Original Summary

Last week, I ran out of tokens on my Claude Code subscription. Again. As someone who codes quite a lot using LLMs (like most of us these days, let’s face it), it got me thinking: Why are we spending so many tokens on programming language syntax designed primarily for humans? Braces, closing quotes, unnecessary spaces, new lines long variable names… all of these make code easier for humans to read. But if LLMs are now writing and modifying a large part of our code do they really need all of that? Of course, there’s a problem: I still review the code. I still need to understand it, debug it, and ask the LLM to make corrections. So after some planning, I created TL - a token-efficient language designed for LLMs. The idea is simple - remove or shorten as much unnecessary syntax as possible while keeping enough structure for an LLM to reliably understand and generate the code. I benchmarked TL against several open source projects and saw roughly 30 to 60% fewer tokens , depending on the codebase. But I definitely don’t want to review that compressed gibberish myself 😅 So I also built a VS Code extension that converts TL into normal human-readable code for reviewing and editing. The LLM works with the compact representation. You work with normal code. Right now, TL only works with NodeJS. If people find the idea useful, I’d like to expand it to other programming languages and potentially turn TL into its own language independent intermediate representation. I’d love for you guys to check it out and give me feedback. https://github.com/krdanny/tl-lang Does this idea make sense to you? Would you actually use something like this in your AI coding workflow?   submitted by   /u/Ok-Condition7148 [link]   [comments]

  • 情报分类：商业与市场研究
  • 分类依据：内容涉及商业、投资或市场动态
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/3 03:21:15