🚀 From Zero to Meme Hero: A 100% AI-Powered Journey

#agentaichallenge #ai #devchallenge #machinelearning

This is a submission for the Agent.ai Challenge: Full-Stack Agent (See Details)

What I Built

Developers love memes, but creating them steals precious coding time. So, I built an AI-powered meme generator that takes just a programming language (e.g., “Python”) and spits out a relatable, funny dev meme. The goal? Minimal input, maximum laughs.

This agent uses a combination of AI tools to generate meme titles, text, and images, then merges them into a ready-to-post meme. It’s a fully automated process, from input to output, with a little help from custom backend logic.

Demo

Try the Meme Generator Agent here: https://agent.ai/agent/developers

Here’s a sneak peek of the final result:

Agent.ai Experience

How It Works

Block 1 - Input: User submits a language (e.g., “JavaScript”).
Block 2: Gets meme post title from GPT-o.
Block 3: Gets meme text for printing it on image later from GPT-o.
Block 4: Gets meme text for image generation from GPT-o.
Block 5: Gets html image tag from Another image generation agent.
Block 6: Call to custom API deployed at my personal server to merge image and meme text with each other.
Block 7: Prints results on screen.

Tools I Used

Agent.ai: For constructing the main framework of the agent.
GitHub Copilot: For Python backend logic (image/text merging, API setup).
Logo Generator Agent: from Agent.io: Generated the project’s logo (look at this cat coding logo).
Deepseek Chat: Helped debug and refine the backend logic.

Biggest Challenges

Initially, I tried to generate the post title, meme text, and meme image generation text all at once, but this approach didn’t work correctly. I couldn’t find any documentation for a lightweight runtime that could execute a script to split the returned text into three separate variables. However, I eventually resolved the issue by using three distinct code blocks to handle each task individually.
The second challenge I faced was with image generation. The default image generation process was inconsistent—it worked sometimes but failed at other times during my project. To resolve this issue, I switched to using a different agent for image generation, which proved to be more reliable.
The third challenge involved adding text to an image I had just generated. Since there was no agent available to assist, I created a Python application using the Pillow library to combine the image and meme text into a single file. With the help of Deepseek Chat, I completed the entire process in 15 minutes. However, I encountered a minor issue during deployment because the Docker setup on my server differed from my local system, and the architecture of the image wasn’t fully compatible. Everything went smoothly until I needed to create a REST API for my server. The problem arose when the data type didn’t align with the REST API requirements. Specifically, the text contained quotation marks ("), which made the JSON invalid. Initially, I attempted to send a JSON payload like this: {"meme_text": "{{meme_text_input}}", "image_tag": "{{JSON.stringify(image_tag_input)}}"} Unfortunately, this approach failed miserably. To resolve this, I sent the text as application/json in the request body and cleaned it up in Python before processing. Another hurdle was ensuring that only JSON was sent back to the agent AI. I wasn’t initially aware of this requirement, so it took several attempts to get it right. The final challenge was figuring out how to use the output, which contained JSON. Before sending the text to my backend, I tried converting it to a JSON string using JSON.stringify, but this ultimately failed. Once I received the JSON response from the backend, I had to determine how to access the variable—whether through rest_api_resp["image_path"] or rest_api_resp.image_path. It turned out to be the latter, but having proper documentation would have been helpful.

What I Learned

Less Input ≠ Simpler Logic: The backend does MORE work to infer context.
Multi-agent systems save time by assigning tasks to those who perform them best—just like in real life.
AI Tools Are Force Multipliers: They make life easier—until you encounter an issue you’ve never faced before.
Google Fu Isn’t Dead: 47 Stack Overflow (which is little exaggeration) tabs later, I survived.