legacy-sourcegraph

Sourcegraph is a tool that simplifies reading, writing, and fixing code in large and complex codebases. It offers features such as code search across repositories and hosts, code intelligence for navigation and references, and the ability to roll out large-scale changes and track migrations. Sourcegraph can be used on the cloud or self-hosted, with public code search available on Sourcegraph.com. The tool provides high-level architecture documentation, database setup best practices, Go and documentation style guides, tips for modifying the GraphQL API, and guidelines for contributing.

Sourcegraph is a tool that simplifies reading, writing, and fixing code in large and complex codebases. It offers features such as code search across repositories, code intelligence for code navigation and history tracing, and the ability to roll out large-scale changes to multiple repositories simultaneously. Sourcegraph can be used on the cloud or self-hosted, and provides public code search on Sourcegraph.com. The tool is designed to enhance code understanding and collaboration within development teams.

Sourcegraph is a code search and navigation tool that helps developers read, write, and fix code in large, complex codebases. It provides features such as code search across all repositories and branches, code intelligence for navigation and refactoring, and the ability to fix and refactor code across multiple repositories at once.

Fluid is an open source Kubernetes-native Distributed Dataset Orchestrator and Accelerator for data-intensive applications, such as big data and AI applications. It implements dataset abstraction, scalable cache runtime, automated data operations, elasticity and scheduling, and is runtime platform agnostic. Key concepts include Dataset and Runtime. Prerequisites include Kubernetes version > 1.16, Golang 1.18+, and Helm 3. The tool offers features like accelerating remote file accessing, machine learning, accelerating PVC, preloading dataset, and on-the-fly dataset cache scaling. Contributions are welcomed, and the project is under the Apache 2.0 license with a vendor-neutral approach.

ReproModel is an open-source toolbox designed to boost AI research efficiency by enabling researchers to reproduce, compare, train, and test AI models faster. It provides standardized models, dataloaders, and processing procedures, allowing researchers to focus on new datasets and model development. With a no-code solution, users can access benchmark and SOTA models and datasets, utilize training visualizations, extract code for publication, and leverage an LLM-powered automated methodology description writer. The toolbox helps researchers modularize development, compare pipeline performance reproducibly, and reduce time for model development, computation, and writing. Future versions aim to facilitate building upon state-of-the-art research by loading previously published study IDs with verified code, experiments, and results stored in the system.

Applio is a VITS-based Voice Conversion tool focused on simplicity, quality, and performance. It features a user-friendly interface, cross-platform compatibility, and a range of customization options. Applio is suitable for various tasks such as voice cloning, voice conversion, and audio editing. Its key features include a modular codebase, hop length implementation, translations in over 30 languages, optimized requirements, streamlined installation, hybrid F0 estimation, easy-to-use UI, optimized code and dependencies, plugin system, overtraining detector, model search, enhancements in pretrained models, voice blender, accessibility improvements, new F0 extraction methods, output format selection, hashing system, model download system, TTS enhancements, split audio, Discord presence, Flask integration, and support tab.

OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. It provides a common API to deliver inference solutions on various platforms, including CPU, GPU, NPU, and heterogeneous devices. OpenVINO™ supports pre-trained models from Open Model Zoo and popular frameworks like TensorFlow, PyTorch, and ONNX. Key components of OpenVINO™ include the OpenVINO™ Runtime, plugins for different hardware devices, frontends for reading models from native framework formats, and the OpenVINO Model Converter (OVC) for adjusting models for optimal execution on target devices.

ClearML is a suite of tools designed to streamline the machine learning workflow. It includes an experiment manager, MLOps/LLMOps, data management, and model serving capabilities. ClearML is open-source and offers a free tier hosting option. It supports various ML/DL frameworks and integrates with Jupyter Notebook and PyCharm. ClearML provides extensive logging capabilities, including source control info, execution environment, hyper-parameters, and experiment outputs. It also offers automation features, such as remote job execution and pipeline creation. ClearML is designed to be easy to integrate, requiring only two lines of code to add to existing scripts. It aims to improve collaboration, visibility, and data transparency within ML teams.

Slips is a powerful endpoint behavioral intrusion prevention and detection system that uses machine learning to detect malicious behaviors in network traffic. It can work with network traffic in real-time, PCAP files, and network flows from tools like Suricata, Zeek/Bro, and Argus. Slips threat detection is based on machine learning models, threat intelligence feeds, and expert heuristics. It gathers evidence of malicious behavior and triggers alerts when enough evidence is accumulated. The tool is Python-based and supported on Linux and MacOS, with blocking features only on Linux. Slips relies on Zeek network analysis framework and Redis for interprocess communication. It offers a graphical user interface for easy monitoring and analysis.

DataDreamer is a powerful open-source Python library designed for prompting, synthetic data generation, and training workflows. It is simple, efficient, and research-grade, allowing users to create prompting workflows, generate synthetic datasets, and train models with ease. The library is built for researchers, by researchers, focusing on correctness, best practices, and reproducibility. It offers features like aggressive caching, resumability, support for bleeding-edge techniques, and easy sharing of datasets and models. DataDreamer enables users to run multi-step prompting workflows, generate synthetic datasets for various tasks, and train models by aligning, fine-tuning, instruction-tuning, and distilling them using existing or synthetic data.

Kiln is an intuitive tool for fine-tuning LLM models, generating synthetic data, and collaborating on datasets. It offers desktop apps for Windows, MacOS, and Linux, zero-code fine-tuning for various models, interactive data generation, and Git-based version control. Users can easily collaborate with QA, PM, and subject matter experts, generate auto-prompts, and work with a wide range of models and providers. The tool is open-source, privacy-first, and supports structured data tasks in JSON format. Kiln is free to use and helps build high-quality AI products with datasets, facilitates collaboration between technical and non-technical teams, allows comparison of models and techniques without code, ensures structured data integrity, and prioritizes user privacy.

Spider is a high-performance web crawler and indexer designed to handle data curation workloads efficiently. It offers features such as concurrency, streaming, decentralization, headless Chrome rendering, HTTP proxies, cron jobs, subscriptions, smart mode, blacklisting, whitelisting, budgeting depth, dynamic AI prompt scripting, CSS scraping, and more. Users can easily get started with the Spider Cloud hosted service or set up local installations with spider-cli. The tool supports integration with Node.js and Python for additional flexibility. With a focus on speed and scalability, Spider is ideal for extracting and organizing data from the web.

Docq is a private and secure GenAI tool designed to extract knowledge from business documents, enabling users to find answers independently. It allows data to stay within organizational boundaries, supports self-hosting with various cloud vendors, and offers multi-model and multi-modal capabilities. Docq is extensible, open-source (AGPLv3), and provides commercial licensing options. The tool aims to be a turnkey solution for organizations to adopt AI innovation safely, with plans for future features like more data ingestion options and model fine-tuning.

Extractous offers a fast and efficient solution for extracting content and metadata from various document types such as PDF, Word, HTML, and many other formats. It is built with Rust, providing high performance, memory safety, and multi-threading capabilities. The tool eliminates the need for external services or APIs, making data processing pipelines faster and more efficient. It supports multiple file formats, including Microsoft Office, OpenOffice, PDF, spreadsheets, web documents, e-books, text files, images, and email formats. Extractous provides a clear and simple API for extracting text and metadata content, with upcoming support for JavaScript/TypeScript. It is free for commercial use under the Apache 2.0 License.

The openfoodfacts-ai repository is dedicated to tracking and storing experimental AI endeavors, models training, and wishlists related to nutrition table detection, category prediction, logos and labels detection, spellcheck, and other AI projects for Open Food Facts. It serves as a hub for integrating AI models into production and collaborating on AI-related issues. The repository also hosts trained models and datasets for public use and experimentation.

Aria is a Zotero plugin that serves as an AI Research Assistant powered by Large Language Models (LLMs). It offers features like drag-and-drop referencing, autocompletion for creators and tags, visual analysis using GPT-4 Vision, and saving chats as notes and annotations. Aria requires the OpenAI GPT-4 model family and provides a configurable interface through preferences. Users can install Aria by downloading the latest release from GitHub and activating it in Zotero. The tool allows users to interact with Zotero library through conversational AI and probabilistic models, with the ability to troubleshoot errors and provide feedback for improvement.

Cody is a free, open-source AI coding assistant that can write and fix code, provide AI-generated autocomplete, and answer your coding questions. Cody fetches relevant code context from across your entire codebase to write better code that uses more of your codebase's APIs, impls, and idioms, with less hallucination.

Sourcegraph is a tool that simplifies reading, writing, and fixing code in large and complex codebases. It offers features such as code search across repositories and hosts, code intelligence for navigation and references, and the ability to roll out large-scale changes and track migrations. Sourcegraph can be used on the cloud or self-hosted, with public code search available on Sourcegraph.com. The tool provides high-level architecture documentation, database setup best practices, Go and documentation style guides, tips for modifying the GraphQL API, and guidelines for contributing.

Sourcegraph is a code search and navigation tool that helps developers read, write, and fix code in large, complex codebases. It provides features such as code search across all repositories and branches, code intelligence for navigation and refactoring, and the ability to fix and refactor code across multiple repositories at once.

Sourcegraph is a tool that simplifies reading, writing, and fixing code in large and complex codebases. It offers features such as code search across repositories, code intelligence for code navigation and history tracing, and the ability to roll out large-scale changes to multiple repositories simultaneously. Sourcegraph can be used on the cloud or self-hosted, and provides public code search on Sourcegraph.com. The tool is designed to enhance code understanding and collaboration within development teams.

Aide is an Open Source AI-native code editor that combines the powerful features of VS Code with advanced AI capabilities. It provides a combined chat + edit flow, proactive agents for fixing errors, inline editing widget, intelligent code completion, and AST navigation. Aide is designed to be an intelligent coding companion, helping users write better code faster while maintaining control over the development process.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Nvidia GPU exporter for prometheus, using `nvidia-smi` binary to gather metrics.

Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.