Best AI tools for< Classify Bugs >

Trezy Classifier is a powerful API designed for transaction enrichment, categorization, and company identification. It offers global coverage, 350+ categories, VAT estimation, and more. The API goes beyond simple categorization to provide enriched data for each transaction, making it easy to relate to ledger accounts. With features like supplier intelligence, VAT estimation, and simple integration, Trezy Classifier empowers users to gain real profitability insights from their transactions.

JobtitlesAI is a machine-learning API that sorts job titles into two categories: field (sales, finance, I.T...) and position (executive, management, assistant...). It can be used in spreadsheets, Hubspot, or via API. JobtitlesAI is multilingual and GDPR compliant.

Charm is an AI-powered spreadsheet assistant that helps users clean messy data, create content, summarize feedback, classify sales leads, and generate dummy data. It is a Google Sheets add-on that automates tasks that are impossible to do with traditional formulas. Charm is used by hundreds of analysts, marketers, product managers, and more.

Pointly is an intelligent, cloud-based B2B software solution that enables efficient automatic and advanced manual classification in 3D point clouds. It offers innovative AI techniques for fast and precise data classification and vectorization, transforming point cloud analysis into an enjoyable and efficient workflow. Pointly provides standard and custom classifiers, tools for classification and vectorization, API and on-premise classification options, collaboration features, secure cloud processing, and scalability for handling large-scale point cloud data.

Taylor is a deterministic AI tool that empowers Business & Engineering teams to enhance data at scale through bulk classification. It allows users to structure freeform text, enrich metadata, and customize enrichments according to specific needs. Taylor provides high impact, easy-to-use features for total control over classification and extraction models, enabling users to drive business impact from day one. With powerful integrations and simple customization options, Taylor brings powerful machine learning capabilities to users' fingertips.

FranzAI LLM Playground is an AI-powered tool that helps you extract, classify, and analyze unstructured text data. It leverages transformer models to provide accurate and meaningful results, enabling you to build data applications faster and more efficiently. With FranzAI, you can accelerate product and content classification, enhance data interpretation, and advance data extraction processes, unlocking key insights from your textual data.

Eigen Technologies is an AI-powered data extraction platform designed for business users to automate the extraction of data from various documents. The platform offers solutions for intelligent document processing and automation, enabling users to streamline business processes, make informed decisions, and achieve significant efficiency gains. Eigen's platform is purpose-built to deliver real ROI by reducing manual processes, improving data accuracy, and accelerating decision-making across industries such as corporates, banks, financial services, insurance, law, and manufacturing. With features like generative insights, table extraction, pre-processing hub, and model governance, Eigen empowers users to automate data extraction workflows efficiently. The platform is known for its unmatched accuracy, speed, and capability, providing customers with a flexible and scalable solution that integrates seamlessly with existing systems.

Nightfall AI is a comprehensive data security platform that leverages AI technology to protect sensitive data in the AI-driven enterprise. It offers solutions for data loss prevention, data protection, and data privacy for AI applications. Nightfall scans all types of enterprise data, monitors high-risk activities, and enables secure, AI-driven productivity without hindering end-users. The platform integrates seamlessly with enterprise apps and devices, providing immediate response to data exposure incidents. Nightfall is trusted by innovative organizations for its holistic approach to data security and compliance.

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

Roboflow is a platform that provides tools for building and deploying computer vision models. It offers a range of features, including data annotation, model training, and deployment. Roboflow is used by over 250,000 engineers to create datasets, train models, and deploy to production.

Cohere is a leading provider of artificial intelligence (AI) tools and services. Our mission is to make AI accessible and useful to everyone, from individual developers to large enterprises. We offer a range of AI tools and services, including natural language processing, computer vision, and machine learning. Our tools are used by businesses of all sizes to improve customer service, automate tasks, and gain insights from data.

Hive AI provides a suite of AI models and solutions for understanding, searching, and generating content. Their AI models can be integrated into applications via APIs, enabling developers to add advanced content understanding capabilities to their products. Hive AI's solutions are used by businesses in various industries, including digital platforms, sports, media, and marketing, to streamline content moderation, automate image search and authentication, measure sponsorships, and monetize ad inventory.

Predibase is a platform for fine-tuning and serving Large Language Models (LLMs). It provides a cost-effective and efficient way to train and deploy LLMs for a variety of tasks, including classification, information extraction, customer sentiment analysis, customer support, code generation, and named entity recognition. Predibase is built on proven open-source technology, including LoRAX, Ludwig, and Horovod.

Levity is an AI-powered email automation tool designed specifically for the freight industry. It connects to your inbox, categorizes incoming emails, extracts critical information, and pushes it to your TMS, allowing you to focus on building customer relationships instead of manual data entry and repetitive tasks.

Liner is a free and easy-to-use tool that allows users to train machine learning models without writing any code. It provides a user-friendly interface that guides users through the process of importing data, selecting a model, and training the model. Liner also offers a variety of pre-trained models that can be used for common tasks such as image classification, text classification, and object detection. With Liner, users can quickly and easily create and deploy machine learning applications without the need for specialized knowledge or expertise.

Cogniflow is a no-code AI platform that allows users to build and deploy custom AI models without any coding experience. The platform provides a variety of pre-built AI models that can be used for a variety of tasks, including customer service, HR, operations, and more. Cogniflow also offers a variety of integrations with other applications, making it easy to connect your AI models to your existing workflow.

Landing AI is a computer vision platform and AI software company that provides a cloud-based platform for building and deploying computer vision applications. The platform includes a library of pre-trained models, a set of tools for data labeling and model training, and a deployment service that allows users to deploy their models to the cloud or edge devices. Landing AI's platform is used by a variety of industries, including automotive, electronics, food and beverage, medical devices, life sciences, agriculture, manufacturing, infrastructure, and pharma.

This website provides a platform for users to apply artificial intelligence (AI) to their work. Users can access a variety of AI tools and resources, including pre-trained models, datasets, and tutorials. The website also provides a community forum where users can connect with other AI enthusiasts and experts.

Custom Vision is a cognitive service provided by Microsoft that offers a user-friendly platform for creating custom computer vision models. Users can easily train the models by providing labeled images, allowing them to tailor the models to their specific needs. The service simplifies the process of implementing visual intelligence into applications, making it accessible even to those without extensive machine learning expertise.

Nesa is a global blockchain network that brings AI on-chain, allowing applications and protocols to seamlessly integrate with AI. It offers secure execution for critical inference, a private AI network, and a global AI model repository. Nesa supports various AI models for tasks like text classification, content summarization, image generation, language translation, and more. The platform is backed by a team with extensive experience in AI and deep learning, with numerous awards and recognitions in the field.

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.

Invariant Analyzer is an open-source scanner designed for LLM-based AI agents to find bugs, vulnerabilities, and security threats. It scans agent execution traces to identify issues like looping behavior, data leaks, prompt injections, and unsafe code execution. The tool offers a library of built-in checkers, an expressive policy language, data flow analysis, real-time monitoring, and extensible architecture for custom checkers. It helps developers debug AI agents, scan for security violations, and prevent security issues and data breaches during runtime. The analyzer leverages deep contextual understanding and a purpose-built rule matching engine for security policy enforcement.

Semantic Cache is a tool for caching natural text based on semantic similarity. It allows for classifying text into categories, caching AI responses, and reducing API latency by responding to similar queries with cached values. The tool stores cache entries by meaning, handles synonyms, supports multiple languages, understands complex queries, and offers easy integration with Node.js applications. Users can set a custom proximity threshold for filtering results. The tool is ideal for tasks involving querying or retrieving information based on meaning, such as natural language classification or caching AI responses.

The collection is actively updated with the help of an internal literature search engine.

Recognize is a smart media tagging tool for Nextcloud that automatically categorizes photos and music by recognizing faces, animals, landscapes, food, vehicles, buildings, landmarks, monuments, music genres, and human actions in videos. It uses pre-trained models for object detection, landmark recognition, face comparison, music genre classification, and video classification. The tool ensures privacy by processing images locally without sending data to cloud providers. However, it cannot process end-to-end encrypted files. Recognize is rated positively for ethical AI practices in terms of open-source software, freely available models, and training data transparency, except for music genre recognition due to limited access to training data.

Autolabel is a Python library designed to label, clean, and enrich text datasets using Large Language Models (LLMs). It provides a simple 3-step process for labeling data, supports various NLP tasks, and offers features like confidence estimation, explanations, and state management. Users can access Refuel hosted LLMs for labeling and confidence estimation, and the library supports commercial and open source LLMs from providers like OpenAI, Anthropic, HuggingFace, and Google. Autolabel aims to streamline the labeling process for machine learning tasks by leveraging state-of-the-art LLM techniques and minimizing costs and experimentation time.

SuperAdapters is a tool designed to finetune Large Language Models (LLMs) with various adapters on different platforms. It supports models like Bloom, LLaMA, ChatGLM, Qwen, Baichuan, Mixtral, Phi, and more. Users can finetune LLMs on Windows, Linux, and Mac M1/2, handle train/test data with Terminal, File, or DataBase, and perform tasks like CausalLM and SequenceClassification. The tool provides detailed instructions on how to use different models with specific adapters for tasks like finetuning and inference. It also includes requirements for CentOS, Ubuntu, and MacOS, along with information on LLM downloads and data formats. Additionally, it offers parameters for finetuning and inference, as well as options for web and API-based inference.

AIDE is a machine learning code generation agent that can generate solutions for machine learning tasks from natural language descriptions. It has the following features: 1. **Instruct with Natural Language**: Describe your problem or additional requirements and expert insights, all in natural language. 2. **Deliver Solution in Source Code**: AIDE will generate Python scripts for the **tested** machine learning pipeline. Enjoy full transparency, reproducibility, and the freedom to further improve the source code! 3. **Iterative Optimization**: AIDE iteratively runs, debugs, evaluates, and improves the ML code, all by itself. 4. **Visualization**: We also provide tools to visualize the solution tree produced by AIDE for a better understanding of its experimentation process. This gives you insights not only about what works but also what doesn't. AIDE has been benchmarked on over 60 Kaggle data science competitions and has demonstrated impressive performance, surpassing 50% of Kaggle participants on average. It is particularly well-suited for tasks that require complex data preprocessing, feature engineering, and model selection.

This repository contains the complete archive of the 2024 version of the 'Intelligent Computing System' experiment at the University of Chinese Academy of Sciences. The experiment content for 2024 has undergone extensive adjustments to the knowledge system and experimental topics, including the transition from TensorFlow to PyTorch, significant modifications to previous code, and the addition of experiments with large models. The project is continuously updated in line with the course progress, currently up to the seventh experiment. Updates include the addition of experiments like YOLOv5 in Experiment 5-3, updates to theoretical teaching materials, and fixes for bugs in Experiment 6 code. The repository also includes experiment manuals, questions, and answers for various experiments, with some data sets hosted on Baidu Cloud due to size limitations on GitHub.

Eden AI aims to simplify the use and deployment of AI technologies by providing a unique API that connects to all the best AI engines. With the rise of **AI as a Service** , a lot of companies provide off-the-shelf trained models that you can access directly through an API. These companies are either the tech giants (Google, Microsoft , Amazon) or other smaller, more specialized companies, and there are hundreds of them. Some of the most known are : DeepL (translation), OpenAI (text and image analysis), AssemblyAI (speech analysis). There are **hundreds of companies** doing that. We're regrouping the best ones **in one place** !

LLM2Vec is a simple recipe to convert decoder-only LLMs into text encoders. It consists of 3 simple steps: 1) enabling bidirectional attention, 2) training with masked next token prediction, and 3) unsupervised contrastive learning. The model can be further fine-tuned to achieve state-of-the-art performance.

Superagent is an open-source AI assistant framework and API that allows developers to add powerful AI assistants to their applications. These assistants use large language models (LLMs), retrieval augmented generation (RAG), and generative AI to help users with a variety of tasks, including question answering, chatbot development, content generation, data aggregation, and workflow automation. Superagent is backed by Y Combinator and is part of YC W24.

AI Flow is an open-source, user-friendly UI application that empowers you to seamlessly connect multiple AI models together, specifically leveraging the capabilities of multiples AI APIs such as OpenAI, StabilityAI and Replicate. In a nutshell, AI Flow provides a visual platform for crafting and managing AI-driven workflows, thereby facilitating diverse and dynamic AI interactions.

**deep** doctection is a Python library that orchestrates document extraction and document layout analysis tasks using deep learning models. It does not implement models but enables you to build pipelines using highly acknowledged libraries for object detection, OCR and selected NLP tasks and provides an integrated framework for fine-tuning, evaluating and running models. For more specific text processing tasks use one of the many other great NLP libraries. **deep** doctection focuses on applications and is made for those who want to solve real world problems related to document extraction from PDFs or scans in various image formats. **deep** doctection provides model wrappers of supported libraries for various tasks to be integrated into pipelines. Its core function does not depend on any specific deep learning library. Selected models for the following tasks are currently supported: * Document layout analysis including table recognition in Tensorflow with **Tensorpack**, or PyTorch with **Detectron2**, * OCR with support of **Tesseract**, **DocTr** (Tensorflow and PyTorch implementations available) and a wrapper to an API for a commercial solution, * Text mining for native PDFs with **pdfplumber**, * Language detection with **fastText**, * Deskewing and rotating images with **jdeskew**. * Document and token classification with all LayoutLM models provided by the **Transformer library**. (Yes, you can use any LayoutLM-model with any of the provided OCR-or pdfplumber tools straight away!). * Table detection and table structure recognition with **table-transformer**. * There is a small dataset for token classification available and a lot of new tutorials to show, how to train and evaluate this dataset using LayoutLMv1, LayoutLMv2, LayoutXLM and LayoutLMv3. * Comprehensive configuration of **analyzer** like choosing different models, output parsing, OCR selection. Check this notebook or the docs for more infos. * Document layout analysis and table recognition now runs with **Torchscript** (CPU) as well and **Detectron2** is not required anymore for basic inference. * [**new**] More angle predictors for determining the rotation of a document based on **Tesseract** and **DocTr** (not contained in the built-in Analyzer). * [**new**] Token classification with **LiLT** via **transformers**. We have added a model wrapper for token classification with LiLT and added a some LiLT models to the model catalog that seem to look promising, especially if you want to train a model on non-english data. The training script for LayoutLM can be used for LiLT as well and we will be providing a notebook on how to train a model on a custom dataset soon. **deep** doctection provides on top of that methods for pre-processing inputs to models like cropping or resizing and to post-process results, like validating duplicate outputs, relating words to detected layout segments or ordering words into contiguous text. You will get an output in JSON format that you can customize even further by yourself. Have a look at the **introduction notebook** in the notebook repo for an easy start. Check the **release notes** for recent updates. **deep** doctection or its support libraries provide pre-trained models that are in most of the cases available at the **Hugging Face Model Hub** or that will be automatically downloaded once requested. For instance, you can find pre-trained object detection models from the Tensorpack or Detectron2 framework for coarse layout analysis, table cell detection and table recognition. Training is a substantial part to get pipelines ready on some specific domain, let it be document layout analysis, document classification or NER. **deep** doctection provides training scripts for models that are based on trainers developed from the library that hosts the model code. Moreover, **deep** doctection hosts code to some well established datasets like **Publaynet** that makes it easy to experiment. It also contains mappings from widely used data formats like COCO and it has a dataset framework (akin to **datasets** so that setting up training on a custom dataset becomes very easy. **This notebook** shows you how to do this. **deep** doctection comes equipped with a framework that allows you to evaluate predictions of a single or multiple models in a pipeline against some ground truth. Check again **here** how it is done. Having set up a pipeline it takes you a few lines of code to instantiate the pipeline and after a for loop all pages will be processed through the pipeline.

Deepgram JavaScript SDK. Power your apps with world-class speech and Language AI models.

BAML is a config file format for declaring LLM functions that you can then use in TypeScript or Python. With BAML you can Classify or Extract any structured data using Anthropic, OpenAI or local models (using Ollama) ## Resources  [Discord Community](https://discord.gg/boundaryml)  [Follow us on Twitter](https://twitter.com/boundaryml) * Discord Office Hours - Come ask us anything! We hold office hours most days (9am - 12pm PST). * Documentation - Learn BAML * Documentation - BAML Syntax Reference * Documentation - Prompt engineering tips * Boundary Studio - Observability and more #### Starter projects * BAML + NextJS 14 * BAML + FastAPI + Streaming ## Motivation Calling LLMs in your code is frustrating: * your code uses types everywhere: classes, enums, and arrays * but LLMs speak English, not types BAML makes calling LLMs easy by taking a type-first approach that lives fully in your codebase: 1. Define what your LLM output type is in a .baml file, with rich syntax to describe any field (even enum values) 2. Declare your prompt in the .baml config using those types 3. Add additional LLM config like retries or redundancy 4. Transpile the .baml files to a callable Python or TS function with a type-safe interface. (VSCode extension does this for you automatically). We were inspired by similar patterns for type safety: protobuf and OpenAPI for RPCs, Prisma and SQLAlchemy for databases. BAML guarantees type safety for LLMs and comes with tools to give you a great developer experience:  Jump to BAML code or how Flexible Parsing works without additional LLM calls. | BAML Tooling | Capabilities | | ----------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | BAML Compiler install | Transpiles BAML code to a native Python / Typescript library (you only need it for development, never for releases) Works on Mac, Windows, Linux  | | VSCode Extension install | Syntax highlighting for BAML files Real-time prompt preview Testing UI | | Boundary Studio open (not open source) | Type-safe observability Labeling |

Actual AI is a project designed to categorize uncategorized transactions for Actual Budget using OpenAI or OpenAI specification compatible API. It sends requests to the OpenAI API to classify transactions based on their description, amount, and notes. Transactions that cannot be classified are marked as 'not guessed' in notes. The tool allows users to sync accounts before classification and classify transactions on a cron schedule. Guessed transactions are marked in notes for easy review.

Just upload a numerical dataset for classification task, will apply data analysis and machine learning steps to make a best model possible.

GPT used to classify prompts as valid inputs or injection attempts. Json output.

NACE (Nomenclature of Economic Activities) is the European statistical classification of economic activities. This is not an official product. Official information here: https://nacev2.com/en

Import Export Compliance | Tariff Classification | Shipping Queries | Logistics & Supply Chain Solutions

Expert in LAStools with in-depth command line knowledge.

GICS is a classification standard developed by MSCI and S&P Dow Jones Indices. This GPT is not a MSCI and S&P product. Official website : https://www.msci.com/our-solutions/indexes/gics

Expert in UNSPSC Codes (United Nations Standard Products and Services Code®).

Expert in IATA DGR for Transportation of Dangerous Goods.

Assists with DGL coding, focusing on edge classification and link prediction.

Classifies articles into knowledge domains. source code: https://homun.posetmage.com/Agents/

Super astronomer identifying clouds in English and Chinese, sharing facts in Chinese.

What would you say if I told you there is an app on the market that can tell you if you have a hot dog or not a hot dog.

Direct answers on Lie groups for the knowledgeable.

A bilingual porcelain classification assistant.

Medical Device Expert on MDR 2017/745, IVDR 2017/746 and related MDCG guidance

Assists in identifying wildlife.

I guide on optimizing Korean sentences for embeddings.

guess the era of the image!

I identify various rocks from images and advise consulting a geologist for certainty.

Analize uploaded files and assists in tasks based on their content.