Best AI tools for< Improve Systems >

Elixir is an AI tool designed for observability and testing of AI voice agents. It offers features such as automated testing, call review, monitoring, analytics, tracing, scoring, and reviewing. Elixir helps in simulating realistic test calls, analyzing conversations, identifying mistakes, and debugging issues with audio snippets and call transcripts. It provides detailed traces for complex abstractions, streamlines manual review processes, and allows for simulating thousands of calls for full test coverage. The tool is suitable for monitoring agent performance, detecting anomalies in real-time, and improving conversational systems through human-in-the-loop feedback.

Spatial.ai is a customer segmentation platform that helps businesses understand their customers' social, mobile, and web behaviors. This data can be used to create targeted marketing campaigns, make better location decisions, and develop predictive models. Spatial.ai's data is built directly from organic consumer behavior, which means richer insights and higher accuracy.

Kira Systems is a machine learning contract search, review, and analysis software that helps businesses identify, extract, and analyze content in their contracts and documents. It uses patented machine learning technology to extract concepts and data points with high efficiency and accuracy. Kira also has built-in intelligence that streamlines the contract review process with out-of-the-box smart fields. Businesses can also create their own smart fields to find specific data points using Kira's no-code machine learning tool. Kira's adaptive workflows allow businesses to organize, track, and export results. Kira has a partner ecosystem that allows businesses to transform how teams work with their contracts.

This website is an AI tool that helps senior developers analyze AI tech debt. AI tech debt is the technical debt that accumulates when AI systems are developed and deployed. It can be difficult to identify and quantify AI tech debt, but it can have a significant impact on the performance and reliability of AI systems. This tool uses a variety of techniques to analyze AI tech debt, including static analysis, dynamic analysis, and machine learning. It can help senior developers to identify and quantify AI tech debt, and to develop strategies to reduce it.

Assessment Systems is an online testing platform that provides cost-effective, AI-driven solutions to develop, deliver, and analyze high-stakes exams. With Assessment Systems, you can build and deliver smarter exams faster, thanks to modern psychometrics and AI like computerized adaptive testing, multistage testing, or automated item generation. You can also deliver exams flexibly: paper, online testing unproctored, online proctored, and test centers (yours or ours). Assessment Systems also offers item banking software to build better tests in less time, with collaborative item development brought to life with versioning, user roles, metadata, workflow management, multimedia, automated item generation, and much more.

SambaNova Systems is an AI platform that revolutionizes AI workloads by offering an enterprise-grade full stack platform purpose-built for generative AI. It provides state-of-the-art AI and deep learning capabilities to help customers outcompete their peers. SambaNova delivers the only enterprise-grade full stack platform, from chips to models, designed for generative AI in the enterprise. The platform includes the SN40L Full Stack Platform with 1T+ parameter models, Composition of Experts, and Samba Apps. SambaNova also offers resources to accelerate AI journeys and solutions for various industries like financial services, healthcare, manufacturing, and more.

Vellum AI is an AI platform that supports using Microsoft Azure hosted OpenAI models. It offers tools for prompt engineering, semantic search, prompt chaining, evaluations, and monitoring. Vellum enables users to build AI systems with features like workflow automation, document analysis, fine-tuning, Q&A over documents, intent classification, summarization, vector search, chatbots, blog generation, sentiment analysis, and more. The platform is backed by top VCs and founders of well-known companies, providing a complete solution for building LLM-powered applications.

Laudio is an intelligent platform designed to help frontline leaders in health systems streamline their work and prioritize high-impact actions with their teams. By integrating essential workflows and leveraging AI technology, Laudio aims to drive large-scale change by saving time, standardizing best practices, and improving employee engagement and patient experience.

HireFlow.net is an AI-powered platform designed to optimize resumes and enhance job prospects. The website offers a free resume checker that leverages advanced Artificial Intelligence technology to provide personalized feedback and suggestions for improving resumes. Users can also access features such as CV analysis, cover letter and resignation letter generators, and expert insights to stand out in the competitive job market.

Refraction is an AI-powered code generation tool designed to help developers learn, improve, and generate code effortlessly. It offers a wide range of features such as bug detection, code conversion, function creation, CSP generation, CSS style conversion, debug statement addition, diagram generation, documentation creation, code explanation, code improvement, concept learning, CI/CD pipeline creation, SQL query generation, code refactoring, regex generation, style checking, type addition, and unit test generation. With support for 56 programming languages, Refraction is a versatile tool trusted by innovative companies worldwide to streamline software development processes using the magic of AI.

Abacus.AI is the world's first AI platform where AI, not humans, build Applied AI agents and systems at scale. Using generative AI and other novel neural net techniques, AI can build LLM apps, gen AI agents, and predictive applied AI systems at scale.

Promptmate.io is an AI-powered app builder that allows users to create customized applications based on leading AI systems. With Promptmate, users can combine different AI systems, add external data, and automate processes to streamline their workflows. The platform offers a range of features, including pre-built app templates, bulk processing, and data extenders, making it easy for users to build and deploy AI-powered applications without the need for coding.

Radiology Business is an AI tool designed to provide insights and solutions for professionals in the radiology field. The platform covers a wide range of topics including management, imaging, technology, and conferences. It offers news, analysis, and resources to help radiologists stay informed and make informed decisions. Radiology Business aims to leverage artificial intelligence to improve workflow efficiency and enhance the overall experience in the radiology ecosystem.

Helper Systems has developed technology that restores the trust between students who want to use AI tools for research and faculty who need to ensure academic integrity. With kOS (pronounced chaos), students can easily provide proof of work using a platform that significantly simplifies and enhances the research process in ways never before possible. Add PDF files from your desktop, shared drives or the web. Annotate them if you desire. Use AI responsibly, knowing when information is generated from your research vs. the web. Instantly create a presentation of all your resources. Share and prove your work. Try other cool features that offer a unique way to find, organize, discover, archive, and present information.

The website offers a range of AI tools and applications such as Akira AI, XAI, Neural AI OS, and more, designed to help businesses in various industries enhance decision-making processes, automate operations, and improve efficiency. It provides solutions for data management, analytics, AI transformation, and AI risk management. The platform aims to transform industries by harnessing the power of agentic workflows and decision intelligence, making businesses truly decision-centric.

BugFree.ai is an AI-powered platform designed to help users practice system design and behavior interviews, similar to Leetcode. The platform offers a range of features to assist users in preparing for technical interviews, including mock interviews, real-time feedback, and personalized study plans. With BugFree.ai, users can improve their problem-solving skills and gain confidence in tackling complex interview questions.

Nuance is a Conversational AI platform specializing in Healthcare and Customer Engagement. It offers AI solutions and services that transform the way organizations work, connect, and interact with others. Nuance provides industry-leading AI technology and deep vertical expertise to address challenges and accelerate business results, from healthcare solutions to customer engagement. The platform aims to amplify users' ability to help others and advance the effectiveness of organizations, ultimately making a positive impact on the world.

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.

Overjet is the #1 Dental AI Platform for providers and payers, offering artificial intelligence solutions to enhance clinical care and administrative efficiency in the dental industry. The platform leverages AI technology to improve oral health by providing clinically precise, efficient, and patient-centric services. Overjet is recognized by Forbes as one of the top 50 AI companies shaping the future, trusted by leading payers and providers in the dental field. It offers features such as Clinical Intelligence Platform for providers and Claim Intelligence Platform for payers, empowering teams to achieve better patient outcomes and streamline claims processes.

Connecterra is an intelligent data platform designed specifically for the dairy industry. It provides farmers, advisors, and enterprises with a comprehensive suite of tools to collect, analyze, and visualize their farm data. With advanced AI capabilities, Connecterra helps users identify trends, make informed decisions, and improve their overall operational efficiency.

A toolkit for agent autonomy, evolution, and governance enabling agents to learn from experience, collaborate, communicate, and build new tools within governance guardrails. It focuses on autonomous evolution, agent self-discovery, governance firmware, self-building systems, and agent-centric architecture. The toolkit leverages existing frameworks to enable agent autonomy and self-governance, moving towards truly autonomous AI systems.

MATLAB-Simulink-Challenge-Project-Hub is a repository aimed at contributing to the progress of engineering and science by providing challenge projects with real industry relevance and societal impact. The repository offers a wide range of projects covering various technology trends such as Artificial Intelligence, Autonomous Vehicles, Big Data, Computer Vision, and Sustainability. Participants can gain practical skills with MATLAB and Simulink while making a significant contribution to science and engineering. The projects are designed to enhance expertise in areas like Sustainability and Renewable Energy, Control, Modeling and Simulation, Machine Learning, and Robotics. By participating in these projects, individuals can receive official recognition for their problem-solving skills from technology leaders at MathWorks and earn rewards upon project completion.

Motia is an AI agent framework designed for software engineers to create, test, and deploy production-ready AI agents quickly. It provides a code-first approach, allowing developers to write agent logic in familiar languages and visualize execution in real-time. With Motia, developers can focus on business logic rather than infrastructure, offering zero infrastructure headaches, multi-language support, composable steps, built-in observability, instant APIs, and full control over AI logic. Ideal for building sophisticated agents and intelligent automations, Motia's event-driven architecture and modular steps enable the creation of GenAI-powered workflows, decision-making systems, and data processing pipelines.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

The Next-Generation LLM-based Recommender Systems Survey is a comprehensive overview of the latest advancements in recommender systems leveraging Large Language Models (LLMs). The survey covers various paradigms, approaches, and applications of LLMs in recommendation tasks, including generative and non-generative models, multimodal recommendations, personalized explanations, and industrial deployment. It discusses the comparison with existing surveys, different paradigms, and specific works in the field. The survey also addresses challenges and future directions in the domain of LLM-based recommender systems.

Foyle is a project focused on building agents to assist software developers in deploying and operating software. It aims to improve agent performance by collecting human feedback on agent suggestions and human examples of reasoning traces. Foyle utilizes a literate environment using vscode notebooks to interact with infrastructure, capturing prompts, AI-provided answers, and user corrections. The goal is to continuously retrain AI to enhance performance. Additionally, Foyle emphasizes the importance of reasoning traces for training agents to work with internal systems, providing a self-documenting process for operations and troubleshooting.

STILL is an open-source project exploring slow-thinking reasoning systems, focusing on o1-like reasoning systems. The project has released technical reports on enhancing LLM reasoning with reward-guided tree search algorithms and implementing slow-thinking reasoning systems using an imitate, explore, and self-improve framework. The project aims to replicate the capabilities of industry-level reasoning systems by fine-tuning reasoning models with long-form thought data and iteratively refining training datasets.

RSS Can is a tool designed to simplify and improve RSS feed management. It supports various systems and architectures, including Linux and macOS. Users can download the binary from the GitHub release page or use the Docker image for easy deployment. The tool provides CLI parameters and environment variables for customization. It offers features such as memory and Redis cache services, web service configuration, and rule directory settings. The project aims to support RSS pipeline flow, NLP tasks, integration with open-source software rules, and tools like a quick RSS rules generator.

The Lakera PINT Benchmark provides a neutral evaluation method for prompt injection detection systems, offering a dataset of English inputs with prompt injections, jailbreaks, benign inputs, user-agent chats, and public document excerpts. The dataset is designed to be challenging and representative, with plans for future enhancements. The benchmark aims to be unbiased and accurate, welcoming contributions to improve prompt injection detection. Users can evaluate prompt injection detection systems using the provided Jupyter Notebook. The dataset structure is specified in YAML format, allowing users to prepare their datasets for benchmarking. Evaluation examples and resources are provided to assist users in evaluating prompt injection detection models and tools.

Akagi is a project designed to help users understand and improve their performance in Majsoul game matches in real-time. It provides educational insights and tools for analyzing gameplay. Users can install Akagi on Windows or Mac systems and follow the setup instructions to enhance their gaming experience. The project aims to offer features like Autoplay, Auto Ron, and integration with MajsoulUnlocker. It also focuses on enhancing user safety by providing guidelines to minimize the risk of account suspension. Akagi is a tool that combines MITM interception, AI decision-making, and user interaction to optimize gameplay strategies and performance.

DevOpsGPT is a revolutionary tool designed to streamline your workflow and empower you to build systems and automate tasks with ease. Tired of spending hours on repetitive DevOps tasks? DevOpsGPT is here to help! Whether you're setting up infrastructure, speeding up deployments, or tackling any other DevOps challenge, our app can make your life easier and more productive. With DevOpsGPT, you can expect faster task completion, simplified workflows, and increased efficiency. Ready to experience the DevOpsGPT difference? Visit our website, sign in or create an account, start exploring the features, and share your feedback to help us improve. DevOpsGPT will become an essential tool in your DevOps toolkit.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Aid2 is a tool designed to authorize iOS devices and install apps similar to iTools. After authorizing with Aid2, the IPA files can be installed without entering the app ID and password. This second version of Aid supports both Windows and Mac systems, although the Mac system has not been fully tested yet. Version 2.1 added the functionality to install IPA files. Version 2.5 streamlined the authorization process, executing it on each device using a single thread to reduce code complexity and improve authorization speed. The tool requires a compilation environment with Vcpkg, gRPC, Protobuf, and OpenSSL, and users need to have access to a VPN for successful configuration.

SuperBench is a validation and profiling tool for AI infrastructure. It provides a comprehensive set of tests and benchmarks to evaluate the performance and reliability of AI systems. The tool helps users identify bottlenecks, optimize configurations, and ensure the stability of their AI infrastructure. SuperBench is designed to streamline the validation process and improve the overall efficiency of AI deployments.

MindsDB is a platform for customizing AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data. MindsDB "enhances" SQL syntax with AI capabilities to make it accessible for developers worldwide. With MindsDB’s nearly 200 integrations, any developer can create AI customized for their purpose, faster and more securely. Their AI systems will constantly improve themselves — using companies’ own data, in real-time.

The Codebase Context Specification (CCS) project aims to standardize embedding contextual information within codebases to enhance understanding for both AI and human developers. It introduces a convention similar to `.env` and `.editorconfig` files but focused on documenting code for both AI and humans. By providing structured contextual metadata, collaborative documentation guidelines, and standardized context files, developers can improve code comprehension, collaboration, and development efficiency. The project includes a linter for validating context files and provides guidelines for using the specification with AI assistants. Tooling recommendations suggest creating memory systems, IDE plugins, AI model integrations, and agents for context creation and utilization. Future directions include integration with existing documentation systems, dynamic context generation, and support for explicit context overriding.

Awesome-Papers-Autonomous-Agent is a curated collection of recent papers focusing on autonomous agents, specifically interested in RL-based agents and LLM-based agents. The repository aims to provide a comprehensive resource for researchers and practitioners interested in intelligent agents that can achieve goals, acquire knowledge, and continually improve. The collection includes papers on various topics such as instruction following, building agents based on world models, using language as knowledge, leveraging LLMs as a tool, generalization across tasks, continual learning, combining RL and LLM, transformer-based policies, trajectory to language, trajectory prediction, multimodal agents, training LLMs for generalization and adaptation, task-specific designing, multi-agent systems, experimental analysis, benchmarking, applications, algorithm design, and combining with RL.

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 |

Advanced RAG Techniques is a comprehensive collection of cutting-edge Retrieval-Augmented Generation (RAG) tutorials aimed at enhancing the accuracy, efficiency, and contextual richness of RAG systems. The repository serves as a hub for state-of-the-art RAG enhancements, comprehensive documentation, practical implementation guidelines, and regular updates with the latest advancements. It covers a wide range of techniques from foundational RAG methods to advanced retrieval methods, iterative and adaptive techniques, evaluation processes, explainability and transparency features, and advanced architectures integrating knowledge graphs and recursive processing.

LLM4IR-Survey is a collection of papers related to large language models for information retrieval, organized according to the survey paper 'Large Language Models for Information Retrieval: A Survey'. It covers various aspects such as query rewriting, retrievers, rerankers, readers, search agents, and more, providing insights into the integration of large language models with information retrieval systems.

A guide for creating systems that improve society, named Quijote.

Advises on e-procurement systems to optimize purchasing processes.

Tech expert helping improve your internet speed

The AI Internal Family Systems Chatbot

Mentor for designers on creating modern design systems

Formal, yet adaptable linear systems expert.

Guides on library blockchain systems.

UX/UI designer for design systems specs

Ensures IT systems integrity through comprehensive auditing.

Mentor-like computer science professor specializing in operating systems, making complex concepts accessible.

Expert in water filtration systems

Expert in guiding schools in developing holistic discipline policies, integrating restorative practices, and educational systems enhancement.

Accountable AI represents a step forward in creating a more ethical, transparent, and responsible AI system, tailored to meet the demands of users who prioritize accountability and unbiased information in their AI interactions.

Aiding in systemic thinking and analysis

Encouraging STM32 coding expert

I conduct mock system design interviews.

Tech-savvy manager for computer-based problem-solving.

Skilled in teaching C++ 98 with clear, beginner-friendly examples.

Experto en GRASP, General Responsibility Assignment Software Patterns

C programming tutor offering guidance, debugging, and style tips.