If you don’t run Pi locally you’re falling behind…

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

Source video:
- Title: If you don’t run Pi locally you’re falling behind…
- URL: https://www.youtube.com/watch?v=jcUqsNpDDDk
- Topic: Creative Automation
- My current learning frame: Set up Pi from scratch — install, provider login, a project agents.md, the web-access extension, and one prompt template you actually repeat daily — then have Pi itself make the next improvement to its own config.
- Why this matters: New playlist item from David Ondrej; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:14 / Evidence 1: "even more than cloth code and CEX. Also, it's one of the fastest growing repositories in all of GitHub, meaning soon enough, I think Pi will become the most popular AI agent in the world. Right now, we're..."
- 6:02 / Evidence 2: "basically just like updating the system prompt without messing up any of the default things that Mario added. The third option is creating agents.mmd file either globally or inside of any folder you're working on with context specific..."
- 10:56 / Evidence 3: "set up a new skill and it can do it. So, a lot of you are limiting the AI by your own limited prompts, by your own low-level prompts because you're not giving it ambitious enough of tasks."
- 13:39 / Evidence 4: "remember every single message put it into agents.mmd file. The second thing is a bit more advanced and that is prompt templates. So this is not just simple prompts. This is more like slash commands. So if you..."
- 16:12 / Evidence 5: "Pi keybinds, extensions, skills, prompts, themes. Just it will reload your entire PI agent. And boom, just like that. If we do SL review, it works now. And as you can see, we have the SL review skill..."
- 32:44 / Evidence 6: "You can use any model. If you prefer cloud models, you can use that. And you're fully in control. And that agent is driving Codex, which is right now the most powerful coding harness. Plus, you can save..."
- 36:36 / Evidence 7: "Make sure to fix this. Read what the codexes are doing. Okay. So what happened is they wrote the same index.html. So now PI agent is going to correct them and you can see that it send the..."

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 "If you don’t run Pi locally you’re falling behind…", 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.