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 test AI agent applications in TypeScript. Covers Vitest setup, strategies for mocking LLM responses, snapshot testing for agent outputs, deterministic tool testing, and CI integration for reliable agent test suites.
Deep dive into TypeScript streaming patterns essential for AI applications. Learn ReadableStream construction, TransformStreams for processing, async iterators for consumption, Server-Sent Events for browser delivery, and backpressure handling.
Implement SSE streaming, token-by-token display, typing simulation delays, and progressive loading states that make chat agents feel fast and human-like even when LLM inference takes several seconds.
Explore what agentic AI is, how autonomous AI systems work, and why 44% of enterprises are deploying or assessing AI agents to transform their business workflows in 2026.
Learn the technical strategies behind dramatic inference cost reduction, from precision quantization and speculative decoding to batching optimizations and hardware-software co-design.
Foundation models are the core infrastructure layer behind modern AI applications. Learn what they are, how pre-training and fine-tuning work, and how to select the right foundation model for your use case.
Learn the complete LLM training pipeline from pre-training on internet-scale data through supervised fine-tuning and RLHF alignment, with practical code examples at each stage.
Learn how LLMs break text into tokens using BPE, WordPiece, and SentencePiece algorithms, and how tokenization impacts cost, performance, and application design.
Design a single unified API that serves chat, voice, and task-based AI agents through a common interface. Learn channel abstraction, response normalization, and how to handle the unique requirements of each modality without code duplication.
