Verol
Stop AI hallucinations
Verol launched on June 15, 2026 with 98 votes, #406 of 22,647 launches that month and more than 95% of that year's launches.
What it does
Stop asking LLMs "is this true?" when they hallucinate, they will just lie again. Verol fixes this by adding an independent verification layer to ChatGPT, Claude, and Gemini. It parses answers, executes real-time web lookups, and validates sources through a dedicated backend pipeline. You get instant verdicts, confidence metrics, and clickable sources. No data tracking; history stays local. 5 free runs to test it. Plans from $4.99/mo.
Does a similar job
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- FAFactual AI Q&A – Answers based on Huberman Lab transcripts2022 · huberman.rile.yt · ▲120
This is a quick prototype I built for semantic search and factual question answering using embeddings and GPT-3. It tries to solve the LLM hallucination issue by guiding it only to answer questions from the given context instead of making things up. If you ask something not covered in an episode, it should say that it doesn't know rather than providing a plausible, but potentially incorrect response. It uses Whisper to transcribe, text-embedding-ada-002 to embed, Pinecone.io to search, and text-davinci-003 to generate the answer. More examples and explanations here:…
Open-source model and scorecard for measuring hallucinations in LLMs2023 · vectara.com · ▲65Hi 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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