OpenCode + FREE Kimi K3, GLM-5.2 API: IT ACTUALLY WORKS!

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

Source video:
- Title: OpenCode + FREE Kimi K3, GLM-5.2 API: IT ACTUALLY WORKS!
- URL: https://www.youtube.com/watch?v=fMzVZvK9frI
- Topic: Creative Automation
- My current learning frame: Connect both Nvidia NIM and ZenMLX as providers inside OpenCode, then run the same real coding task through Kimi K3 and one Nvidia catalog model to compare long-horizon follow-through versus quick-response speed.
- 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:
- 1:11 / Evidence 1: "popular agentic coding tools out there. But, the main reason I'm using it for this video is that both of the model sources we're going to use, Nvidia and ZenML, exist as official providers inside open code. So,..."
- 2:52 / Evidence 2: "context window. You've got Mini Max M3 preview, which is the big multimodal one that can handle text, images, and video and is built for long horizon coding and design workflows. You've got Step 3.7 flash, which is..."
- 4:44 / Evidence 3: "their pay-as-you-go system. So, you're getting Moonshot's flagship model, which is a 2.8 trillion parameter model with their Kimmy Delta attention setup and a full 1 million token context window at zero cost. I'm saying for a limited..."
- 6:59 / Evidence 4: "is just ridiculous. And switching between all of these is just a model picker away inside Open Code. You connect both providers once and then you swap models per task. That's the whole workflow. Now, before we wrap..."
- 8:39 / Evidence 5: "this video a thumbs up and subscribe to my channel. I'll see you in the next one. Until then, bye. >>..."

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 creative workflow board with critique criteria and review checkpoints.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Brief -> Source -> Generation -> Selection -> Edit -> Taste Review
   - 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 "OpenCode + FREE Kimi K3, GLM-5.2 API: IT ACTUALLY WORKS!", not a generic Creative Automation 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.