Best AI tools for< Improve Performance >

EXCELR8 is an AI-infused platform designed to help leaders and teams improve performance and retention to empower sustainable growth. It offers a range of tools and features such as performance and engagement surveys, AI-powered insights and data analysis, tailored learning and development, goals and action planning dashboards, and performance and retention management systems. The platform provides clear and actionable data, specific recommendations for immediate implementation, and personalized development opportunities. EXCELR8 aims to revolutionize the way organizations build and retain high-performance teams by leveraging AI technology and science-backed tools.

Uplift is an AI-powered platform that optimizes human movement performance by providing insights for sports performance, sports medicine, and sports media. It offers products like Uplift Capture and Uplift Vision to enhance performance, minimize risk, and elevate broadcasting experiences. The platform is utilized by Major League Baseball, professional sports teams, elite performance coaches, and Division I athletics programs. Uplift uses AI-powered movement analysis to unlock unrivaled insights for athletes and broadcasters, helping them improve performance, reduce injury risk, and engage fans effectively.

Elsevier is an information analytics business that supports researchers and healthcare professionals in advancing science and improving healthcare outcomes. They provide high-quality data and analytics to help researchers, librarians, and research leaders address challenges at every stage of the research journey. Elsevier offers researcher tools, research management solutions, and evaluation services to enhance productivity and research impact.

Allego Conversation Intelligence is an AI-powered platform that helps revenue teams enhance performance and revenue by analyzing sales conversations. The platform enables users to capture, review, coach, and share valuable insights from conversations with customers. It leverages AI-driven analysis to reveal successful outcomes and provides data-driven performance coaching to improve sales performance. Allego Conversation Intelligence allows users to search, filter, and leverage call intelligence to uncover key moments that impact revenue. Trusted by top companies, the platform offers integrations with existing systems and resources for modern sales teams.

DINGR is an AI-powered solution designed to help gamers analyze their performance in League of Legends. The tool uses AI algorithms to provide accurate insights into gameplay, comparing performance metrics with friends and offering suggestions for improvement. DINGR is currently in development, with a focus on enhancing the gaming experience through data-driven analysis and personalized feedback.

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

Dexa.ai is an AI-powered tool designed to verify user identity and ensure secure connections. It quickly verifies human users, reviews connection security, and provides real-time responses. By leveraging AI technology, Dexa.ai enhances performance and security for users. The tool is essential for websites that require user verification and secure connections to prevent unauthorized access and ensure data protection.

Observe.AI is a conversation intelligence software designed for contact centers, offering a suite of AI-powered tools to enhance customer interactions, support agents in real-time, and improve overall customer satisfaction. The platform combines advanced analytics, automation, and real-time assistance to drive continuous improvement and boost operational efficiency. Trusted by over 350 enterprises worldwide, Observe.AI helps businesses transform customer conversations into actionable insights, leading to measurable results such as increased sales conversions, improved compliance adherence, and enhanced customer sentiment.

New Relic is an AI monitoring platform that offers an all-in-one observability solution for monitoring, debugging, and improving the entire technology stack. With over 30 capabilities and 750+ integrations, New Relic provides the power of AI to help users gain insights and optimize performance across various aspects of their infrastructure, applications, and digital experiences.

Lamini is an enterprise-level LLM platform that offers precise recall with Memory Tuning, enabling teams to achieve over 95% accuracy even with large amounts of specific data. It guarantees JSON output and delivers massive throughput for inference. Lamini is designed to be deployed anywhere, including air-gapped environments, and supports training and inference on Nvidia or AMD GPUs. The platform is known for its factual LLMs and reengineered decoder that ensures 100% schema accuracy in the JSON output.

cloudNito is an AI-driven platform that specializes in cloud cost optimization and management for businesses using AWS services. The platform offers automated cost optimization, comprehensive insights and analytics, unified cloud management, anomaly detection, cost and usage explorer, recommendations for waste reduction, and resource optimization. By leveraging advanced AI solutions, cloudNito aims to help businesses efficiently manage their AWS cloud resources, reduce costs, and enhance performance.

Jochem is an AI tool designed to provide accurate answers quickly and enhance knowledge on-the-go. It helps users get instant answers to their questions, connects them with experts within the company, and continuously learns to improve performance. Jochem eliminates the need to search through files and articles by offering a smart matching system based on expertise. It also allows users to easily add and update the knowledge base, ensuring full control and transparency.

Smarter Sales is a sales call data management and automation tool that helps businesses streamline their sales processes, improve performance metrics, and save time. It integrates with popular video conferencing platforms like Zoom, Teams, and Meets to automatically pull call recordings for analysis. The tool also automates CRM data entry, providing instant, personalized feedback post-call. Managers can access detailed performance dashboards and summarized email reports to make data-driven coaching decisions. Smarter Sales is fully customizable, allowing businesses to set their own CRM data preferences and extract specific data from each call. The tool also offers personalized AI learning materials and stunning chart creation capabilities to help businesses better understand their sales data and improve their sales strategies.

Intelogos is a workforce analytics software that combines people and workforce analytics with artificial intelligence to provide insights into employee performance and well-being. It offers features such as performance management, time tracking, attendance tracking, burnout prevention, and remote employee monitoring. The application helps organizations improve decision-making, optimize performance, and boost productivity by analyzing employee data and providing actionable recommendations.

Adext is an AI-powered platform that offers real-time ad spend optimization for Google, YouTube, Instagram, and Facebook Ads. It provides an advanced end-to-end solution for marketing teams by automatically optimizing audience segments and budget allocations using proprietary Machine Learning algorithms. Adext aims to deliver exceptional performance and increased return on ad spend for advertisers and agencies through AI-driven ad allocation. The platform offers benefits such as daily budget updates, autonomous daily changes, and transparent operation within the user's own accounts. Adext also provides free digital marketing consultancy to help businesses navigate the digital marketing landscape.

Betterworks is an intelligent performance management platform that simplifies performance management, fosters greater manager effectiveness, higher employee engagement, and intelligent decision-making for HR leaders and organizations. It offers features such as AI for HR analytics & insights, integrations, accessibility, security, and manager effectiveness. Betterworks is designed to help organizations align their workforce around strategic priorities, drive collaboration, and continuously improve performance.

Second Nature is an AI-powered sales training software that offers life-like role-playing simulations to enhance sales skills and productivity. It provides personalized AI role plays, customized simulations, and virtual pitch partners to help sales teams practice conversations, improve performance, and drive results. The platform features AI avatars, template libraries, and various training modules for different industries and use cases.

Qualtrics XM is a leading Experience Management Software that helps businesses optimize customer experiences, employee engagement, and market research. The platform leverages specialized AI to uncover insights from data, prioritize actions, and empower users to enhance customer and employee experience outcomes. Qualtrics XM offers solutions for Customer Experience, Employee Experience, Strategy & Research, and more, enabling organizations to drive growth and improve performance.

Utsaluna is an AI-driven platform that empowers performers of all levels by providing insights, coaching, and tools to unlock their full potential. The platform offers personalized feedback, tracks progress, and connects performers to talent seekers. Utsaluna focuses on leveraging AI to enhance creativity, automate technical aspects of coaching, and provide comprehensive support for success in show business. It aims to create a global community that nurtures creativity and empowers talent through actionable insights and accessible tools.

Had-a Call is an AI-powered call management platform that revolutionizes customer outreach by breaking down language barriers and building stronger relationships with customers worldwide. It offers multi-language call center software, real-time call performance analytics, and scalable solutions for data-driven customer engagement. With Had-a Call, businesses can effortlessly scale their call center operations and make data-driven decisions to drive customer engagement and improve performance.

HuixiangDou2 is a robustly optimized GraphRAG approach that integrates multiple open-source projects to improve performance in graph-based augmented generation. It conducts comparative experiments and achieves a significant score increase, leading to a GraphRAG implementation with recognized performance. The repository provides code improvements, dense retrieval for querying entities and relationships, real domain knowledge testing, and impact analysis on accuracy.

Koordinator is a QoS based scheduling system for hybrid orchestration workloads on Kubernetes. It aims to improve runtime efficiency and reliability of latency sensitive workloads and batch jobs, simplify resource-related configuration tuning, and increase pod deployment density. It enhances Kubernetes user experience by optimizing resource utilization, improving performance, providing flexible scheduling policies, and easy integration into existing clusters.

Akagi is a project designed to help users understand and improve their performance in Majsoul game matches in real-time. It provides educational insights and tools for analyzing gameplay. Users can install Akagi on Windows or Mac systems and follow the setup instructions to enhance their gaming experience. The project aims to offer features like Autoplay, Auto Ron, and integration with MajsoulUnlocker. It also focuses on enhancing user safety by providing guidelines to minimize the risk of account suspension. Akagi is a tool that combines MITM interception, AI decision-making, and user interaction to optimize gameplay strategies and performance.

Palico AI is a tech stack designed for rapid iteration of LLM applications. It allows users to preview changes instantly, improve performance through experiments, debug issues with logs and tracing, deploy applications behind a REST API, and manage applications with a UI control panel. Users have complete flexibility in building their applications with Palico, integrating with various tools and libraries. The tool enables users to swap models, prompts, and logic easily using AppConfig. It also facilitates performance improvement through experiments and provides options for deploying applications to cloud providers or using managed hosting. Contributions to the project are welcomed, with easy ways to get involved by picking issues labeled as 'good first issue'.

AvaTaR is a novel and automatic framework that optimizes an LLM agent to effectively use provided tools and improve performance on a given task/domain. It designs a comparator module to provide insightful prompts to the LLM agent via reasoning between positive and negative examples from training data.

Implementation of Double DQN reinforcement learning for OpenAI Gym environments with discrete action spaces. The algorithm aims to improve sample efficiency by using two uncorrelated Q-Networks to prevent overestimation of Q-values. By updating parameters periodically, the model reduces computation time and enhances training performance. The tool is based on the Double DQN method proposed by Hasselt in 2010.

Ape is an AI prompt engineer tool powered by the open-source library 'ape-core', developed by Weavel. It allows users to generate AI prompts efficiently and effectively. The tool is designed to enhance productivity by providing syntax highlighting for '.prompt' files and welcoming contributions to improve its capabilities and performance. Users can seek help and support through the issue tracker or join the Ape community Discord server. Ape is licensed under the MIT License and credits Stanford NLP's DSPy project for inspiration.

This repository contains a library for constraining LLM generation to structured output, enforcing a JSON schema for precise data types and property names. It includes an acceptor/state machine framework, JSON acceptor, and JSON schema acceptor for guiding decoding in LLMs. The library provides reference implementations using Apple's MLX library and examples for function calling tasks. The tool aims to improve LLM output quality by ensuring adherence to a schema, reducing unnecessary output, and enhancing performance through pre-emptive decoding. Evaluations show performance benchmarks and comparisons with and without schema constraints.

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.

This repository contains the code for LongRAG, a framework that enhances retrieval-augmented generation with long-context LLMs. LongRAG introduces a 'long retriever' and a 'long reader' to improve performance by using a 4K-token retrieval unit, offering insights into combining RAG with long-context LLMs. The repo provides instructions for installation, quick start, corpus preparation, long retriever, and long reader.

Pwnagotchi is an AI tool leveraging bettercap to learn from WiFi environments and maximize crackable WPA key material. It uses LSTM with MLP feature extractor for A2C agent, learning over epochs to improve performance in various WiFi environments. Units can cooperate using a custom parasite protocol. Visit https://www.pwnagotchi.ai for documentation and community links.

llm_rl is a repository that combines llm (language model) and rl (reinforcement learning) techniques. It likely focuses on using language models in reinforcement learning tasks, such as natural language understanding and generation. The repository may contain implementations of algorithms that leverage both llm and rl to improve performance in various tasks. Developers interested in exploring the intersection of language models and reinforcement learning may find this repository useful for research and experimentation.

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.

This repository contains the training and inference code needed to replicate the winning solution to the AI Mathematical Olympiad - Progress Prize 1. It consists of fine-tuning DeepSeekMath-Base 7B, high-quality training datasets, a self-consistency decoding algorithm, and carefully chosen validation sets. The training methodology involves Chain of Thought (CoT) and Tool Integrated Reasoning (TIR) training stages. Two datasets, NuminaMath-CoT and NuminaMath-TIR, were used to fine-tune the models. The models were trained using open-source libraries like TRL, PyTorch, vLLM, and DeepSpeed. Post-training quantization to 8-bit precision was done to improve performance on Kaggle's T4 GPUs. The project structure includes scripts for training, quantization, and inference, along with necessary installation instructions and hardware/software specifications.

MatMul-Free LM is a language model architecture that eliminates the need for Matrix Multiplication (MatMul) operations. This repository provides an implementation of MatMul-Free LM that is compatible with the 🤗 Transformers library. It evaluates how the scaling law fits to different parameter models and compares the efficiency of the architecture in leveraging additional compute to improve performance. The repo includes pre-trained models, model implementations compatible with 🤗 Transformers library, and generation examples for text using the 🤗 text generation APIs.

PrefixQuant is an official PyTorch implementation for static quantization that outperforms dynamic quantization in Large Language Models (LLMs) by utilizing prefixed outliers. The tool provides functionalities for quantization, inference, and visualization of activation distributions. Users can fine-tune quantization settings and evaluate pre-quantized models for tasks like PIQA, ARC, Hellaswag, and Winogrande. The approach aims to improve performance and efficiency in LLMs through innovative quantization techniques.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

EvalKit is an open-source TypeScript library for evaluating and improving the performance of large language models (LLMs). It helps developers ensure the reliability, accuracy, and trustworthiness of their AI models. The library provides various metrics such as Bias Detection, Coherence, Faithfulness, Hallucination, Intent Detection, and Semantic Similarity. EvalKit is designed to be user-friendly with detailed documentation, tutorials, and recipes for different use cases and LLM providers. It requires Node.js 18+ and an OpenAI API Key for installation and usage. Contributions from the community are welcome under the Apache 2.0 License.

APOLLO is a memory-efficient optimizer designed for large language model (LLM) pre-training and full-parameter fine-tuning. It offers SGD-like memory cost with AdamW-level performance. The optimizer integrates low-rank approximation and optimizer state redundancy reduction to achieve significant memory savings while maintaining or surpassing the performance of Adam(W). Key contributions include structured learning rate updates for LLM training, approximated channel-wise gradient scaling in a low-rank auxiliary space, and minimal-rank tensor-wise gradient scaling. APOLLO aims to optimize memory efficiency during training large language models.

RouteLLM is a framework for serving and evaluating LLM routers. It allows users to launch an OpenAI-compatible API that routes requests to the best model based on cost thresholds. Trained routers are provided to reduce costs while maintaining performance. Users can easily extend the framework, compare router performance, and calibrate cost thresholds. RouteLLM supports multiple routing strategies and benchmarks, offering a lightweight server and evaluation framework. It enables users to evaluate routers on benchmarks, calibrate thresholds, and modify model pairs. Contributions for adding new routers and benchmarks are welcome.

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