This repository serves as a companion to the Build AI-Powered Apps with OpenAI and Node.js course on Frontend Masters. It includes course notes and provides alternative approaches for deprecated Langchain methods by installing the Langchain community module and importing loaders for document processing from PDFs and YouTube videos.

The Google Gen AI JavaScript SDK is an experimental SDK for TypeScript and JavaScript developers to build applications powered by Gemini. It supports both the Gemini Developer API and Vertex AI. The SDK is designed to work with Gemini 2.0 features. Users can access API features through the GoogleGenAI classes, which provide submodules for querying models, managing caches, creating chats, uploading files, and starting live sessions. The SDK also allows for function calling to interact with external systems. Users can find more samples in the GitHub samples directory.

Ax is a Typescript library that allows users to build intelligent agents inspired by agentic workflows and the Stanford DSP paper. It seamlessly integrates with multiple Large Language Models (LLMs) and VectorDBs to create RAG pipelines or collaborative agents capable of solving complex problems. The library offers advanced features such as streaming validation, multi-modal DSP, and automatic prompt tuning using optimizers. Users can easily convert documents of any format to text, perform smart chunking, embedding, and querying, and ensure output validation while streaming. Ax is production-ready, written in Typescript, and has zero dependencies.

Langfun is a Python library that aims to make language models (LM) fun to work with. It enables a programming model that flows naturally, resembling the human thought process. Langfun emphasizes the reuse and combination of language pieces to form prompts, thereby accelerating innovation. Unlike other LM frameworks, which feed program-generated data into the LM, langfun takes a distinct approach: It starts with natural language, allowing for seamless interactions between language and program logic, and concludes with natural language and optional structured output. Consequently, langfun can aptly be described as Language as functions, capturing the core of its methodology.

mcp-ui is a collection of SDKs that bring interactive web components to the Model Context Protocol (MCP). It allows servers to define reusable UI snippets, render them securely in the client, and react to their actions in the MCP host environment. The SDKs include @mcp-ui/server (TypeScript) for generating UI resources on the server, @mcp-ui/client (TypeScript) for rendering UI components on the client, and mcp_ui_server (Ruby) for generating UI resources in a Ruby environment. The project is an experimental community playground for MCP UI ideas, with rapid iteration and enhancements.

Parseable is a full stack observability platform designed to ingest, analyze, and extract insights from various types of telemetry data. It can be run locally, in the cloud, or as a managed service. The platform offers features like high availability, smart cache, alerts, role-based access control, OAuth2 support, and OpenTelemetry integration. Users can easily ingest data, query logs, and access the dashboard to monitor and analyze data. Parseable provides a seamless experience for observability and monitoring tasks.

Jina-Serve is a framework for building and deploying AI services that communicate via gRPC, HTTP and WebSockets. It provides native support for major ML frameworks and data types, high-performance service design with scaling and dynamic batching, LLM serving with streaming output, built-in Docker integration and Executor Hub, one-click deployment to Jina AI Cloud, and enterprise-ready features with Kubernetes and Docker Compose support. Users can create gRPC-based AI services, build pipelines, scale services locally with replicas, shards, and dynamic batching, deploy to the cloud using Kubernetes, Docker Compose, or JCloud, and enable token-by-token streaming for responsive LLM applications.

LitData is a tool designed for blazingly fast, distributed streaming of training data from any cloud storage. It allows users to transform and optimize data in cloud storage environments efficiently and intuitively, supporting various data types like images, text, video, audio, geo-spatial, and multimodal data. LitData integrates smoothly with frameworks such as LitGPT and PyTorch, enabling seamless streaming of data to multiple machines. Key features include multi-GPU/multi-node support, easy data mixing, pause & resume functionality, support for profiling, memory footprint reduction, cache size configuration, and on-prem optimizations. The tool also provides benchmarks for measuring streaming speed and conversion efficiency, along with runnable templates for different data types. LitData enables infinite cloud data processing by utilizing the Lightning.ai platform to scale data processing with optimized machines.

AnglE is a library for training state-of-the-art BERT/LLM-based sentence embeddings with just a few lines of code. It also serves as a general sentence embedding inference framework, allowing for inferring a variety of transformer-based sentence embeddings. The library supports various loss functions such as AnglE loss, Contrastive loss, CoSENT loss, and Espresso loss. It provides backbones like BERT-based models, LLM-based models, and Bi-directional LLM-based models for training on single or multi-GPU setups. AnglE has achieved significant performance on various benchmarks and offers official pretrained models for both BERT-based and LLM-based models.

Bespoke Curator is an open-source tool for data curation and structured data extraction. It provides a Python library for generating synthetic data at scale, with features like programmability, performance optimization, caching, and integration with HuggingFace Datasets. The tool includes a Curator Viewer for dataset visualization and offers a rich set of functionalities for creating and refining data generation strategies.

LLM Scraper is a TypeScript library that allows you to convert any webpages into structured data using LLMs. It supports Local (GGUF), OpenAI, Groq chat models, and schemas defined with Zod. With full type-safety in TypeScript and based on the Playwright framework, it offers streaming when crawling multiple pages and supports four input modes: html, markdown, text, and image.

mLoRA (Multi-LoRA Fine-Tune) is an open-source framework for efficient fine-tuning of multiple Large Language Models (LLMs) using LoRA and its variants. It allows concurrent fine-tuning of multiple LoRA adapters with a shared base model, efficient pipeline parallelism algorithm, support for various LoRA variant algorithms, and reinforcement learning preference alignment algorithms. mLoRA helps save computational and memory resources when training multiple adapters simultaneously, achieving high performance on consumer hardware.

uzu is a high-performance inference engine for AI models on Apple Silicon. It features a simple, high-level API, hybrid architecture for GPU kernel computation, unified model configurations, traceable computations, and utilizes unified memory on Apple devices. The tool provides a CLI mode for running models, supports its own model format, and offers prebuilt Swift and TypeScript frameworks for bindings. Users can quickly start by adding the uzu dependency to their Cargo.toml and creating an inference Session with a specific model and configuration. Performance benchmarks show metrics for various models on Apple M2, highlighting the tokens/s speed for each model compared to llama.cpp with bf16/f16 precision.

Pixeltable is a Python library designed for ML Engineers and Data Scientists to focus on exploration, modeling, and app development without the need to handle data plumbing. It provides a declarative interface for working with text, images, embeddings, and video, enabling users to store, transform, index, and iterate on data within a single table interface. Pixeltable is persistent, acting as a database unlike in-memory Python libraries such as Pandas. It offers features like data storage and versioning, combined data and model lineage, indexing, orchestration of multimodal workloads, incremental updates, and automatic production-ready code generation. The tool emphasizes transparency, reproducibility, cost-saving through incremental data changes, and seamless integration with existing Python code and libraries.

This repository contains the code for the paper 'Towards Economical Inference: Enabling DeepSeek's Multi-Head Latent Attention in Any Transformer-based LLMs'. It provides tools for fine-tuning and evaluating Llama models, converting models between different frameworks, processing datasets, and performing specific model training tasks like Partial-RoPE Fine-Tuning and Multiple-Head Latent Attention Fine-Tuning. The repository also includes commands for model evaluation using Lighteval and LongBench, along with necessary environment setup instructions.

The Mistral JavaScript client is a library that allows you to interact with the Mistral AI API. With this client, you can perform various tasks such as listing models, chatting with streaming, chatting without streaming, and generating embeddings. To use the client, you can install it in your project using npm and then set up the client with your API key. Once the client is set up, you can use it to perform the desired tasks. For example, you can use the client to chat with a model by providing a list of messages. The client will then return the response from the model. You can also use the client to generate embeddings for a given input. The embeddings can then be used for various downstream tasks such as clustering or classification.

Gemini Pro Vision Playground is a simple project aimed at assisting developers in utilizing the Gemini Pro Vision and Gemini Pro AI models for building applications. It provides a playground environment for experimenting with these models and integrating them into apps. The project includes instructions for setting up the Google AI API key and running the development server to visualize the results. Developers can learn more about the Gemini API documentation and Next.js framework through the provided resources. The project encourages contributions and feedback from the community.

Shards is a high-performance, multi-platform, type-safe programming language designed for visual development. It is a dataflow visual programming language that enables building full-fledged apps and games without traditional coding. Shards features automatic type checking, optimized shard implementations for high performance, and an intuitive visual workflow for beginners. The language allows seamless round-trip engineering between code and visual models, empowering users to create multi-platform apps easily. Shards also powers an upcoming AI-powered game creation system, enabling real-time collaboration and game development in a low to no-code environment.

Eidos is an extensible framework for managing personal data in one place. It runs inside the browser as a PWA with offline support. It integrates AI features for translation, summarization, and data interaction. Users can customize Eidos with Prompt extension, JavaScript for Formula functions, TypeScript/JavaScript for data processing logic, and build apps using any framework. Eidos is developer-friendly with API & SDK, and uses SQLite standardization for data tables.

This repository serves as a companion to the Build AI-Powered Apps with OpenAI and Node.js course on Frontend Masters. It includes course notes and provides alternative approaches for deprecated Langchain methods by installing the Langchain community module and importing loaders for document processing from PDFs and YouTube videos.

NeoHaskell is a newcomer-friendly and productive dialect of Haskell. It aims to be easy to learn and use, while also powerful enough for app development with minimal effort and maximum confidence. The project prioritizes design and documentation before implementation, with ongoing work on design documents for community sharing.

BlackFriday-GPTs-Prompts is a repository that provides a collection of prompts and jailbreaks for various purposes such as programming, marketing, academic, job hunting, game, creative tasks, prompt engineering, business, productivity, and lifestyle. It introduces AiDark.net, an autonomous AI software engineer named Devin, capable of working collaboratively or independently on tasks for review. The repository offers prompts that can be used in GPTOS, along with demo videos showcasing an Android self-coding app builder.

AIDE-Plus is a comprehensive tool for Android app development, offering support for various Java syntax versions, Gradle and Maven build systems, ProGuard, AndroidX, CMake builds, APK/AAB generation, code coloring customization, data binding, and APK signing. It also provides features like AAPT2, D8, runtimeOnly, compileOnly, libgdxNatives, manifest merging, Shizuku installation support, and syntax auto-completion. The tool aims to streamline the development process and enhance the user experience by addressing common issues and providing advanced functionalities.

Superplatform is a microservices platform focused on distributed AI management and development. It enables users to self-host AI models, build backendless AI apps, develop microservices-based AI applications, and deploy third-party AI apps easily. The platform supports running open-source AI models privately, building apps leveraging AI models, and utilizing a microservices-based communal backend for diverse projects.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

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

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.