Why I switched to Pi...

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

Source video:
- Title: Why I switched to Pi...
- URL: https://www.youtube.com/watch?v=MsPhMhfvgD4
- Topic: Codex + Claude Workflows
- My current learning frame: Install Pi, add one community extension from the package catalog and write one of your own (a custom tool or a system-prompt context injector), then prototype a tiny hosted agent with the coding agent SDK using a custom resource loader for skills and guardrails.
- Why this matters: New playlist item from AI Jason; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:14 / Evidence 1: "about Pi agent, why it is one of the best option for you to build agent system, and in the end how can you use Pi to build some agentic system like Post AI from scratch, which is..."
- 1:50 / Evidence 2: "better?" My answer for the past few weeks just they are same. There's no real difference between those coding agents anymore. But why did Open Claw choose Pi Agent as the base to build upon? Because CloudCode, CodeX,..."
- 4:56 / Evidence 3: "popular Cloud Code Codex feature already implemented by someone that you can plug into your Pi Agent, like the Go feature, the MCP adapter, the Chrome browser access, the ask user question tool, sub agents, plan mode, and..."
- 6:58 / Evidence 4: "can utilize agent loops, large language model OS, and the session management, but also extend it with MCP, memory sub agent, ACP, and many other things. And those type of customizability would be otherwise very difficult to achieve..."
- 10:13 / Evidence 5: "time you actually don't need to learn building those extension yourself, cuz you can just talk to the Pi agent, it will self-evolve. However, the real power of Pi, from my point of view, is actually not just..."
- 12:49 / Evidence 6: "can also use Pi agent package to build web hosted agent product like this Posia replica I did. But there are some nuance you need to handle because the Pi e-coding agent SDK at default is designed quite..."
- 14:47 / Evidence 7: "hosted agent system that can launch and run company autonomously. It is using pi's coding agent SDK as agent runtime. Breaking down into 11 different agents with orchestrator that's built around task entity and persist the state and..."

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 "Why I switched to Pi...", 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.