This content originally appeared on DEV Community and was authored by Batorgil Davaajantsan
Developers love memes but creating them steals coding time...
Hey devs! Ever wondered what happens when you let AI handle everything—from code to logos? I just built my first AI agent that generates ready-to-post developer memes with barely any input (just a programming language!). Here’s how I did it, start to finish, using AI tools like Copilot, Agent.ai, and more.
✨ Project Breakdown
The Goal: Create an AI agent that takes only a programming language (e.g., “Python”) and spits out a relatable, funny dev meme. Minimal input, maximum laughs.
Tools I Used:
- Agent.ai to construct mean frame 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).
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.
🔥 Biggest Challenges?
(Spoiler: It wasn’t the AI… it was my own hubris.)
- 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.
🚀 Try It Yourself!
Meme Generator Agent: https://agent.ai/agent/developers
Custom Text+Image API: https://gitlab.com/batorgil.it/meme-text-attacher
PS: Shoutout to Copilot for debugging my spaghetti code at 2 AM. 🍝
PPS: Should I add a “meme review” AI agent next? Let’s vote!
What i initially wanted to build was creating a was automation from generating meme to posting on it dedicated Facebook page or IG but i'm too lazy to do it. There was a scheduler and posting thing like agent in there i'm just lazy is it me only.
😂 Fun Fact
I used AI agents to:
- Design the logo (shoutout to Agent.io’s cat-loving AI).
- Write this post’s first draft (thanks, Deepseek Chat!).
- Debug my code (Copilot, you chaotic genius).
Questions for You (to improve the post!):
Would a step-by-step tutorial interest you?
Should I deep-dive into the joke-generation logic?
Want to see the AI-generated logo? (Spoiler: It’s gloriously chaotic.)
Let me know in the comments! 👀
Final Thought: Building with AI feels like riding a rocket… until it crashes into a JSON-shaped black hole. 🚀💥*
This post falls under the Full-Stack Agent Dev Challenge category. Let’s have some fun with it!
This content originally appeared on DEV Community and was authored by Batorgil Davaajantsan
Batorgil Davaajantsan | Sciencx (2025-01-23T15:24:06+00:00) 🚀 From Zero to Meme Hero: A 100% AI-Powered Journey. Retrieved from https://www.scien.cx/2025/01/23/%f0%9f%9a%80-from-zero-to-meme-hero-a-100-ai-powered-journey/
Please log in to upload a file.
There are no updates yet.
Click the Upload button above to add an update.