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: Pick one small service you run in Docker and decide its right home by testing it under Podman for a rootless CI build and sketching whether it could ship as a Wasm module — writing down which of stateless-edge versus stateful-heavy it really is.
- 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..."

Video-aware target:
- Prompt lane: AI interface control
- Mechanism to extract: Extract how the interface gives the user control over context, visual quality, generated artifacts, and handoff.
- Artifact to produce: A UI control-surface critique sheet with context inputs, artifact visibility, review criteria, and implementation handoff.
- Artifact must include: context input; visual target; preview/review step; implementation handoff; quality rubric

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, transcript support, what the demo proves, confidence, and what still needs verification.
3. Extract the actual teachable mechanism from the video: Extract how the interface gives the user control over context, visual quality, generated artifacts, and handoff. Do not invent claims that are not supported by the title, lesson frame, or transcript anchors.
4. Build a reusable learning artifact: A UI control-surface critique sheet with context inputs, artifact visibility, review criteria, and implementation handoff.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Context -> Generation surface -> Preview -> Critique -> Implementation handoff
   - answers to these source questions: What does the interface let the user control? | What artifact becomes visible? | What critique or handoff step closes the loop?
   - 3 concrete examples that apply the video idea to real agentic work, such as design.md handoff; Figma-to-code review; UI reference library translation
   - 2 failure modes the video helps prevent, chosen from the transcript evidence and these likely risks: generic UI inspiration; visual output with no critique; handoff that lacks implementation criteria
   - a checklist for the next real workflow, focused on: context, preview, artifact visibility, critique, handoff
   - one practical exercise with a clear done signal: Turn one UI demo into a design-review checklist for a real product screen.
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.
- Cite transcript anchors for every claim about design context, UI generation, preview, critique, or handoff.
- Prefer operational examples, failure modes, and reusable artifacts over broad definitions.
- Call out uncertainty instead of smoothing over weak evidence.
- Avoid these generic drifts: generic design tips; visual hype without inspection; screenshots without implementation criteria.
- If evidence is weak or missing, stop and say what transcript segment or timestamp needs review instead of guessing.
- Finish with a concise artifact I could paste into my learning app.