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What is a ReAct Agent? How It Thinks and Acts, Explained
A ReAct Agent is an AI Agent built using the ReAct (Reasoning + Acting) pattern. It runs in a loop where the LLM thinks, takes an action, and observes the result.
What are Multi-Agent Systems and When Should We Use Them?
A Multi-Agent System is a setup where two or more LLM-driven agents, each with its own prompt and tools, work together on a shared task. Let's see when to use one.
How Does AI Agent Memory Work? The Memory Stack Explained
AI Agent Memory is the system that lets an LLM remember things across messages, sessions, and users. We will learn the memory stack and how it works.
What is an AI Agent? How It Works
An AI Agent is a system where an LLM takes a goal, decides the steps, uses tools, and keeps working in a loop until the goal is done. We will learn how it works.
What is RMSNorm? Root Mean Square Layer Normalization Explained
RMSNorm is a simpler and faster version of LayerNorm that keeps only the re-scaling step. It powers most modern LLMs like Llama, Mistral, and DeepSeek.
What is DeepSeek-V4 and How Does It Work? Architecture Explained
DeepSeek-V4 is a family of open Mixture-of-Experts models that supports a one-million-token context at about a tenth of the cost of DeepSeek-V3.2.
What is LoRA (Low-Rank Adaptation) and How Does It Fine-Tune LLMs?
LoRA (Low-Rank Adaptation) fine-tunes a large model without updating all its weights. It keeps the model frozen and learns a tiny pair of extra matrices.
What is RoPE (Rotary Position Embedding)? The Math Behind It
RoPE (Rotary Position Embedding) gives position information to a Transformer by rotating the Q and K vectors by an angle that depends on the token's position.
What is Grouped Query Attention (GQA) and Why Do LLMs Use It?
Grouped-Query Attention (GQA) divides the attention heads into groups, where all the heads in a group share the same Key and Value, but each has its own Query.