Best AI tools for< Issue Engineer >

Stepsize AI is an AI-powered reporting tool for software development teams. It analyzes issue tracker activity to generate automated weekly updates on team and project progress. Stepsize AI provides metrics with automatic commentary, project-level AI insights, and intelligent delivery risk surfacing. It offers tailored insights, complete visibility, and unified focus, helping teams stay aligned and make timely decisions.

DigiCord is an AI-powered Discord bot that provides access to a wide range of large language models (LLMs) such as GPT-3.5, GPT-4, Claude, and more. It allows users to converse with AI, generate content, analyze images and data, and perform various tasks, all within the Discord server environment. DigiCord aims to democratize AI tools and technologies, making them more accessible, cost-efficient, and user-friendly for a diverse range of users, from students and digital artists to software engineers and entrepreneurs.

Elessar is an AI-powered platform designed to enhance engineering productivity by providing automatic documentation, reporting, and visibility for development teams. It seamlessly integrates with existing ecosystems, generates pull request changelogs, automates Notion documentation, offers Slack bot functionality, provides VS Code extension for easy code understanding, and links with Linear for issue tracking. Elessar ensures data privacy and security by following SOC II compliant policies and encrypting data at rest and in transit. It does not use data for training AI models. With Elessar, organizations can streamline communication, improve visibility, and boost productivity.

Devin AI, developed by Cognition Labs, is the world's first fully autonomous AI software engineer. It streamlines software development by handling complex tasks, allowing engineers to focus on more challenging aspects. Devin AI possesses advanced programming skills, can manage complex tasks, understands and learns contextually, integrates with developer tools, and offers collaborative features. It can build and deploy applications, detect and fix bugs, contribute to open-source projects, train AI models, and handle GitHub repositories. Devin AI has demonstrated strong performance in issue resolution, surpassing previous AI models. It is currently in early access, with plans for future enhancements and integration with various development tools and platforms.

Replit GPT Assistant is a tool that acts as a Replit-informed assistant, helping developers address their issues. It provides solutions to common problems faced by developers when using Replit, such as lower Node version errors and issues with updating environment variables.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. The error code is 'DEPLOYMENT_NOT_FOUND' with an ID of 'sin1::llkll-1726766020379-aede70059d09'. Users are directed to refer to the documentation for further information and troubleshooting.

The website appears to be experiencing an 'Access Denied' error, preventing users from accessing a specific page related to a domain sale. The error message indicates that the user lacks permission to view the content. The issue may be related to server restrictions or misconfiguration. The error reference number is provided for troubleshooting purposes.

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::z2jxr-1736614143090-03728368920f'. Users are directed to refer to the documentation for further information and troubleshooting.

The website is currently experiencing a temporary service outage, indicated by the '503 Service Temporarily Unavailable' error message. This error typically occurs when the server is unable to handle the request due to maintenance, overload, or other issues. The 'nginx' reference suggests that the website is using the Nginx web server software. Users encountering this error are advised to wait and try accessing the site later.

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::t6p58-1734886683342-4b66840b1e2d) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website is a simple error page indicating that the requested page is not found. It provides a message stating '404 - Not found' and 'Oops! Page not found'. The user is advised to go back home by clicking a link provided on the page.

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 page displays a '404 Not Found' error message indicating that the train has not arrived at the station. It suggests checking network settings to confirm domain provisioning. The message advises visitors to inform the owner if they are stuck at the station. The page also provides a request ID for reference.

LogRocket is a session replay, product analytics, and issue detection platform that helps software teams deliver the best web and mobile experiences. With LogRocket, you can see exactly what users experienced on your app, as well as DOM playback, console and network logs, errors, and performance data. You can also surface the most impactful user issues with JavaScript errors, network errors, stack traces, automatic triaging, and alerting. LogRocket also provides product analytics to help you understand how users are interacting with your app, and UX analytics to help you visualize how users experience your app at both the individual and aggregate level.

The website is experiencing an application error, which indicates a technical issue preventing the proper functioning of the application. An application error can occur due to various reasons such as bugs in the code, server issues, or database problems. Users encountering an application error may not be able to access the intended features or content of the website until the issue is resolved by the developers.

Speakaide.com is a website that currently faces an issue with an invalid SSL certificate. The error code 526 indicates a problem with the SSL certificate, leading to a host error on the site. Visitors are advised to wait a few minutes and try again, while website owners are instructed to ensure a valid SSL certificate is configured by their hosting provider. The site appears to be hosted on Cloudflare, and the error message provides troubleshooting information for resolving the SSL certificate issue.

Simpleblog.ai is a website that unfortunately experienced a connection timeout issue, resulting in an Error code 522. The error occurred due to a timeout between Cloudflare's network and the origin web server, preventing the web page from being displayed. Visitors are advised to try accessing the website again after a few minutes, while website owners are encouraged to contact their hosting provider for assistance in resolving the issue. The error code 522 typically indicates that the request was able to connect to the web server but did not complete, often due to resource constraints on the server.

The website is experiencing an application error related to a client-side exception. Users are advised to check the browser console for more information on the error. The error message indicates a problem with the application's functionality on the user's end, prompting further investigation to identify and resolve the issue.

Resolvd is an AI-powered incident resolution platform that creates a knowledge base of logs, data sources, and apps to autonomously diagnose and resolve incidents. It helps reduce time to response, correlates events across data sources, and provides automated insights for faster issue resolution. With features like simple data querying, automated anomaly detection, and in-workflow integration with existing systems, Resolvd aims to streamline incident response processes and empower engineers with actionable insights.

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.

Tegon is an open-source AI-First issue tracking tool designed for engineering teams. It aims to simplify task management by leveraging AI and integrations to automate task creation, prioritize tasks, and enhance bug resolution. Tegon offers features like issues tracking, automatic title generation, AI-generated labels and assignees, custom views, and upcoming features like sprints and task prioritization. It integrates with GitHub, Slack, and Sentry to streamline issue tracking processes. Tegon also plans to introduce AI Agents like PR Agent and Bug Agent to enhance product management and bug resolution. Contributions are welcome, and the product is licensed under the MIT License.

Ape is an AI prompt engineer tool powered by the open-source library 'ape-core', developed by Weavel. It allows users to generate AI prompts efficiently and effectively. The tool is designed to enhance productivity by providing syntax highlighting for '.prompt' files and welcoming contributions to improve its capabilities and performance. Users can seek help and support through the issue tracker or join the Ape community Discord server. Ape is licensed under the MIT License and credits Stanford NLP's DSPy project for inspiration.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

Bugbug is a tool developed by Mozilla that leverages machine learning techniques to assist with bug and quality management, as well as other software engineering tasks like test selection and defect prediction. It provides various classifiers to suggest assignees, detect patches likely to be backed-out, classify bugs, assign product/components, distinguish between bugs and feature requests, detect bugs needing documentation, identify invalid issues, verify bugs needing QA, detect regressions, select relevant tests, track bugs, and more. Bugbug can be trained and tested using Python scripts, and it offers the ability to run model training tasks on Taskcluster. The project structure includes modules for data mining, bug/commit feature extraction, model implementations, NLP utilities, label handling, bug history playback, and GitHub issue retrieval.

The AI Engineering Hub is a repository that provides in-depth tutorials on LLMs and RAGs, real-world AI agent applications, and examples to implement, adapt, and scale in projects. It caters to beginners, practitioners, and researchers, offering resources for all skill levels to experiment and succeed in AI engineering.

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

The Prompt Engineering Holy Grail repository is a curated resource for prompt engineering enthusiasts, providing essential resources, tools, templates, and best practices to support learning and working in prompt engineering. It covers a wide range of topics related to prompt engineering, from beginner fundamentals to advanced techniques, and includes sections on learning resources, online courses, books, prompt generation tools, prompt management platforms, prompt testing and experimentation, prompt crafting libraries, prompt libraries and datasets, prompt engineering communities, freelance and job opportunities, contributing guidelines, code of conduct, support for the project, and contact information.

Dewhale is a GitHub-Powered AI tool designed for effortless development. It utilizes prompt engineering techniques under the GPT-4 model to issue commands, allowing users to generate code with lower usage costs and easy customization. The tool seamlessly integrates with GitHub, providing version control, code review, and collaborative features. Users can join discussions on the design philosophy of Dewhale and explore detailed instructions and examples for setting up and using the tool.

LLM4AD is an open-source Python-based platform leveraging Large Language Models (LLMs) for Automatic Algorithm Design (AD). It provides unified interfaces for methods, tasks, and LLMs, along with features like evaluation acceleration, secure evaluation, logs, GUI support, and more. The platform was originally developed for optimization tasks but is versatile enough to be used in other areas such as machine learning, science discovery, game theory, and engineering design. It offers various search methods and algorithm design tasks across different domains. LLM4AD supports remote LLM API, local HuggingFace LLM deployment, and custom LLM interfaces. The project is licensed under the MIT License and welcomes contributions, collaborations, and issue reports.

Dash AI is an open-source coding assistant for Flutter developers. It is designed to not only write code but also run and debug it, allowing it to assist beyond code completion and automate routine tasks. Dash AI is powered by Gemini, integrated with the Dart Analyzer, and specifically tailored for Flutter engineers. The vision for Dash AI is to create a single-command assistant that can automate tedious development tasks, enabling developers to focus on creativity and innovation. It aims to assist with the entire process of engineering a feature for an app, from breaking down the task into steps to generating exploratory tests and iterating on the code until the feature is complete. To achieve this vision, Dash AI is working on providing LLMs with the same access and information that human developers have, including full contextual knowledge, the latest syntax and dependencies data, and the ability to write, run, and debug code. Dash AI welcomes contributions from the community, including feature requests, issue fixes, and participation in discussions. The project is committed to building a coding assistant that empowers all Flutter developers.

OpenDevin is an open-source project aiming to replicate Devin, an autonomous AI software engineer capable of executing complex engineering tasks and collaborating actively with users on software development projects. The project aspires to enhance and innovate upon Devin through the power of the open-source community. Users can contribute to the project by developing core functionalities, frontend interface, or sandboxing solutions, participating in research and evaluation of LLMs in software engineering, and providing feedback and testing on the OpenDevin toolset.

CogAgent is an advanced intelligent agent model designed for automating operations on graphical interfaces across various computing devices. It supports platforms like Windows, macOS, and Android, enabling users to issue commands, capture device screenshots, and perform automated operations. The model requires a minimum of 29GB of GPU memory for inference at BF16 precision and offers capabilities for executing tasks like sending Christmas greetings and sending emails. Users can interact with the model by providing task descriptions, platform specifications, and desired output formats.

**Prompt flow** is a suite of development tools designed to streamline the end-to-end development cycle of LLM-based AI applications, from ideation, prototyping, testing, evaluation to production deployment and monitoring. It makes prompt engineering much easier and enables you to build LLM apps with production quality.

Agentless is an open-source tool designed for automatically solving software development problems. It follows a two-phase process of localization and repair to identify faults in specific files, classes, and functions, and generate candidate patches for fixing issues. The tool is aimed at simplifying the software development process by automating issue resolution and patch generation.

The 'Taming LLMs' repository provides a practical guide to the pitfalls and challenges associated with Large Language Models (LLMs) when building applications. It focuses on key limitations and implementation pitfalls, offering practical Python examples and open source solutions to help engineers and technical leaders navigate these challenges. The repository aims to equip readers with the knowledge to harness the power of LLMs while avoiding their inherent limitations.

Dockershrink is an AI-powered Commandline Tool designed to help reduce the size of Docker images. It combines traditional Rule-based analysis with Generative AI techniques to optimize Image configurations. The tool supports NodeJS applications and aims to save costs on storage, data transfer, and build times while increasing developer productivity. By automatically applying advanced optimization techniques, Dockershrink simplifies the process for engineers and organizations, resulting in significant savings and efficiency improvements.

LLM-Deploy focuses on the theory and practice of model/LLM reasoning and deployment, aiming to be your partner in mastering the art of LLM reasoning and deployment. Whether you are a newcomer to this field or a senior professional seeking to deepen your skills, you can find the key path to successfully deploy large language models here. The project covers reasoning and deployment theories, model and service optimization practices, and outputs from experienced engineers. It serves as a valuable resource for algorithm engineers and individuals interested in reasoning deployment.

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's no-code Connector Builder or low-code CDK. Airbyte is used by data engineers and analysts at companies of all sizes to build and manage their data pipelines.

Airbyte is an open-source data integration platform that makes it easy to move data from any source to any destination. With Airbyte, you can build and manage data pipelines without writing any code. Airbyte provides a library of pre-built connectors that make it easy to connect to popular data sources and destinations. You can also create your own connectors using Airbyte's low-code Connector Development Kit (CDK). Airbyte is used by data engineers and analysts at companies of all sizes to move data for a variety of purposes, including data warehousing, data analysis, and machine learning.

MetaGPT is a multi-agent framework that enables GPT to work in a software company, collaborating to tackle more complex tasks. It assigns different roles to GPTs to form a collaborative entity for complex tasks. MetaGPT takes a one-line requirement as input and outputs user stories, competitive analysis, requirements, data structures, APIs, documents, etc. Internally, MetaGPT includes product managers, architects, project managers, and engineers. It provides the entire process of a software company along with carefully orchestrated SOPs. MetaGPT's core philosophy is "Code = SOP(Team)", materializing SOP and applying it to teams composed of LLMs.

Gives structure to messy GitHub issues.

Translates GitHub issues from Japanese to English.

I'm Logic Pro X's manual. Let me answer your questions, troubleshoot whatever issue you're having and get you back into the groove!

The best assistant for IT admins, get to the root cause of any issue

Clone and replicate the source site using a screenshot, while enabling continuous development and optimization capabilities. - 通过截图复制源站点前端代码，同时具备持续开发和优化功能。Any Issue: contact me @X: https://twitter.com/tb_xy09

Translates network issues into Wireshark filter syntax.

Learn Linux by Hands-on Labs and AI

Prioritizes major 3D printing issues with solutions.

Analyzes code for accessibility issues & provides recommendations

Product Engineer helps find inventive solutions to engineering problems.

A JavaScript expert solving coding issues with clarity and depth.

An mechanical engineering tutor, aiding with concepts and problem-solving.

Provides expert help for Laravel development questions and challenges.

Expert in diagnosing and fixing GPT issues.

Guides thermal management solutions for efficient system performance.

An assistant to help audio DIY'ers of any level, and anyone curios about audio to identify issues, find information, and general assistance in their journey.

The pressing environmental issues we face today require novel approaches and technological advancements to effectively mitigate their impacts. GaiaAI offers a range of tools and modes to promote sustainable practices and enhance environmental stewardship.

Assists in resolving technical issues in field operations.

制定和实施质量管理体系；审核和改进质量管理体系；培训和指导员；处理质量问题；与其他部门协调；持续改进

Java Security Expert offering advice on best practices and resolving security issues.