OpenCode + NVIDIA: Use Minimax M3, Gemma4, Nemotron 3 & GLM for Free

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

Source video:
- Title: OpenCode + NVIDIA: Use Minimax M3, Gemma4, Nemotron 3 & GLM for Free
- URL: https://www.youtube.com/watch?v=G7dSIip5jF4
- Topic: Codex + Claude Workflows
- My current learning frame: Use Nemotron local agents as a transcript-backed codex + claude workflows walkthrough: at 0:27, it frames yet, open code is an open-source AI coding agent that runs in your terminal.
- Why this matters: New playlist item from AI Stack Engineer; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:27 / Evidence 1: "yet, open code is an open-source AI coding agent that runs in your terminal. It is built by the SST team, written in Go, and it sits in that same category as Claude code, Cursor's agent, or Aider."
- 2:09 / Evidence 2: "are not locked into any single model. You point open code at whatever endpoint you want, and that is exactly the door NVIDIA build walks through. Go to build.nvidia.com/models. You will see what NVIDIA actually opened up here."
- 4:36 / Evidence 3: "For coding agents, this is the one I would reach for when the task has visual inputs, like a UI screenshot or a design mock, or when the session needs to run long. The second one is Nvidia's..."
- 7:06 / Evidence 4: "latency matters more than reasoning depth, diffusion Gemma is a real option. All right, so the integration. To wire this up, first head to build.nvidia.com. Sign in or create a free developer account. Open any model page and..."
- 9:12 / Evidence 5: "free NVIDIA tier has rate limits that can hit you on heavier tool use sessions, and individual model availability can change as NVIDIA rotates between free and partner endpoints. Some of these models, like Minimax M3, are listed..."

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 routing matrix for when to use Codex, Claude, browser checks, or manual review.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Inspect -> Plan -> Edit -> Verify -> Review -> Route
   - 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 + NVIDIA: Use Minimax M3, Gemma4, Nemotron 3 & GLM for Free", not a generic Codex + Claude Workflows 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.