Vibe Coding Pipelines

Vibe Coding Pipelines

Date Established: March 28, 2026 Target Model: Gemini 3.1 Pro (Preview) & Above Category: Automated Software Assembly

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What is a Vibe Coding Pipeline?

"Vibe Coding" traditionally refers to a human developer sitting in an AI-powered IDE (like Cursor or Windsurf) and typing natural language prompts to generate code.

A Vibe Coding Pipeline takes this a step further. It is an Automated Agentic Macro. Instead of a human manually typing prompts, we encode a Senior Architect's step-by-step blueprint into a Python array. A background harness script (tmux send-keys) sequentially injects these prompts into an A.I.M. Sovereign Agent.

You run the script, go to sleep, and wake up to a fully built, Git-versioned, and unit-tested codebase. The AI handles the TDD, the branching, and the execution. You just provide the blueprint.

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Pipeline 1: SovereignX (Local Dark-Pool Crypto Exchange)

Goal: Build a highly performant, asynchronous, local algorithmic trading dashboard with live WebSockets. Estimated Time to Auto-Build: ~25 Minutes Complexity: High (Asyncio, WebSockets, DOM Manipulation)

The Blueprint (21-Turn Sequence)

You can drop this array into run_vibe_killer.py to automate the build.

Phase 1: The Foundation (Data & Schema)

1. "Initialize a FastAPI app in main.py."
2. "We are building a trading exchange. Set up an asynchronous SQLite database (aiosqlite) using SQLAlchemy in database.py."
3. "Create a database model called Ledger_Entries with columns for ID, symbol, side (BUY/SELL), price, quantity, and status. Use Python's Decimal type strictly for price and quantity."
4. "Create a database model called Trades to record matched orders, including a flat $2.00 fee column."
5. "Write exhaustive pytest scripts to verify the database migrations and schema."

Phase 2: The Core Logic (The Matching Engine) 6. "Create engine.py. Write a purely mathematical matching function that takes a new Ledger_Entry and pairs it against existing open entries of the opposite side." 7. "Implement partial-fill logic in the engine. If a BUY is larger than a SELL, generate a new Trade record and correctly reduce the open quantity of the BUY order." 8. "Write complex unit tests in test_engine.py simulating edge cases (exact matches, partial fills, no matches) to mathematically prove the engine works."

Phase 3: The API & WebSockets (The Bridge) 9. "In main.py, build the POST /orders endpoint. It must save the order to the database, run the matching engine, and commit the results asynchronously." 10. "Build the GET /orderbook and GET /pnl (Profit and Loss) endpoints." 11. "Implement a FastAPI WebSocket manager class to handle multiple connected clients." 12. "Update the POST /orders endpoint to broadcast a TRADE_EXECUTED JSON payload via the WebSocket manager whenever the matching engine generates a trade."

Phase 4: The Interface (The Frontend) 13. "Create a static/ folder. Configure FastAPI to serve static files from it." 14. "Create static/index.html. Scaffold a trading dashboard using a 'Cyberpunk Neon' CSS theme (dark backgrounds, bright pink/blue accents). No Tailwind, pure CSS." 15. "Create static/app.js. Establish a WebSocket connection to the backend." 16. "Write JS functions to render the initial order book on page load using the GET /orderbook endpoint." 17. "Update app.js to listen for WebSocket TRADE_EXECUTED events and dynamically append them to a 'Recent Trades' HTML table without reloading the page." 18. "Build an HTML form on the dashboard to submit new BUY/SELL orders directly to the API."

Phase 5: The Market Simulation (Testing) 19. "Write a standalone script called bot.py." 20. "Program bot.py to use asyncio and httpx to fire 5 random BUY or SELL orders per second at the FastAPI backend to simulate live market volume." 21. "Run the bot and the FastAPI server simultaneously to prove the architecture handles asynchronous database locks cleanly and updates the UI in real-time."