Mapping LLMs over excel saved my passion for game dev

原始链接: https://danieltan.weblog.lol/2025/06/map-llms-excel-saved-my-passion-for-game-dev

Facing data entry burnout in his card game project, the developer transitioned from Unity's scriptable objects and custom editors (which proved cumbersome for complex, nested data) to storing game assets as C# code. This allowed for easier editing, type-checking, and management. He then leveraged LLMs to automate the conversion of data from Excel to C# code. Recognizing LLMs' susceptibility to "context poisoning," he adopted a structured approach. First, he designed a detailed prompt (with the LLM's assistance) that guided the LLM to parse Excel data, identify spell components, map them to existing C# functions, and propose new functions if needed. This prompt included specific mapping rules and identified nested structures. The resulting workflow involved a human-driven problem analysis, LLM-assisted brainstorming, human solution selection, and LLM-assisted code implementation. This approach proved significantly more efficient than manual data entry, allowing the developer to focus on problem-solving and development rather than tedious data input.

This Hacker News thread discusses a weblog post about using LLMs (likely GPT models) and spreadsheets (like Excel) to improve game development workflows. The original author found that leveraging LLMs to process and reformat game data reinvigorated their passion for the process. Commenters discussed the potential for deeper LLM integration within Google Workspace apps (like Sheets and Slides), highlighting existing features such as the `=AI` function in Sheets but suggesting more could be done. Others debated alternative data storage methods for game development, with ScriptableObjects in Unity being a popular suggestion. Some requested more specifics on the prompts, models, and results used in the original post. One commenter suggested that LLMs are good at reformatting and data manipulation. Finally, someone pointed out that the SPL programming language also used spreadsheets for coding.
相关文章

原文

Biscuit immediately but LLMs struggled even after extended analysis to design something similar even when it was aware of Macaroons. That being said, claude worked the best and grok/openai/deepseek all failed to even propose decentralized, attenuated authorization.

A typical workflow looks like this

  1. analyzing the problem (humans work best here)
  2. brainstorming a solution (AI can rapidly pattern match against known solutions. Humans have the ability to consume the latest information and analyze if any are relevant for the AI to research on)
  3. choosing the best solution (humans work best here)
  4. implementing the solution (AI can help here by filling in boilerplate code)