Original Summary

We built an AI code review agent that remembers our team's coding conventions. Most AI code reviewers can identify common issues like using "print()" instead of proper logging, missing type hints, or weak error handling. But we wanted to explore a different question: Can an AI code reviewer remember what a specific team has already decided? That's why we built ReviewMind using Hindsight for persistent team memory. The workflow is simple: RECALL → REVIEW → FEEDBACK → RETAIN → IMPROVE Before reviewing code, ReviewMind recalls relevant team knowledge and provides it to the AI reviewer. When developers accept meaningful feedback, that knowledge can be retained and used in future reviews. So instead of simply saying: «"Don't use "print()"."» It can recognize: «"Our team convention recommends structured logging instead of "print()"."» Tech Stack - Next.js + TypeScript - Tailwind CSS - FastAPI - Groq - Hindsight - Python One of the main things we learned is that memory isn't the same as context. Rather than continuously adding more information to prompts, useful team knowledge can be stored and retrieved when it's relevant. We're now exploring things like GitHub PR integration, repository-specific memory, conflicting conventions, and better memory consolidation.   submitted by   /u/anonymouskittu [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：2026/9/30 01:22:17