Agentic AI, LLM engineering, and the models behind modern automation — multi-agent systems, LLM evaluation and comparisons, RAG, fine-tuning, AI infrastructure, security, and production AI engineering.
From the blog
Million-token context windows enable entire codebase analysis, full document processing, and multi-session reasoning. Explore the technical advances and practical applications of extended context in LLMs.
Explore the Mistral family of open-source models, from the efficient 7B to the powerful Mixtral 8x22B mixture-of-experts. Learn model selection, API setup, and agent integration patterns.
Mixture of Experts has become the dominant architecture for large-scale open-source models. Learn how MoE works, why 60% of recent open releases adopt it, and what efficiency gains it delivers.
Understand how Mixture of Experts architectures work, how token routing and expert capacity affect performance, and what MoE models mean for designing efficient agentic systems.
Design and build a mobile-first AI agent chat interface with responsive layouts, proper touch targets, virtual keyboard handling, and progressive web app patterns.
Design and implement a model router that classifies query complexity and directs agent requests to the most cost-effective model. Build fallback chains, measure routing accuracy, and optimize per-query costs.
Build a behavioral monitoring system for AI agents that establishes baselines, detects anomalies in tool usage and output patterns, triggers alerts, and implements automated shutdown for runaway agents.
Build a monitoring system for AI agent data pipelines that tracks ingestion metrics, detects data drift, alerts on failures, and enforces SLAs to keep your agent's knowledge base fresh and reliable.
Learn how to evaluate multi-agent AI systems by measuring handoff accuracy, information retention across agents, routing correctness, and end-to-end coordination quality.
