Deep dives · Open source · Built to ship
Engineering courses
built for builders.
Structured, deep-dive curricula - starting with AI, infrastructure, and security, with more domains on the way — for engineers who want to understand and operate production systems, not just use them.
Available Courses
LLM Systems Engineering
GPU architecture, inference engines, distributed training, RAG pipelines, and production observability — everything you need to build and operate LLMs at scale.
Backend Engineering for the AI Era
Build, scale, and operate production backends that serve both human and AI traffic — APIs and streaming, storage from Postgres to vector stores, caching, queues, distributed systems, reliability, observability, and the patterns that emerged when LLMs became the slowest dependency in your stack.
From Code to Internet: Deployment & Operations
Everything that happens after the code compiles. DNS, Linux servers, NGINX, TLS, systemd, zero-downtime deploys, Docker, CI/CD, monitoring, and on-call — the boring, load-bearing skills that separate a localhost demo from a public URL someone is willing to pay for.
Agentic AI with LLM APIs
Tool use, agent loops, memory, multi-agent orchestration, computer use, and production observability — build LLM agents that ship, not demos.
Application Security Engineering
Threat modeling, the CIA triad, OWASP Top 10, modern crypto, authn/authz, network and cloud hardening, and incident response — the security mental models every engineer should own.
Cloud Security Engineering
Architect, harden and defend cloud-native systems — IAM and STS, VPC and PrivateLink, Kubernetes and admission control, KMS and envelope encryption, supply chain (SLSA, Sigstore), cloud-native detection and incident response.