Github

Introducing Kel, your all-in-one AI-based assistant integrated seamlessly into your Command Line Interface (CLI). Designed to enhance productivity, Kel automates repetitive tasks and introduces efficiency-boosting capabilities to your CLI interactions. 🌐 Multi-Lingual Support: Kel supports multiple Languages for Learning Machines (LLMs), including OpenAI, Anthropic, Ollama, and OpenAI Assistants. This versatile AI integration expands your possibilities and interactions. 💬 Artifact Interaction: Kel introduces a unique feature: interacting with your artifacts through a chat-like interface. Simply upload your files and engage in conversations, adding a conversational element to your CLI experience. 🔑 Personalization with API Keys: Developers can incorporate their API keys for OpenAI or Anthropic, tailoring their assistant for a personalized experience. Future expansions may include other LLMs, offering even more flexibility. 🆓 Free and Open-Source: Kel is committed to accessibility, inviting community contributions on its GitHub platform. This fosters a collaborative environment for ongoing enhancements and ensures availability to all. 📚 Support and Documentation: Access support through a contact-based model and explore comprehensive documentation on the main website. Clear insights into Kel's functionalities empower users to make the most of this versatile tool. 🔧 Continuous Improvement: Kel embraces a community-driven approach to development. Open-source contributions further enhance its capabilities, keeping it dynamic and tailored to users' evolving needs. Kel takes your CLI experience to the next level, with its groundbreaking features. Elevate efficiency and introduce innovative interactions with multi-lingual support, artifact interaction, personalization options, and a commitment to openness. Experience the future of command line assistants with Kel.

Welcome to Pixels2Flutter, an innovative tool designed to revolutionize UI development by converting screenshots into functional Flutter code. With a focus on simplicity and efficiency, Pixels2Flutter aims to streamline the UI development process. 🔮 Conversion Magic: Pixels2Flutter's primary function is to convert UI screenshots into ready-to-use Flutter code, eliminating the need for manual coding from scratch. 💡 Simplicity at Its Core: The tool is designed for ease of use, ensuring that developers can effortlessly translate digital images into functional Flutter components. ⚙️ Flutter Compatibility: Tailored for developers using Flutter, an open-source UI software development kit by Google, Pixels2Flutter directly generates Flutter code from screenshots. 🌉 Bridge the Gap: Pixels2Flutter serves as a bridge between UI design and code implementation, minimizing manual effort and accelerating the development process. ⏱️ Boost Productivity: By automating the conversion process, Pixels2Flutter enhances productivity, allowing developers to focus on refining designs and implementing features. 🎯 Accuracy in Design-to-Code: Achieve greater accuracy in translating design concepts to code, reducing the chances of errors and discrepancies in the development phase. 🔄 Workflow Improvement: Integrate Pixels2Flutter into your workflow to experience a seamless transition from design ideation to Flutter app development. Why Pixels2Flutter: Pixels2Flutter is a game-changer for Flutter developers, offering a practical solution to convert UI screenshots into functional code. With simplicity, accuracy, and workflow improvement at its core, Pixels2Flutter is poised to redefine UI development processes. Conclusion: Elevate your Flutter app development experience with Pixels2Flutter. Say goodbye to manual coding from UI screenshots and embrace a more efficient and accurate design-to-code conversion process. Enhance your workflow and productivity—try Pixels2Flutter today!

Introducing Papermark: The Open-Source Document Sharing Platform 📎 Papermark is a revolutionary open-source document sharing platform that offers a secure and free alternative to DocSend. With a wide range of features designed to enhance your document sharing experience, Papermark is a must-have tool for founders, investors, and anyone looking to securely share their pitch decks and other important documents. Key Features: 🔒 Open Source: Users have the ability to host the platform themselves, ensuring complete control and security over their shared documents. 🌐 Custom Domain: Stand out from the crowd by using your own unique domain, giving your shared documents a personalized touch. 💬 Feedback: Papermark provides valuable feedback for each slide, allowing users to improve their presentations and documents. 📊 Analytics: Built-in analytics provide real-time insights, helping users understand their audience better than ever before. 💵 Free to Use: Papermark is completely free, making it accessible for everyone. Use Cases: Trusted and beloved by over 2000 active users, Papermark is the go-to platform for sharing pitch decks and other important documents. Founders and investors particularly appreciate the platform's automatic link copying to the clipboard feature, as well as the detailed analytics available for each page of their pitch decks. In conclusion, Papermark is the essential tool for secure document sharing with real-time analytics. Its open-source nature, unique features, and user-friendly interface set it apart from other document sharing platforms. Start using Papermark today and experience the difference for yourself!

Introducing Monoid, the innovative open-source platform that transforms APIs into AI agents. With Monoid, you can take your APIs to the next level by providing context and real-time capabilities to Large Language Models (LLMs). 🔆 Key Features: - APIs into Actions: Monoid seamlessly turns APIs into Actions, empowering LLMs to understand context and act as an Agent on your behalf. - Create Agents in Minutes: With just a few simple steps, you can select a Foundational LLM, an Agent Type, and define Actions. No complex setup required, just provide your API and choose the parameters controlled by the AI Agent. - Test APIs as Actions: Simulate an AI Agent by leveraging your API to respond to natural language interactions. Experiment and fine-tune your agent's abilities. - Chat with your Agent: Engage in conversations with your AI Agent as it utilizes multiple Actions, making the interaction feel more human-like and intuitive. - Share Actions and Agents: Contribute to a vibrant network by sharing your Actions and Agents on the Hub. Collaborate with other users and foster innovation. 🌟 ** Revolutionize your AI Agent experience with Monoid! ** Explore various use cases where Monoid can make a difference: 💼 Shopping Assistant: Let AI Agents serve as your business concierge, guiding customers and providing conversational product recommendations. 📞 Customer Support Agent: Empower your customers to resolve issues independently with AI Agents acting as reliable support guides. ⚙️ Workflow Automator: Delegate manual repetitive tasks in IT, engineering, or operations to AI Agents, freeing up valuable time and resources. Experience the power of Monoid and unlock the true potential of your APIs. Transform the way you interact with AI agents today!

Introducing Higgsfield: A Powerful Solution for Multi-Node Training 🚀 Higgsfield, an essential tool in the machine learning landscape, offers a seamless solution for multi-node training without the tears. Let's dive into its impressive features: 🔹 GPU Workload Manager: Efficiently allocates exclusive and non-exclusive access to compute resources (nodes), making it a robust GPU workload manager for user training tasks. 🔹 Support for Trillion-Parameter Models: Supports ZeRO-3 deepspeed API and fully sharded data parallel API of PyTorch, allowing efficient sharding for models with billions to trillions of parameters. 🔹 Comprehensive Framework: Initiates, executes, and monitors the training of large neural networks on allocated nodes, providing a comprehensive framework for seamless training. 🔹 Resource Contention Management: Effectively manages resource contention by maintaining a queue for running experiments, ensuring optimal resource utilization. 🔹 GitHub Integration: Seamlessly integrates with GitHub and GitHub Actions, facilitating continuous integration of machine learning development. Ideal Use Cases for Higgsfield: ✨ Large Language Models: Tailored to train models with billions to trillions of parameters, especially Large Language Models (LLMs). ✨ Efficient GPU Resource Allocation: Perfect for users who require exclusive and non-exclusive access to GPU resources for their training tasks. ✨ Seamless CI/CD: Enables developers to integrate machine learning development seamlessly into GitHub workflows. Higgsfield emerges as a versatile and fault-tolerant solution, streamlining the intricate process of training massive models. With its comprehensive set of features, it empowers developers to navigate the challenges of multi-node training with efficiency and ease.

Introducing Replicate: Simplifying Machine Learning with Ease 🌟 Running machine learning models has never been easier with Replicate, a cloud-based platform that simplifies the entire process. No extensive understanding of machine learning or complex coding required - just a few lines of code and you're ready to go. 💻 **Key Features** 💻 - Run Models with Ease: Use Replicate's Python library or query the API directly to effortlessly run machine learning models. - Wide Range of Models: Explore thousands of shared models from Replicate's vibrant community. From language models to video creation and editing models, image restoration models, and more - the possibilities are endless. - Cog Tool: Leverage Replicate's open-source tool, Cog, to package your machine learning models in a standard, production-ready container. Say goodbye to Python dependency issues, GPU configurations, and Dockerfile assembly woes. - Automatic API and Scale: With Cog, Replicate automatically generates a scalable API server for your model and deploys it on a powerful cluster of GPUs. Experience seamless scaling to handle high traffic and resource optimization during idle periods. - Pay by the Second: Say goodbye to unnecessary costs. With Replicate, you only pay for the time your code is running, saving valuable resources on idle GPUs. 🚀 **Use Cases** 🚀 Replicate opens up a world of possibilities for various applications. Discover the potential of AI with open-source projects developed on Replicate, such as roomGPT by Hassan El Mghari – an AI-powered solution that redesigns rooms in a matter of seconds. Immerse yourself in Zoo by Charlie Holtz, a captivating playground for comparing AI image models. Ready to revolutionize your machine learning journey with Replicate? Try it now and experience a seamless and efficient way to run machine learning models.

📚 NLTK (Natural Language Toolkit) is a leading platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, and a suite of text processing libraries for various tasks in Natural Language Processing (NLP). 🔍 Features: - Text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. - Wrappers for industrial-strength NLP libraries. - An active discussion forum for users to discuss and resolve issues. - A hands-on guide introducing programming fundamentals alongside topics in computational linguistics, plus comprehensive API documentation. ✨ Use Cases: NLTK is suitable for a wide range of users including linguists, engineers, students, educators, researchers, and industry users. Some of the things you can do with NLTK include: - Tokenize and tag text: Break down text into words, phrases, symbols, or other meaningful elements (tokens) and assign parts of speech to each token (tagging). - Identify named entities: Recognize and categorize words that represent proper nouns (named entities) such as person names, organization names, locations etc. - Display a parse tree: Visualize the grammatical structure of a sentence. 💡 Additional Information: NLTK has been praised as a wonderful tool for teaching and working in computational linguistics using Python, and an amazing library to play with natural language. The creators of NLTK have also written a book titled "Natural Language Processing with Python" which provides a practical introduction to programming for language processing. The online version of the book has been updated for Python 3 and NLTK 3.

Introducing Pinokio 🌐 A unique browser designed to simplify application control and management. With Pinokio, users can effortlessly install, run, and programmatically control any application, all with just a single click. 🔹 One-click control: Install, run, and control any application with ease. 🔹 Community Scripts: Explore and utilize scripts shared by the Pinokio community. 🔹 Model Integration: Support for various models like BakLLaVA, Stable Diffusion, LLM, Whisper model, AudioSep, and more. 🔹 Audio and Video Editing: Tools for seamless audio and video editing, including AudioCraft_plus, Audio Webui, AudioLDM 2, and more. 🔹 Text and Image Generation: Harness the power of large language models for text generation and utilize latent consistency models for image generation. 🔹 Tutorial Scripts: Dive into simple script examples that showcase the full potential of Pinokio's APIs. Unlock limitless possibilities with Pinokio and experience its unparalleled features. Use Cases: ✨ Real-time BakLLaV: Allama.cpp with BakLLaVA model captures what it sees. ✨ Stable Diffusion web UI: Launch Stable Diffusion web UI with a single click. ✨ Whisper-WebUI: Easily add subtitles using the whisper model through a user-friendly Web UI. ✨ AudioSep: Efficiently separate anything you describe with precision. ✨ Text Generation WebUI: Experience a Gradio web UI for creating text with Large Language Models. ✨ FaceFusion: Swap and enhance faces effortlessly with this next-generation tool. ✨ IllusionDiffusion: Unleash your creativity and generate stunning illusion artwork with StableDiffusion. ✨ Bark Voice Cloning: Mimic your desired vocal persona by uploading a clean 20-second WAV file. ✨ ModelScope Video2Video: Enhance resolution and spatiotemporal continuity in text-generated and image-generated videos. ✨ Fooocus: Embrace a minimalist Stable Diffusion user interface for enhanced focus. ✨ ComfyUI: Seamlessly navigate Stable Diffusion with a powerful and modular GUI featuring a graph/nodes interface. Experience the convenience and potential of Pinokio today. 🚀

Introducing Synaptic.js, the JavaScript library dedicated to neural networks and deep learning 🧠 📚 Learning Resources and Demos: - Learn XOR: Understand the power of neural networks with this classic machine learning problem. - Discrete Sequence Recall: Dive into training networks to recall discrete sequences, a crucial concept in many applications. - Learn Image Filters: Explore the library's support for image filtering in computer vision tasks. - Paint An Image: Witness the creativity of neural networks in image-related tasks. - Self Organizing Map: Discover the specific neural network type used for clustering and visualization. - Read From Wikipedia: See how text processing and natural language understanding can be achieved using Synaptic.js. 📖 Documentation: - Neurons: Understand the core building blocks of artificial neurons in neural networks. - Networks: Gain insights into the internal structures and interconnections of neural networks in Synaptic.js. - Layers: Learn about the organization and functionality of layers within neural networks. - Trainer: Explore the training process of neural networks using Synaptic.js. - Architect: Design the architecture of neural networks tailored to specific tasks. Unlock the possibilities of JavaScript-powered neural networks with Synaptic.js!

🚀 Keras: Unlocking the Power of Deep Learning 🔑 Key Features: - User-Friendly API: Simplifies the creation and experimentation of neural network models. - Modularity: Build complex models by connecting various building blocks. - Extensive Compatibility: Seamlessly integrates with TensorFlow, Theano, and Microsoft Cognitive Toolkit. - Diverse Applications: Versatile support for image recognition, natural language processing, and more. - Community and Support: Access valuable resources, documentation, and a network of experts. - Customizable: Fine-tune models for a wide range of applications and research projects. - Open Source: Drive innovation and collaboration in the deep learning community. 📚 Resources: - Documentation: Extensive, invaluable resource for users of all levels. - Code Examples: Numerous tutorials to quickly grasp deep learning fundamentals. - Online Communities: Forums and communities to seek help and stay updated. - Books and Courses: Deep dive into Keras and deep learning. - Pre-trained Models: Save time and computational resources in your projects. 🌟 In Summary: Keras, the versatile deep learning library, is a valuable tool for beginners and experienced researchers alike. With its user-friendly interface, extensive compatibility, and supportive community, Keras empowers you to build and train neural networks for a wide array of applications. Dive into the world of deep learning with confidence and let Keras unleash your potential.

Introducing TFLearn, a user-friendly and modular deep learning library that builds upon the power of TensorFlow. With its intuitive interface and full transparency with TensorFlow, TFLearn aims to simplify the experimentation process with deep neural networks. 🔑 Key Features: - High-Level API: Easily implement deep neural networks with TFLearn's user-friendly API, suitable for beginners and experts alike. Tutorials and examples are available to guide users in getting started. - Modular Architecture: TFLearn's modular design allows for fast prototyping, with customizable and combinable neural network layers, regularizers, optimizers, and metrics. - Full Transparency Over TensorFlow: While operating on TensorFlow, TFLearn ensures complete transparency. Users can still work independently with TensorFlow tensors when necessary. - Training Support: TFLearn offers powerful helper functions for training any TensorFlow graph, accommodating multiple inputs, outputs, and optimizers for versatile deep learning tasks. - Graph Visualization: Effortlessly visualize the deep learning graph constructed with TFLearn, providing insights into weights, gradients, activations, and more. An invaluable tool for debugging and model understanding. - Device Placement: TFLearn simplifies device placement, effortlessly utilizing multiple CPUs or GPUs for training deep neural networks. 🚀 Supported Deep Learning Models: TFLearn's high-level API supports various cutting-edge models, including Convolutional Neural Networks (Convolutions), Long Short-Term Memory Networks (LSTM), Bidirectional Recurrent Neural Networks (BiRNN), Batch Normalization (BatchNorm), Parametric Rectified Linear Unit (PReLU), Residual Networks (ResNets), and Generative Networks (e.g., Generative Adversarial Networks, GANs). TFLearn keeps up with the latest techniques, ensuring access to the most recent advancements in the field. 💡 Compatibility: Please note that the latest version of TFLearn (v0.3) is compatible with TensorFlow versions 1.0 and later. Ensure the appropriate TensorFlow version is used when working with TFLearn. For the most up-to-date information and resources, we recommend visiting the official website or repository for TFLearn. Explore the simplicity and power of TFLearn today and unlock the potential of deep learning with ease.

Introducing OpenCV: Your Real-Time Optimized Computer Vision Library 📸 🔬 Key Features: - Open Source: OpenCV is open source and free for commercial use. - Optimized: Highly optimized library focused on real-time applications. - Cross-Platform: Supports C++, Python, and Java interfaces across multiple platforms. 🏢 Services: - OpenCV.ai: Hire a team of AI experts. - OpenCV University: A trustworthy destination for learning Computer Vision, Deep Learning, and AI. - OpenCV Face Recognition: Combines the world's largest Computer Vision library with top-rated Face Recognition technology. 📚 Courses offered by OpenCV University: - Mastering OpenCV with Python - Fundamentals of CV & IP - Deep Learning with PyTorch - Deep Learning with TensorFlow & Keras - Computer Vision & Deep Learning Applications - Mastering Generative AI for Art 💼 Partnerships: OpenCV has partnerships with notable companies like Intel, its Platinum Member, offering Gold Membership and Development Partnership options. 🔎 Use Cases: OpenCV is widely used in AI and ML applications, especially in face recognition technology. It is also extensively utilized in educational courses teaching Computer Vision, Deep Learning, and AI. Discover the power of OpenCV, the ultimate tool for real-time Computer Vision! 🚀

Introducing Megatron: 🤖 Megatron, developed by NVIDIA's Applied Deep Learning Research team, is a robust transformer model designed to advance research in large transformer language models. With three iterations available, Megatron offers high performance and versatility for a wide range of applications. Key Highlights: 💡 - Efficient Model Parallelism: Incorporating model-parallel techniques, Megatron ensures smooth and scalable model training, especially for large transformer models like GPT, BERT, and T5. - Mixed Precision: Megatron embraces mixed precision to optimize hardware resources and enhance the training of large-scale language models. Projects Utilizing Megatron: 🚀 Megatron has been applied in various notable projects, such as: - Studies on BERT and GPT - Advancements in Biomedical Domain Language Models - End-to-End Training of Neural Retrievers for Open-Domain Question Answering - Large Scale Multi-Actor Generative Dialog Modeling - Local Knowledge Powered Conversational Agents - MEGATRON-CNTRL: Controllable Story Generation with External Knowledge - Advancements in the RACE Reading Comprehension Dataset Leaderboard - Training Question Answering Models From Synthetic Data - Detecting Social Biases with Few-shot Instruction Prompts - Exploring Domain-Adaptive Training for Detoxifying Language Models - Leveraging DeepSpeed and Megatron for Training Megatron-Turing NLG 530B NeMo Megatron: 🌐 Megatron finds application in NeMo Megatron, a comprehensive framework for constructing and training advanced natural language processing models with billions or even trillions of parameters. This framework is particularly beneficial for enterprises engaged in large-scale NLP projects. Scalability: 📈 Megatron's codebase enables efficient training of massive language models with hundreds of billions of parameters. From GPT models with 1 billion to a staggering 1 trillion parameters, Megatron demonstrates impressive linear scaling across various GPU setups and model sizes. Benchmark results utilizing the Selene supercomputer by NVIDIA highlight the outstanding performance capabilities of Megatron. Experience the power of Megatron for your language model training needs.

Introducing Text-To-Text Transfer Transformer (T5) ✨: an innovative creation by Google Research, pushing the boundaries of transfer learning. T5 is a powerful unified transformer, pre-trained on a vast text corpus, and capable of delivering cutting-edge performance across various NLP tasks. Its versatile library, designed to facilitate model development, allows for easy reproduction of experiments from the project's paper and offers essential modules for training and fine-tuning models on a range of text-to-text tasks. 🔑 Key Features: - t5.data: Package providing Task objects for tf.data.Datasets. - t5.evaluation: Metrics and utilities for evaluation. - t5.models: Shims for connecting Tasks and Mixtures to a model implementation. 🔧 Usage: - Dataset Preparation: Supports Tasks, TfdsTasks, TextLineTasks, and TSV files. - Installation: Simple pip installation process. - Setting up TPUs on GCP: Configure variables based on your project, zone, and GCS bucket. - Training, Fine-tuning, Eval, Decode, Export: A variety of commands provided for these operations. - GPU Usage: Supports efficient GPU utilization. 💡 Use Cases: - Reproducing Experiments: Successfully recreate the experiments from the project's paper. - Model Development: Utilize the library's modules for training and fine-tuning models. The T5 code is open-source and available on GitHub under the Apache-2.0 license. Join the exploration of transfer learning capabilities with T5 today! 🌟

Introducing MLJAR: a versatile platform that caters to a spectrum of machine learning capabilities. 🚀 🔬 Automated Machine Learning (AutoML): Simplify data prediction and training with MLJAR's AutoML feature. It ensures fairness in machine learning with eXplainable AI (XAI) and provides comprehensive documentation and fairness metrics. 📚 Notebook Conversion to Web App: Transform your notebooks into dynamic web applications, dashboards, and reports effortlessly. Deploy them using Mercury Cloud to bring your insights to life. 📊 Python Plots Generation with LLMs: PlotAI utilizes Large Language Models to generate Python code for data visualization. Convert your DataFrame and prompt into visually appealing plots. 💡 Building SaaS: The SaaSitive tutorial guides you in constructing and deploying a Software as a Service (SaaS) application from scratch. Leverage React and Django to turn your SaaS vision into a reality. ⚙️ Server and Website Monitoring: MLJAR incorporates machine learning to detect anomalies swiftly. Stay proactive with timely email notifications to prevent potential downtimes and maintain system reliability. 🤖 Machine Learning with Django: DeployMachineLearning offers invaluable insights for navigating the complex landscape of machine learning development. MLJAR meets the needs of various user groups: 🧪 Data Scientists: Empower data scientists to predict, train, and comprehend their data effectively with AutoML and Machine Learning with Django. 💻 Developers: Convert your notebooks into interactive web apps and build SaaS applications using React and Django. 🏢 Businesses: Support server and website monitoring to prevent downtimes and quickly detect anomalies. Maintain a reliable online presence. Whether you're a data scientist, developer, or business, MLJAR has something to offer. Explore the possibilities now!

Introducing YData Fabric platform: the ultimate data-centric AI tool 👨‍💻 Features: - Synthetic Data Generation: Mimics real data, protecting sensitive information and enhancing model efficiency. - Data Catalog: Profiling and managing data, with an interactive catalog to track changes and drifts. - Data-Centric AI: A proactive approach to AI development, enhancing ML workflows. - Open-Source Tools: ydata-synthetic and ydata-profiling for generating tabular and time-series data, and one-line Exploratory Data Analysis (EDA) experience. - Community: A network of data-centric AI experts and thought-leaders. Use Cases: - Financial Services: Boosts efficiency in payment sector. - Telecommunications: Manages data involved in communication. - Utilities: Handles electricity, water supply, and public service data. - Retail: Secures data for top brands. - Healthcare & Pharma: Unleashes the power of health sector data. Benefits: - Productivity: Boosts data scientist productivity by 10x. - Faster Delivery: Enables 25% faster AI model delivery. - Time-to-Market Reduction: Cuts time-to-market by up to 50%. - Model Performance Boost: Improves model performance by up to 20% through enhanced data quality. With over 12,000 data scientists using it daily and more than 52 million downloads, YData is the trusted platform for high-quality data generation and management capabilities.

🚀 Introducing Pezzo: The Open-Source AI Development Toolkit 🤖 Features: ✨ Effortless Prompt and Version Management: Seamlessly handle AI prompts and versions for swift solution delivery. ✨ Conversion-Boosting Capabilities: Implement A/B testing, targeting, and experiments for improved conversion rates. ✨ Cost Optimization: Maximize efficiency, potentially saving up to 50% in AI operations. ✨ Real-Time Trading and Advanced Charts: Access advanced features like real-time trading and charts to enhance your development experience. ✨ Comprehensive Troubleshooting: Benefit from execution history, time-travel, debugging, and test suites for confident deployment. Use Cases: 🔧 Efficient AI Development: Simplify and expedite AI tasks with Pezzo's array of features. 👥 Collaborative AI Development: Encourage teamwork and collective contributions to enhance AI impact. 💰 Cost-Effective Operations: Optimize spending while maintaining productivity in AI endeavors. Why Use Pezzo: Pezzo is a comprehensive AI development toolkit designed to streamline the entire AI development journey. With a keen focus on collaboration, cost efficiency, and troubleshooting, it empowers users to harness AI's power with ease. Its open-source nature and upcoming cloud service make it an invaluable resource for AI enthusiasts and professionals alike. Join the Pezzo community on GitHub and sign up for early access today! 👉 Learn more about Pezzo and get started on your AI projects now!