
BattleDome.ai
Compare LLMs. Expose hallucinations. Find the truth.
What it does
4 AI models battle. TruthLock fact-checks every response. ThunderScore picks the winner. Don’t trust one AI, verify with four. Battledome pulls independent answers from OpenAI, Anthropic, xAI (Grok), and Google (Gemini), then scores accuracy, flags hallucinations, and shows where they agree — and where they don’t — so you can confidently choose the best answer.
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- OSOpen-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…


Truth MirrorJun 2026 · wuyijia.gumroad.com · ▲2Real-time AI hallucination detection. Don't trust, verify.
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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

