Let’s Copilot is a gamified application integrated into Microsoft Teams that aids in the adoption of Microsoft 365 Copilot. It begins with assessing the user’s AI skill level and role within the organization to personalize the learning experience. Users engage in interactive lessons that build their AI skills and understanding of Microsoft 365 Copilot. Practical and hands-on, these lessons empower users to apply what they learn directly to their work tasks, ultimately enhancing productivity and streamlining workflows.

LLM Agents Example provides a hands-on codebase for building AI agents in Python. It demonstrates registering custom tools (web search, math solver via WolframAlpha, CSV analyzer, Python REPL), creating chat and retrieval-based agents, and connecting to vector stores for document question answering. The repo illustrates patterns for maintaining conversational memory, dispatching tool calls dynamically, and chaining multiple LLM prompts to solve complex tasks. Users learn how to integrate third-party APIs, structure agent workflows, and extend the framework with new capabilities—serving as a practical guide for developer experimentation and prototyping.

Luminaries AI is an advanced platform combining machine learning and expert knowledge to offer personalized coaching. The platform helps users set clear goals, connect relevant data to train the AI, define operational rules and audience criteria, and ultimately drive impactful campaigns. Luminaries AI is perfect for those seeking to enhance their skills, improve decision-making, and achieve significant personal or professional development. By automating and personalizing the coaching process, it aims to foster growth effectively and efficiently.

Mockaroni is a platform designed to generate custom synthetic text data that looks and feels similar to real-world data. The generated data can be used for various applications such as training AI and machine learning models, testing algorithms, and more. With customizable templates and advanced generation algorithms, Mockaroni ensures your models are well-prepared for real-world data scenarios, enhancing their efficiency and effectiveness.

Proposal Copycat is an AI-powered Chrome extension designed for freelancers. It helps users train the AI with their successful past Upwork proposals to write new, personalized proposals. The extension securely collects these proposals, requires the addition of portfolio projects, and allows for customization of the AI prompts for optimal results. It integrates seamlessly with ChatGPT/Claude for generating tailored proposals. This tool is ideal for busy professionals who want to maintain the quality of their work while increasing their proposal output and leveraging AI technology.

SelfYAI offers a comprehensive, no-code interface for designing, training, and deploying AI agents tailored to your specific business needs. Users can import data from CRM systems, spreadsheets, and databases, then configure custom workflows and conversational flows with simple drag-and-drop tools. Agents maintain context using memory modules and can be deployed across websites, Slack, Teams, and API endpoints. Built-in analytics track interaction volume, resolution rates, and user feedback, supporting iterative improvements. With robust security features and role-based access controls, SelfYAI ensures data privacy and compliance while scaling AI-driven automation effortlessly.

Side School is dedicated to integrating AI into the workplace through practical training programs. It provides tailored courses that empower teams to leverage AI tools effectively, enhancing productivity and innovation. Participants engage with industry-specific case studies, ensuring that they gain knowledge that directly applies to their roles. The curriculum is designed to meet the needs of various departments, enabling teams to embrace AI as a vital part of their work processes.

TexasHoldemAgent provides a modular environment built on Python to train, evaluate, and deploy an AI-powered poker player for heads-up limit Texas Hold’em. It integrates a custom simulation engine with deep reinforcement learning algorithms, including DQN, for iterative policy improvement. Key capabilities include hand state encoding, action space definition (fold, call, raise), reward shaping, and real-time decision evaluation. Users can customize learning parameters, leverage CPU/GPU acceleration, monitor training progress, and load or save trained models. The framework supports batch simulation to test various strategies, generate performance metrics, and visualize win rates, empowering researchers, developers, and poker enthusiasts to experiment with AI-driven gameplay strategies.

Why Try AI is a practical resource for exploring the capabilities and applications of generative AI technologies. Founded by Daniel Nest, the platform emphasizes a no-nonsense approach to AI, cutting through the jargon to deliver tools, tips, and insights that are accessible to both beginners and seasoned users. From coding simple games to generating creative visual content, Why Try AI aims to demystify AI and make it usable for everyone. With constant updates on AI news and developments, it serves as a reliable guide to staying current in this rapidly evolving field.

14 Days of AI provides a fun and effective learning experience with 30-minute bitesized lessons delivered daily. This no-code journey guides you through the basics of AI, helping you to master new skills quickly. Starting from creating your first AI assistant to generating professional images and analyzing data with AI, each day focuses on practical and useful applications, ensuring you gain valuable knowledge and skills. Join thousands of learners and start your AI journey, enhancing your tech capabilities and making a real impact.

PromptWars is an online platform where AI prompt enthusiasts can compete with each other. Users can create, share, and refine their AI prompts in a competitive environment. The platform allows individuals to hone their skills, gain feedback from the community, and stay updated with the latest trends in AI prompt engineering. With various challenges and contests, it’s a lively community for those passionate about AI and language models.

AI Agents for Rock Paper Scissors is an open-source Python project that demonstrates how to build, train, and evaluate different AI strategies—random play, rule-based pattern recognition, and reinforcement learning (Q-learning)—in the classic Rock-Paper-Scissors game. It provides modular agent classes, a configurable game runner, performance logging, and visualization utilities. Users can easily swap agents, adjust learning parameters, and explore AI behavior in competitive scenarios.

Incribo is a platform that simplifies the creation of high-quality synthetic data for AI model training. It allows users to generate 3D models, audio, and other data types, crucial for various fields like augmentation, gaming, architecture, and product designs. By synthesizing data with real-world variations in features, movements, and expressions, it enhances AI training and reduces the reliance on expensive and time-consuming data collection processes.

Cerebras AI Agent leverages the unique architecture of the Cerebras Wafer Scale Engine to expedite deep learning model training. It provides unparalleled performance by enabling the training of deep neural networks with high speed and substantial data throughput, transforming research into tangible results. Its capabilities help organizations manage large-scale AI projects efficiently, ensuring researchers can focus on innovation rather than hardware limitations.

Bexy AI is a ChatGPT-powered, 24/7 sales assistant chatbot designed to engage visitors, handle their queries, and recommend products. Its AI capabilities allow proactive engagement at crucial moments, reducing cart abandonment and increasing conversion rates. It adapts to your brand’s voice and integrates seamlessly with your store, providing a premium shopping experience. Supporting over 100 languages, Bexy AI also offers custom training to enhance its performance based on your specific business needs.

Build Club offers a streamlined path to an AI career, featuring certifications from major AI companies, practical learning with the latest AI tools, and opportunities to solve real-world challenges. The platform connects you with a community of AI learners, engineers, and founders, providing hands-on learning through paid challenges. Earn money and gain experience while building projects that address genuine business needs, all for free.

Cayodis is a web application that serves as a platform for users to discover, share, and delve into a plethora of historical and general interest topics. Through an intuitive interface and the power of AI, it guides users to explore subjects that spark their curiosity and enhance their knowledge. It's an entertainment hub that combines education and engagement seamlessly.

DataCamp is an online learning platform that specializes in teaching data science, AI, and various programming languages like Python and SQL. With over 490 courses, users can learn from industry experts through video tutorials, coding exercises, and real-world projects. DataCamp also offers certifications to validate your skills and make you job-market ready.

DebateBotAI is an educational platform that simulates debates with an AI-driven opponent. Users can choose from different difficulty levels, ranging from beginner to expert, making it suitable for individuals at any stage of their debate journey. The platform supports over 45 languages and allows extensive interaction with up to 2000 messages initially and 50 messages per day thereafter. This service is offered for a one-time payment of $4.99, providing users with a cost-effective way to enhance their debating skills and critical thinking abilities.

DQN-Deep-Q-Network-Atari-Breakout-TensorFlow provides a complete implementation of the DQN algorithm tailored for the Atari Breakout environment. It uses a convolutional neural network to approximate Q-values, applies experience replay to break correlations between sequential observations, and employs a periodically updated target network to stabilize training. The agent follows an epsilon-greedy policy for exploration and can be trained from scratch on raw pixel input. The repository includes configuration files, training scripts to monitor reward growth over episodes, evaluation scripts to test trained models, and TensorBoard utilities for visualizing training metrics. Users can adjust hyperparameters such as learning rate, replay buffer size, and batch size to experiment with different setups.