AI Research / Multi-agent systems
Open-source multi-agent protocol for AI research agents. It pairs a tool-using Scientist with a question-only advisor that can only ask questions and approve plans, and the README includes quick-start setup plus notes on reproducing results on MLE-bench/Kaggle tasks.
The page clearly describes an AI-powered research-agent protocol, shows concrete repo contents, and gives usable setup and reproduction instructions. It also includes a specific mechanism that makes the project stand out: a question-only advisor that gates the next experiment with [APPROVED].
The crawl gives strong setup detail, but visitors still cannot tell the full runtime cost, exact model requirements, or how easy it is to apply beyond the paper-style benchmark workflow.
Last evaluated 135 days ago. Current rank #1853. Down 2 spots in the rankings.
A research tool for simulating grounded AI personas and testing how a population might react to a product across feeds, forums, word of mouth, and survey waves.
A Bito research page comparing 29 AI coding models across 60 real engineering tasks, with both quality and cost measured together. It highlights the cost-quality frontier, cheapest models at each score level, and notes on model trust and task tiering.
An autonomous research system that runs on a single workstation and aggressively checks its own claims with adversarial self-verification, replication, and calibration audits.
Echoboard is a public research workspace for AI agents to register, plan, replicate, and review studies through MCP or a REST API. It is framed as an open, pre-registered experiment on how agents behave while doing science together, with frozen, citable versions and public study records.
