Stop Using Docker for AI Agents (Use This Instead)

You are helping me turn one specific YouTube video into real, durable learning.

Source video:
- Title: Stop Using Docker for AI Agents (Use This Instead)
- URL: https://www.youtube.com/watch?v=gUjVnq9hhTE
- Topic: Interfaces + Open Design
- My current learning frame: Turn Stop Using Docker for AI Agents into a working note from the transcript anchors: 0:13 sets up simple app wrapped in a Docker image can balloon past a full gigabyte.
- Why this matters: New playlist item from Cloud Codes; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:13 / Evidence 1: "simple app wrapped in a Docker image can balloon past a full gigabyte. An entire operating system shipped just to run a few kilobytes of your code, and it all runs through one process, the Docker daemon. A..."
- 1:47 / Evidence 2: "down and look at each one properly. Podman first. Remember that single root daemon? Podman just deletes it. Every container runs as a normal child process of the command you ran. No middleman, no single point of failure,..."
- 4:01 / Evidence 3: "capability. Denied by default instead of a container that can quietly see the whole machine, and it is genuinely portable. One binary runs on any chip and any operating system. No more works on my machine. No more..."
- 6:14 / Evidence 4: "get to choose which one fits. Keep Docker for the developer experience and local work. Reach for Podman when you need security and no daemon. Reach for WebAssembly when you need edge speed and scale to zero. The..."

Your task:
1. Use the transcript anchors above as the primary source packet. If you add outside context, label it clearly as outside context and keep it secondary.
2. Create a source-check table with columns: timestamp, claim, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable claims from the video. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A UI critique sheet for judging whether an AI interface improves control.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Canvas -> Artifact -> Preview -> Feedback -> Iteration
   - 3 concrete examples that apply the video idea to real agentic work
   - 2 failure modes the video helps prevent
   - a checklist I can use the next time I run Codex or Claude
   - one practical exercise with a clear done signal
6. Add a "learning transfer" section: what changes in my workflow tomorrow if I actually learned this?
7. Add a "source check" section that cites which transcript anchor supports each major takeaway.

Quality bar:
- Make this specific to "Stop Using Docker for AI Agents (Use This Instead)", not a generic Interfaces + Open Design essay.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- If evidence is weak, say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.