AI Research / Content Detection
An arXiv paper on detecting AI-generated commercial web content from structural signals rather than word-level text alone. The abstract reports a high-performing detector, attribution results, and released code and artifacts.
The page clearly describes an AI research project with a concrete method, evaluation results, and released pipeline/code/artifacts. It is useful for people tracking AI content detection and provides enough evidence for a public listing.
The crawl only shows the abstract page, so visitors cannot see the full paper, the actual code repository, or whether the released artifacts are easy to reuse.
Last evaluated 45 days ago. Current rank #2575. Up 1 spot 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.
