Zed + Gemma 4: 100% Free, Local & Unlimited Coding Via Ollama

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

Source video:
- Title: Zed + Gemma 4: 100% Free, Local & Unlimited Coding Via Ollama
- URL: https://www.youtube.com/watch?v=qrbACey05Xo
- Topic: Agentic Engineering
- My current learning frame: Pull Gemma 4 12B with 'ollama pull', connect it in Zed's agent settings (auto-detected, no API key), then complete one real task each way — a local inline refactor and a unit-test generation in the agent panel — and note the token speed and quality on your hardware.
- 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:
- 0:18 / Evidence 1: "serious traction in the open-source dev community, and over the last year, the team has been quietly stacking on AI features. There's an agent panel for chatting with models, inline edits where you highlight code and ask for..."
- 2:21 / Evidence 2: "the whole system. Context window goes up to 256K tokens. Native function calling is built in and the coding benchmarks are solid for a model in this size range. Now to plug it into Zed, there are three..."
- 4:46 / Evidence 3: "For inline edits, select some code in the editor. Hit the inline assist shortcut and ask for a change. Zed pipes the request through a llama. The model runs locally and the response streams back into the buffer."
- 6:30 / Evidence 4: "error messages or design mockups, and the model handles them natively. And the coding quality holds up well on small refactors, explanations, and boilerplate. If you're on a lighter-spec laptop, drop down to Gemma 4 E4B, which fits..."
- 8:36 / Evidence 5: "project. Zed lets you switch the active model on a per thread basis. So, you can have a Gemma 4 12B thread running for fast local edits, a Claude thread running for harder architectural questions, and a Codex..."

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 task packet that a coding agent could execute without wandering.
5. Include:
   - a plain-English definition of the core idea
   - a diagram or structured model using this sequence: Intent -> Task Packet -> Agent Run -> Evidence -> Review -> Standard
   - 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 "Zed + Gemma 4: 100% Free, Local & Unlimited Coding Via Ollama", not a generic Agentic Engineering 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.