import google.generativeai as genai
import json

# Configure with your API key
genai.configure(api_key='YOUR_API_KEY')
model = genai.GenerativeModel('gemini-pro')

# Core prompt to generate 2035 Hacker News front page
prompt = """Generate a simulated Hacker News front page from the year 2035.

Include:
1. 5-10 headlines with realistic tech topics from 2035
2. Brief discussion points for each thread
3. Simulated community engagement metrics (karma, comments)
4. Mix of technical breakthroughs, ethical debates, and startup launches

Format as JSON with: title, discussion, upvotes, comments

Example structure:
{
  "posts": [
    {
      "title": "Post-AGI Economic Models: Universal Basic Compute Implemented in Norway",
      "discussion": "Debate about whether compute allocation should replace currency",
      "upvotes": 487,
      "comments": 203
    }
  ]
}"""

# Generate the content
try:
    response = model.generate_content(prompt)
    future_hn = json.loads(response.text)
    
    # Display results
    for i, post in enumerate(future_hn['posts'], 1):
        print(f"{i}. {post['title']}")
        print(f"   💬 {post['comments']} comments | ⬆️ {post['upvotes']} votes")
        print(f"   {post['discussion']}")
        print()
        
except Exception as e:
    print(f"Error: {e}")
    print("\nRaw response:")
    print(response.text if 'response' in locals() else 'No response')

When AI Writes the Future

A developer has fed Google's Gemini Pro 3 a unique prompt: generate the headlines, discussion points, and community engagement metrics for Hacker News a decade from now. The resulting simulated 2035 front page is a captivating artifact. It's not a forecast but a complex hallucination, blending plausible tech trends with the model's inherent biases and training data limitations.

What Gemini Sees in 2035

The AI-generated page is a mix of the familiar and the fantastical. You'll find threads on "Post-AGI economic models" alongside debates about quantum-resistant cryptography becoming standard. There are Show HNs for projects like "A fully local, 10-trillion parameter model that runs on a solar-powered Raspberry Pi 12." The community discussions feature high karma counts and heated debates, mimicking today's HN culture with futuristic jargon.

This exercise highlights a core tension in generative AI: its ability to extrapolate versus its tendency to confabulate. The page feels authentic because it perfectly replicates the structure and tone of Hacker News. The headlines follow recognizable patterns, and the comment sentiment rings true. Yet, the specific technologies are often vague amalgamations of current buzzwords pushed to an extreme.

Why This Hallucination Matters

This isn't just a fun parlor trick. It's a stark demonstration of how AI models, trained on our present-day discourse, project a future that is fundamentally anchored in today's ideas. The "2035" page is obsessed with scaling current paradigms—bigger models, faster chips, more pervasive automation. It shows a surprising lack of conceptual breakthroughs that fall outside its training distribution.

For developers and tech leaders, the takeaway is critical: AI is a powerful tool for pattern recognition, but a poor tool for genuine invention. It can remix and recombine existing concepts with stunning fluency, but the "front page from the future" is ultimately a reflection of our collective present. The truly disruptive technology of 2035—the equivalent of today's AI boom emerging from the mobile era—is likely something this model cannot conceive of because its seeds aren't yet in its training data.

The Verdict: Imagination vs. Extrapolation

Comparing Gemini Pro 3's output to a real forecasting exercise reveals the gap. A human futurist might consider societal shifts, ethical reckonings, or black swan events. The AI produces a coherent but derivative timeline where today's trends simply continue. It's a comparison between linear extrapolation and nonlinear imagination.

The project serves as both a warning and a tool. Use AI to brainstorm and explore permutations, but never mistake its confident, detailed hallucinations for foresight. The real future will be written by humans asking questions the models haven't yet been trained to answer.