AI · alternatives · 2026
24 alternatives to Open-source study to measure end user satisfaction levels with LLMs
The LLM challenge - an online study - aims to answer a simple question: what is the quality corridor that matters to end users when interacting with LLMs? At what point do users stop seeing a quality difference and at what point do users get frustrated by poor LLM quality. The project is an Apache 2.0 licensed open source project available on Github: https://github.com/open-llm-initiative/llm-challenge. And the challenge is hosted on AWS as a single-page web app, where users see greeting text, followed by a randomly selected prompt and a LLM response, which they must rate…
The LLM Challenge is an open-source online study that measures end-user satisfaction with large language models across real-world tasks. Participants rate randomly selected LLM responses on a 1-5 scale across use cases… 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 study to measure end user satisfaction levels with LLMs launched in 2024; newer entries below may have overtaken it.
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Measuring the quality corridor that matters to end users
2024 · its alternatives →
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LLM Stats▲293I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Oct 2025 · llm-stats.com · its alternatives →
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Hey HN, I made Browser-Use, an open-source tool that lets (all Langchain supported) LLMs execute tasks directly in the browser just with function calling. It allows you to build agents that interact with web elements using natural language prompts. We created a layer that simplifies website interaction for LLMs by extracting xPaths and interactive elements like buttons and input fields (and other fancy things). This enables you to design custom web automation and scraping functions without manual inspection through DevTools. Hasn't this been done a lot of times? Good question, as a general…
2024 · github.com · its alternatives →
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I spent a few hours last weekend testing whether AI can replace code by executing directly. Built a contact manager where every HTTP request goes to an LLM with three tools: database (SQLite), webResponse (HTML/JSON/JS), and updateMemory (feedback). No routes, no controllers, no business logic. The AI designs schemas on first request, generates UIs from paths alone, and evolves based on natural language feedback. It works—forms submit, data persists, APIs return JSON—but it's catastrophically slow (30-60s per request), absurdly expensive ($0.05/request), and has zero UI…
Nov 2025 · github.com · its alternatives →
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LLM SEO Report▲302Check your brand's visibility on ChatGPT and Google Gemini
2025 · llmseoreport.com · its alternatives →
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Hey HN, we've just finished building a dynamic router for LLMs, which takes each prompt and sends it to the most appropriate model and provider. We'd love to know what you think! Here is a quick(ish) screen-recroding explaining how it works: https://youtu.be/ZpY6SIkBosE Best results when training a custom router on your own prompt data: https://youtu.be/9JYqNbIEac0 The router balances user preferences for quality, speed and cost. The end result is higher quality and faster LLM responses at lower cost. The quality for each candidate LLM is predicted ahead of time…
2024 · unify.ai · its alternatives →
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Open source Google Reviews analyzer project using LLM
2023 · github.com · its alternatives →
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Twigg▲157Hi HN, we’re Jamie and Matti, co-founders of Twigg. During our master’s we continually found the same pain points cropping up when using LLMs. The linear nature of typical LLMs interfaces - like ChatGPT and Claude - made it really easy to get lost without any easy way to visualise or navigate your project. Worst of all, none of them are well suited for long term projects. We found ourselves spending days using the same chat, only for it to eventually break. Transferring context from one chat to another is also cumbersome. We decided to build something more intuitive to the ways humans think.…
2025 · twigg.ai · its alternatives →
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Compare LLMs on your data, measure, and pick the best.
Apr 2026 · trismik.com · its alternatives →
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Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…
2025 · theophilecantelob.re · its alternatives →
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Humans vs LLMs▲138Hi HN, We just launched [AI Page Ready](https://aipageready.com) — a tool to check if your web pages are discoverable and understandable by AI tools like ChatGPT, Claude, Gemini, and Perplexity. It runs 40+ checks across six areas: Discoverability & Crawlability, Structured Data & Semantics, Content Quality, Answer-Ready Formatting, Accessibility & Rendering, and Deep AI LLM-specific checks. Would love your feedback — especially if you work in eCommerce, SEO, content, SaaS, or are trying to get traffic from AI tools. (Also open to ideas on what else to add to the tool!)
2025 · aipageready.com · its alternatives →
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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 · its alternatives →
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Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…
2023 · github.com · its alternatives →
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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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The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
2023 · github.com · its alternatives →
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A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →
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Find the Best LLM for Your Needs through E2E Testing - Yuyz0112/relia
2024 · github.com · its alternatives →
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Hello HN! Pierre and Paul here. We are building an open source text analytics tool for user inputs and LLM app outputs The repo is https://github.com/phospho-app/phospho and landing is https://phospho.ai Most people building with LLMs today don’t have quantified evaluation and usage metrics on the interactions between users and their product. The only solution is to read every message (or a sample) to get a sense of what is going on. You can't improve your product without understanding who your users are and how they are using it. Nobody would launch a website…
2024 · github.com · its alternatives →
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Shopping online can be pretty tedious if you care about the quality of what you are buying. The time it takes can scale pretty proportionally to some combination of the value, relative importance, and complexity of the purchase. I think LLMs can help expedite and personalize the online shopping experience to a degree marketplaces just don't currently provide. I don't think anybody really wants to read the product descriptions of a bunch of products, read reviews, do pairwise product comparisons, and find the best deals across different websites? Or am I wrong? That's why I think this…
2024 · claros.so · its alternatives →
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Spelltest framework simulates conversations between AI ‘synthetic users' in an environment to test and refine LLM-based applications. It ensures your app converse with utmost accuracy and relevance. Post-chat, Spelltest assesses responses, providing qualitative and quantitative feedback on performance. Suitable for both chat and completion modes. When to use: - After modifying your prompt. - When your LLM provider updates. - As a CI step for you repo. All feedback and collaborations appreciated!
2023 · github.com · its alternatives →
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Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…
2024 · github.com · its alternatives →
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Test and compare results from Large Language Models (LLMs) simultaneously, including Anthropic's Claude 1.2, OpenAI's GPT-4, GPT-3.5 turbo, and Cohere's CommandXL Nightly. Analyze the results side-by-side in a single UI on top of LLM APIs.
2023 · retool.com · its alternatives →
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Hey HN! I built Experiment to solve a common frustration in LLM development: the lack of proper tools for prompt engineering experimentation. Here's what makes it different: Key Features: - Load and edit chat completion logs from CSV files - Fork and modify specific conversation entries - Run inference via Anthropic, Mistral, and OpenAI - Define custom tools using JSONSchema format - Visual tool usage analysis with collapsible, sorted key-value pairs - Full mobile support and available as installable PWA Technical Highlights: - Built with React using custom isomorphic architecture -…
2025 · github.com · its alternatives →
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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 →