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Products that do what dreamGPT: What if LLM hallucinations were a feature and not a bug? does
The first GPT-based solution that uses hallucinations from LLMs for divergent thinking to generate new and novel ideas. Hallucinations are often seen as a negative thing, but what if they could be used for our advantage? dreamGPT is here to show you how. The goal of dreamGPT is to explore as many possibilities as possible, as opposed to most other GPT-based solutions which are focused on solving specific problems.
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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…
2023 · vectara.com
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2023 · sagittarius.greg.technology
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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:…
2022 · huberman.rile.yt
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Turn PDFs into courses with AI without irrelevant additions
2025
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2023 · github.com
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I forked a PyTorch DeepDream implementation and added video support with temporal consistency. It produces smooth DeepDream videos with minimal flickering, and is highly flexible including many parameters and supports multiple pretrained image classifiers including GoogLeNet. Check out the repo for sample videos! Features: - Optical flow warps previous hallucinations into the current frame - Occlusion masking prevents ghosting and hallucination transfer when objects move - Advanced parameters (layers, octaves, iterations) still work - Works on GPU, CPU, and Apple Silicon
Jan 2026 · github.com
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This is another one of my automate-my-life projects - I'm constantly asking the same question to different AIs since there's always the hope of getting a better answer somewhere else. Maybe ChatGPT's answer is too short, so I ask Perplexity. But I realize that's hallucinated, so I try Gemini. That answer sounds right, but I cross-reference with Claude just to make sure. This doesn't really apply to math/coding (where o1 or Gemini can probably one-shot an excellent response), but more to online search, where information is more fluid and there's no "right" search engine + text…
2024 · ithy.com
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2023 · github.com
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Hi HN, We've been frustrated with how confidently LLMs hallucinate—a dangerous flaw in high-stakes domains like health and medicine. The standard "I am not an expert" disclaimer feels insufficient since we all ignore those statements. Our approach is a RAG/agentic system built to solve this. It runs on ~40M+ scientific papers, but goes beyond simple retrieval. A multi-agent workflow decomposes queries, cross-references claims against multiple sources, and synthesizes answers, ensuring every key statement is cited directly from the literature. Beyond the literature, our agent system has…
2025 · my-openhealth.com
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Hello hackernews, I am looking for feedback for Labophase.com which is a app that focuses on returning results from multiple ai models at the same time. I currently support: GPT4-Turbo, Claude-2, Google PaLM2, Llama2, Mistral, and OpenOrca. Working on supporting Gemini soonTM. I built it to solve a couple of personal pains I experienced. After reaching out to a couple of users in r/localllama, seems that people would have similar approach to address hallucination, availability, and comparing ai models. Initial feedback came in with some surprises that I'm hoping to get feedback from the…
2023 · labophase.com
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We built tooling that connects LLMs directly to case law databases with citation verification to address hallucination in legal AI. Think of it as giving the model access to actual legal sources instead of relying on training data.
Feb 2026 · openjuris.org
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Hey HN! We’ve been experimenting with integrating multimodal models directly into creative workflows, and ended up building an AI-first image editor using OpenAI’s new `gpt-image-1` (from GPT-4o) inside our SDK. Instead of prompting in ChatGPT and pasting outputs into a design tool, this lets you generate, edit, and remix images all in one canvas. This allows for really interesting new workflows, like quickly mixing multiple images, or creating visual prompts by using annotations and reference on the canvas. Some key details: - Built with our plugin system in CE.SDK (CreativeEditor SDK) -…
2025 · img.ly
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I've built an app that extracts interpretable 'circuits' from models using the GPT-2 architecture. These circuits reveal how specific inputs influence the probabilities of the next token in a sequence. While some tutorials present theoretical examples of how feedforward layers and attention heads may produce predictions, this app provides concrete examples of how information flows through an LLM. You can see, for example, the formation of features that search for simple grammatical patterns and trace their construction back to the use of more primitive features. Feel free to reach out with…
2024 · peterlai.github.io
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