Large Language Models

In this tutorial, we walk you through building an advanced AI Agent that not only chats but also remembers. We start from scratch and demonstrate how to combine a lightweight LLM, FAISS vector search, and a summarization mechanism to create both short-term and long-term memory. By working together with embeddings and auto-distilled facts, we can craft an agent that adapts to our instructions, recalls important details in future conversations, and intelligently compresses context, ensuring the interaction remains smooth and efficient.

A well-crafted prompt is essential for obtaining accurate and relevant outputs from LLMs (Large Language Models). Prompt design enables users new to machine learning to control model output with minimal overhead.

With the demand for customizable, private, and offline artificial intelligence tools on the rise, many developers and hobbyists are searching for open-source large language models (LLMs) that are both easy to host locally and gentle on system resources. The AI landscape in 2025 offers several strong contenders for those seeking efficient, privacy-preserving LLM deployments without enterprise-scale hardware.

The tech world was rocked in January 2025 when DeepSeek, a Chinese AI company, launched a new chatbot that has quickly become a game-changer in the artificial intelligence space. With its impressive capabilities and low cost, DeepSeek's large-language model is posing a direct challenge to established U.S. tech giants like OpenAI, Meta, and Microsoft.

In the rapidly evolving landscape of artificial intelligence and energy management, BitEnergy AI has introduced a groundbreaking algorithm known as L-Mul. This innovative approach aims to optimize energy consumption and enhance the efficiency of renewable energy systems. In this blog, we will explore what the L-Mul algorithm is, how it works, its implications for the energy sector, and its potential impact on our daily lives.