When AI Meets Humor: Lessons from Building a GitHub Roast Bo

Introduction

Ever wondered what would happen if you combined the raw data of a developer’s GitHub activity with the snarky tone of a stand‑up comic? That was the premise behind GitHub Roast, a playful AI tool that generates tongue‑in‑cheek “roasts” of public GitHub profiles. What started as a personal side‑project shared on X quickly attracted over a thousand visitors per day, forcing the creator to think beyond the joke and address real‑world concerns like scalability, user experience, and ethical AI.

The Spark

The idea sprouted during a lazy weekend when I was scrolling through my feed on X (formerly Twitter). A friend posted a meme about “GitHub contributions looking like a desert,” and I thought: What if an algorithm could weaponize that observation into a personalized roast? The concept was simple—feed a public GitHub username into an LLM, pull a few stats (commit frequency, language mix, repo count), and let the model generate a witty, slightly sarcastic paragraph.

Building the Roast Engine

1. Data Retrieval – The GitHub REST API provides a wealth of public information without authentication for basic queries. I fetched the user’s profile data, recent commit counts, language breakdown, and starred repositories. To keep the app lightweight, I cached responses for 15 minutes using Vercel’s edge‑caching layer.

2. Prompt Engineering – The heart of the system is a carefully crafted prompt that tells the LLM (OpenAI’s GPT‑4 at the time) to adopt a roast‑style voice while staying respectful. Example prompt: ` You are a witty comedian. Write a short roast of a GitHub user based on the following stats: {stats}. Keep it funny, avoid personal attacks, and end with a constructive tip. ` Iterating on this prompt was crucial; early versions produced overly harsh language that risked violating GitHub’s Terms of Service.

3. Serverless Deployment – I used Vercel’s serverless functions to host the API endpoint. The function receives a username, calls GitHub, builds the prompt, queries the LLM, and returns the roast. The serverless model kept costs low while handling the bursty traffic spikes that followed the X post.

Traffic Surge and Scaling

Within 24 hours of the tweet, the site logged ~1,200 unique visitors per day, with peaks of 5,000 when a popular tech influencer retweeted it. This forced three quick upgrades:

- Rate Limiting – GitHub’s unauthenticated API limit is 60 requests per hour per IP. I introduced a per‑IP token bucket to avoid hitting the ceiling and added optional personal access token support for power users. - Cold‑Start Mitigation – Vercel’s serverless functions can suffer from cold starts. I added a warm‑up cron job that pings the endpoint every five minutes, keeping the container alive during high‑traffic periods. - Analytics & Feedback Loop – By embedding a tiny “Was this roast helpful?” widget, I collected user sentiment data that guided subsequent prompt tweaks.

Balancing Fun and Constructive Feedback

The core challenge was ensuring the roasts stayed entertaining without crossing into harassment. I implemented a two‑step safety filter:

1. Content Moderation – Before returning the LLM output, the text passes through OpenAI’s moderation endpoint. Any flagged content triggers a fallback generic roast. 2. User Opt‑Out – Visitors can hide their username from future roasts via a simple cookie, respecting privacy and giving control back to the user.

Community Feedback and Iteration

The Hacker News (HN) community proved invaluable. Commenters suggested:

- Adding a “positive spin” mode that highlights strengths instead of mocking weaknesses. - Providing a downloadable markdown snippet so developers can paste the roast into their README. - Integrating with GitHub Actions to automatically generate a roast after a release.

Implementing these ideas not only broadened the tool’s appeal but also turned a novelty into a small productivity aid.

Ethical Considerations

Even a light‑hearted AI can reinforce biases. To mitigate this, I:

- Limited the model to public data only—no private repos or email addresses. - Ensured the roast never mentions protected characteristics (gender, ethnicity, etc.). - Added a disclaimer stating the roast is generated by an AI and is not a professional assessment.

These steps helped keep the project aligned with responsible AI principles while still delivering humor.

Takeaways for Makers

- Start Small, Iterate Fast – A weekend prototype can uncover real user demand. Use that signal to prioritize features. - Mind the API Limits – Public APIs often have strict rate caps. Build caching and rate‑limiting early to avoid service interruptions. - Safety First – Even jokes need moderation. Leverage existing moderation APIs rather than reinventing the wheel. - Listen to the Community – Platforms like HN and X provide rapid, candid feedback that can shape product direction. - Design for Scale from Day One – Serverless platforms make scaling painless, but cold starts and caching still require attention.

Conclusion

GitHub Roast turned a whimsical idea into a daily traffic driver, proving that humor‑centric AI can be both engaging and instructive. By focusing on robust engineering, responsible content generation, and community‑driven iteration, the project evolved from a meme to a modest tool that developers actually find useful. If you’re contemplating your own AI side‑project, remember: a good roast is just a prompt away, but a great product needs thoughtful architecture and ethical guardrails.

Sources: https://gitroast-kappa.vercel.app/