Best AI tools for< Resolve Dependencies >

Explosion is a software company specializing in developer tools and tailored solutions for AI, Machine Learning, and Natural Language Processing (NLP). They are the makers of spaCy, one of the leading open-source libraries for advanced NLP. The company offers consulting services and builds developer tools for various AI-related tasks, such as coreference resolution, dependency parsing, image classification, named entity recognition, and more.

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Remy is an AI-powered platform designed to help product security and compliance teams resolve security risks early. It offers scalable design review capabilities, automates review initiation, generates tailored questions, and provides clear metrics and audit trails. Remy aims to augment and scale product security teams by ensuring full visibility on risky engineering plans and automating tedious review processes. The platform is built for enterprise readiness, offering SSO for convenient logins, scalability, and customization.

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AI Judge is an online platform that helps to generate a verdict and resolve disputes in a fair and impartial manner. It allows both parties involved in a dispute to present their arguments and evidence, after which the AI system analyzes the information and generates a clear and concise verdict. The platform aims to provide an additional perspective for the contending parties to consider during the dispute resolution process, ensuring impartial decisions based on the merits of each case.

SupportGuy is a customer support platform powered by ChatGPT. It allows businesses to create Slack bots that are trained on their knowledge base, enabling them to resolve customer queries 10x faster. The platform is easy to set up and use, and it offers a variety of features and advantages that make it a valuable tool for businesses of all sizes.

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Molin AI is an advanced AI tool designed to revolutionize customer support in e-commerce. It offers a wide range of features such as product recommendations, multilingual support, content creation, customer analytics, and business copilot assistance. Molin uses state-of-the-art AI technology to provide accurate and instant solutions to customer queries, generate product descriptions, and create marketing content in multiple languages. The tool is highly versatile, integrating seamlessly with various e-commerce platforms and websites. Molin aims to streamline support processes, increase sales, and enhance the overall customer experience.

Chatterdocs is a platform that allows users to create custom, GPT-powered chatbots in minutes. With Chatterdocs, users can harness the power of AI to resolve support questions, capture leads, and book appointments, all without coding. The platform is easy to use, and users can build and customize their chatbots in minutes. Chatterdocs also offers a variety of features, such as the ability to connect to other tools and services, embed chatbots on websites, and collect valuable insights from visitor questions.

Ada is an AI-powered customer service automation platform that helps businesses resolve more customer inquiries with less effort. It offers a range of features, including automated resolution of up to 75% of customer inquiries, seamless integration with existing tech stacks, and continuous improvement through guidance and AI coaching.

My AskAI is an AI chatbot designed to provide customer support. It can answer questions, resolve issues, and provide information about products and services. My AskAI is easy to use and can be integrated with a variety of platforms. It is a valuable tool for businesses looking to improve their customer service.

Spot AI is a Video AI platform that transforms cameras into intelligent tools to secure, protect, and optimize operations. It offers features such as real-time visibility, incident resolution, worker safety, and training. The platform includes AI agents, semantic search, and state-of-the-art video AI models to drive business outcomes and enhance productivity. Spot AI is trusted by over 1,000 organizations to reduce workplace injuries, improve incident resolution time, and increase operational throughput.

BigPanda is an AI-powered ITOps platform that helps teams gain efficiency, improve service quality, and reduce costs. It provides automated detection and alert intelligence, automated investigation and incident intelligence, automated remediation and workflow automation, and unified analytics and ready-to-use dashboards.

BigPanda is an AI-powered ITOps platform that helps businesses automatically identify actionable alerts, proactively prevent incidents, and ensure service availability. It uses advanced AI/ML algorithms to analyze large volumes of data from various sources, including monitoring tools, event logs, and ticketing systems. BigPanda's platform provides a unified view of IT operations, enabling teams to quickly identify and resolve issues before they impact business-critical services.

Magneo is an AI-driven communication platform that caters to schools and local businesses, offering automated solutions to streamline processes such as admission, retention, and enrollment. The platform provides 24/7 support, empowering staff to assist students effectively. Magneo leverages AI to enhance customer service, improve online reviews, automate payments, and collect valuable data insights. Additionally, it offers features like robotic process automation, support for diverse learners, and explainable AI models for compliance.

Blip is an AI-powered communication platform that offers solutions for businesses to revolutionize their digital communication. With Blip, users can create chatbots and intelligent contacts to provide quick, efficient, and personalized conversations. The platform integrates with various systems and offers over 90 extensions and open APIs to streamline communication processes. Blip focuses on providing personalized and humanized conversations, resolving issues, and increasing conversion rates across different digital channels. The platform is designed to cater to various market segments and business needs, offering integrated solutions for customer service, marketing, sales, and more.

PolyAI is a website that offers a conversational platform powered by lifelike, adaptable, engaging, and dynamic voice AI technology. It aims to enhance customer experience by resolving over 50% of calls and consistently delivering the best brand experience. The platform transforms call centers into revenue generators by handling calls in multiple languages, increasing customer satisfaction, and generating revenue through longer calls. PolyAI helps businesses improve operational efficiency, customer loyalty, and agent satisfaction through actionable insights and modernizing the customer experience stack.

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Gladly is an AI-powered Customer Service Platform that focuses on delivering exceptional customer experiences by centering on people rather than tickets. It offers a unified solution for customer communication across various channels, combining AI technology with human support to enhance efficiency, decrease costs, and drive revenue growth. Gladly stands out for its customer-centric approach, personalized self-service, and the ability to maintain a single lifelong conversation with customers. The platform is trusted by world-renowned brands and has proven success stories in improving customer interactions and agent productivity.

Front is an AI-powered platform designed to enhance customer service and operational efficiency. It offers a comprehensive suite of tools for managing shared inboxes, live chat support, team collaboration, AI chatbots, knowledge base management, workflow automation, integrations, and analytics. Front caters to various industries such as technology, financial services, logistics, manufacturing, professional services, and travel, providing solutions for customer service, operations, inbound sales, account management, and success. The platform aims to deliver exceptional customer experiences at scale by leveraging real-time collaboration, automation, and AI capabilities.

StepWise is a code-first, event-driven workflow framework for .NET designed to help users build complex workflows in a simple and efficient way. It allows users to define workflows using C# code, visualize and execute workflows from a browser, execute steps in parallel, and resolve dependencies automatically. StepWise also features an AI assistant called `Geeno` in its WebUI to help users run and analyze workflows with ease.

OSWorld is a benchmarking tool designed to evaluate multimodal agents for open-ended tasks in real computer environments. It provides a platform for running experiments, setting up virtual machines, and interacting with the environment using Python scripts. Users can install the tool on their desktop or server, manage dependencies with Conda, and run benchmark tasks. The tool supports actions like executing commands, checking for specific results, and evaluating agent performance. OSWorld aims to facilitate research in AI by providing a standardized environment for testing and comparing different agent baselines.

libLLM is an open-source project designed for efficient inference of large language models (LLM) on personal computers and mobile devices. It is optimized to run smoothly on common devices, written in C++14 without external dependencies, and supports CUDA for accelerated inference. Users can build the tool for CPU only or with CUDA support, and run libLLM from the command line. Additionally, there are API examples available for Python and the tool can export Huggingface models.

The fsdp_qlora repository provides a script for training Large Language Models (LLMs) with Quantized LoRA and Fully Sharded Data Parallelism (FSDP). It integrates FSDP+QLoRA into the Axolotl platform and offers installation instructions for dependencies like llama-recipes, fastcore, and PyTorch. Users can finetune Llama-2 70B on Dual 24GB GPUs using the provided command. The script supports various training options including full params fine-tuning, LoRA fine-tuning, custom LoRA fine-tuning, quantized LoRA fine-tuning, and more. It also discusses low memory loading, mixed precision training, and comparisons to existing trainers. The repository addresses limitations and provides examples for training with different configurations, including BnB QLoRA and HQQ QLoRA. Additionally, it offers SLURM training support and instructions for adding support for a new model.

mllm is a fast and lightweight multimodal LLM inference engine for mobile and edge devices. It is a Plain C/C++ implementation without dependencies, optimized for multimodal LLMs like fuyu-8B, and supports ARM NEON and x86 AVX2. The engine offers 4-bit and 6-bit integer quantization, making it suitable for intelligent personal agents, text-based image searching/retrieval, screen VQA, and various mobile applications without compromising user privacy.

Resonance is a framework designed to facilitate interoperability and messaging between services in your infrastructure and beyond. It provides AI capabilities and takes full advantage of asynchronous PHP, built on top of Swoole. With Resonance, you can: * Chat with Open-Source LLMs: Create prompt controllers to directly answer user's prompts. LLM takes care of determining user's intention, so you can focus on taking appropriate action. * Asynchronous Where it Matters: Respond asynchronously to incoming RPC or WebSocket messages (or both combined) with little overhead. You can set up all the asynchronous features using attributes. No elaborate configuration is needed. * Simple Things Remain Simple: Writing HTTP controllers is similar to how it's done in the synchronous code. Controllers have new exciting features that take advantage of the asynchronous environment. * Consistency is Key: You can keep the same approach to writing software no matter the size of your project. There are no growing central configuration files or service dependencies registries. Every relation between code modules is local to those modules. * Promises in PHP: Resonance provides a partial implementation of Promise/A+ spec to handle various asynchronous tasks. * GraphQL Out of the Box: You can build elaborate GraphQL schemas by using just the PHP attributes. Resonance takes care of reusing SQL queries and optimizing the resources' usage. All fields can be resolved asynchronously.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

SimpleAI is a self-hosted alternative to the not-so-open AI API, focused on replicating main endpoints for LLM such as text completion, chat, edits, and embeddings. It allows quick experimentation with different models, creating benchmarks, and handling specific use cases without relying on external services. Users can integrate and declare models through gRPC, query endpoints using Swagger UI or API, and resolve common issues like CORS with FastAPI middleware. The project is open for contributions and welcomes PRs, issues, documentation, and more.

PatchWork is an open-source framework designed for automating development tasks using large language models. It enables users to automate workflows such as PR reviews, bug fixing, security patching, and more through a self-hosted CLI agent and preferred LLMs. The framework consists of reusable atomic actions called Steps, customizable LLM prompts known as Prompt Templates, and LLM-assisted automations called Patchflows. Users can run Patchflows locally in their CLI/IDE or as part of CI/CD pipelines. PatchWork offers predefined patchflows like AutoFix, PRReview, GenerateREADME, DependencyUpgrade, and ResolveIssue, with the flexibility to create custom patchflows. Prompt templates are used to pass queries to LLMs and can be customized. Contributions to new patchflows, steps, and the core framework are encouraged, with chat assistants available to aid in the process. The roadmap includes expanding the patchflow library, introducing a debugger and validation module, supporting large-scale code embeddings, parallelization, fine-tuned models, and an open-source GUI. PatchWork is licensed under AGPL-3.0 terms, while custom patchflows and steps can be shared using the Apache-2.0 licensed patchwork template repository.

llama_cpp.rb provides Ruby bindings for the llama.cpp, a library that allows you to use the Llama language model in your Ruby applications. Llama is a large language model that can be used for a variety of natural language processing tasks, such as text generation, translation, and question answering. This gem is still under development and may undergo many changes in the future.

The stable-diffusion.cpp repository provides an implementation for inferring stable diffusion in pure C/C++. It offers features such as support for different versions of stable diffusion, lightweight and dependency-free implementation, various quantization support, memory-efficient CPU inference, GPU acceleration, and more. Users can download the built executable program or build it manually. The repository also includes instructions for downloading weights, building from scratch, using different acceleration methods, running the tool, converting weights, and utilizing various features like Flash Attention, ESRGAN upscaling, PhotoMaker support, and more. Additionally, it mentions future TODOs and provides information on memory requirements, bindings, UIs, contributors, and references.

The WhatsApp AI Assistant repository offers a chatbot named Sydney that serves as an AI-powered personal assistant. It utilizes Language Model (LLM) technology to provide various features such as Google/Bing searching, Google Calendar integration, communication capabilities, group chat compatibility, voice message support, basic text reminders, image recognition, and more. Users can interact with Sydney through natural language queries and voice messages. The chatbot can transcribe voice messages using either the Whisper API or a local method. Additionally, Sydney can be used in group chats by mentioning her username or replying to her last message. The repository welcomes contributions in the form of issue reports, pull requests, and requests for new tools. The creators of the project, Veigamann and Luisotee, are open to job opportunities and can be contacted through their GitHub profiles.

Booster is a powerful inference accelerator designed for scaling large language models within production environments or for experimental purposes. It is built with performance and scaling in mind, supporting various CPUs and GPUs, including Nvidia CUDA, Apple Metal, and OpenCL cards. The tool can split large models across multiple GPUs, offering fast inference on machines with beefy GPUs. It supports both regular FP16/FP32 models and quantised versions, along with popular LLM architectures. Additionally, Booster features proprietary Janus Sampling for code generation and non-English languages.

PyLLMCore is a light-weighted interface with Large Language Models with native support for llama.cpp, OpenAI API, and Azure deployments. It offers a Pythonic API that is simple to use, with structures provided by the standard library dataclasses module. The high-level API includes the assistants module for easy swapping between models. PyLLMCore supports various models including those compatible with llama.cpp, OpenAI, and Azure APIs. It covers use cases such as parsing, summarizing, question answering, hallucinations reduction, context size management, and tokenizing. The tool allows users to interact with language models for tasks like parsing text, summarizing content, answering questions, reducing hallucinations, managing context size, and tokenizing text.

Llama3.java is a practical Llama 3 inference tool implemented in a single Java file. It serves as the successor of llama2.java and is designed for testing and tuning compiler optimizations and features on the JVM, especially for the Graal compiler. The tool features a GGUF format parser, Llama 3 tokenizer, Grouped-Query Attention inference, support for Q8_0 and Q4_0 quantizations, fast matrix-vector multiplication routines using Java's Vector API, and a simple CLI with 'chat' and 'instruct' modes. Users can download quantized .gguf files from huggingface.co for model usage and can also manually quantize to pure 'Q4_0'. The tool requires Java 21+ and supports running from source or building a JAR file for execution. Performance benchmarks show varying tokens/s rates for different models and implementations on different hardware setups.

RAPTOR introduces a novel approach to retrieval-augmented language models by constructing a recursive tree structure from documents. This allows for more efficient and context-aware information retrieval across large texts, addressing common limitations in traditional language models. Users can add documents to the tree, answer questions based on indexed documents, save and load the tree, and extend RAPTOR with custom summarization, question-answering, and embedding models. The tool is designed to be flexible and customizable for various NLP tasks.

optillm is an OpenAI API compatible optimizing inference proxy implementing state-of-the-art techniques to enhance accuracy and performance of LLMs, focusing on reasoning over coding, logical, and mathematical queries. By leveraging additional compute at inference time, it surpasses frontier models across diverse tasks.

Codefuse-ModelCache is a semantic cache for large language models (LLMs) that aims to optimize services by introducing a caching mechanism. It helps reduce the cost of inference deployment, improve model performance and efficiency, and provide scalable services for large models. The project facilitates sharing and exchanging technologies related to large model semantic cache through open-source collaboration.

DocETL is a tool for creating and executing data processing pipelines, especially suited for complex document processing tasks. It offers a low-code, declarative YAML interface to define LLM-powered operations on complex data. Ideal for maximizing correctness and output quality for semantic processing on a collection of data, representing complex tasks via map-reduce, maximizing LLM accuracy, handling long documents, and automating task retries based on validation criteria.

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