Original Summary

For the past few months, in our free time, a colleague and I have been building a tool for hardware designers. It’s called ACE , and before moving forward, we’d like to see if it actually makes sense. The idea stems from a personal frustration. If you ask ChatGPT for the noise figure of an amplifier, it responds with absolute confidence. But it’s not actually reading the datasheet: it just hallucinates the most likely number based on its training data. As a result, you get a value measured at a different frequency, or from a similar model, or a "typical" value presented as guaranteed—and nobody tells you where it came from. It also does math "by eye": one mistake in an RF chain calculation, and the whole result is wrong, written with the exact same confidence as a correct one. You only notice it when the board arrives. ACE , on the other hand, answers strictly based on datasheets. Every value displays the exact page and measurement conditions right next to it, so you can double-check it in ten seconds. The code handles the calculations using transparent formulas, not the AI model. If a data point isn't there, it tells you instead of making it up. Right now, it's a prototype: it includes about 1,500 datasheets, mostly RF, and some features shown in the video are still in the early stages. To the hardware designers out there: would you use this? And what would it take for you to truly trust the numbers? We are especially looking for critical feedback.   submitted by   /u/Suspicious-Hunt1682 [link]   [comments]

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/10/13 15:24:07