Direct API access to OpenAI's models — the canonical example of buying credits and pasting a key into something you built.
Pick this whenA generated app needs its own model access and you want the vendor relationship direct rather than routed.
What makes it different
The teaching example for the whole API-key pattern: create key, top up credits, paste into the app
Cost reality check — the BC2 AI project generator's whole session cost 3-4 cents
What it does
Serves OpenAI models to your own applications with a per-token bill
Powers features in vibe-coded apps that need an LLM
What it does not do
Common wrong expectationsDoesn't come free — but the amounts are small enough that fear of cost is usually misplaced
Doesn't belong in front-end code; keys go server-side (Supabase edge functions or equivalent)
Where it sits in a stack
Anywhere your own software needs a model and you've settled on the vendor.
ShelfModels & model access
Our status · not-usingThe pipeline calls no model APIs directly.
For your projectsThe 3-4 cent figure is a useful anchor when estimating any future API-backed feature — the cost objection is usually theoretical.
| Session | Coverage | Moment | What happened |
|---|
| Basecamp 2: Vibe Coding | demonstrated | 1:22:56 | Key creation in console, credits top-up, powers the AI project generator; total spend for the session's feature ~3-4 cents (2:29:42) |
| Basecamp 3: Introduction to n8n — I | demonstrated | 0:37:53 | Credential setup walkthrough as the canonical API-key example |
| Basecamp 4: Introduction to n8n — II | demonstrated | 1:36:01 | Agent LLM; agent-prompt-vs-chat-input error debugged live; picked for high thinking |
| Session 19: Content Strategy Pipeline — the YouTube Growth Stack, Built by an Agent Team of Chats | demonstrated | | Keys created live; realtime-voice rejections all session; chained Transcribe + 4o-mini fallback |
| Session 20: Complete LinkedIn Agency Automation — Carousels + Newsletter from One Content System | demonstrated | | Responses API structured output + GPT Image; Azure-variable hijack fought off with explicit env isolation; 429 throttling diagnosed live |
| Session 22: Automate Admin Tasks — Advanced RAG (Enterprise 'Internal Perplexity' Built Live) | demonstrated | | Demo LLM + embeddings; stale-key failure diagnosed live |
| 24/7 Chief of Staff: Building 'Jarvis' - Hermes Agent as the Brain on an Azure VM, DeepSeek Harness as the Pluggable Face, Agent Client Protocol Instead of MCP, a Realtime Voice Orb, and Tailscale to Keep It Yours | demonstrated | 2:03:00 | API key for DSH; realtime voice API |
| Day 3: n8n From Zero — Triggers, Fixed vs Expression, an AI Spam Classifier, and Your First Lead-Routing Workflow | demonstrated | | GPT-4.1 nano attempted; pricing walkthrough |
| Day 4: Your First AI Employee — Brain/Memory/Tools/Prompt, a Pinecone RAG Support Agent in n8n, and When NOT to Use an Agent | demonstrated | | GPT-4.1 mini for classifier and agent brain; text-embedding-3-small |
| Day 9: Webhook vs API for Good — Apify Scraper, cURL Import, Structured Output, Gmail Drafts, then the Native-Node Rewrite | demonstrated | | GPT-5 / GPT-5 mini chat model |
| An n8n Content-Repurposing Workflow: Google Sheet Paragraph to Tweet, LinkedIn Post and Shorts Script | demonstrated | | |
| Hands-On Project: a Chat-Triggered n8n Agent with Search, Manus Repurposing, and a Weekend Relay Assignment | demonstrated | | |
| Building a Web App with Replit: Vibe Coding Explained, the Tool Landscape, and a Live Build of a Business Site with an AI Sales Chatbot | demonstrated | | Project-scoped API key |
| Basecamp 6: Voice Agents | explained | 1:07:52 | Retell's suggested models; counterintuitive tested finding: 4.1 beats 5.1 for voice work |
| Session 18: Nano Banana + Veo 3.1 Multi-Shot Video — UGC Genie, an AI-UGC Product Built Live | explained | | Magic-wand prompt-enhancement endpoint built in, key deliberately not added on stage |
| Day 1: APIs, Webhooks, How LLMs Predict, CoStar vs Meta Prompting, Local Models, and a Live RAG Build | explained | | API docs used to show URL + key + body anatomy; Playground needs paid credits |
| Day 6: Voice Agents That Talk Like Humans — the 5-Step Pipeline, Retell Settings That Matter, the 5-Section Prompt, cal.com Booking, and Call Logging | explained | | GPT-4.1 as the voice LLM |
| Workbooks 1–5: Guided Builds with Completion Proof | explained | | Workbook 5's model access: key handling doctrine, small funded balance, usage limits, JSON output mode |
OpenAI API