using tools and recursive sub-calls.

An in-depth look at ByteDance's Seedance 2.0, exploring its breakthrough in temporal consistency, physics, and the infrastructure requirements for developers integrating SOTA video models.

An exhaustive analysis of 125+ local LLMs including Qwen 3, DeepSeek R1, and GLM-5, categorized by hardware requirements from 8GB RAM to workstation setups.

An in-depth exploration of why agent frameworks remain essential as LLMs evolve, focusing on system architecture, observability, and the importance of high-performance API backends like n1n.ai.

A deep dive into the Model Context Protocol (MCP) compared to traditional Direct API calls for building context-aware AI agents and high-performance applications.

A comprehensive guide on moving beyond basic RAG to build scalable, enterprise-grade AI systems using advanced ingestion, hybrid search, and agentic reasoning.