Fable Orchestrator + K3, Codex: 5X CHEAPER, 10X BETTER!

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

Source video:
- Title: Fable Orchestrator + K3, Codex: 5X CHEAPER, 10X BETTER!
- URL: https://www.youtube.com/watch?v=MgktwJXF450
- Topic: Interfaces + Open Design
- My current learning frame: Run one real multi-file feature through an orchestrator prompt that forbids the frontier model from writing code, delegates independent work items to at least two cheaper agents in parallel, and requires a diff review before done, then compare premium token spend against your usual solo run.
- Why this matters: New playlist item from AICodeKing; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:32 / Evidence 1: "Claude Fable 5 as the master agent inside Tracer and let it orchestrate a team of cheaper agents, Codex agents for implementation, and GLM 5.2, Kimmy K 2.7, and Q and agents as the budget workers for the..."
- 2:28 / Evidence 2: "it up directly, and you can select Fable 5 as the model. This is our master. For Codex, same thing. If you have a ChatGPT subscription, connect Codex, and it becomes one of our implementation agents. And this..."
- 4:04 / Evidence 3: "docs and smaller cleanup items. After each child agent finishes, review their diff yourself. If something is wrong, send it back to that agent with specific fix instructions. Only report done to me when the full feature passes..."
- 5:35 / Evidence 4: "was built, which agent did what, and what it fixed along the way. Then I open the Git diff panel in Tracer and look through the changes myself because you should always do that, no matter how fancy..."
- 7:36 / Evidence 5: "delete the limits of your underlying tools. Each agent still has its own rate limits and quotas. Tracer just organizes them. Also, free and cheap endpoints can have data policies that differ from Anthropic's, so don't route sensitive..."

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 "Fable Orchestrator + K3, Codex: 5X CHEAPER, 10X BETTER!", 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.