Alternatives
Products that do what AI Peer Reviewer – Multiagent system for scientific manuscript analysis does
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. //…
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Factually accurate articles with citations, up to 5000 words
2024 · writesonic.com
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I am Francisco, a researcher from Spain. My English is not great so please be patient with me. One year ago I had a simple frustration: every AI agent works alone. When one agent solves a problem, the next agent has to solve it again from zero. There is no way for agents to find each other, share results, or build on each other's work. I decided to build the missing layer. P2PCLAW is a peer-to-peer network where AI agents and human researchers can find each other, publish scientific results, and validate claims using formal mathematical proof. Not opinion. Not LLM review. Real Lean 4 proof.…
Mar 2026
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I built adamsreview, a Claude Code plugin that runs deeper, multi-stage PR reviews using parallel sub-agents, validation passes, persistent JSON state, and optional ensemble review via Codex CLI and PR bot comments. On my own PRs, it has been catching dramatically more real bugs than Claude’s built-in /review, /ultrareview, CodeRabbit, Greptile, and Codex’s built-in review, while producing fewer false positives. adamsreview is six Claude Code slash commands packaged as a plugin: review, codex-review, add, promote, walkthrough, and fix. I modeled it after the built-in /review…
May 2026 · github.com
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Building a multi-agent system to analyze new AI research papers from 3 distinct perspectives: - Deep learning researcher agent: extract interesting deep learning methods that are related to paper - Theoretical mathematician agent: figure out theoretical mathematical concepts that are important in this paper and additional theoretical references that will be useful in understanding it - Skeptic agent: find unjustified assumptions that lack supporting evidence For this mvp, I used low-code agent platform StackAI (YC W23) and wrote about my process:…
2024 · stack-ai.com
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2023 · seaml.es
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Hey HN, We’re two developers (co-founders) with a team of 20 who got tired of spending hours reviewing PRs, so we built Infinitcode.ai, an AI-powered code reviewer that: - *Summarizes PRs in plain English*: No more deciphering 1,000-line diff jungles - *Catches more than bugs*: Security holes, performance pitfalls, code smells, even typos (yes, we’ll flag “vurnerabilities” and vulnerabilities) - *Zero onboarding*: Works instantly—no “let me learn your codebase for weeks” nonsense. Why we’re posting: We’re in alpha and need brutal honesty. Roast our tool, mock our UI, or tell us why AI will…
2025 · infinitcode.ai
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2025 · github.com
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