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Cameron Jones is an AI tool developed by a Cognitive Science PhD student focusing on persuasion, deception, and social intelligence in humans and Large Language Models (LLMs). The tool analyzes LLM performance on tasks like the False Belief task and the Turing test. It also compares humans and LLMs on theory of mind evaluation. Cameron Jones provides select publications, recent media, and projects related to understanding, grounding, and reference in LLMs.

Fahim AI is a premier destination for in-depth reviews and analyses of the latest advancements in artificial intelligence and software technology. The website empowers tech enthusiasts, professionals, and casual readers with reliable information to stay ahead in the rapidly evolving digital world. It offers comprehensive evaluations of cutting-edge AI systems, detailed reviews of software solutions, insights on the latest tech gadgets integrated with AI capabilities, side-by-side product comparisons, easy-to-follow tutorials and guides, expert opinions from industry leaders, and a community platform for sharing experiences and connecting with like-minded individuals.

The website is a platform that provides reviews and recommendations for AI presentation makers. It offers in-depth guides on various AI presentation generators and helps users choose the best one for their needs. The site features detailed reviews of different AI presentation software, including their features, pros, and cons. Users can find information on popular AI tools like Plus AI, Canva, Beautiful.ai, and more. The platform also includes comparisons between different AI tools, pricing details, and evaluations of AI outputs and design components.

The website is a platform that provides comprehensive and unbiased reviews of various AI tools and applications. It aims to help users, especially small businesses, make informed decisions about selecting the right AI tools to enhance productivity and stay ahead of the competition. The site offers detailed comparisons, interviews with AI innovators, expert tips, and insights into the future of artificial intelligence. It also features blog posts on AI-related topics, free resources, and a newsletter for staying updated on the latest AI trends and tools.

1st things 1st is an online tool that helps users prioritize tasks and make decisions. It offers two prioritization tools: intuitive and smart. The intuitive tool allows users to compare options in pairs and organize them based on personal preferences. The smart tool uses AI-powered autosuggestion and fast evaluations to help users make confident and informed decisions. 1st things 1st also provides customizable templates and allows users to export their priorities to their favorite productivity apps. The tool is designed to help users clarify their goals, make complex decisions, and achieve their objectives.

Weights & Biases is an AI tool that offers documentation, guides, tutorials, and support for using AI models in applications. The platform provides two main products: W&B Weave for integrating AI models into code and W&B Models for building custom AI models. Users can access features such as tracing, output evaluation, cost estimates, hyperparameter sweeps, model registry, and more. Weights & Biases aims to simplify the process of working with AI models and improving model reproducibility.

Everypixel.com is a website that provides image analysis and enhancement services. Users can upload images to the platform for evaluation and improvement, such as identifying the quality of the image, detecting objects, and enhancing colors. The platform utilizes advanced algorithms to analyze images and provide users with valuable insights to enhance their visual content. Everypixel.com aims to help users improve the quality of their images for various purposes, such as social media, marketing, and personal projects.

MindpoolAI is a tool that allows users to access multiple leading AI models with a single query. This means that users can get the answers they are looking for, spark ideas, and fuel their work, creativity, and curiosity. MindpoolAI is easy to use and does not require any technical expertise. Users simply need to enter their prompt and select the AI models they want to compare. MindpoolAI will then send the query to the selected models and present the results in an easy-to-understand format.

usefulAI is a platform that allows users to easily add AI features to their products in minutes. Users can find AI features that best meet their needs, test them using the platform's playground, and integrate them into their products through a single API. The platform offers a user-friendly playground to test and compare AI solutions, provides pricing and metrics for evaluation, and allows integration within applications using a single API. usefulAI aims to provide practical AI engines in one place, without hype, for users to leverage in their products.

Deal Protectors is an AI-driven website designed to help users evaluate their car deals to ensure they are getting the best possible price. The platform allows users to upload their deal or input essential details for analysis by an advanced AI machine. Deal Protectors aims to provide transparency, empowerment, and community support in the car purchasing process by comparing deals with national and regional averages. Additionally, the website features a protection forum for sharing dealership experiences, a game called 'Beat The Dealer,' and testimonials from satisfied customers.

Rawbot is an AI model comparison tool that simplifies the process of selecting the best AI models for projects and applications. It allows users to compare AI models side-by-side, understand their strengths and weaknesses, and make informed decisions. Rawbot supports a wide range of AI models and helps users optimize performance, identify customization opportunities, analyze cost and efficiency, and make informed decisions for successful outcomes in research, development, and business applications.

How Attractive Am I is an AI-powered tool that analyzes facial features to calculate an attractiveness score. By evaluating symmetry and proportions, the tool provides personalized beauty scores. Users can upload a photo to discover their true beauty potential. The tool ensures accuracy by providing guidelines for taking photos and offers a fun and insightful way to understand facial appeal.

functime is an AI tool designed for time-series machine learning at scale. It provides a comprehensive set of functions and resources for users to perform forecasting tasks efficiently. With features like scoring, ranking, and plotting functions, functime simplifies the evaluation process of thousands of forecasts simultaneously. Additionally, it offers an AI copilot feature to analyze and compare trends, seasonality, and causal factors in forecasts. The tool also includes detailed API reference documentation for seamless integration.

KAYAK is a comprehensive travel search engine that allows users to search for flights, hotels, and rental cars from hundreds of travel sites. The platform offers a wide range of features to help users plan their trips efficiently and find the best travel deals. With KAYAK, users can explore flight options, track flights, access travel tips, and plan their travel itineraries. The website compares prices from various sources to provide users with a convenient way to book their travel arrangements.

Contentable.ai is a platform for comparing multiple AI models, rapidly moving from prototyping to production, and management of your custom AI solutions across multiple vendors. It allows users to test multiple AI models in seconds, compare models side-by-side across top AI providers, collaborate on AI models with their team seamlessly, design complex AI workflows without coding, and pay as they go.

Sofon is a knowledge aggregation and curation platform that provides users with personalized insights on topics they care about. It aggregates and curates knowledge shared across 1,000+ articles, podcasts, and books, delivering a personalized stream of ideas to users. Sofon uses AI to compare ideas across hundreds of people on any question, saving users thousands of hours of curation. Users can indicate the people they want to learn from, and Sofon will curate insights across all their knowledge. Users can receive an idealetter, which is a unique combination of ideas across all the people they've selected around a common theme, delivered at an interval of their choice.

LLM Clash is a web-based application that allows users to compare the outputs of different large language models (LLMs) on a given task. Users can input a prompt and select which LLMs they want to compare. The application will then display the outputs of the LLMs side-by-side, allowing users to compare their strengths and weaknesses.

Prompt Octopus is a free tool that allows you to compare multiple prompts side-by-side. You can add as many prompts as you need and view the responses in real-time. This can be helpful for fine-tuning your prompts and getting the best possible results from your AI model.

Gemini is a multi-modal AI model, developed by Google. It is designed to understand and generate human language, and can be used for a variety of tasks, including question answering, translation, and dialogue generation. ChatGPT is a large language model, developed by OpenAI. It is also designed to understand and generate human language, and can be used for a variety of tasks, including question answering, translation, and dialogue generation.

LLM Price Check is an AI tool designed to compare and calculate the latest prices for Large Language Models (LLM) APIs from leading providers such as OpenAI, Anthropic, Google, and more. Users can use the streamlined tool to optimize their AI budget efficiently by comparing pricing, sorting by various parameters, and searching for specific models. The tool provides a comprehensive overview of pricing information to help users make informed decisions when selecting an LLM API provider.

Can AI Code is a self-evaluating interview tool for AI coding models. It includes interview questions written by humans and tests taken by AI, inference scripts for common API providers and CUDA-enabled quantization runtimes, a Docker-based sandbox environment for validating untrusted Python and NodeJS code, and the ability to evaluate the impact of prompting techniques and sampling parameters on large language model (LLM) coding performance. Users can also assess LLM coding performance degradation due to quantization. The tool provides test suites for evaluating LLM coding performance, a webapp for exploring results, and comparison scripts for evaluations. It supports multiple interviewers for API and CUDA runtimes, with detailed instructions on running the tool in different environments. The repository structure includes folders for interviews, prompts, parameters, evaluation scripts, comparison scripts, and more.

SuperKnowa is a fast framework to build Enterprise RAG (Retriever Augmented Generation) Pipelines at Scale, powered by watsonx. It accelerates Enterprise Generative AI applications to get prod-ready solutions quickly on private data. The framework provides pluggable components for tackling various Generative AI use cases using Large Language Models (LLMs), allowing users to assemble building blocks to address challenges in AI-driven text generation. SuperKnowa is battle-tested from 1M to 200M private knowledge base & scaled to billions of retriever tokens.

LLM AutoEval is a tool that simplifies the process of evaluating Large Language Models (LLMs) using a convenient Colab notebook. It automates the setup and execution of evaluations using RunPod, allowing users to customize evaluation parameters and generate summaries that can be uploaded to GitHub Gist for easy sharing and reference. LLM AutoEval supports various benchmark suites, including Nous, Lighteval, and Open LLM, enabling users to compare their results with existing models and leaderboards.

The RAG Experiment Accelerator is a versatile tool that helps you conduct experiments and evaluations using Azure AI Search and RAG pattern. It offers a rich set of features, including experiment setup, integration with Azure AI Search, Azure Machine Learning, MLFlow, and Azure OpenAI, multiple document chunking strategies, query generation, multiple search types, sub-querying, re-ranking, metrics and evaluation, report generation, and multi-lingual support. The tool is designed to make it easier and faster to run experiments and evaluations of search queries and quality of response from OpenAI, and is useful for researchers, data scientists, and developers who want to test the performance of different search and OpenAI related hyperparameters, compare the effectiveness of various search strategies, fine-tune and optimize parameters, find the best combination of hyperparameters, and generate detailed reports and visualizations from experiment results.

This repository contains scripts and tools for evaluating a chat app that uses the RAG architecture. It provides parameters to assess the quality and style of answers generated by the chat app, including system prompt, search parameters, and GPT model parameters. The tools facilitate running evaluations, with examples of evaluations on a sample chat app. The repo also offers guidance on cost estimation, setting up the project, deploying a GPT-4 model, generating ground truth data, running evaluations, and measuring the app's ability to say 'I don't know'. Users can customize evaluations, view results, and compare runs using provided tools.

Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

ChainForge is a visual programming environment for battle-testing prompts to LLMs. It is geared towards early-stage, quick-and-dirty exploration of prompts, chat responses, and response quality that goes beyond ad-hoc chatting with individual LLMs. With ChainForge, you can: * Query multiple LLMs at once to test prompt ideas and variations quickly and effectively. * Compare response quality across prompt permutations, across models, and across model settings to choose the best prompt and model for your use case. * Setup evaluation metrics (scoring function) and immediately visualize results across prompts, prompt parameters, models, and model settings. * Hold multiple conversations at once across template parameters and chat models. Template not just prompts, but follow-up chat messages, and inspect and evaluate outputs at each turn of a chat conversation. ChainForge comes with a number of example evaluation flows to give you a sense of what's possible, including 188 example flows generated from benchmarks in OpenAI evals. This is an open beta of Chainforge. We support model providers OpenAI, HuggingFace, Anthropic, Google PaLM2, Azure OpenAI endpoints, and Dalai-hosted models Alpaca and Llama. You can change the exact model and individual model settings. Visualization nodes support numeric and boolean evaluation metrics. ChainForge is built on ReactFlow and Flask.

Raga LLM Hub is a comprehensive evaluation toolkit for Language and Learning Models (LLMs) with over 100 meticulously designed metrics. It allows developers and organizations to evaluate and compare LLMs effectively, establishing guardrails for LLMs and Retrieval Augmented Generation (RAG) applications. The platform assesses aspects like Relevance & Understanding, Content Quality, Hallucination, Safety & Bias, Context Relevance, Guardrails, and Vulnerability scanning, along with Metric-Based Tests for quantitative analysis. It helps teams identify and fix issues throughout the LLM lifecycle, revolutionizing reliability and trustworthiness.

The Korean SAT LLM Leaderboard is a benchmarking project that allows users to test their fine-tuned Korean language models on a 10-year dataset of the Korean College Scholastic Ability Test (CSAT). The project provides a platform to compare human academic ability with the performance of large language models (LLMs) on various question types to assess reading comprehension, critical thinking, and sentence interpretation skills. It aims to share benchmark data, utilize a reliable evaluation dataset curated by the Korea Institute for Curriculum and Evaluation, provide annual updates to prevent data leakage, and promote open-source LLM advancement for achieving top-tier performance on the Korean CSAT.

Promptfoo is a tool for testing and evaluating LLM output quality. With promptfoo, you can build reliable prompts, models, and RAGs with benchmarks specific to your use-case, speed up evaluations with caching, concurrency, and live reloading, score outputs automatically by defining metrics, use as a CLI, library, or in CI/CD, and use OpenAI, Anthropic, Azure, Google, HuggingFace, open-source models like Llama, or integrate custom API providers for any LLM API.

UpTrain is an open-source unified platform to evaluate and improve Generative AI applications. We provide grades for 20+ preconfigured evaluations (covering language, code, embedding use cases), perform root cause analysis on failure cases and give insights on how to resolve them.

Q-Bench is a benchmark for general-purpose foundation models on low-level vision, focusing on multi-modality LLMs performance. It includes three realms for low-level vision: perception, description, and assessment. The benchmark datasets LLVisionQA and LLDescribe are collected for perception and description tasks, with open submission-based evaluation. An abstract evaluation code is provided for assessment using public datasets. The tool can be used with the datasets API for single images and image pairs, allowing for automatic download and usage. Various tasks and evaluations are available for testing MLLMs on low-level vision tasks.

DashInfer is a C++ runtime tool designed to deliver production-level implementations highly optimized for various hardware architectures, including x86 and ARMv9. It supports Continuous Batching and NUMA-Aware capabilities for CPU, and can fully utilize modern server-grade CPUs to host large language models (LLMs) up to 14B in size. With lightweight architecture, high precision, support for mainstream open-source LLMs, post-training quantization, optimized computation kernels, NUMA-aware design, and multi-language API interfaces, DashInfer provides a versatile solution for efficient inference tasks. It supports x86 CPUs with AVX2 instruction set and ARMv9 CPUs with SVE instruction set, along with various data types like FP32, BF16, and InstantQuant. DashInfer also offers single-NUMA and multi-NUMA architectures for model inference, with detailed performance tests and inference accuracy evaluations available. The tool is supported on mainstream Linux server operating systems and provides documentation and examples for easy integration and usage.

Evidently is an open-source Python library designed for evaluating, testing, and monitoring machine learning (ML) and large language model (LLM) powered systems. It offers a wide range of functionalities, including working with tabular, text data, and embeddings, supporting predictive and generative systems, providing over 100 built-in metrics for data drift detection and LLM evaluation, allowing for custom metrics and tests, enabling both offline evaluations and live monitoring, and offering an open architecture for easy data export and integration with existing tools. Users can utilize Evidently for one-off evaluations using Reports or Test Suites in Python, or opt for real-time monitoring through the Dashboard service.

LLM price compass is an open-source tool for comparing inference costs on different GPUs across various cloud providers. It collects benchmark data to help users select the right GPU, cloud, and provider for their models. The project aims to provide insights into fixed per token costs from different providers, aiding in decision-making for model deployment.

Eval-Scope is a framework for evaluating and improving large language models (LLMs). It provides a set of commonly used test datasets, metrics, and a unified model interface for generating and evaluating LLM responses. Eval-Scope also includes an automatic evaluator that can score objective questions and use expert models to evaluate complex tasks. Additionally, it offers a visual report generator, an arena mode for comparing multiple models, and a variety of other features to support LLM evaluation and development.

OpenLIT is an OpenTelemetry-native GenAI and LLM Application Observability tool. It's designed to make the integration process of observability into GenAI projects as easy as pie – literally, with just **a single line of code**. Whether you're working with popular LLM Libraries such as OpenAI and HuggingFace or leveraging vector databases like ChromaDB, OpenLIT ensures your applications are monitored seamlessly, providing critical insights to improve performance and reliability.

Athina is an open-source library designed to help engineers improve the reliability and performance of Large Language Models (LLMs) through eval-driven development. It offers plug-and-play preset evals for catching and preventing bad outputs, measuring model performance, running experiments, A/B testing models, detecting regressions, and monitoring production data. Athina provides a solution to the flaws in current LLM developer workflows by offering rapid experimentation, customizable evaluators, integrated dashboard, consistent metrics, historical record tracking, and easy setup. It includes preset evaluators for RAG applications and summarization accuracy, as well as the ability to write custom evals. Athina's evals can run on both development and production environments, providing consistent metrics and removing the need for manual infrastructure setup.

Eval-Scope is a framework designed to support the evaluation of large language models (LLMs) by providing pre-configured benchmark datasets, common evaluation metrics, model integration, automatic evaluation for objective questions, complex task evaluation using expert models, reports generation, visualization tools, and model inference performance evaluation. It is lightweight, easy to customize, supports new dataset integration, model hosting on ModelScope, deployment of locally hosted models, and rich evaluation metrics. Eval-Scope also supports various evaluation modes like single mode, pairwise-baseline mode, and pairwise (all) mode, making it suitable for assessing and improving LLMs.

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