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Getting LLMs to Generate Funny Memes: Surprisingly Hard

2025-01-06

A University of Waterloo intern attempted to build an app using LLMs and the Greptile API to generate memes that roast GitHub repositories. The process proved unexpectedly challenging. Directly prompting the LLM for roasts yielded generic results. The solution involved separating the task into code analysis (using Greptile to pinpoint specific issues) and roast generation (using the LLM to create targeted humor). Image generation proved difficult due to limitations in handling text, leading to the use of pre-built meme templates and node-canvas for text insertion. Despite the hurdles, the project culminated in reporoast.com, a website capable of generating custom code-roasting memes.

How an AI Code Review Bot Learned to Shut Up

2024-12-21

Greptile's AI code review bot initially faced criticism for generating excessive comments. To address this, they experimented with prompt engineering and having the LLM evaluate its own comments, but these methods proved ineffective. Their breakthrough came from vectorizing past comments, clustering them in a vector database, and filtering out new comments similar to those previously downvoted. This approach boosted the developer address rate from 19% to over 55%, significantly reducing LLM noise.