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 horizontally scale AI agent systems to handle thousands of concurrent sessions using stateless design, session affinity, load balancing, and auto-scaling strategies that maintain conversation coherence under heavy load.
Master the Hugging Face Transformers library for agent development. Learn model loading, pipeline APIs, chat templates, generation parameters, and how to integrate local models into agent workflows.
Implement seamless human handoff in AI support systems with intelligent escalation triggers, full context transfer, agent assist mode, and routing logic that preserves conversation continuity.
Hybrid architectures that interleave transformer attention layers with state-space model blocks like Mamba deliver faster inference and lower memory usage. Learn how they work and when to use them.
Design a hybrid agent system that runs fast local inference on edge devices for simple tasks and routes complex requests to cloud models, with seamless fallback and synchronization patterns.
Learn how to implement hybrid search for RAG by combining BM25 keyword search with vector similarity, using reciprocal rank fusion and re-ranking to maximize retrieval quality.
Implement idempotency patterns for AI agent tool calls to ensure retries never cause duplicate bookings, double charges, or repeated notifications. Covers idempotency keys, state checking, and tool-level design.
Master idempotent API design for AI agent systems. Learn how to implement idempotency keys, request deduplication, and fingerprinting so agents can safely retry failed requests without duplicate side effects.
Learn how to implement checkpointing for AI agents — serializing agent state, persisting progress to disk or database, and resuming interrupted tasks with idempotent operations and recovery strategies.
