HallucinationBuster
AI research tool where YOU choose which publishers to trust
What it does
ChatGPT fabricates 20-56% of academic citations. HallucinationBuster takes a different approach: it searches real academic databases, lets you pick which publishers and years to include, then processes ONLY your approved papers. AI-powered topic clustering, relevance scoring, and RAG chat, all grounded in verified sources with clickable DOIs. You control the sources. The AI never sees a paper you didn't approve. Free tier available, no credit card needed.
Does the same job
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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…
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- IMI'm a teacher and built an AI presentation tool2025 · ▲260
Hi, I'm a high school teacher from Australia and I've built what I'd like to think is a pretty nifty ChatGPT powered presentation tool for teachers. I'd love it if you could have a look at it and give me some of your feedback. I don't think there's much overlap with the HN crowd and school teachers, but I've been coming here for many years and thought I'd post here and see what you all think. Check it out if you have a minute and I'd be super happy to hear your feedback too. https://www.slidehero.ai/ You can jump in and have a play with the tool all you like ;) Cheers, Eli
- 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:…
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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…
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