Everything about AI, in one place.

Get more done with the tools that already exist.

For anyone who wants to work faster with ChatGPT, Claude, Cursor and similar tools — prompting, workflows, and everyday automation. No coding required.

Build agents that plan, call tools, and get things done.

LLM APIs, RAG, tool calling, agent frameworks (LangGraph, CrewAI, Claude Agent SDK), MCP, and evaluation — the core build path for engineers shipping agents.

Design systems where multiple agents work together.

Routing, context handoff, task decomposition, and production architecture for systems of agents rather than a single one.

Run agents in production without losing sleep.

Observability, guardrails, evaluation, cost and usage tracking, and safety for agents once they're live.

From software engineer to production AI engineer and agent architect.

The complete engineering course: LLM applications, RAG, tools and agents, workflow patterns, MCP, memory, evaluation, security, production engineering, AWS architecture, system design and five hands-on labs.

Work with a customer and turn a vague problem into an AI solution that runs.

How to run a customer project from first conversation to handover: discovery, scoping and success numbers, prototyping on real data, connecting to customer systems safely, rollout and trust.

Prove whether an AI system is good enough, and keep checking after launch.

Define quality with rubrics, build test sets, use code checks and model judges, evaluate RAG and agents, analyze errors, set release gates and monitor live quality. Ends with interview practice.

Protect AI systems from attacks, data leaks and misuse.

Threat modeling, prompt injection, data leakage, agent and tool security, supply chain risk, red teaming, guardrails and monitoring, and incident response. Ends with interview practice.

Design, build and ship client-ready websites with AI.

The full workflow from brief to launch: design judgement, briefing AI, iterating on generated code, copy and SEO, quality and accessibility, hosting, and winning and delivering client projects.

Create images, 3D assets and motion that people pay for.

Image generation craft, consistent brand styles, product imagery, 3D cleanup and web delivery, AR, AI video, and the licensing and ethics of selling AI-assisted visual work.

Decide where AI is worth using, and turn the plan into funded projects.

Strategy for managers and consultants: finding and ranking use cases, the business case, risk and governance, change management, and pilots that scale.

Set the rules for safe AI use and check that people follow them.

AI inventory and risk tiers, policies, standards and controls, laws and frameworks, privacy and intellectual property, fairness and transparency, vendor risk, assurance, incidents and regulators. Ends with interview practice.

Decide what AI product to build, for whom, and how to measure it.

Problem framing and research, judging feasibility, requirements with evaluation plans, designing AI experiences, quality-cost-speed trade-offs, safe launches, metrics and feedback loops, and working with engineers, legal and leaders. Ends with interview practice.

Build reliable AI workflows for small businesses — and sell them.

Design automations, choose platforms, build dependable AI steps with validation, implement three reference workflows, make them reliable and secure, then package and price the service.

Turn AI skills into income — with honest expectations.

Pick a niche that pays, package and price your services, find clients with a weekly system, deliver safely, sell digital products without hype, and follow a 90-day plan.

Model answers, system-design practice and mock interviews.

80+ interview questions with strong answers across LLMs, RAG, agents, MCP, security, evaluation, production, AWS and staff-level topics — plus coding drills, a design method and a four-week mock-interview plan.