Best AI tools for< Benchmark Product Performance >

Particl is an AI-powered platform that automates competitor intelligence for modern retail businesses. It provides real-time sales, pricing, and sentiment data across various e-commerce channels. Particl's AI technology tracks sales, inventory, pricing, assortment, and sentiment to help users quickly identify profitable opportunities in the market. The platform offers features such as benchmarking performance, automated e-commerce intelligence, competitor research, product research, assortment analysis, and promotions monitoring. With easy-to-use tools and robust AI capabilities, Particl aims to elevate team workflows and capabilities in strategic planning, product launches, and market analysis.

Woven Insights is an AI-driven Fashion Retail Market & Consumer Insights solution that empowers fashion businesses with data-driven decision-making capabilities. It provides competitive intelligence, performance monitoring analytics, product assortment optimization, market insights, consumer insights, and pricing strategies to help businesses succeed in the retail market. With features like insights-driven competitive benchmarking, real-time market insights, product performance tracking, in-depth market analytics, and sentiment analysis, Woven Insights offers a comprehensive solution for businesses of all sizes. The application also offers bespoke data analysis, AI insights, natural language query, and easy collaboration tools to enhance decision-making processes. Woven Insights aims to democratize fashion intelligence by providing affordable pricing and accessible insights to help businesses stay ahead of the competition.

UserTesting is a Human Insight Platform that allows organizations to quickly gain a first-person understanding of customer experiences, enabling them to build greater customer empathy. The platform offers comprehensive testing capabilities, insights identification, performance measurement, and insights sharing across organizations. UserTesting empowers users to run tests for free, see what customers experience, and turn feedback into better designs efficiently. With features like AI Insights Hub, integrations, mobile testing, and templates, UserTesting helps users target diverse audiences, validate findings confidently, measure and benchmark performance, and boost consumer trust. Trusted by leading brands, UserTesting provides human insights that drive innovation, improve customer experiences, and enhance product development.

Reflection 70B is a next-gen open-source LLM powered by Llama 70B, offering groundbreaking self-correction capabilities that outsmart GPT-4. It provides advanced AI-powered conversations, assists with various tasks, and excels in accuracy and reliability. Users can engage in human-like conversations, receive assistance in research, coding, creative writing, and problem-solving, all while benefiting from its innovative self-correction mechanism. Reflection 70B sets new standards in AI performance and is designed to enhance productivity and decision-making across multiple domains.

Lunary is an AI developer platform designed to bring AI applications to production. It offers a comprehensive set of tools to manage, improve, and protect LLM apps. With features like Logs, Metrics, Prompts, Evaluations, and Threads, Lunary empowers users to monitor and optimize their AI agents effectively. The platform supports tasks such as tracing errors, labeling data for fine-tuning, optimizing costs, running benchmarks, and testing open-source models. Lunary also facilitates collaboration with non-technical teammates through features like A/B testing, versioning, and clean source-code management.

SeeMe Index is an AI tool for inclusive marketing decisions. It helps brands and consumers by measuring brands' consumer-facing inclusivity efforts across public advertisements, product lineup, and DEI commitments. The tool utilizes responsible AI to score brands, develop industry benchmarks, and provide consulting to improve inclusivity. SeeMe Index awards the highest-scoring brands with an 'Inclusive Certification', offering consumers an unbiased way to identify inclusive brands.

SaaSlidator is an AI-powered application designed to help users validate their project ideas efficiently and effectively. By providing a project name and description, SaaSlidator offers valuable insights to support decision-making on whether to proceed with building and launching a minimum viable product (MVP). The platform leverages AI algorithms to analyze data, offer market demand insights, competition analysis, and assess the feasibility of project ideas. With features like rapid validation, monetization suggestions, and benchmarking data, SaaSlidator aims to streamline the idea validation process and empower users to make informed decisions for successful project development.

Embedl is an AI tool that specializes in developing advanced solutions for efficient AI deployment in embedded systems. With a focus on deep learning optimization, Embedl offers a cost-effective solution that reduces energy consumption and accelerates product development cycles. The platform caters to industries such as automotive, aerospace, and IoT, providing cutting-edge AI products that drive innovation and competitive advantage.

Janus Pro AI is an advanced unified multimodal AI model that combines image understanding and generation capabilities. It incorporates optimized training strategies, expanded training data, and larger model scaling to achieve significant advancements in both multimodal understanding and text-to-image generation tasks. Janus Pro features a decoupled visual encoding system, outperforming leading models like DALL-E 3 and Stable Diffusion in benchmark tests. It offers open-source compatibility, vision processing specifications, cost-effective scalability, and an optimized training framework.

Optimove is a Customer-Led Marketing Platform that leverages a real-time Customer Data Platform (CDP) to orchestrate personalized multichannel campaigns optimized by AI. It enables businesses to deliver personalized experiences in real-time across various channels such as web, app, and marketing channels. With a focus on customer-led marketing, Optimove helps brands improve customer KPIs through data-driven campaigns and top-tier personalization. The platform offers a range of resources, including industry benchmarks, marketing guides, success stories, and best practices, to help users achieve marketing mastery.

Junbi.ai is an AI-powered insights platform designed for YouTube advertisers. It offers AI-powered creative insights for YouTube ads, allowing users to benchmark their ads, predict performance, and test quickly and easily with fully AI-powered technology. The platform also includes expoze.io API for attention prediction on images or videos, with scientifically valid results and developer-friendly features for easy integration into software applications.

HelloData is an AI-powered multifamily market analysis platform that automates market surveys, unit-level rent analysis, concessions monitoring, and development feasibility reports. It provides financial analysis tools to underwrite multifamily deals quickly and accurately. With custom query builders and Proptech APIs, users can analyze and download market data in bulk. HelloData is used by over 15,000 multifamily professionals to save time on market research and deal analysis, offering real-time property data and insights for operators, developers, investors, brokers, and Proptech companies.

ARC Prize is a platform hosting a $1,000,000+ public competition aimed at beating and open-sourcing a solution to the ARC-AGI benchmark. The platform is dedicated to advancing open artificial general intelligence (AGI) for the public benefit. It provides a formal benchmark, ARC-AGI, created by François Chollet, to measure progress towards AGI by testing the ability to efficiently acquire new skills and solve open-ended problems. ARC Prize encourages participants to try solving test puzzles to identify patterns and improve their AGI skills.

Report Card AI is an AI Writing Assistant that helps users generate high-quality, unique, and personalized report card comments. It allows users to create a quality benchmark by writing their first draft of comments with the assistance of AI technology. The tool is designed to streamline the report card writing process for teachers, ensuring error-free and eloquently written comments that meet specific character count requirements. With features like 'rephrase', 'Max Character Count', and easy exporting options, Report Card AI aims to enhance efficiency and accuracy in creating report card comments.

Perspect is an AI-powered platform designed for high-performance software teams. It offers real-time insights into team contributions and impact, optimizing developer experience, and rewarding high-performers. With 50+ integrations, Perspect enables visualization of impact, benchmarking performance, and uses machine learning models to identify and eliminate blockers. The platform is deeply integrated with web3 wallets and offers built-in reward mechanisms. Managers can align resources around crucial KPIs, identify top talent, and prevent burnout. Perspect aims to enhance team productivity and employee retention through AI and ML technologies.

Gorilla is an AI tool that integrates a large language model (LLM) with massive APIs to enable users to interact with a wide range of services. It offers features such as training the model to support parallel functions, benchmarking LLMs on function-calling capabilities, and providing a runtime for executing LLM-generated actions like code and API calls. Gorilla is open-source and focuses on enhancing interaction between apps and services with human-out-of-loop functionality.

Trend Hunter is an AI-powered platform that offers a wide range of services to accelerate innovation and provide insights into trends and opportunities. With a vast database of ideas and innovations, Trend Hunter helps individuals and organizations stay ahead of the curve by offering trend reports, newsletters, training programs, and custom services. The platform also provides personalized assessments to enhance innovation potential and offers resources such as books, keynotes, and online courses to foster creativity and strategic thinking.

JaanchAI is an AI-powered tool that provides valuable insights for e-commerce businesses. It utilizes artificial intelligence algorithms to analyze data and trends in the e-commerce industry, helping businesses make informed decisions to optimize their operations and increase sales. With JaanchAI, users can gain a competitive edge by leveraging advanced analytics and predictive modeling techniques tailored for the e-commerce sector.

The Deepfake Detection Challenge Dataset is a project initiated by Facebook AI to accelerate the development of new ways to detect deepfake videos. The dataset consists of over 100,000 videos and was created in collaboration with industry leaders and academic experts. It includes two versions: a preview dataset with 5k videos and a full dataset with 124k videos, each featuring facial modification algorithms. The dataset was used in a Kaggle competition to create better models for detecting manipulated media. The top-performing models achieved high accuracy on the public dataset but faced challenges when tested against the black box dataset, highlighting the importance of generalization in deepfake detection. The project aims to encourage the research community to continue advancing in detecting harmful manipulated media.

Clarity AI is an AI-powered technology platform that offers a Sustainability Tech Kit for sustainable investing, shopping, reporting, and benchmarking. The platform provides built-in sustainability technology with customizable solutions for various needs related to data, methodologies, and tools. It seamlessly integrates into workflows, offering scalable and flexible end-to-end SaaS tools to address sustainability use cases. Clarity AI leverages powerful AI and machine learning to analyze vast amounts of data points, ensuring reliable and transparent data coverage. The platform is designed to empower users to assess, analyze, and report on sustainability aspects efficiently and confidently.

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.

TrustLLM is a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. The document explains how to use the trustllm python package to help you assess the performance of your LLM in trustworthiness more quickly. For more details about TrustLLM, please refer to project website.

Langfuse is a powerful tool that helps you develop, monitor, and test your LLM applications. With Langfuse, you can: * **Develop:** Instrument your app and start ingesting traces to Langfuse, inspect and debug complex logs, and manage, version, and deploy prompts from within Langfuse. * **Monitor:** Track metrics (cost, latency, quality) and gain insights from dashboards & data exports, collect and calculate scores for your LLM completions, run model-based evaluations, collect user feedback, and manually score observations in Langfuse. * **Test:** Track and test app behaviour before deploying a new version, test expected in and output pairs and benchmark performance before deploying, and track versions and releases in your application. Langfuse is easy to get started with and offers a generous free tier. You can sign up for Langfuse Cloud or deploy Langfuse locally or on your own infrastructure. Langfuse also offers a variety of integrations to make it easy to connect to your LLM applications.

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

MNN is a highly efficient and lightweight deep learning framework that supports inference and training of deep learning models. It has industry-leading performance for on-device inference and training. MNN has been integrated into various Alibaba Inc. apps and is used in scenarios like live broadcast, short video capture, search recommendation, and product searching by image. It is also utilized on embedded devices such as IoT. MNN-LLM and MNN-Diffusion are specific runtime solutions developed based on the MNN engine for deploying language models and diffusion models locally on different platforms. The framework is optimized for devices, supports various neural networks, and offers high performance with optimized assembly code and GPU support. MNN is versatile, easy to use, and supports hybrid computing on multiple devices.

SageAttention is an official implementation of an accurate 8-bit attention mechanism for plug-and-play inference acceleration. It is optimized for RTX4090 and RTX3090 GPUs, providing performance improvements for specific GPU architectures. The tool offers a technique called 'smooth_k' to ensure accuracy in processing FP16/BF16 data. Users can easily replace 'scaled_dot_product_attention' with SageAttention for faster video processing.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

Generative Fusion Decoding (GFD) is a novel shallow fusion framework that integrates Large Language Models (LLMs) into multi-modal text recognition systems such as automatic speech recognition (ASR) and optical character recognition (OCR). GFD operates across mismatched token spaces of different models by mapping text token space to byte token space, enabling seamless fusion during the decoding process. It simplifies the complexity of aligning different model sample spaces, allows LLMs to correct errors in tandem with the recognition model, increases robustness in long-form speech recognition, and enables fusing recognition models deficient in Chinese text recognition with LLMs extensively trained on Chinese. GFD significantly improves performance in ASR and OCR tasks, offering a unified solution for leveraging existing pre-trained models through step-by-step fusion.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

The Aleph Alpha Intelligence Layer️ offers a comprehensive suite of development tools for crafting solutions that harness the capabilities of large language models (LLMs). With a unified framework for LLM-based workflows, it facilitates seamless AI product development, from prototyping and prompt experimentation to result evaluation and deployment. The Intelligence Layer SDK provides features such as Composability, Evaluability, and Traceability, along with examples to get started. It supports local installation using poetry, integration with Docker, and access to LLM endpoints for tutorials and tasks like Summarization, Question Answering, Classification, Evaluation, and Parameter Optimization. The tool also offers pre-configured tasks for tasks like Classify, QA, Search, and Summarize, serving as a foundation for custom development.

The main goal of llama.cpp is to enable LLM inference with minimal setup and state-of-the-art performance on a wide range of hardware - locally and in the cloud. It provides a Plain C/C++ implementation without any dependencies, optimized for Apple silicon via ARM NEON, Accelerate and Metal frameworks, and supports various architectures like AVX, AVX2, AVX512, and AMX. It offers integer quantization for faster inference, custom CUDA kernels for NVIDIA GPUs, Vulkan and SYCL backend support, and CPU+GPU hybrid inference. llama.cpp is the main playground for developing new features for the ggml library, supporting various models and providing tools and infrastructure for LLM deployment.

Nitro is a high-efficiency C++ inference engine for edge computing, powering Jan. It is lightweight and embeddable, ideal for product integration. The binary of nitro after zipped is only ~3mb in size with none to minimal dependencies (if you use a GPU need CUDA for example) make it desirable for any edge/server deployment.

This repository provides advanced AI explainability for PyTorch, offering state-of-the-art methods for Explainable AI in computer vision. It includes a comprehensive collection of Pixel Attribution methods for various tasks like Classification, Object Detection, Semantic Segmentation, and more. The package supports high performance with full batch image support and includes metrics for evaluating and tuning explanations. Users can visualize and interpret model predictions, making it suitable for both production and model development scenarios.

Holloway's Upscaler is a consolidation of various compiled open-source AI image/video upscaling products for a CLI-friendly image and video upscaling program. It provides low-cost AI upscaling software that can run locally on a laptop, programmable for albums and videos, reliable for large video files, and works without GUI overheads. The repository supports hardware testing on various systems and provides important notes on GPU compatibility, video types, and image decoding bugs. Dependencies include ffmpeg and ffprobe for video processing. The user manual covers installation, setup pathing, calling for help, upscaling images and videos, and contributing back to the project. Benchmarks are provided for performance evaluation on different hardware setups.

The LLM Prompt Tuning Playbook is a comprehensive guide for improving the performance of post-trained Language Models (LLMs) through effective prompting strategies. It covers topics such as pre-training vs. post-training, considerations for prompting, a rudimentary style guide for prompts, and a procedure for iterating on new system instructions. The playbook emphasizes the importance of clear, concise, and explicit instructions to guide LLMs in generating desired outputs. It also highlights the iterative nature of prompt development and the need for systematic evaluation of model responses.

Auto-Deep-Research is an open-source and cost-efficient alternative to OpenAI's Deep Research, based on the AutoAgent framework. It offers high performance, universal LLM support, flexible interaction, cost-efficiency, file support, and one-click launch. Users can seamlessly integrate with various LLMs, handle file uploads, and start instantly with a simple command. The tool aims to provide a fully-automated and personalized AI assistant at a lower cost, catering to community needs and showcasing the potential of AutoAgent for practical AI applications.

KanTV is an open-source project that focuses on studying and practicing state-of-the-art AI technology in real applications and scenarios, such as online TV playback, transcription, translation, and video/audio recording. It is derived from the original ijkplayer project and includes many enhancements and new features, including: * Watching online TV and local media using a customized FFmpeg 6.1. * Recording online TV to automatically generate videos. * Studying ASR (Automatic Speech Recognition) using whisper.cpp. * Studying LLM (Large Language Model) using llama.cpp. * Studying SD (Text to Image by Stable Diffusion) using stablediffusion.cpp. * Generating real-time English subtitles for English online TV using whisper.cpp. * Running/experiencing LLM on Xiaomi 14 using llama.cpp. * Setting up a customized playlist and using the software to watch the content for R&D activity. * Refactoring the UI to be closer to a real commercial Android application (currently only supports English). Some goals of this project are: * To provide a well-maintained "workbench" for ASR researchers interested in practicing state-of-the-art AI technology in real scenarios on mobile devices (currently focusing on Android). * To provide a well-maintained "workbench" for LLM researchers interested in practicing state-of-the-art AI technology in real scenarios on mobile devices (currently focusing on Android). * To create an Android "turn-key project" for AI experts/researchers (who may not be familiar with regular Android software development) to focus on device-side AI R&D activity, where part of the AI R&D activity (algorithm improvement, model training, model generation, algorithm validation, model validation, performance benchmark, etc.) can be done very easily using Android Studio IDE and a powerful Android phone.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Awesome LLM for APR is a repository dedicated to exploring the capabilities of Large Language Models (LLMs) in Automated Program Repair (APR). It provides a comprehensive collection of research papers, tools, and resources related to using LLMs for various scenarios such as repairing semantic bugs, security vulnerabilities, syntax errors, programming problems, static warnings, self-debugging, type errors, web UI tests, smart contracts, hardware bugs, performance bugs, API misuses, crash bugs, test case repairs, formal proofs, GitHub issues, code reviews, motion planners, human studies, and patch correctness assessments. The repository serves as a valuable reference for researchers and practitioners interested in leveraging LLMs for automated program repair.

A strategic SaaS analyst for CXOs, with a focus on market trends and benchmarks.

Free to use oracle for analyzing Net Promoter Score data.

Analyzes contracts vs benchmarks, grades alignment

Benchmark HVAC GPT model with unmatched expertise and forward-thinking solutions, powered by OpenAI

ESG performance assessment and reporting guide.

Compensation Advisor for HR and managers (v0.4)

Quizzes users on advanced machine learning concepts.

Guides businesses in managing global tax liabilities efficiently.

I provide monthly salary data in euros, using a structured format for global job roles.

Ensures software performance meets organizational standards and expectations.