Pi Coding Agent vs Claude Code vs OpenCode — I Tested All 3

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

Source video:
- Title: Pi Coding Agent vs Claude Code vs OpenCode — I Tested All 3
- URL: https://www.youtube.com/watch?v=8pfPf8Wmcrc
- Topic: Creative Automation
- My current learning frame: Install Pi from npm, log in through your existing GitHub Copilot subscription with /login, add a short agents.md, then give it one self-contained front-end task (like a single-file three.js demo) and compare the experience against your current agent's prompts and plan-mode friction.
- Why this matters: New playlist item from Roman Knox; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:40 / Evidence 1: "own, stripped to the bone, yellow by default, >> >> no permission prompts, no background bash, no swarm of sub agents, just the model and a tiny harness around it. And honestly, I didn't think I'd switch. I've..."
- 3:49 / Evidence 2: "editor instead of the terminal box, control G opens it in my system editor. Small thing, but I use it constantly, okay? Now, the actual test. I want to see how Pi performs on a creative front-end task..."
- 5:47 / Evidence 3: "Just a model, a few tools, and a clean session file I can fork later if I want to try a different approach. If I compare this to Claude Code, the difference is mostly philosophical. Claude Code has..."
- 7:29 / Evidence 4: "for this one. The link to the docs and the GitHub repo are in the description. Catch you in the next video."

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 "Pi Coding Agent vs Claude Code vs OpenCode — I Tested All 3", 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.