Ever spend way too long hunting across scattered sources just to find "which benchmark actually fits this task"? Title: Benchmark Radar: A Living Database and Search Engine for AI Benchmarks and Evaluation URL: ❓ What is Benchmark Radar? 💡 It's a search engine that crawls 37 sources daily, arXiv, Hugging Face, GitHub and more, and pulls each benchmark's paper, dataset, repository, and score history into one place, with a CLI for offline queries. ❓ How much data are we talking about? 💡 1,283 source records from four catalogs, covering 12,916 numeric scores across 790 benchmarks. Daily collection alone logs 11,068 observations across 6,546 distinct artifacts. ❓ Why can't scores just be compared directly? 💡 Only 82 of the scored records use a verified 0-100 percentage scale, the rest use different or unverified scales, and roughly half lack a known release date, so naive comparisons would be misleading. ❓ So what's the fix? 💡 Instead of forcing comparability, it preserves source identity and citations so readers can inspect the evaluation conditions themselves. #LLMEval# #Benchmarks#