PlanExe is a planning AI tool that helps users generate detailed plans based on vague descriptions. It offers a Gradio-based web interface for easy input and output. Users can choose between running models in the cloud or locally on a high-end computer. The tool aims to provide a straightforward path to planning various tasks efficiently.

Mage is an open-source data pipeline tool for transforming and integrating data. It offers an easy developer experience, engineering best practices built-in, and data as a first-class citizen. Mage makes it easy to build, preview, and launch data pipelines, and provides observability and scaling capabilities. It supports data integrations, streaming pipelines, and dbt integration.

Eko is a lightweight and flexible command-line tool for managing environment variables in your projects. It allows you to easily set, get, and delete environment variables for different environments, making it simple to manage configurations across development, staging, and production environments. With Eko, you can streamline your workflow and ensure consistency in your application settings without the need for complex setup or configuration files.

CAMEL is an open-source library designed for the study of autonomous and communicative agents. We believe that studying these agents on a large scale offers valuable insights into their behaviors, capabilities, and potential risks. To facilitate research in this field, we implement and support various types of agents, tasks, prompts, models, and simulated environments.

The FuzzyAI Fuzzer is a powerful tool for automated LLM fuzzing, designed to help developers and security researchers identify jailbreaks and mitigate potential security vulnerabilities in their LLM APIs. It supports various fuzzing techniques, provides input generation capabilities, can be easily integrated into existing workflows, and offers an extensible architecture for customization and extension. The tool includes attacks like ArtPrompt, Taxonomy-based paraphrasing, Many-shot jailbreaking, Genetic algorithm, Hallucinations, DAN (Do Anything Now), WordGame, Crescendo, ActorAttack, Back To The Past, Please, Thought Experiment, and Default. It supports models from providers like Anthropic, OpenAI, Gemini, Azure, Bedrock, AI21, and Ollama, with the ability to add support for newer models. The tool also supports various cloud APIs and datasets for testing and experimentation.

Findto is a decentralized search tool for the Web and AI that puts people in control of algorithms. It aims to provide a better search experience by offering diverse sources, privacy and carbon level information, trends exploration, autosuggest, voice search, and more. Findto encourages a free search experience and promotes a healthier internet by empowering users with democratic choices.

Starmoon is an affordable, compact AI-enabled device that can understand and respond to your emotions with empathy. It offers supportive conversations and personalized learning assistance. The device is cost-effective, voice-enabled, open-source, compact, and aims to reduce screen time. Users can assemble the device themselves using off-the-shelf components and deploy it locally for data privacy. Starmoon integrates various APIs for AI language models, speech-to-text, text-to-speech, and emotion intelligence. The hardware setup involves components like ESP32S3, microphone, amplifier, speaker, LED light, and button, along with software setup instructions for developers. The project also includes a web app, backend API, and background task dashboard for monitoring and management.

Buffer of Thoughts (BoT) is a thought-augmented reasoning framework designed to enhance the accuracy, efficiency, and robustness of large language models (LLMs). It introduces a meta-buffer to store high-level thought-templates distilled from problem-solving processes, enabling adaptive reasoning for efficient problem-solving. The framework includes a buffer-manager to dynamically update the meta-buffer, ensuring scalability and stability. BoT achieves significant performance improvements on reasoning-intensive tasks and demonstrates superior generalization ability and robustness while being cost-effective compared to other methods.

Comet Opik is a repository containing two main services: a frontend and a backend. It provides a Python SDK for easy installation. Users can run the full application locally with minikube, following specific installation prerequisites. The repository structure includes directories for applications like Opik backend, with detailed instructions available in the README files. Users can manage the installation using simple k8s commands and interact with the application via URLs for checking the running application and API documentation. The repository aims to facilitate local development and testing of Opik using Kubernetes technology.

SciCode is a challenging benchmark designed to evaluate the capabilities of language models (LMs) in generating code for solving realistic scientific research problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 subdomains from 6 domains. The benchmark offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. SciCode demonstrates a realistic workflow of identifying critical science concepts and facts and transforming them into computation and simulation code, aiming to help showcase LLMs' progress towards assisting scientists and contribute to the future building and evaluation of scientific AI.

DinoPal is an AI voice assistant residing in the Mac menu bar, offering real-time voice and video chat, screen sharing, online search, and multilingual support. It provides various AI assistants with unique strengths and characteristics to meet different conversational needs. Users can easily install DinoPal and access different communication modes, with a call time limit of 30 minutes. User feedback can be shared in the Discord community. DinoPal is powered by Google Gemini & Pipecat.

SimpleAICV_pytorch_training_examples is a repository that provides simple training and testing examples for various computer vision tasks such as image classification, object detection, semantic segmentation, instance segmentation, knowledge distillation, contrastive learning, masked image modeling, OCR text detection, OCR text recognition, human matting, salient object detection, interactive segmentation, image inpainting, and diffusion model tasks. The repository includes support for multiple datasets and networks, along with instructions on how to prepare datasets, train and test models, and use gradio demos. It also offers pretrained models and experiment records for download from huggingface or Baidu-Netdisk. The repository requires specific environments and package installations to run effectively.

QServe is a serving system designed for efficient and accurate Large Language Models (LLM) on GPUs with W4A8KV4 quantization. It achieves higher throughput compared to leading industry solutions, allowing users to achieve A100-level throughput on cheaper L40S GPUs. The system introduces the QoQ quantization algorithm with 4-bit weight, 8-bit activation, and 4-bit KV cache, addressing runtime overhead challenges. QServe improves serving throughput for various LLM models by implementing compute-aware weight reordering, register-level parallelism, and fused attention memory-bound techniques.

SuperDuperDB is a Python framework for integrating AI models, APIs, and vector search engines directly with your existing databases, including hosting of your own models, streaming inference and scalable model training/fine-tuning. Build, deploy and manage any AI application without the need for complex pipelines, infrastructure as well as specialized vector databases, and moving our data there, by integrating AI at your data's source: - Generative AI, LLMs, RAG, vector search - Standard machine learning use-cases (classification, segmentation, regression, forecasting recommendation etc.) - Custom AI use-cases involving specialized models - Even the most complex applications/workflows in which different models work together SuperDuperDB is **not** a database. Think `db = superduper(db)`: SuperDuperDB transforms your databases into an intelligent platform that allows you to leverage the full AI and Python ecosystem. A single development and deployment environment for all your AI applications in one place, fully scalable and easy to manage.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates tasks such as data cleaning, preprocessing, and complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. It aims to expedite the data analysis process, making it accessible to all, regardless of their expertise in data analysis. The tool is built to empower users to process data and achieve high-quality visualizations with unparalleled efficiency, and to execute high-performance modeling with the best strategies. Future enhancements include Natural Language Processing (NLP), neural networks, and object detection utilizing YOLO, broadening its capabilities to meet diverse data analysis needs.

PlanExe is a planning AI tool that helps users generate detailed plans based on vague descriptions. It offers a Gradio-based web interface for easy input and output. Users can choose between running models in the cloud or locally on a high-end computer. The tool aims to provide a straightforward path to planning various tasks efficiently.

HuggingFists is a low-code data flow tool that enables convenient use of LLM and HuggingFace models. It provides functionalities similar to Langchain, allowing users to design, debug, and manage data processing workflows, create and schedule workflow jobs, manage resources environment, and handle various data artifact resources. The tool also offers account management for users, allowing centralized management of data source accounts and API accounts. Users can access Hugging Face models through the Inference API or locally deployed models, as well as datasets on Hugging Face. HuggingFists supports breakpoint debugging, branch selection, function calls, workflow variables, and more to assist users in developing complex data processing workflows.

Backend.AI Web UI is a user-friendly web and app interface designed to make AI accessible for end-users, DevOps, and SysAdmins. It provides features for session management, inference service management, pipeline management, storage management, node management, statistics, configurations, license checking, plugins, help & manuals, kernel management, user management, keypair management, manager settings, proxy mode support, service information, and integration with the Backend.AI Web Server. The tool supports various devices, offers a built-in websocket proxy feature, and allows for versatile usage across different platforms. Users can easily manage resources, run environment-supported apps, access a web-based terminal, use Visual Studio Code editor, manage experiments, set up autoscaling, manage pipelines, handle storage, monitor nodes, view statistics, configure settings, and more.

The Apache Airflow Python Client provides a range of REST API endpoints for managing Airflow metadata objects. It supports CRUD operations for resources, with endpoints accepting and returning JSON. Users can create, read, update, and delete resources. The API design follows conventions with consistent naming and field formats. Update mask is available for patch endpoints to specify fields for update. API versioning is not synchronized with Airflow releases, and changes go through a deprecation phase. The tool supports various authentication methods and error responses follow RFC 7807 format.

The Modal Python library provides convenient, on-demand access to serverless cloud compute from Python scripts on your local computer. It allows users to easily integrate serverless cloud computing into their Python scripts, providing a seamless experience for accessing cloud resources. The library simplifies the process of interacting with cloud services, enabling developers to focus on their applications' logic rather than infrastructure management. With detailed documentation and support available through the Modal Slack channel, users can quickly get started and leverage the power of serverless computing in their projects.

MEGREZ is a modern and elegant open-source high-performance computing platform that efficiently manages GPU resources. It allows for easy container instance creation, supports multiple nodes/multiple GPUs, modern UI environment isolation, customizable performance configurations, and user data isolation. The platform also comes with pre-installed deep learning environments, supports multiple users, features a VSCode web version, resource performance monitoring dashboard, and Jupyter Notebook support.

Cortex.cpp is an open-source platform designed as the brain for robots, offering functionalities such as vision, speech, language, tabular data processing, and action. It provides an AI platform for running AI models with multi-engine support, hardware optimization with automatic GPU detection, and an OpenAI-compatible API. Users can download models from the Hugging Face model hub, run models, manage resources, and access advanced features like multiple quantizations and engine management. The tool is under active development, promising rapid improvements for users.

MLE-Agent is an intelligent companion designed for machine learning engineers and researchers. It features autonomous baseline creation, integration with Arxiv and Papers with Code, smart debugging, file system organization, comprehensive tools integration, and an interactive CLI chat interface for seamless AI engineering and research workflows.

LLM-Minutes-of-Meeting is a project showcasing NLP & LLM's capability to summarize long meetings and automate the task of delegating Minutes of Meeting(MoM) emails. It converts audio/video files to text, generates editable MoM, and aims to develop a real-time python web-application for meeting automation. The tool features keyword highlighting, topic tagging, export in various formats, user-friendly interface, and uses Celery for asynchronous processing. It is designed for corporate meetings, educational institutions, legal and medical fields, accessibility, and event coverage.

PlanExe is a planning AI tool that helps users generate detailed plans based on vague descriptions. It offers a Gradio-based web interface for easy input and output. Users can choose between running models in the cloud or locally on a high-end computer. The tool aims to provide a straightforward path to planning various tasks efficiently.

crewAI-examples is a collection of examples showcasing the usage of crewAI framework for automating processes in role-playing AI agent collaboration. It includes basic and advanced examples such as marketing strategy, surprise trip, match to proposal, find job candidates demo, create job posting, trip planner, create Instagram post, markdown validator, game generator, using Azure OpenAI API, stock analysis, landing page generator, and CrewAI + LangGraph integration.

ChatGPT Queue Prompts is a collection of prompt chains designed to enhance interactions with large language models like ChatGPT. These prompt chains help build context for the AI before performing specific tasks, improving performance. Users can copy and paste prompt chains into the ChatGPT Queue extension to process prompts in sequence. The repository includes example prompt chains for tasks like conducting AI company research, building SEO optimized blog posts, creating courses, revising resumes, enriching leads for CRM, personal finance document creation, workout and nutrition plans, marketing plans, and more.

OpsPilot is an AI-powered operations navigator developed by the WeOps team. It leverages deep learning and LLM technologies to make operations plans interactive and generalize and reason about local operations knowledge. OpsPilot can be integrated with web applications in the form of a chatbot and primarily provides the following capabilities: 1. Operations capability precipitation: By depositing operations knowledge, operations skills, and troubleshooting actions, when solving problems, it acts as a navigator and guides users to solve operations problems through dialogue. 2. Local knowledge Q&A: By indexing local knowledge and Internet knowledge and combining the capabilities of LLM, it answers users' various operations questions. 3. LLM chat: When the problem is beyond the scope of OpsPilot's ability to handle, it uses LLM's capabilities to solve various long-tail problems.

A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO) for solving vehicle routing problems. Users can interact with the GMPRO data model through forms, tables, and maps to construct scenarios, tune constraints, and visualize routes. The application is intended for exploration purposes only and should not be deployed in production. Users are responsible for billing related to cloud resources and API usage. It is important to understand the pricing models for Maps Platform and Route Optimization before using the application.

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It leverages Large Language Models (LLMs) to interact with spreadsheets like a human expert, enabling non-expert users to complete tasks on complex software such as Google Sheets and Excel via a language interface. The tool observes spreadsheet states, polishes generated solutions based on external action documents and error feedback, and aims to improve success rate and efficiency. SheetCopilot offers a dataset with diverse task categories and operations, supporting operations like entry & manipulation, management, formatting, charts, and pivot tables. Users can interact with SheetCopilot in Excel or Google Sheets, executing tasks like calculating revenue, creating pivot tables, and plotting charts. The tool's evaluation includes performance comparisons with leading LLMs and VBA-based methods on specific datasets, showcasing its capabilities in controlling various aspects of a spreadsheet.

A web application to explore the capabilities of Google Maps Platform Route Optimization (GMPRO). It helps users understand the data model and functions of the API by presenting interactive forms, tables, and maps. The tool is intended for exploratory use only and should not be deployed in production. Users can construct scenarios, tune constraint parameters, and visualize routes before implementing their own solutions for integrating Route Optimization into their business processes. The application incurs charges related to cloud resources and API usage, and users should be cautious about generating high usage volumes, especially for large scenarios.