Guides
Comprehensive AI guides
AI Coding Assistants: The Complete Guide (2026)
Comprehensive guide to AI coding tools — GitHub Copilot, Cursor, Claude Code, Codeium, and open-source alternatives. Reviews, comparisons, and tutorials.
The Complete Guide to Running LLMs Locally (2026)
Everything you need to know about running large language models on your own hardware — from Ollama to llama.cpp, GPU requirements, and optimization tips.
The Best Open Source AI Tools in 2026
Curated directory of the best open-source AI tools — LLMs, image generators, coding assistants, RAG frameworks, and more. Reviews and comparisons included.
RAG (Retrieval-Augmented Generation): The Definitive Guide
Everything about RAG systems — architecture, vector databases, embeddings, chunking strategies, and step-by-step tutorials for building production RAG.
Understanding Large Language Models: From Theory to Practice
A comprehensive resource on LLMs — how they work, key architectures (Transformer, attention), training methods, and practical applications.
How to Choose a GPU for Machine Learning (2026)
Choosing a GPU for machine learning depends on budget, task type, and VRAM needs. Consider tiers from $500 to over $2000, with options like NVIDIA's RTX 4090, A100, H100, and AMD's MI300X. Cloud alternatives offer flexibility. Select based on training, inference, or RAG requirements.
RAG vs Fine-tuning: When to Use Each Approach
In 2026, RAG and fine-tuning enhance large language models for specific tasks. RAG uses an external knowledge base for context, offering lower costs and fast setup but limited by KB quality. Fine-tuning requires task-specific data, ensuring high accuracy but at higher costs and longer implementation times. Both methods offer deployment flexibility with unique advantages.
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