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
Explore the tradeoffs between edge and cloud AI agent deployment, including latency benefits, privacy advantages, cost reduction strategies, and decision frameworks for choosing the right approach.
Learn how to install Ollama, pull Llama models, serve them through OpenAI-compatible endpoints, and integrate local LLMs into your AI agent pipelines for private, cost-free inference.
Understand how GPTQ, AWQ, and GGUF quantization compress large language models to fit consumer GPUs. Compare quality tradeoffs, memory requirements, and practical deployment strategies.
A practical guide to running open-source language models on your own hardware using Ollama, vLLM, and llama.cpp, covering installation, model management, API compatibility, and performance optimization.
Learn how to build safety evaluation pipelines for AI agents including red team datasets, automated harm scanning, policy compliance scoring, and production safety monitoring.
Implement the Saga pattern for AI agent systems to manage multi-step transactions with compensating actions, rollback support, and saga orchestration for reliable distributed workflows.
Learn how to sandbox AI agent tool execution using Docker containers, restricted file systems, timeout enforcement, and resource limits to prevent agents from causing damage through code execution tools.
Navigate the technical and organizational scaling challenges of growing an AI agent platform from zero to ten thousand customers, covering database migrations, architectural evolution, performance bottlenecks, and team scaling.
Learn how to schedule AI agent tasks with cron expressions, implement idempotent recurring analyses, prevent overlapping runs, and build periodic reporting pipelines that run reliably in production.
