Best AI tools for< Resolve Deployment Issue >

The website page displays a 402: PAYMENT_REQUIRED error message indicating that the deployment has been disabled. It provides a code (DEPLOYMENT_DISABLED ID: sin1::wrwtg-1727542950481-16e8d7d3f9ae) and advises visitors to contact the website owner or try again later. If the visitor is the owner, they are directed to read the documentation section for further guidance.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::vphzj-1726938947015-5b9ee22b5622) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::gwh5l-1728060486264-1caee7008fee) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::zdhct-1723140771934-b5e5ad909fad'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::ggptb-1727542270172-dbd5ec692f5f) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::zgchc-1727888586770-95afe6303495). The message suggests checking the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. Users encountering this error are advised to refer to the documentation for more information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::tszrz-1723627812794-26f3e29ebbda). Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message along with a code and ID indicating a deployment not found issue. Users encountering this error are directed to refer to the documentation for further information and troubleshooting.

Prodvana is an intelligent deployment platform that helps businesses automate and streamline their software deployment process. It provides a variety of features to help businesses improve the speed, reliability, and security of their deployments. Prodvana is a cloud-based platform that can be used with any type of infrastructure, including on-premises, hybrid, and multi-cloud environments. It is also compatible with a wide range of DevOps tools and technologies. Prodvana's key features include: Intent-based deployments: Prodvana uses intent-based deployment technology to automate the deployment process. This means that businesses can simply specify their deployment goals, and Prodvana will automatically generate and execute the necessary steps to achieve those goals. This can save businesses a significant amount of time and effort. Guardrails for deployments: Prodvana provides a variety of guardrails to help businesses ensure the security and reliability of their deployments. These guardrails include approvals, database validations, automatic deployment validation, and simple interfaces to add custom guardrails. This helps businesses to prevent errors and reduce the risk of outages. Frictionless DevEx: Prodvana provides a frictionless developer experience by tracking commits through the infrastructure, ensuring complete visibility beyond just Docker images. This helps developers to quickly identify and resolve issues, and it also makes it easier to collaborate with other team members. Intelligence with Clairvoyance: Prodvana's Clairvoyance feature provides businesses with insights into the impact of their deployments before they are executed. This helps businesses to make more informed decisions about their deployments and to avoid potential problems. Easy integrations: Prodvana integrates seamlessly with a variety of DevOps tools and technologies. This makes it easy for businesses to use Prodvana with their existing workflows and processes.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::l44g5-1727283130745-f99c9f7f28f4) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::qhrjt-1726765433586-bc18f7adaa0c) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::lpcgw-1726939089118-f134fdcd683c) for reference. Users are directed to consult the documentation for further information and troubleshooting.

The website page displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code 'DEPLOYMENT_NOT_FOUND' and an ID 'sin1::fd55k-1727629228031-7a0d0ffffbbb'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::k52hk-1728147113444-17b9d56f17fd) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::7crbp-1720289011850-d12041b250e9) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::22md2-1720772812453-4893618e160a) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the requested deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::ltfvp-1727369324219-e2d8330c3f8d) for reference. Users are directed to check the documentation for further information and troubleshooting.

Fini is a customer support AI chatbot that helps businesses resolve customer queries, increase retention, revenue, and activation. It integrates with various platforms like Zendesk, Slack, and Discord, and can be customized to match the tone and character of the business. Fini uses machine learning to learn from customer interactions and improve its responses over time. It offers features such as seamless human routing, multi-channel deployment, and feedback-based learning.

Yellow.ai is a leading provider of AI-powered customer service automation solutions. Its Dynamic Automation Platform (DAP) is built on multi-LLM architecture and continuously trains on billions of conversations for scale, speed, and accuracy. Yellow.ai's platform leverages the latest advancements in NLP and generative AI to deliver empathetic and context-aware conversations that exceed customer expectations across channels. With its enterprise-grade security, advanced analytics, and zero-setup bot deployment, Yellow.ai helps businesses transform their customer and employee experiences with AI-powered automation.

Llama-github is a powerful tool that helps retrieve relevant code snippets, issues, and repository information from GitHub based on queries. It empowers AI agents and developers to solve coding tasks efficiently. With features like intelligent GitHub retrieval, repository pool caching, LLM-powered question analysis, and comprehensive context generation, llama-github excels at providing valuable knowledge context for development needs. It supports asynchronous processing, flexible LLM integration, robust authentication options, and logging/error handling for smooth operations and troubleshooting. The vision is to seamlessly integrate with GitHub for AI-driven development solutions, while the roadmap focuses on empowering LLMs to automatically resolve complex coding tasks.

Airbroke is an open-source error catcher tool designed for modern web applications. It provides a PostgreSQL-based backend with an Airbrake-compatible HTTP collector endpoint and a React-based frontend for error management. The tool focuses on simplicity, maintaining a small database footprint even under heavy data ingestion. Users can ask AI about issues, replay HTTP exceptions, and save/manage bookmarks for important occurrences. Airbroke supports multiple OAuth providers for secure user authentication and offers occurrence charts for better insights into error occurrences. The tool can be deployed in various ways, including building from source, using Docker images, deploying on Vercel, Render.com, Kubernetes with Helm, or Docker Compose. It requires Node.js, PostgreSQL, and specific system resources for deployment.

Qwen-TensorRT-LLM is a project developed for the NVIDIA TensorRT Hackathon 2023, focusing on accelerating inference for the Qwen-7B-Chat model using TRT-LLM. The project offers various functionalities such as FP16/BF16 support, INT8 and INT4 quantization options, Tensor Parallel for multi-GPU parallelism, web demo setup with gradio, Triton API deployment for maximum throughput/concurrency, fastapi integration for openai requests, CLI interaction, and langchain support. It supports models like qwen2, qwen, and qwen-vl for both base and chat models. The project also provides tutorials on Bilibili and blogs for adapting Qwen models in NVIDIA TensorRT-LLM, along with hardware requirements and quick start guides for different model types and quantization methods.

runpod-worker-comfy is a serverless API tool that allows users to run any ComfyUI workflow to generate an image. Users can provide input images as base64-encoded strings, and the generated image can be returned as a base64-encoded string or uploaded to AWS S3. The tool is built on Ubuntu + NVIDIA CUDA and provides features like built-in checkpoints and VAE models. Users can configure environment variables to upload images to AWS S3 and interact with the RunPod API to generate images. The tool also supports local testing and deployment to Docker hub using Github Actions.

The AWS Lex Web UI is a sample Amazon Lex web interface that provides a chatbot UI component for integration into websites. It supports voice and text interactions, Lex response cards, and programmable configuration using JavaScript. The interface can be used as a full-page chatbot UI or embedded as a widget. It offers mobile-ready responsive UI, seamless voice-text switching, and interactive messaging support. The project includes CloudFormation templates for easy deployment and customization. Users can modify configurations, integrate the UI into existing sites, and deploy using various methods like CloudFormation, pre-built libraries, or npm installation.

A chat interface using open source models, eg OpenAssistant or Llama. It is a SvelteKit app and it powers the HuggingChat app on hf.co/chat.

LangServe helps developers deploy `LangChain` runnables and chains as a REST API. This library is integrated with FastAPI and uses pydantic for data validation. In addition, it provides a client that can be used to call into runnables deployed on a server. A JavaScript client is available in LangChain.js.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

FoundationaLLM is a platform designed for deploying, scaling, securing, and governing generative AI in enterprises. It allows users to create AI agents grounded in enterprise data, integrate REST APIs, experiment with large language models, centrally manage AI agents and assets, deploy scalable vectorization data pipelines, enable non-developer users to create their own AI agents, control access with role-based access controls, and harness capabilities from Azure AI and Azure OpenAI. The platform simplifies integration with enterprise data sources, provides fine-grain security controls, load balances across multiple endpoints, and is extensible to new data sources and orchestrators. FoundationaLLM addresses the need for customized copilots or AI agents that are secure, licensed, flexible, and suitable for enterprise-scale production.

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.

LARS is an application that enables users to run Large Language Models (LLMs) locally on their devices, upload their own documents, and engage in conversations where the LLM grounds its responses with the uploaded content. The application focuses on Retrieval Augmented Generation (RAG) to increase accuracy and reduce AI-generated inaccuracies. LARS provides advanced citations, supports various file formats, allows follow-up questions, provides full chat history, and offers customization options for LLM settings. Users can force enable or disable RAG, change system prompts, and tweak advanced LLM settings. The application also supports GPU-accelerated inferencing, multiple embedding models, and text extraction methods. LARS is open-source and aims to be the ultimate RAG-centric LLM application.

The TensorRT-LLM Backend is a Triton backend designed to serve TensorRT-LLM models with Triton Inference Server. It supports features like inflight batching, paged attention, and more. Users can access the backend through pre-built Docker containers or build it using scripts provided in the repository. The backend can be used to create models for tasks like tokenizing, inferencing, de-tokenizing, ensemble modeling, and more. Users can interact with the backend using provided client scripts and query the server for metrics related to request handling, memory usage, KV cache blocks, and more. Testing for the backend can be done following the instructions in the 'ci/README.md' file.

Analyze the following text from a github repository (name and readme text at end) . Then, generate a JSON object with the following keys and provide the corresponding information for each key, in lowercase letters: 'description' (detailed description of the repo, must be less than 400 words，Ensure that no line breaks and quotation marks.),'for_jobs' (List 5 jobs suitable for this tool,in lowercase letters), 'ai_keywords' (keywords of the tool,user may use those keyword to find the tool,in lowercase letters), 'for_tasks' (list of 5 specific tasks user can use this tool to do,in lowercase letters), 'answer' (in english languages)

Suno AI API is an open-source project that allows developers to integrate the music generation capabilities of Suno.ai into their own applications. The API provides a simple and convenient way to generate music, lyrics, and other audio content using Suno.ai's powerful AI models. With Suno AI API, developers can easily add music generation functionality to their apps, websites, and other projects.

Skyvern automates browser-based workflows using LLMs and computer vision. It provides a simple API endpoint to fully automate manual workflows, replacing brittle or unreliable automation solutions. Traditional approaches to browser automations required writing custom scripts for websites, often relying on DOM parsing and XPath-based interactions which would break whenever the website layouts changed. Instead of only relying on code-defined XPath interactions, Skyvern adds computer vision and LLMs to the mix to parse items in the viewport in real-time, create a plan for interaction and interact with them. This approach gives us a few advantages: 1. Skyvern can operate on websites it’s never seen before, as it’s able to map visual elements to actions necessary to complete a workflow, without any customized code 2. Skyvern is resistant to website layout changes, as there are no pre-determined XPaths or other selectors our system is looking for while trying to navigate 3. Skyvern leverages LLMs to reason through interactions to ensure we can cover complex situations. Examples include: 1. If you wanted to get an auto insurance quote from Geico, the answer to a common question “Were you eligible to drive at 18?” could be inferred from the driver receiving their license at age 16 2. If you were doing competitor analysis, it’s understanding that an Arnold Palmer 22 oz can at 7/11 is almost definitely the same product as a 23 oz can at Gopuff (even though the sizes are slightly different, which could be a rounding error!) Want to see examples of Skyvern in action? Jump to #real-world-examples-of- skyvern

A Kubernetes error debugger offering diagnostic and resolution guidance.

A personal co-pilot/tutor for Davinci Resolve

Expert in DaVinci Resolve for editing, effects, and troubleshooting

Incorporates evolving psychology principles for family and friendship healing.

Your empathetic conflict resolution coach. Get guidance from Mike to resolve life conflicts and rehearse conversations beforehand.

Helps resolve Game Maker & GML related questions.

Expert in Adobe Creative Cloud, DaVinci Resolve, and Blender.

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Experte en résolution de conflit et communication non violente

Mediator for couple's chats.

JUEZ GPT es una herramienta diseñada para arbitrar conflictos y tomar decisiones de manera pragmática, justa y lógica, proporcionando veredictos claros

A place to finally find out if you were wrong in an argument that's been bothering you

Using "Crucial Conversations," I can help you handle work and home challenges with confidence and clarity.

Assists in conflict resolution in a professional setting 🤝

An expert in offering the right advice on solving any neighbor issues you might have.

Resolves complex customer complaints to ensure satisfaction.

Your go-to advisor for moral dilemmas, offering direct, honest, and memorable guidance.

Guides strong, empathetic responses for complex interpersonal challenges.

Expert in transforming conflict into harmony and offering empathetic peace advice with ancient wisdom in combination with modern AI technologies, as well as with the Nonflict way of Million Peacemakers.

L'Ambassadeur Virtuel est un agent d'intelligence artificielle spécialisé dans la diplomatie et la résolution de conflits. Conçu pour gérer une grande variété de situations, il excelle dans la communication interculturelle, la négociation, et le maintien de la paix.