Best AI tools for< Reward Researchers >

Huntr is the world's first bug bounty platform for AI/ML. It provides a single place for security researchers to submit vulnerabilities, ensuring the security and stability of AI/ML applications, including those powered by Open Source Software (OSS).

MyShell is an AI application that enables users to build, share, and own AI agents. It serves as a platform connecting users, creators, and open-source AI researchers. With MyShell, users can interact with AI friends and work companions, such as Shizuku and Emma 01 03, through voice and video conversations. The application empowers creators to leverage generative AI models to transform ideas into AI-native apps quickly. MyShell fosters a creator economy in the AI-native era, allowing anyone to become a creator, take ownership of their work, and be rewarded for their ideas.

Bagel is an AI & Cryptography Research Lab that focuses on making open source AI monetizable by leveraging novel cryptography techniques. Their innovative fine-tuning technology tracks the evolution of AI models, ensuring every contribution is rewarded. Bagel is built for autonomous AIs with large resource requirements and offers permissionless infrastructure for seamless information flow between machines and humans. The lab is dedicated to privacy-preserving machine learning through advanced cryptography schemes.

Hive3 is the world's first competitive AI league. It is a platform where AI creators can compete in design challenges for the chance to win cash and credibility. Brands partner with Hive3 to sponsor image and video design challenges, and all users need to do is bring their favorite AI tools and unleash their creativity. Competitions drop weekly, giving users the chance to push their limits with brand-specific challenges. Winners receive cash prizes, and brands often hire creators for private projects.

Text Enhancer is a free online AI-powered tool that helps users enhance and improve their writing. It offers a range of features, including text enhancement, rewriting, and advanced paraphrasing capabilities. The tool is designed to be user-friendly and provides quick and efficient assistance to refine and elevate the quality of writing. Text Enhancer is ideal for students, professionals, and everyday writers who want to improve the clarity, eliminate redundancy, and ensure their writing is impactful and engaging.

FOXSY.AI is an AI application that combines robotics and AI to create fully autonomous humanoid robot soccer players. The project aims to achieve the RoboCup final goal of having a team of robots win a soccer game against the winner of the most recent World Cup. The $FOXSY token powers the implementation of robotics and AI research, enabling users to engage with the RoboCup mechanics for entertainment value. The application offers various tools and features for users to participate in online tournaments, customize players, and analyze game strategies.

Tutor AI is a personal AI tutor designed to assist life-long learners in achieving their academic and personal goals. The platform offers a customizable learning pathway to enhance skills in various subjects such as Machine Learning, French, and Mindfulness. Users can access free online courses, get help with homework through Homeworkify, and even participate in the affiliate program. With Tutor AI, users can take their learning to the next level and excel in school, work, and life.

Zora is an adaptive storytelling platform for learning that offers personalized and engaging reading experiences for users of all ages. Named after Zora Neale Hurston, the platform allows users to create their own characters, explore genres matching their interests, and deepen reading comprehension skills and vocabulary through adaptive storytelling. Zora aims to make learning fun and effective by providing a gamified approach to skill mastery and comprehension. The platform offers age-appropriate and safe content, with a focus on continuous development of interactive features to enhance the reading experience.

The website page contains a collection of blog posts by Sam Altman, discussing various topics related to AI, technology, entrepreneurship, and personal reflections. The posts cover a wide range of subjects, including the development of AI tools, reflections on company progress, personal experiences, and insights on leadership and innovation. Sam Altman shares his thoughts on the impact of AI technology, the challenges and rewards of building a company, and the importance of long-term vision and persistence in achieving success.

Neurochain AI is a decentralized AI-as-a-Service (DeAIAS) network that provides an innovative solution for building, launching, and using AI-powered decentralized applications (dApps). It offers a community-driven approach to AI development, incentivizing contributors with $NCN rewards. The platform aims to address challenges in the centralized AI landscape by democratizing AI development and leveraging global computing resources. Neurochain AI also features a community-powered content generation engine and is developing its own independent blockchain. The team behind Neurochain AI includes experienced professionals in infrastructure, cryptography, computer science, and AI research.

One Click Crypto is an AI-powered platform focused on educating users about Crypto and Decentralized Finance (DeFi). The platform offers a personalized experience by utilizing an AI recommendation engine to create yield farming portfolios tailored to individual users. Users can connect their wallets to start building their dream DeFi portfolios. Additionally, the platform features an Airdrop Tracker to simplify the process of hunting for extra crypto rewards and a Points Farming Community with early adopter incentives and a referral program. One Click Crypto emphasizes educational content and research, and it does not provide investment or financial advice.

UPTO3 is a decentralized event knowledge graph protocol that aims to provide consensus verification for Web3 events by turning them into NFTs. It allows users to mint and verify events, with rewards based on the results. The platform promotes transparency, open data, and unbiased analysis through economic incentives. UPTO3 will be built on Blast(L2) and offers features such as event minting as NFTs, permissionless access, and decentralized validation tasks.

Tomo Cafe is an RPG-ish AI companion application designed to boost productivity by providing users with accountable AI buddies for study, work, and more. Users can engage with unique virtual characters, set study targets, interact in 3D, and unlock character backstories to stay motivated and focused. The application offers a gamified experience with rewards, Pomodoro timer, and immersive storytelling to enhance the learning process.

Vouchery.io is an all-in-one promotional engine designed to help businesses orchestrate and deliver the right incentives at every stage of the customer lifecycle. It offers features such as Coupons & Discounts, Loyalty Program, Gift Cards & Vouchers, and Referral Program. The platform is AI-powered, enabling contextual, predictive marketing promotions and special offers to drive customer engagement. Vouchery allows users to create, distribute, and manage promo campaigns efficiently, with capabilities to analyze data, maximize promo ROI, manage and collaborate on promotions, detect coupon abuse, and personalize incentives. Trusted by leading brands globally, Vouchery aims to help businesses scale their promotional infrastructure and prevent fraud through its headless architecture and machine learning technology.

PurplePro is an AI-powered loyalty club platform designed to help businesses launch and manage loyalty programs effortlessly. With features like referral management, streaks, quizzes, variable rewards, and automated triggers, PurplePro aims to enhance customer engagement, retention, and acquisition. The platform offers advanced customization and segmentation options, making it suitable for direct-to-consumer (D2C) brands looking to boost customer loyalty and increase revenue. PurplePro's AI capabilities enable users to create and implement effective loyalty campaigns in just a few clicks, without the need for coding knowledge. The platform also provides a seamless integration with Shopify, making it easy for businesses to set up and activate their loyalty programs.

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.

The Advantage Club is an AI-powered Employee Engagement Platform that offers solutions for workforce engagement through various features such as recognition, marketplace, wellness, incentive automation, communities, and pulse tracking. It digitizes rewarding processes, curates personalized vouchers and gifts, elevates well-being with a holistic wellness platform, automates sales contests, fosters inclusion through communities, and captures employee sentiments through surveys and quizzes. The platform integrates with global HRIS and communication tools, provides real-time analytics, and offers a seamless user experience for both employees and administrators.

Community Hub is a free-to-use, AI-powered community management platform that helps you automate tasks, reward members, and keep your community engaged. With Community Hub, you can:

Saara Inc is an AI tool for eCommerce that focuses on maximizing profits by leveraging AI-powered automation and smart agents. The platform helps online stores increase profitability by addressing challenges such as high return rates, operational costs, and customer churn. By enhancing loyalty, reducing expenses, and streamlining processes through automation and AI, Saara enables businesses to achieve sustainable growth and long-term profitability.

Almonds Ai is a powerful and scalable AI-driven platform that focuses on channel engagement for businesses. It offers solutions such as B2B loyalty programs, interactive product learning, and hybrid/virtual events to enhance partner engagement and drive revenue growth. With features like platform customization, dedicated customer support, data & AI engine, and global recognition, Almonds Ai aims to deliver measurable conversions and return on experience for its users. The platform caters to various industries including technology, retail, auto, and banking, helping businesses engage, educate, and reward their channel partners effectively.

This repository focuses on test-time computing, exploring various strategies such as test-time adaptation, modifying the input, editing the representation, calibrating the output, test-time reasoning, and search strategies. It covers topics like self-supervised test-time training, in-context learning, activation steering, nearest neighbor models, reward modeling, and multimodal reasoning. The repository provides resources including papers and code for researchers and practitioners interested in enhancing the reasoning capabilities of large language models.

The Awesome-LLM-Post-training repository is a curated collection of influential papers, code implementations, benchmarks, and resources related to Large Language Models (LLMs) Post-Training Methodologies. It covers various aspects of LLMs, including reasoning, decision-making, reinforcement learning, reward learning, policy optimization, explainability, multimodal agents, benchmarks, tutorials, libraries, and implementations. The repository aims to provide a comprehensive overview and resources for researchers and practitioners interested in advancing LLM technologies.

MM-RLHF is a comprehensive project for aligning Multimodal Large Language Models (MLLMs) with human preferences. It includes a high-quality MLLM alignment dataset, a Critique-Based MLLM reward model, a novel alignment algorithm MM-DPO, and benchmarks for reward models and multimodal safety. The dataset covers image understanding, video understanding, and safety-related tasks with model-generated responses and human-annotated scores. The reward model generates critiques of candidate texts before assigning scores for enhanced interpretability. MM-DPO is an alignment algorithm that achieves performance gains with simple adjustments to the DPO framework. The project enables consistent performance improvements across 10 dimensions and 27 benchmarks for open-source MLLMs.

The Arcade Learning Environment (ALE) is a simple framework that allows researchers and hobbyists to develop AI agents for Atari 2600 games. It is built on top of the Atari 2600 emulator Stella and separates the details of emulation from agent design. The ALE currently supports three different interfaces: C++, Python, and OpenAI Gym.

NeMo Framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). The primary objective of NeMo is to provide a scalable framework for researchers and developers from industry and academia to more easily implement and design new generative AI models by being able to leverage existing code and pretrained models.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

SwiftSage is a tool designed for conducting experiments in the field of machine learning and artificial intelligence. It provides a platform for researchers and developers to implement and test various algorithms and models. The tool is particularly useful for exploring new ideas and conducting experiments in a controlled environment. SwiftSage aims to streamline the process of developing and testing machine learning models, making it easier for users to iterate on their ideas and achieve better results. With its user-friendly interface and powerful features, SwiftSage is a valuable tool for anyone working in the field of AI and ML.

This repository is a collection of research notes and resources focusing on training large language models (LLMs) and Reinforcement Learning from Human Feedback (RLHF). It includes methodologies, techniques, and state-of-the-art approaches for optimizing preferences and model alignment in LLM training. The purpose is to serve as a reference for researchers and engineers interested in reinforcement learning, large language models, model alignment, and alternative RL-based methods.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It provides pytorch and **python-first** , low and high level abstractions for RL that are intended to be **efficient** , **modular** , **documented** and properly **tested**. The code is aimed at supporting research in RL. Most of it is written in python in a highly modular way, such that researchers can easily swap components, transform them or write new ones with little effort.

Awesome-AGI is a curated list of resources related to Artificial General Intelligence (AGI), including models, pipelines, applications, and concepts. It provides a comprehensive overview of the current state of AGI research and development, covering various aspects such as model training, fine-tuning, deployment, and applications in different domains. The repository also includes resources on prompt engineering, RLHF, LLM vocabulary expansion, long text generation, hallucination mitigation, controllability and safety, and text detection. It serves as a valuable resource for researchers, practitioners, and anyone interested in the field of AGI.

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.

Qlib is an open-source, AI-oriented quantitative investment platform that supports diverse machine learning modeling paradigms, including supervised learning, market dynamics modeling, and reinforcement learning. It covers the entire chain of quantitative investment, from alpha seeking to order execution. The platform empowers researchers to explore ideas and implement productions using AI technologies in quantitative investment. Qlib collaboratively solves key challenges in quantitative investment by releasing state-of-the-art research works in various paradigms. It provides a full ML pipeline for data processing, model training, and back-testing, enabling users to perform tasks such as forecasting market patterns, adapting to market dynamics, and modeling continuous investment decisions.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

LLM4Opt is a collection of references and papers focusing on applying Large Language Models (LLMs) for diverse optimization tasks. The repository includes research papers, tutorials, workshops, competitions, and related collections related to LLMs in optimization. It covers a wide range of topics such as algorithm search, code generation, machine learning, science, industry, and more. The goal is to provide a comprehensive resource for researchers and practitioners interested in leveraging LLMs for optimization tasks.

This repository is a curated collection of resources for fine-tuning Large Language Models (LLMs) like GPT, BERT, RoBERTa, and their variants. It includes tutorials, papers, tools, frameworks, and best practices to aid researchers, data scientists, and machine learning practitioners in adapting pre-trained models to specific tasks and domains. The resources cover a wide range of topics related to fine-tuning LLMs, providing valuable insights and guidelines to streamline the process and enhance model performance.

The 'Awesome LLMs Planning Reasoning' repository is a curated collection focusing on exploring the capabilities of Large Language Models (LLMs) in planning and reasoning tasks. It includes research papers, code repositories, and benchmarks that delve into innovative techniques, reasoning limitations, and standardized evaluations related to LLMs' performance in complex cognitive tasks. The repository serves as a comprehensive resource for researchers, developers, and enthusiasts interested in understanding the advancements and challenges in leveraging LLMs for planning and reasoning in real-world scenarios.

The repository 'Awesome LLM Reasoning Openai-o1 Survey' provides a collection of survey papers and related works on OpenAI o1, focusing on topics such as LLM reasoning, self-play reinforcement learning, complex logic reasoning, and scaling law. It includes papers from various institutions and researchers, showcasing advancements in reasoning bootstrapping, reasoning scaling law, self-play learning, step-wise and process-based optimization, and applications beyond math. The repository serves as a valuable resource for researchers interested in exploring the intersection of language models and reasoning techniques.

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Ti aiuto a conoscere soluzioni per il settore HR legate alla Gamification e agli Assessment Game

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Decision Journal can help you with decision making, keeping track of the decisions you've made, and helping you review them later on.

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