PostgreSQL Full-Text Search: When You Don't Need Elasticsearch
Build fast, accurate search with PostgreSQL. Complete guide to tsvector, tsquery, GIN indexes, and ranking. Benchmarks show when Postgres beats Elasticsearch.
AI
Artificial intelligence, machine learning, and data science tutorials and implementations
Fine-Tuning LLMs with QLoRA: Run a 7B Model on a Single GPU
Train your own LLM with QLoRA using just 8GB VRAM. Complete Python tutorial with Hugging Face, code examples, and practical tips for efficient fine-tuning.
Vector Databases in Python: Pinecone vs Weaviate vs FAISS Compared
Compare Pinecone, Weaviate, and FAISS for vector search in Python. Real benchmarks, code examples, and guidance on choosing the right solution for your AI application.
How Elasticsearch Really Works: A Look Under the Hood
Learn how Elasticsearch indexes, searches, and ranks documents. Inverted indexes, BM25 scoring, shards, segments, and text analysis explained with real examples.
Building RAG from Scratch: A Python Guide with LangChain
Build a production-ready RAG system with Python and LangChain. Learn document loading, chunking, embeddings, vector stores, and retrieval strategies with working code.
Expose Local LLMs with ngrok: Ollama and LM Studio
Access your local LLMs remotely using ngrok. Setup for Ollama and LM Studio, security tips, and real use cases.
Grok-1: 314B Total Parameters, 25B Active Per Token (MoE Explained)
Grok-1 has 314 billion total parameters but only ~25B activate per token due to MoE architecture. Hardware requirements: 80GB+ VRAM. Full technical breakdown.
CUDA + Python for LLMs: Real Performance Gains
Speed up LLM inference with CUDA and Python. Actual benchmarks, setup guide, and code that works.
How to Train an LLM: Fine-Tuning Guide with Python & Hugging Face
Step-by-step guide to train and fine-tune a large language model (LLM). Python code, hardware requirements, dataset preparation, and deployment tips.
AI in Medical Marketing: Simulation Models That Work
Using AI and multi-agent simulation to optimize medical marketing funnels. Research-backed strategies for patient engagement.
Machine Learning with Ruby: A Practical Guide
Learn to integrate machine learning into Ruby apps using ruby-fann for neural networks. Working examples, training tips, and when Ruby ML actually makes sense.
Machine Learning in Healthcare: What's Actually Working
Real applications of machine learning in healthcare - from diagnosis to drug discovery. What works, what doesn't, and where we're headed.