How I Built a Tweet Generator That Actually Works
(And Why Most AI Tools Get It Wrong)

I was frustrated watching friends struggle with AI-generated content that screamed "I used ChatGPT." You know the type—those generic "Plot twist:" threads and "Most people don't realize..." openers that make you want to close the app immediately.

Coming from a background that spans diplomacy, international trade, and eventually programming (yes, I switched to CS in my mid-20s), I've always been fascinated by communication patterns. So when I started building a social media tool, I approached the tweet generation problem differently.

The Problem with Current AI Tools

Most AI tweet generators work like this:

User enters a topic
LLM generates generic content based on training data
Output feels robotic and inauthentic

The result? Content that sounds like it came from the same bot, regardless of who's supposedly "writing" it.

My Approach: Pattern Recognition + Personal Style

Instead of just prompting an LLM, I built a system that:

1. Analyzes the user's existing content

to understand their unique voice:

// Extract user's writing style from their tweets
if (user.personalizedInfo?.permissionForWritingStyle === true && 
    user.personalizedInfo.viralTweets?.length > 0) {
    userTweets = user.personalizedInfo.viralTweets
        .map(t => `"${t.text || t.full_text || t.content}"`)
        .join('\n');
}

2. Studies viral patterns from specific creators

they want to emulate:

// Fetch and analyze top-performing tweets from chosen creators
const topTweets = allTweets
    .sort((a, b) => (b.stats.likes + b.stats.retweets) - (a.stats.likes + a.stats.retweets))
    .slice(0, tweetsPerCreator);

3. Separates style from substance

in the final generation:

User's tweets → writing style
Creator's tweets → content patterns and ideas
LLM combines them intelligently

The Content Quality Challenge

The trickiest part wasn't the generation—it was filtering quality content. Most APIs return everything, including promotional posts, event announcements, and those dreaded "GM" tweets.

I built a content quality scoring system that evaluates:

Text substance (length, word count, sentence structure)
Content value indicators (questions, insights, actionable advice)
Penalties for low-value patterns (simple greetings, event promotions)

const calculateContentQuality = (tweet) => {
    let score = 0;
    const text = tweet.text || '';
    
    // Prefer substantial content
    if (textLength >= 100) score += 30;
    else if (textLength < 10) score -= 20;
    
    // Detect and penalize low-value patterns
    const lowValuePatterns = [
        /^(gm|good morning|gn|good night)[\s!]*$/i,
        /live\s*(now|soon)/i,
        /join\s*(me|us)\s*(live|now)/i
    ];
    
    for (const pattern of lowValuePatterns) {
        if (pattern.test(text)) {
            score -= 30;
            break;
        }
    }
    
    return Math.max(0, score);
};

This alone improved output quality dramatically.

Beyond Generation: The Full Pipeline

What started as a tweet generator evolved into something more comprehensive:

Auto-scheduling: Users can queue content for optimal posting times
Image suggestions: AI suggests relevant visuals for each post
Community targeting: Recommends which Twitter communities to engage with
Performance tracking: Analytics on what actually performs well

Technical Lessons Learned

1. Context windows matter more than model choice

I experimented with different models, but the real breakthrough came from better context management—feeding the LLM exactly the right amount of relevant information.

2. Quality filtering > quantity generation

Better to generate 10 great tweets than 50 mediocre ones. The scoring system was worth the extra complexity.

3. User agency is crucial

The system suggests patterns and ideas but never forces a specific voice. Users maintain control over their authentic self-expression.

The Authenticity Paradox

Here's what I find interesting: by systematically analyzing what makes content engaging, the tool actually helps people be more authentically themselves online. It's not about copying someone else—it's about understanding why certain communication patterns work and adapting them to your unique perspective.

The entrepreneurs using it aren't becoming clones of each other. They're becoming better versions of their own voice.

What's Next?

I'm exploring how these patterns apply beyond Twitter—LinkedIn posts, newsletter writing, even technical documentation. Currently expanding SupaBird to handle these different content types while maintaining the same principle: effective communication follows learnable patterns, but authenticity can't be automated.

If you're interested in the technical details or want to discuss the ethics of AI-assisted content creation, I'd love to hear your thoughts.