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 three strategies for isolating tenant data in vector databases — namespaces, metadata filtering, and separate indexes — with tradeoffs for security, performance, and cost at scale.
Design and build a multi-tenant AI agent platform with proper tenant isolation, resource quotas, data segregation, per-tenant billing, and shared infrastructure that scales efficiently without cross-tenant data leakage.
Implement multi-tenant authentication for AI agent platforms using FastAPI. Learn tenant identification, JWT claims design, row-level data isolation, and cross-tenant prevention strategies.
Deep dive into multi-token prediction and speculative decoding techniques that deliver up to 3x faster AI agent response times without sacrificing output quality.
Learn how to architect agents that maintain reasoning chains across multiple LLM invocations, accumulate state progressively, and refine their analysis through iterative thinking.
Learn how to design AI agent architectures that detect user languages, localize prompts, translate responses, and manage multilingual content pipelines for global audiences.
Learn how to design multimodal AI agent architectures that route inputs across text, image, audio, and video modalities. Covers fusion strategies, modality-specific processors, and unified reasoning pipelines.
Understand when to use multiprocessing versus asyncio for AI agent workloads. Learn CPU-bound vs I/O-bound trade-offs, ProcessPoolExecutor, and hybrid patterns.
Learn how to build AI agent automations with n8n. Covers workflow design with AI nodes, triggers, integrations with 400+ services, and self-hosting for full control over your agent infrastructure.
