MirahLabs Engineering Blog
Technical insights, architectural deep-dives, and system designs authored by our product engineers and AI research leads.
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.
Understanding Transformer Architecture: Attention Is All You Need
A deep-dive into the Transformer model that revolutionized natural language processing—from self-attention heads to positional encoding and multi-head parallelism.
AI Agent Evaluation Frameworks: Ragas, TruLens, and Phoenix
Evaluating LLM outputs is notoriously difficult. Learn how to use automated evaluation frameworks to measure RAG faithfulness, answer relevance, and context precision.
AI Agents and Tool Use: Building Autonomous Workflows with LangGraph
LangGraph enables stateful, multi-step AI agent workflows with cyclic graphs. Learn how to build reliable autonomous agents that use tools, handle errors, and maintain state.
Fine-Tuning LLMs with LoRA: A Practical Guide
Low-Rank Adaptation (LoRA) lets you fine-tune large language models efficiently with minimal GPU memory. Learn how to apply LoRA to domain-specific AI tasks step by step.
Diffusion Models Explained: DALL-E 3 and Stable Diffusion Mechanics
Understand the math and mechanics behind modern generative image models: forward diffusion, reverse denoising U-Nets, and classifier-free guidance.