AI Peer Reviewer – Multiagent system for scientific manuscript analysis
After waiting 8 months for a journal response or two months for co-author feedback that consisted of "looks good" and a single comma change, we built an AI-powered peer review system that helps researchers improve their manuscripts rapidly before submission. The system uses multiple specialized agents to analyze different aspects of scientific papers, from methodology to writing quality. Key features: 24 specialized agents analyzing sections, scientific rigor, and writing quality // Detailed feedback with actionable recommendations. // PDF report generation. //…
In plain words
AI Peer Reviewer is a system designed for researchers seeking detailed feedback on scientific manuscripts before submission. It uses 24 specialized AI agents to analyze different aspects of papers, including methodology, scientific rigor, and writing quality. The system provides actionable recommendations and generates PDF reports. Users can access it through a cloud version or deploy a self-hosted version from GitHub. It supports custom review criteria and journal-specific requirements, offering feedback within one to two working days.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
After waiting 8 months for a journal response or two months for co-author feedback that consisted of "looks good" and a single comma change, we built an AI-powered peer review system that helps researchers improve their manuscripts rapidly before submission. The system uses multiple specialized agents to analyze different aspects of scientific papers, from methodology to writing quality. Key features: 24 specialized agents analyzing sections, scientific rigor, and writing quality // Detailed feedback with actionable recommendations. // PDF report generation. // Support for custom review criteria and target journals. Two ways to use it: 1. Cloud version (free during testing): https://www.rigorous.company - Upload your manuscript - Get a comprehensive PDF report within 1–2 working days - No setup required 2. Self-hosted version (GitHub): https://github.com/robertjakob/rigorous - Use your own OpenAI API keys - Full control over the review process - Customize agents and criteria - MIT licensed The system is particularly useful for researchers preparing manuscripts before submission to co-authors or target journals. Would love to get feedback from the HN community, especially from PhDs and researchers across all academic fields. The project is open source and we welcome contributions! GitHub: https://github.com/robertjakob/rigorous Cloud version: https://www.rigorous.company
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2025
the whole month →
- C9
Life & fun · 2025 · felixrieseberg.github.io
