AI · alternatives · 2026
24 alternatives to Open-source model and scorecard for measuring hallucinations in LLMs
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…
An open-source model and evaluation tool for detecting hallucinations in large language models, particularly in retrieval augmented generation systems. It identifies instances where LLMs fabricate details not present… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Open-source model and scorecard for measuring hallucinations in LLMs launched in 2023; newer entries below may have overtaken it.
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Athina AI▲509Monitor LLMs and automatically detect hallucinations in prod
2024 · athina.ai · its alternatives →
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I spent a lot of time and money on this rather big side project of mine that attempts to replicate the mechanistic interpretability research on proprietary LLMs that was quite popular this year and produced great research papers by Anthropic [1], OpenAI [2] and Deepmind [3]. I am quite proud of this project and since I consider myself the target audience for HackerNews did I think that maybe some of you would appreciate this open research replication as well. Happy to answer any questions or face any feedback. Cheers [1]…
2024 · github.com · its alternatives →
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io · its alternatives →
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TensorZero▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…
2025 · github.com · its alternatives →
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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 · its alternatives →
- 6NM
Hey HN! Mustafa, Lizzie, and Varun here from NomadicML (https://nomadicml.com). We’re excited to show you Nomadic (https://github.com/nomadic-ml/nomadic): a platform focused on parameter search to continuously optimize AI systems. Here’s a simple demo notebook where you get the best-performing, statistically significant configurations for your RAG — and improve hallucination metrics by 4X in just 5 minutes — with a single Nomadic experiment: https://tinyurl.com/4xmaryyw Our lightweight library is now live on PyPI (`pip install nomadic`). Try one…
2024 · its alternatives →
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Sup AI▲103Hi HN. I'm Ken, a 20-year-old Stanford CS student. I built Sup AI. I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations begin. My dad Scott (AI Research Scientist at TRI) is my research partner on this. He sends me papers…
Apr 2026 · sup.ai · its alternatives →
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Dream 7B▲191Powerful Open Diffusion LLM, Beyond Autoregressive
2025 · hkunlp.github.io · its alternatives →
- 9FA
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 · its alternatives →
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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.
2023 · github.com · its alternatives →
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Everything you need to evaluate & improve prompts and LLMs
2023 · dioptra.ai · its alternatives →
- 12WM
Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…
2024 · glhf.chat · its alternatives →
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I've built an advanced RAG (Retrieval-Augmented Generation) pipeline from scratch to demystify the complex mechanics of modern LLM-powered Question Answering systems. This repository features: -- An implementation of a sub-question query engine from scratch to answer complex user questions. -- Illustrative explanations that unveil the inner workings of the system. -- An analysis of the challenges I faced while working with the system, like prompt engineering and cost estimation. -- Qualitative comparison with similar frameworks like LlamaIndex, offering a broader perspective. Key Takeaway:…
2023 · github.com · its alternatives →
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Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: https://docs.ragas.io/en/stable/, docs: https://docs.ragas.io/. Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year. Problems Ragas can solve - How do you choose the best components for your RAG, such as the retriever, reranker, and LLM? - How do you formulate a test dataset…
2024 · github.com · its alternatives →
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RagMetrics▲13We built live AI evaluation to detect hallucinations, grounding issues, and drift as outputs are generated. This helps teams move GenAI from pilots into production.
2025 · ragmetrics.ai · its alternatives →
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Hallucinations are still a major blocker for deploying reliable retrieval-augmented generation (RAG) systems, especially in complex domains like medical or legal. Most existing hallucination detectors rely on full LLM inference (expensive, slow), or struggle with long-context inputs. I built LettuceDetect — an open-source, encoder-only framework that detects hallucinated spans in LLM-generated answers based on the retrieved context. No LLMs needed, and it much more efficiently. Highlights: - Token-level hallucination detection (unsupported spans flagged based on retrieved evidence) - Built…
2025 · github.com · its alternatives →
- 17AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
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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 · its alternatives →
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Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…
2024 · github.com · its alternatives →
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Real-time Hallucination Detection for LLMs
Nov 2025 · hallucinationdetector.co · its alternatives →
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Zero-cost epistemic uncertainty quantification & hallucination detection for LLMs (90,000x faster than Semantic Entropy) - Adarshent/Spnda
Sep 2026 · github.com · its alternatives →
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We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
2024 · github.com · its alternatives →
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Synthetic data generation is an essential step in training and evaluating LLMs/Agents/RAG pipelines, but tooling around this is still lacking. We're introducing Curator, an open-source library designed to streamline the data curation process. While there are many libraries to prompt LLMs, the semantics of generating synthetic data is different from prompting. For example, we need to process a large number of prompts (sometimes in millions or more) while accepting some failures, utilize several stages of prompting, incorporate human feedback, and filter out bad data using verifiers…
2025 · github.com · its alternatives →
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AI research tool where YOU choose which publishers to trust
Mar 2026 · hallucinationbuster.com · its alternatives →
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- Nomadic – Minimize RAG Hallucinations with 1 Hyperparameter Experiment alternatives
Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →