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 implement Named Entity Recognition in AI agent pipelines using spaCy and LLMs, covering entity types, custom entity training, and real-time extraction strategies.
Build AI agent communication systems using NATS messaging, covering subject-based routing, queue groups for load balancing, request/reply patterns, and JetStream for persistent message delivery.
Master the art of negative prompting — learn how to set boundaries, use guardrails, and constrain LLM behavior by specifying what to avoid. Includes real patterns for production prompt safety.
Explore neural rendering techniques like NeRF and Gaussian splatting that generate photorealistic 3D scenes from ordinary photographs using AI.
Learn how to build full-stack AI agent applications using Next.js API routes. Covers streaming responses, middleware for authentication, edge runtime considerations, conversation persistence, and production patterns for server-side agent logic.
Build an AI agent that classifies incoming alerts by urgency and type, selects the optimal delivery channel for each notification, bundles related alerts to reduce noise, and ensures critical issues reach the right people immediately.
Master OAuth2 authorization code, client credentials, and PKCE flows for AI agent integrations. Includes FastAPI examples for connecting agents to external APIs securely.
Implement the Observer pattern with an event bus for AI agent systems — enabling decoupled, publish-subscribe communication between agents for flexible coordination.
Design effective on-call systems for AI agents with PagerDuty setup, rotation design, escalation policies, alert routing, and post-incident review processes tailored to the unique demands of autonomous agent systems.
