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
Learn how to build agent factory patterns that dynamically create, manage, and clean up specialist agents based on runtime task requirements. Covers object pools, lifecycle management, and resource cleanup.
Learn how AI agents select the right tool from a large toolset. Covers tool routing strategies, writing descriptions that guide selection, handling the too-many-tools problem, and building intelligent tool dispatchers.
Analyze the true cost structure of agentic AI systems, from the 'thinking tax' to multi-step token multiplication. Learn strategies to optimize cost-per-resolution by 60-80%.
Master LLM cost management — understand API pricing models, input vs output token economics, prompt caching, model routing, and practical strategies to reduce your AI spend by 80% or more.
Edge AI runs inference directly on devices, eliminating cloud latency and enabling real-time decisions. Learn how on-device AI works and where it delivers the most value.
Build an AI agent that processes inbound emails, detects intent, generates contextual responses, and manages threaded conversations using FastAPI and IMAP integration.
Optimize embedding costs for AI agent systems with practical strategies for caching embeddings, selecting cost-effective models, batch sizing, and storage optimization. Reduce embedding spend by 60-80%.
Learn when to use cosine similarity, Euclidean distance, or dot product for vector search, how normalization affects results, and practical guidance on choosing dimensions and metrics.
Compare embedding models for RAG pipelines across dimensions, retrieval quality, latency, and cost — including OpenAI text-embedding-3, Cohere embed-v3, and open-source sentence-transformers alternatives.
