When AI Coding Tools Don't Live Up to the Hype: A Real-World Test

What happens when experienced developers try to build something "simple" with beginner-friendly AI coding platforms

After discussing mental models for the AI age, we decided to put theory into practice. The goal seemed straightforward: build a web application that displays LinkedIn posts from recent connections. What followed was an hour-long journey through the current state of AI coding tools that revealed both their promise and their significant limitations.

The Challenge: A "Simple" LinkedIn Feed

The requirements were clear:

• Display 10-20 LinkedIn posts

• Only from connections made in the last 6 months

• Posts from the last 30 days

• Clicking a post opens it in LinkedIn for interaction

Simple enough, right? This is exactly the kind of task that AI coding evangelists claim anyone can accomplish with natural language prompts.

Tool #1: Lovable - Pretty UI, No Substance

The Promise: A well-funded platform that supposedly builds real applications from simple prompts.

The Reality: Lovable created an impressive-looking mockup with fake data—Jennifer Lee, Lisa Thompson, David Wilson—but no actual LinkedIn integration. The interface looked professional, but clicking "Sign in with LinkedIn" did nothing. The tool acknowledged it would need "backend functionality, including potentially saving API keys" but made no attempt to implement this.

Key Issue: It created the appearance of a working app without any functional backend.

Tool #2: Aider - Professional But Incomplete

The Approach: Command-line driven AI coding that creates actual files and runs real code.

The Progress: Aider built a Python Flask application with proper LinkedIn OAuth setup. It created multiple files, set up a web server, and even got to a functioning login screen.

The Roadblock: Authentication configuration became complex, requiring manual setup of redirect URLs, API credentials, and environment variables. Even with professional development knowledge, we got stuck on OAuth callback configuration.

Observation: Much more technically capable than browser-based tools, but still requires significant developer intervention.

Tool #3: V0 - Polished Prototypes Only

The Experience: V0 created another professional-looking interface with the same fake data (Sarah Johnson, Michael Chen, Priya Patel).

The Limitation: Like Lovable, it explicitly stated: "This is a demonstration prototype. In a real implementation, you would need LinkedIn API integration, OAuth authentication, data storage."

The Pattern: Beautiful mockups that simulate functionality without building it.

The Curious Case of Identical Mock Data

Something strange emerged: both Lovable and V0 generated nearly identical fake profiles—Sarah Johnson, Michael Chen, Priya Patel. This suggests these tools may be drawing from similar training data or template pools, raising questions about their actual creativity and problem-solving capabilities.

What We Learned: The Gap Between Hype and Reality

For Beginners: The Promise Falls Short

Tools marketed as "anyone can build apps" hit immediate walls with real-world requirements:

• API integrations require technical knowledge

• Authentication is complex

• Data handling needs proper backend setup

• Deployment requires infrastructure understanding

For Professionals: Mixed Results

Even experienced developers found limitations:

• Aider showed the most promise but required significant hand-holding

• Browser-based tools created impressive demos but no real functionality

• The "just describe what you want" approach breaks down with complex integrations

The Successful Counter-Example

John showed a working Slack-to-GitHub integration he and Lucas built previously with Aider. The key difference? Simpler, incremental steps rather than attempting to build everything at once.

Their approach:

1. Do step one

2. Do step two

3. Do step three

4. Now make steps 1, 2, and 3 work together

The Reality Check: What Actually Works

AI Coding Tools Excel At:

• Simple, self-contained applications (landing pages, basic games)

• UI mockups and prototypes

• Incremental improvements to existing code

• Specific, narrow tasks ("fix this bug," "add this button")

They Struggle With:

• External API integrations

• Authentication flows

• Complex backend logic

• Real-world deployment requirements

The Skepticism Question

When Alec asked about all the AI-built apps showcased on social media, John's response was telling: Most successful examples come from people who are already professional developers or designers. The "non-technical person builds amazing app" stories are largely absent from real-world experience.

The uncomfortable truth: The people succeeding with AI coding tools already know how to code. They're using AI to accelerate their existing skills, not replace the need for technical knowledge.

Lessons for Realistic Expectations

For Individuals:

• Start with truly simple projects

• Learn basic programming concepts alongside AI tools

• Expect to iterate and debug extensively

• Don't attempt complex integrations as your first project

For Businesses:

• AI coding tools are productivity enhancers, not developer replacements

• Complex applications still require technical expertise

• Budget for significant testing and refinement

• Consider hybrid approaches: AI for prototyping, humans for production

For the Industry:

• We need better intermediate steps between "hello world" and "production app"

• Authentication and API integration remain major hurdles

• The gap between demo and deployment is wider than marketed

The Bottom Line

Our hour-long attempt to build a "simple" LinkedIn feed resulted in polished mockups and half-working prototypes. While AI coding tools have made impressive strides, they're not yet the "anyone can build anything" solution they're often portrayed as.

The most honest assessment? AI coding tools are powerful assistants for people who already understand software development, but they're not yet ready to democratize programming for complete beginners.

The future may be different, but today's reality requires managing expectations and understanding that building real, deployable applications still demands technical knowledge—even with AI help.

Have you tried building something with AI coding tools? What was your experience? The gap between marketing promises and actual capabilities is worth discussing honestly.