AI Engineer (YouTube channel)

What the channel covers

AI Engineer turns its in-person conferences for AI engineers, founders, leaders and researchers into free videos. That means hundreds of talks and long workshops from people building LLM products at companies and startups. The content is practitioner-led: how a team designed its agent, how it evaluates outputs, what broke in production and what it changed.

Talks are grouped into tracks, so you can follow a single theme across many speakers instead of watching at random.

Playlists to start with

• Full Workshops: AI Engineer World's Fair 2026: hands-on sessions that run long enough to build something.
• AIE World's Fair 2026 Complete Playlist: everything from the latest flagship event.
• Agentic Engineering @ AI Engineer and Computer Use (CUA) @ AI Engineer: building and running agents.
• Context Engineering @ AI Engineer and Memory & Continual Learning @ AI Engineer: what goes into the model's context and how systems remember.
• Arize Evals Track @ AI Engineer: measuring quality, the skill most demos skip.
• Snyk Security Track @ AI Engineer: security for LLM apps and agents.
• Platform Engineering (Sandboxing, Orchestration etc) @ AI Engineer and Local AI @ AI Engineer: infrastructure and running models yourself.
• FDE (Forward Deployed Engineering) @ AI Engineer: the customer-facing engineering role around AI products.

Who it suits

Developers who already call an LLM API and want to move from a demo to something reliable. It also suits engineering leads choosing an approach, and students who want to know what the job of an AI engineer actually involves. Complete beginners will find many talks assume too much; learn the basics of LLM APIs first.

How to learn from it without getting stuck in tutorial loops

Conference talks are inspiring and easy to forget. Make them count:

• Watch by problem, not by speaker. Pick the problem you have this week (evals, retrieval, tool calling) and watch two or three talks on it.
• Take one idea into your project after each talk, and note whether it helped.
• Do the workshops with the code open. Pause, type, run. A workshop watched passively is just a long talk.
• Treat vendor talks carefully. Many speakers work for tool companies; separate the general lesson from the product pitch.

Pair it with on Sefism

• The AI engineering roadmap and AI agents and MCP roadmap give the structure; the LLM APIs cheat sheet and MCP cheat sheet are quick references.
• For running LLM apps in production, see the MLOps and LLMOps roadmap and Langfuse documentation.
• On evals and security, read AI Evals: Everything You Need to Know and the OWASP Top 10 for LLM Applications.
• Curious about the forward deployed role? See the Forward deployed and solutions engineering roadmap.

Best for: developers building with LLMs who want practical lessons from teams shipping AI products.