Best AI tools for< Order Tests >

PrepAI is an advanced test generator that uses AI to help educators create high-quality assessments quickly and easily. With PrepAI, teachers can save time, engage students with unique questions, and prepare them for success. PrepAI offers a variety of features to make test creation easy, including multiple content input options, various question formats, and an easy-to-use dashboard. PrepAI also offers a variety of advantages for educators, including the ability to analyze higher-order thinking skills, conduct tests effortlessly, and access unlimited question sets.

BigShort is a real-time stock charting platform designed for day traders and swing traders. It offers a variety of features to help traders make informed decisions, including SmartFlow, which visualizes real-time covert Smart Money activity, and OptionFlow, which shows option blocks, sweeps, and splits in real-time. BigShort also provides backtested and forward-tested leading indicators, as well as live data for all NYSE and Nasdaq tickers.

Cara is an AI-powered mental health therapist that listens, understands, and helps you explore your life's challenges in depth to problem-solve them. Cara understands your texts as seamlessly as your best friend would and is able to respond naturally and give you advice. Cara remembers everything you tell it and can be insightful. Cara is insightful and non-judgemental. Available anytime, anywhere.

Inductor is a developer tool for evaluating, ensuring, and improving the quality of your LLM applications – both during development and in production. It provides a fantastic workflow for continuous testing and evaluation as you develop, so that you always know your LLM app’s quality. Systematically improve quality and cost-effectiveness by actionably understanding your LLM app’s behavior and quickly testing different app variants. Rigorously assess your LLM app’s behavior before you deploy, in order to ensure quality and cost-effectiveness when you’re live. Easily monitor your live traffic: detect and resolve issues, analyze usage in order to improve, and seamlessly feed back into your development process. Inductor makes it easy for engineering and other roles to collaborate: get critical human feedback from non-engineering stakeholders (e.g., PM, UX, or subject matter experts) to ensure that your LLM app is user-ready.

Airparser is an AI-powered email and document parser tool that revolutionizes data extraction by utilizing the GPT parser engine. It allows users to automate the extraction of structured data from various sources such as emails, PDFs, documents, and handwritten texts. With features like automatic extraction, export to multiple platforms, and support for multiple languages, Airparser simplifies data extraction processes for individuals and businesses. The tool ensures data security and offers seamless integration with other applications through APIs and webhooks.

Prelaunch.com is an AI-powered platform that offers bullet-proof insights from ready-to-buy customers. It provides a comprehensive suite of tools for concept testing, price testing, branding and marketing testing, business model evaluation, and more. With a focus on AI idea validation and market research, Prelaunch.com helps businesses understand market competitors, customer personas, and latest trends. The platform also features performance dashboard tracking, surveys for immediate insights, and next-gen focus groups for in-depth interviews. Prelaunch.com is trusted by a community of over 1 million users and 1000+ brands, offering a seamless experience for launching iconic products.

MacWhisper is a native macOS application that utilizes OpenAI's Whisper technology for transcribing audio files into text. It offers a user-friendly interface for recording, transcribing, and editing audio, making it suitable for various use cases such as transcribing meetings, lectures, interviews, and podcasts. The application is designed to protect user privacy by performing all transcriptions locally on the device, ensuring that no data leaves the user's machine.

Galleri is a multi-cancer early detection test that uses a single blood draw to screen for over 50 types of cancer. It is recommended for adults aged 50 or older who are at an elevated risk for cancer. Galleri is not a diagnostic test and does not detect all cancers. A positive result requires confirmatory diagnostic evaluation by medically established procedures (e.g., imaging) to confirm cancer.

Dasha is a conversational AI-as-a-service platform that allows developers to embed realistic voice and text conversational capabilities into their apps or products. With a single integration, developers can create smart conversational apps for web, desktop, mobile, IoT, and call centers. Dasha's declarative programming language, DashaScript, makes it easy to design complex real-world conversations that pass a limited Turing test. Developers can use Dasha to automate call center conversations, recreate the Google Duplex demo, or create no-code GUIs for their users. Dasha's platform is flexible and can be integrated with any platform or programming language. It also offers a free tier for builders and testers.

SiteRecon is an AI-powered mapping tool designed for landscapers, offering features such as automated takeoffs, site condition reports, job plans, account management, and enhancement proposals. It helps landscapers streamline their sales process, improve productivity, and enhance client relations by providing accurate measurements, detailed site audits, and work reports. SiteRecon aims to revolutionize the landscaping industry by leveraging AI technology to simplify property mapping, estimate generation, and job management, ultimately leading to increased efficiency and profitability for landscaping businesses.

Brightpick is an AI-powered order fulfillment solution that revolutionizes warehouse operations by enabling fully automated order picking, consolidation, and dispatch. The Brightpick Autopicker, equipped with AI robots, streamlines the fulfillment process by efficiently picking a wide range of products, from groceries to pharmaceuticals, with precision and speed. Trusted by industry leaders, Brightpick helps businesses cut costs, increase efficiency, and enhance customer service in industries such as Ecommerce, 3PL, E-Grocery, and Medical. With its innovative technology and award-winning solutions, Brightpick is reshaping the future of order fulfillment.

Automaited is an AI application that offers Ada - an AI Agent for automating order processing. Ada handles orders from receipt to ERP entry, extracting, validating, and transferring data to ensure accuracy and efficiency. The application utilizes state-of-the-art AI technology to streamline order processing, saving time, reducing errors, and enabling users to focus on customer satisfaction. With seamless automation, Ada integrates into ERP systems, making order processing effortless, quick, and cost-efficient. Automaited provides tailored automations to make operational processes up to 70% more efficient, enhancing performance and reducing error rates.

Kavida is an AI-powered post PO automation solution for procurement that ensures manufacturers never miss critical orders from suppliers. It coordinates with suppliers, expedites orders, and prevents costly delays. Kavida optimizes inventory, reduces time spent on supplier coordination, and unlocks rapid cash flow by automating PO changes, confirmations, and document management. It offers real-time supply monitoring, early risk detection, and better PO coordination with suppliers. Kavida is a trusted solution used by leading manufacturers across various industries.

chatQR.ai is an AI-powered ordering application that serves as a complete Point Of Sale/Kiosk replacement. It utilizes voice recognition technology combined with the latest Large Language Model (LLM) AI to create a seamless QR code ordering experience for customers. The system is designed to be AI-first, offering mature point of sale features and the ability to integrate the ChatQR Voice Assistant into existing systems. With support for multiple currencies and payment providers like Stripe and Square, chatQR.ai aims to revolutionize the way businesses manage orders and payments.

Super T is an AI-powered T-shirt design tool that allows users to generate unique and stylish T-shirt designs in seconds. With Super T, users can input their ideas, choose from a variety of design options, and purchase their designs in a matter of minutes. Super T is perfect for anyone who wants to create custom T-shirts for themselves, their friends, or their business.

Tiqt is an all-in-one AI-powered platform designed to help WooCommerce businesses grow by simplifying workflow, increasing sales, and providing smart solutions for product and order management. With features like a smart sales assistant chatbot, automated customer support, and product optimization tools, Tiqt aims to revolutionize the e-commerce experience. Say goodbye to lost sales and hello to a seamless shopping journey with Tiqt.

1PX.AI is an AI-powered image resizing tool that allows users to easily resize images without compromising quality. The tool uses advanced algorithms to intelligently adjust image dimensions while preserving important details. With 1PX.AI, users can quickly optimize images for various platforms such as websites, social media, and e-commerce. The intuitive interface and fast processing make it a convenient solution for individuals and businesses looking to enhance their visual content effortlessly.

Music Radio Creative is the largest professional voice-over agency in the world, offering services such as custom voice-overs, AI voice generator, radio jingles, DJ drops, podcast editing, and more. With a team of trained voice actors and AI voices, they provide high-end audio production services for businesses, podcasters, DJs, and radio stations since 2006. The platform caters to all audio and video needs, ensuring a seamless experience for clients seeking top-quality audio solutions.

Sense is an AI-powered tool that helps you organize and search all of your work information in one place. It automatically keeps all documents, links, files, and conversations organized and interrelated, so you can easily find what you need, when you need it. Sense also provides sharing suggestions, so you can never forget to share any piece of information with relevant people. With Sense, you can: * Keep all of your work information organized in one place * Search across all of your apps, websites, and documents * Never forget to share any piece of information with relevant people * Get sharing suggestions * Collaborate with your team more effectively

Responsible AI Licenses (RAIL) is an initiative that empowers developers to restrict the use of their AI technology to prevent irresponsible and harmful applications. They provide licenses with behavioral-use clauses to control specific use-cases and prevent misuse of AI artifacts. The organization aims to standardize RAIL Licenses, develop collaboration tools, and educate developers on responsible AI practices.

allms is a versatile and powerful library designed to streamline the process of querying Large Language Models (LLMs). Developed by Allegro engineers, it simplifies working with LLM applications by providing a user-friendly interface, asynchronous querying, automatic retrying mechanism, error handling, and output parsing. It supports various LLM families hosted on different platforms like OpenAI, Google, Azure, and GCP. The library offers features for configuring endpoint credentials, batch querying with symbolic variables, and forcing structured output format. It also provides documentation, quickstart guides, and instructions for local development, testing, updating documentation, and making new releases.

TestSpark is a plugin for generating unit tests that integrates AI-based test generation tools. It supports LLM-based test generation using OpenAI, HuggingFace, and JetBrains internal AI Assistant platform, as well as local search-based test generation using EvoSuite. Users can configure test generation settings, interact with test cases, view coverage statistics, and integrate tests into projects. The plugin is designed for experimental use to augment existing test suites, not replace manual test writing.

Treds is a Radix Trie based data structure server that stores keys in sorted order, ensuring fast and efficient retrieval. It offers various commands for key/value store, sorted maps store, list store, set store, hash store, and more. Treds provides unique features like optimized querying for keys with common prefixes, sorted key/value pairs, and new commands like DELPREFIX, LNGPREFIX, and PPUBLISH. It is designed for high performance with single-threaded architecture and event loop, utilizing modified Radix trees and Doubly Linked Lists for quick lookup. Treds also supports PubSub functionality and vector store operations for vector search using HNSW algorithm.

Panda is a car interface tool that speaks CAN and CAN FD, running on STM32F413 and STM32H725. It provides safety modes and controls_allowed feature for message handling. The tool ensures code rigor through CI regression tests, including static code analysis, MISRA C:2012 violations check, unit tests, and hardware-in-the-loop tests. The software interface supports Python library, C++ library, and socketcan in kernel. Panda is licensed under the MIT license.

PentestGPT provides advanced AI and integrated tools to help security teams conduct comprehensive penetration tests effortlessly. Scan, exploit, and analyze web applications, networks, and cloud environments with ease and precision, without needing expert skills. The tool utilizes Supabase for data storage and management, and Vercel for hosting the frontend. It offers a local quickstart guide for running the tool locally and a hosted quickstart guide for deploying it in the cloud. PentestGPT aims to simplify the penetration testing process for security professionals and enthusiasts alike.

Yet Another Applied LLM Benchmark is a collection of diverse tests designed to evaluate the capabilities of language models in performing real-world tasks. The benchmark includes tests such as converting code, decompiling bytecode, explaining minified JavaScript, identifying encoding formats, writing parsers, and generating SQL queries. It features a dataflow domain-specific language for easily adding new tests and has nearly 100 tests based on actual scenarios encountered when working with language models. The benchmark aims to assess whether models can effectively handle tasks that users genuinely care about.

The On-board Artificial Intelligence Research (OnAIR) Platform is a framework that enables AI algorithms written in Python to interact with NASA's cFS. It is intended to explore research concepts in autonomous operations in a simulated environment. The platform provides tools for generating environments, handling telemetry data through Redis, running unit tests, and contributing to the repository. Users can set up a conda environment, configure telemetry and Redis examples, run simulations, and conduct unit tests to ensure the functionality of their AI algorithms. The platform also includes guidelines for licensing, copyright, and contributions to the repository.

MSCCL++ is a GPU-driven communication stack for scalable AI applications. It provides a highly efficient and customizable communication stack for distributed GPU applications. MSCCL++ redefines inter-GPU communication interfaces, delivering a highly efficient and customizable communication stack for distributed GPU applications. Its design is specifically tailored to accommodate diverse performance optimization scenarios often encountered in state-of-the-art AI applications. MSCCL++ provides communication abstractions at the lowest level close to hardware and at the highest level close to application API. The lowest level of abstraction is ultra light weight which enables a user to implement logics of data movement for a collective operation such as AllReduce inside a GPU kernel extremely efficiently without worrying about memory ordering of different ops. The modularity of MSCCL++ enables a user to construct the building blocks of MSCCL++ in a high level abstraction in Python and feed them to a CUDA kernel in order to facilitate the user's productivity. MSCCL++ provides fine-grained synchronous and asynchronous 0-copy 1-sided abstracts for communication primitives such as `put()`, `get()`, `signal()`, `flush()`, and `wait()`. The 1-sided abstractions allows a user to asynchronously `put()` their data on the remote GPU as soon as it is ready without requiring the remote side to issue any receive instruction. This enables users to easily implement flexible communication logics, such as overlapping communication with computation, or implementing customized collective communication algorithms without worrying about potential deadlocks. Additionally, the 0-copy capability enables MSCCL++ to directly transfer data between user's buffers without using intermediate internal buffers which saves GPU bandwidth and memory capacity. MSCCL++ provides consistent abstractions regardless of the location of the remote GPU (either on the local node or on a remote node) or the underlying link (either NVLink/xGMI or InfiniBand). This simplifies the code for inter-GPU communication, which is often complex due to memory ordering of GPU/CPU read/writes and therefore, is error-prone.

The ChatGPT R package provides a set of features to assist in R coding. It includes addins like Ask ChatGPT, Comment selected code, Complete selected code, Create unit tests, Create variable name, Document code, Explain selected code, Find issues in the selected code, Optimize selected code, and Refactor selected code. Users can interact with ChatGPT to get code suggestions, explanations, and optimizations. The package helps in improving coding efficiency and quality by providing AI-powered assistance within the RStudio environment.

The Neo4j GraphRAG package for Python is an official repository that provides features for creating and managing vector indexes in Neo4j databases. It aims to offer developers a reliable package with long-term commitment, maintenance, and fast feature updates. The package supports various Python versions and includes functionalities for creating vector indexes, populating them, and performing similarity searches. It also provides guidelines for installation, examples, and development processes such as installing dependencies, making changes, and running tests.

pytest-evals is a minimalistic pytest plugin designed to help evaluate the performance of Language Model (LLM) outputs against test cases. It allows users to test and evaluate LLM prompts against multiple cases, track metrics, and integrate easily with pytest, Jupyter notebooks, and CI/CD pipelines. Users can scale up by running tests in parallel with pytest-xdist and asynchronously with pytest-asyncio. The tool focuses on simplifying evaluation processes without the need for complex frameworks, keeping tests and evaluations together, and emphasizing logic over infrastructure.

AirSwap Protocols is a repository containing smart contracts for developers and traders on the AirSwap peer-to-peer trading network. It includes various packages for functionalities like server registry, atomic token swap, staking, rewards pool, batch token and order calls, libraries, and utils. The repository follows a branching and release process for contracts and tools, with steps for regular development process and individual package features or patches. Users can deploy and verify contracts using specific commands with network flags.

ZipNN is a lossless and near-lossless compression library optimized for numbers/tensors in the Foundation Models environment. It automatically prepares data for compression based on its type, allowing users to focus on core tasks without worrying about compression complexities. The library delivers effective compression techniques for different data types and structures, achieving high compression ratios and rates. ZipNN supports various compression methods like ZSTD, lz4, and snappy, and provides ready-made scripts for file compression/decompression. Users can also manually import the package to compress and decompress data. The library offers advanced configuration options for customization and validation tests for different input and compression types.

Metaflow Service is a metadata service implementation for Metaflow, providing a thin wrapper around a database to keep track of metadata associated with Flows, Runs, Steps, Tasks, and Artifacts. It includes features for managing DB migrations, launching compatible versions of the metadata service, and executing flows locally. The service can be run using Docker or as a standalone service, with options for testing and running unit/integration tests. Users can interact with the service via API endpoints or utility CLI tools.

Minja is a minimalistic C++ Jinja templating engine designed specifically for integration with C++ LLM projects, such as llama.cpp or gemma.cpp. It is not a general-purpose tool but focuses on providing a limited set of filters, tests, and language features tailored for chat templates. The library is header-only, requires C++17, and depends only on nlohmann::json. Minja aims to keep the codebase small, easy to understand, and offers decent performance compared to Python. Users should be cautious when using Minja due to potential security risks, and it is not intended for producing HTML or JavaScript output.

Atlantis is an extensive Java framework based on JBWAPI 2.1.0, designed to simplify bot development for Starcraft. It provides clean and re-usable code, supports all three races with a focus on Terran, automates various tasks like modifying bwapi.ini and managing economy, includes tests and mini-maps, offers customizable build orders, scouts enemy bases, responds to threats, and more. The framework aims to streamline bot development by handling common tasks and providing advanced features for unit selection and decision-making.

Pathway is a Python data processing framework for analytics and AI pipelines over data streams. It's the ideal solution for real-time processing use cases like streaming ETL or RAG pipelines for unstructured data. Pathway comes with an **easy-to-use Python API** , allowing you to seamlessly integrate your favorite Python ML libraries. Pathway code is versatile and robust: **you can use it in both development and production environments, handling both batch and streaming data effectively**. The same code can be used for local development, CI/CD tests, running batch jobs, handling stream replays, and processing data streams. Pathway is powered by a **scalable Rust engine** based on Differential Dataflow and performs incremental computation. Your Pathway code, despite being written in Python, is run by the Rust engine, enabling multithreading, multiprocessing, and distributed computations. All the pipeline is kept in memory and can be easily deployed with **Docker and Kubernetes**. You can install Pathway with pip: `pip install -U pathway` For any questions, you will find the community and team behind the project on Discord.

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=