Pi replaced every other coding agent harness for me

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

Source video:
- Title: Pi replaced every other coding agent harness for me
- URL: https://www.youtube.com/watch?v=jPuN4ilZLdU
- Topic: Interfaces + Open Design
- My current learning frame: Install Pi from pi.dev, scope it to the three models you actually use, work through one real coding question using /tree to branch and summarize instead of restarting the chat, then ask Pi to write you a small extension that transforms messages before they reach the agent.
- Why this matters: New playlist item from Jilles; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:21 / Evidence 1: "test and I don't want it to. Same with Claude code. It has all these instructions and I'm not saying anything of this is bad and for a lot of people, this is probably what you want, but..."
- 1:39 / Evidence 2: "{slash} scope models, you'll see I have these three. Why is this important? Because now I can cycle with control P. Okay? So, Claude Fable 5, Opus 4.8, GPT 5.5. You can also control L to switch through..."
- 3:51 / Evidence 3: "context. So, you could see you could be going back and forth at a glance for a long time. Go back a bit, branch, summarize, >> >> and work your way through a problem. So, instead of you..."
- 5:24 / Evidence 4: "input transforms. Here's another one before agent start. I'm not writing any of this. Pi, the coding agent, is writing this. We can hot reload. Let's reload. Boom, now it's hot reloaded. It will load the extension. I..."

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 "Pi replaced every other coding agent harness for me", 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.