MirahLabs Engineering Blog
Technical insights, architectural deep-dives, and system designs authored by our product engineers and AI research leads.
Graph Neural Networks (GNNs): Concepts and Practical Applications
Graph Neural Networks (GNNs) extend deep learning to non-Euclidean domains. Explore graph convolutions, message passing, and real-world applications in recommendation systems.
Building Production RAG Pipelines with LangChain and PostgreSQL pgvector
How to build Retrieval-Augmented Generation (RAG) systems that ground LLM responses in private documents using LangChain orchestration and PostgreSQL's pgvector extension.
Building Multi-Agent AI Systems with CrewAI
Multi-agent AI systems assign specialized roles to different LLM agents that collaborate to complete complex tasks. Learn how CrewAI orchestrates agent crews for research, writing, code review, and more.
Vector Databases Compared: Pinecone vs Weaviate vs pgvector
A detailed comparison of three leading vector database solutions—Pinecone, Weaviate, and pgvector—covering performance, scalability, cost, and best-fit use cases.
MLOps: Building Reproducible ML Pipelines with MLflow and DVC
Machine learning without MLOps produces science experiments, not production systems. Learn how MLflow tracks experiments and DVC versions datasets to build reproducible, deployable ML pipelines.
Self-Attention vs. State Space Models (Mamba): The Battle for Sequence Modeling
Transformers struggle with O(N^2) context scaling. Discover how State Space Models (SSMs) like Mamba offer linear O(N) scaling for long context windows.