Original Summary

Hi everyone, I'm an undergrad physics student working in a lab, and I built this out of personal frustration with how much time literature reviews eat up during manuscript preparation. Whenever we write or revise a draft across co-authors, the biggest time sink isn't compiling LaTeX—it's the tedious loop of tracking down references: The literature hunt: You write an assertion (e.g., about a known empirical trend or baseline behavior), you know the prior literature exists, but you have to stop writing to spend 30 minutes sifting through search engines to find the exact peer-reviewed paper that proved it. Citation drift: After multiple revision rounds, citations often end up anchored to sentences the cited paper never actually substantiated, leaving you to manually re-review dozens of references before submission. Unsupported claims: Strong assertions left floating without literature at your disposal, which Reviewer 2 immediately flags. The core motive here was simply to save hours of manual literature review without uploading unpublished drafts to cloud servers or using generative models that rewrite text and hallucinate fake DOIs. I built Preflight as an in-browser audit and literature finder: - Scans drafts locally : The manuscript and .bib parser runs entirely client-side in a Web Worker. Your raw drafts never leave your browser sandbox. - Automated claim auditing : It isolates empirical assertions in your text and checks whether they have proper literature backing or if a reference has drifted. - Targeted literature retrieval : For unsourced statements, it queries open academic indices to surface relevant, peer-reviewed papers matching that exact claim context, along with valid BibTeX entries you can drop in immediately. - No generative autocomplete or text rewriting: The goal is just to verify assertions and quickly surface the actual literature you need. The workbench is open to test directly without an account: https://preflightai.tech/workbench I am not a Software Engineer , but I am trying to learn. Your feedback helps me immensely! Maybe it will help me shape this to something that I intended it to be. I'm actively tuning the literature recommendation relevance across different scientific fields. If you have a minute to run a section of a draft through it, any critique on the suggested papers or parser edge cases would be deeply appreciated.   submitted by   /u/stackfrost [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：2026/9/18 04:03:38