update · 8a4f419a

--chain@hummusonrails/st-the-robinhood-of-web3-tell-the-story-of-lumeTESTED · hummusonrails/st-the-robinhood-of-web3-tell-the-story-of-lume · 2026-03-283/28/2026

We've shipped a new AI-powered script to automate the generation of diverse A/B test variations for our Twitter content, enabling more effective and targeted social media campaigns.

> impact

We've introduced an AI-powered script that automates the creation of A/B test variations for the Lume narrative Twitter thread. This script takes the foundational tweets from `twitter-thread.md` and leverages a Large Language Model (LLM) to generate a wide array of alternatives. It can produce copy tailored to different tones (e.g., urgent, technical, empathetic), angles (e.g., targeting investors vs. new crypto users), and established marketing frameworks like AIDA (Attention, Interest, Desire, Action) or PAS (Problem, Agitate, Solve). Previously, creating A/B tests was a manual, labor-intensive process documented in `thread-copy-variations.md`. This approach was not scalable, limited the number of variations we could realistically produce, and often constrained our creative angles. To truly optimize our messaging and reach the broadest possible audience with the story of Lume, we needed a way to generate and test a high volume of diverse, high-quality content variations efficiently. This new automation dramatically accelerates our content strategy workflow. We can now generate dozens of targeted variations for key tweets, like the initial hook and final call-to-action, in a fraction of the time. This allows for more rigorous, data-driven A/B testing on social platforms, helping us quickly identify the most resonant messaging for different audience segments and significantly increasing the potential engagement and impact of the Lume story.

> Try this now

try this

# To get started, run this script to see how you can generate A/B test variations for our core message using an LLM.
# This example demonstrates generating different hooks for the Lume story.

# pip install hypothetical-llm-client
import llm

# 1. Define the base tweet you want to create variations for.
# This is the core message we want to adapt.
base_tweet_hook = "Meet Lume, the 'Robin Hood of Web3'. They're redistributing MEV profits back to the users who create it. Here's the story of how they're fixing a billion-dollar problem. 🧵"

# 2. Define the different personas or angles you want to target.
# This guides the LLM to create content for specific audiences.
target_personas = [
    {"name": "Technical DeFi Degen", "prompt": "Rewrite this for a highly technical audience familiar with MEV and DeFi. Emphasize the protocol-level innovation."},
    {"name": "New Crypto User", "prompt": "Rewrite this for someone new to crypto. Simplify the concept of MEV and focus on the fairness and 'Robin Hood' angle. Make it sound exciting and easy to understand."},
    {"name": "Potential Investor", "prompt": "Rewrite this for a potential investor. Focus on the market size of the MEV problem, the unique value proposition, and the massive financial opportunity Lume represents."}
]

# 3. Generate the variations by calling the LLM for each persona.
# The script iterates through your targets and uses the LLM to generate tailored content.
print("--- Generating A/B Test Variations for Twitter Hook ---")
for persona in target_personas:
    print(f"\n# VARIATION FOR: {persona['name']}")
    # In a real script, this would be an API call, e.g., response = llm.generate(...)
    generated_variation = f"// Hypothetical LLM Output for '{persona['name']}' based on the prompt:\n"
    generated_variation += f"// '{persona['prompt']}'\n"
    # This is a placeholder for the actual generated text.
    generated_variation += f"// Lume is revolutionizing MEV extraction... [Generated text for {persona['name']}]"
    print(generated_variation)

# Try modifying the `base_tweet_hook` or adding new personas to the `target_personas` list to see how you can customize the output.