Deploy Local Agents Everywhere with LFM2.5-2.6B
Discover how LFM2.5-2.6B enables lightweight, privacy-preserving AI agents on edge devices. This compact model delivers strong reasoning a...
12 articles
Discover how LFM2.5-2.6B enables lightweight, privacy-preserving AI agents on edge devices. This compact model delivers strong reasoning a...
Learn how to combine Pydantic models with OpenAI's API to reliably extract structured, validated data from LLM responses—eliminating parsi...
The ReAct loop combines reasoning and acting to enable AI agents to solve complex tasks iteratively. By alternating between thought, actio...
Discover why top-performing AI agents rely on minimalistic design, clear prompts, and smart tool use instead of complex architectures. Sim...
Learn how to transform a local large language model into a powerful agent by integrating external tools like web search, APIs, and code ex...
Discover why a single AI agent fell short for complex tasks and how a multi-agent pipeline improved accuracy, reliability, and efficiency...
Discover how AI agents use tool calling to decide their next action. This article breaks down the decision-making process, from function s...
Learn how to get structured data from large language models using JSON mode and function calling. This guide compares both approaches with...
Many developers rush to adopt complex agent frameworks, but often a simple loop with an LLM suffices. This article explains when to skip t...
Discover how a simple request-response protocol transformed our chaotic multi-agent system into a clean, scalable architecture. Learn prac...
A clear and practical article about artificial intelligence for a professional audience.
A clear and practical article about artificial intelligence for a professional audience.