Vibe coding is so last week. Arch Coding, maybe?

TL;DR

I ran an experiment (fair or not) testing Lovable vs Cursor AI. Cursor got the harder task — I know its blind spots, where it breaks, and where even the best models stop short. For this, I used Cursor’s Claude 3.7 Max model priced at $0.05 per request — supposedly production-grade.

• Lovable delivered a gorgeous UI — colorful dashboards, smooth animations, and what looked like real-time price data. Except every number was fabricated. No API fetches. Mocked data. Hallucinated charts. A false sense of accuracy, dressed up in design.

• Cursor understood the LMPR USDA API flow, structured the Axios calls, and mapped the JSON. But it broke on rendering. No 3D smiley, no micro-animations, and incomplete API parameters. A solid architecture with unfinished plumbing.

• Lovable sells confidence. Cursor delivers structure. Neither shipped something ready.

• This experiment reinforced my shift toward Arch Coding — .md files, tokenized systems, workflows, and architecture-first prompts. Forcing AI agents to behave like junior developers, not interns guessing their way through.

Here’s the exact prompt I gave both Cursor and Lovable — no edits, no tweaks:

“tap into the USDA’s Agricultural Marketing Service (AMS) Market News data—especially the LMPR API and create an app that shows week over week increase for use colorful icons and display increase (sad smiley) and decrease (happy smiley) with amazing micro animations.Create an overall big 3D smiley using 3JS that shows happy or sad this week.

In the data fetch use this: First, call the Table of Contents endpoint to retrieve all available reports: GET https://mpr.datamart.ams.usda.gov/services/v1.1/reports/. Then, for a specific report—for example, the ‘5 Area Daily Weighted Average Direct Slaughter Cattle – Negotiated’ report with slug ID 2466—fetch the summary data by calling: GET https://mpr.datamart.ams.usda.gov/services/v1.1/reports/2466/Summary?q=report_date=03/08/2025, or if the report uses week-ending dates, use q=week_ending_date=03/08/2025. For more granular details, call the detail endpoint: GET https://mpr.datamart.ams.usda.gov/services/v1.1/reports/2466/Detail?q=report_date=03/08/2025. Once you receive the JSON response, convert the data into a structured array listing each commodity’s name, price, change, and percentage change. Finally, prompt the LLM by providing the formatted JSON and asking it to identify the commodity with the largest positive price change, the one with the largest negative price change, summarize overall trends, and suggest market implications.

Cursor

What cursor produced - behind the scenes, it fell to mock Data as it could not connect to the endpoint via Axios but I loved the app/api/usda.ts

Lovable

I have been following lovable's story towards a $10mm ARR - I can see why that UI magic can win hearts.

Agentic Discipline maybe the next frontier

Provided context window and rules allow, something like below maybe the way agents may need to be instructed to provide a guided architecture to provide custom results.

# Architectural rigor example
# Define clear interfaces between AI agents
interface AgentRequest {
  model: string;
  prompt: string;
  maxTokens: number;
  temperature: number;
}

# Establish communication protocols
protocol AgentCommunication {
  request: AgentRequest;
  response: AgentResponse;
  metadata: ExecutionMetadata;
}

Truncated for brevity: comment "MD" and I will "DM" you the rest. Inspired by https://ghuntley.com/stdlib/

Architectural rigor takes time. But it burns fewer tokens, reduces rework, and prevents AI from guessing through the build.