Claude Code,Codex + FREE Opus, Gemini API: FULLY FREE AI Coder is CRAZY!

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

Source video:
- Title: Claude Code,Codex + FREE Opus, Gemini API: FULLY FREE AI Coder is CRAZY!
- URL: https://www.youtube.com/watch?v=sYtNcR9zXvk
- Topic: Interfaces + Open Design
- My current learning frame: Set up CLI Proxy API locally, connect an Anti-Gravity account, and run one real coding task through Claude Code pointed at the proxy to confirm tool calls and streaming work identically to the paid Anthropic endpoint.
- Why this matters: New playlist item from AICodeKing; queued for transcript-backed review, topic mapping, and a practical learning artifact.

Transcript anchors from this exact video:
- 0:21 / Evidence 1: "account, Anti-Gravity gives you access to Gemini 3.1 Pro, Gemini 3.5 Flash, and believe it or not, even Claude models like Opus 4.6, Thinking and Sonnet 4.6, all for free with pretty decent rate limits. Now, that's amazing,..."
- 2:47 / Evidence 2: "thing you should do is look at the API key section in the config and set your own key there. This is the key that your clients will use to talk to the proxy. You can set it..."
- 4:17 / Evidence 3: "obviously the headline here. Ever since Claude Code basically started rug pulling people on limits, everyone's been looking for ways around it. So, for this, you just need to set a few environment variables. First, set the Anthropic..."
- 5:32 / Evidence 4: "from the model list inside open code. Gemini 3.1 Pro Code in open code works really well, to be honest. Then, there's the Codex CLI. For that one, you edit the config.toml file in your .codex folder and..."
- 7:18 / Evidence 5: "window, and they reset over time. Gemini models have pretty generous limits, while the Claude models have tighter ones, which makes sense because Google is paying Anthropic for those. There's also a web management UI that comes with..."

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 "Claude Code,Codex + FREE Opus, Gemini API: FULLY FREE AI Coder is CRAZY!", 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.