Original Summary

AI research tools are increasingly getting good at answering the literature, search, summarise, citing, etc. That half of the job done but the real part is keeping a track and using the entire context and information to see and provide what did this do to your thinking and help you make the research efficient and concrete. For example, one of the observation kills an assumption you made. Another one may or may not backup the conclusion to. Now days or weeks later you're back at the project trying to rebuild your own reasoning from scratch. Why'd I reject that explanation? Which source actually changed my mind? Was that supported by something real, or was it just how I read it at the time? Did those two sources even disagree, or were they measuring different things and I mashed them together? I've been building something around this, keeping the research question, your existing thinking, the outside evidence, the contradictions, the stuff you rejected, and what's still open, all tied to one line of inquiry instead of spread across search history and half-remembered reasoning. New evidence gets checked against what's already there instead of dropped in as another isolated hit. Now if this weakens something you believed, that shows up instead of getting buried in a summary. If two sources disagree, that stays visible instead of getting flattened into "the literature suggests." And if something new follows from your own work plus the evidence, it gets marked as a proposal, not snuck in like it's already your conclusion. That's the actual thing I'm betting on with what I'm building, Aevron. The fix isn't a better search tool, it's a system that tracks your own thinking changing over time, alongside the evidence, instead of leaving you to reconstruct it from memory every time you come back to something. What do you guys think about this approach?   submitted by   /u/mercurias98 [link]   [comments]

  • 情报分类：工作与职业机会
  • 分类依据：内容涉及招聘、求职或职业发展
  • 信息来源：Reddit · SideProject
  • 发布时间：2026/9/24 17:56:33