Best AI tools for< Code Data >

Lightup is a cloud data quality monitoring tool with AI-powered anomaly detection, incident alerts, and data remediation capabilities for modern enterprise data stacks. It specializes in helping large organizations implement successful and sustainable data quality programs quickly and easily. Lightup's pushdown architecture allows for monitoring data content at massive scale without moving or copying data, providing extreme scalability and optimal automation. The tool empowers business users with democratized data quality checks and enables automatic fixing of bad data at enterprise scale.

Mito is a low-code data app infrastructure that allows users to edit spreadsheets and automatically generate Python code. It is designed to help analysts automate their repetitive Excel work and take automation into their own hands. Mito is a Jupyter extension and Streamlit component, so users don't need to set up any new infrastructure. It is easy to get started with Mito, simply install it using pip and start using it in Jupyter or Streamlit.

WhyHive is an AI-powered data analysis tool that helps users find and code key themes in their data, then visualize their findings with beautiful, shareable charts. It is designed to be easy to use, even for those with no prior experience with data analysis. WhyHive can analyze thousands of rows of data in minutes, saving users hours of manual coding time. It can also be used to analyze both quantitative and qualitative data, making it a versatile tool for a variety of research projects.

Airwiz is an AI data analyst tool designed to revolutionize data analysis experiences for Airtable users. It offers seamless integration with Airtable, providing intuitive AI data analysis without the need for complex setups. Users can unlock Python-level data insights by simply asking questions, eliminating the requirement for coding skills. With Airwiz, users can obtain instant, actionable data insights, making data analysis tasks efficient and effortless. The tool has received positive feedback from professionals across various roles, highlighting its transformative impact on data analysis processes.

Blackshark.ai is an AI-based platform that generates a real-time accurate semantic photorealistic 3D digital twin of the entire planet. The platform extracts insights about the planet's infrastructure from satellite and aerial imagery using machine learning at a global scale. It enriches missing attributes with AI to provide a photorealistic, geo-typical, or asset-specific digital twin, which can be used for visualization, simulation, mapping, mixed reality environments, and other enterprise solutions. The platform offers features such as Globe Data Input Sources, No Code Data Labeling, Geointelligence at Scale, 3D Semantic Map, and Synthetic Environments.

Domino Data Lab is an enterprise AI platform that enables data scientists and IT leaders to build, deploy, and manage AI models at scale. It provides a unified platform for accessing data, tools, compute, models, and projects across any environment. Domino also fosters collaboration, establishes best practices, and tracks models in production to accelerate and scale AI while ensuring governance and reducing costs.

Domino Data Lab is an enterprise AI platform that enables users to build, deploy, and manage AI models across any environment. It fosters collaboration, establishes best practices, and ensures governance while reducing costs. The platform provides access to a broad ecosystem of open source and commercial tools, and infrastructure, allowing users to accelerate and scale AI impact. Domino serves as a central hub for AI operations and knowledge, offering integrated workflows, automation, and hybrid multicloud capabilities. It helps users optimize compute utilization, enforce compliance, and centralize knowledge across teams.

Documate is an open-source tool designed to make your documentation site intelligent by embedding AI chat dialogues. It allows users to ask questions based on the content of the site and receive relevant answers. The tool offers hassle-free integration with popular doc site platforms like VitePress, Docusaurus, and Docsify, without requiring AI or LLM knowledge. Users have full control over the code and data, enabling them to choose which content to index. Documate also provides a customizable UI to meet specific needs, all while being developed with care by AirCode.

DataChat is a no-code, AI analytics platform that enables business users to ask questions of their data in plain English, empowering them to gain insights quickly and efficiently. The platform allows users to refine questions, reshape data, and adjust insights without any coding required. DataChat ensures transparency in the workflow and maintains data security by performing all computations within the user's data warehouse. With DataChat, users can easily access real-time insights and make data-driven decisions to enhance their business operations.

Numvio's APEX is a groundbreaking no-code AI platform that empowers businesses to unlock the full potential of their data and drive real impact. With APEX, you can effortlessly implement customized AI solutions, delivered as user-friendly tools or APIs, without the need for complex tech infrastructure or coding expertise. APEX intelligently analyzes your data's potential and provides valuable insights, helping you make informed decisions and drive your business forward.

Webscrape AI is a no-code web scraping tool that allows users to collect data from websites without writing any code. It is easy to use, accurate, and affordable, making it a great option for businesses of all sizes. With Webscrape AI, you can automate your data collection process and free up your time to focus on other tasks.

Sheety is a spreadsheet-like database that lets you build powerful apps without writing any code. It's perfect for teams who need to track data, manage projects, and collaborate on documents.

Datature is an all-in-one platform for building and deploying computer vision models. It provides tools for data management, annotation, training, and deployment, making it easy to develop and implement computer vision solutions. Datature is used by a variety of industries, including healthcare, retail, manufacturing, and agriculture.

fyli is a personalized AI assistant that allows users to supercharge ChatGPT with their own data. With fyli, users can create a personalized AI chat bot without writing a single line of code. fyli also allows users to bring their own data by uploading files directly or connecting to a data source such as a database, Notion, YouTube, Twitter, Slack, or Google Docs. Users can then use the chat UI to ask questions about their data or connect their own chat app. fyli can support chatting on WhatsApp, Telegram, Slack, and more. In the future, fyli will allow users to customize their bot and host it for friends, customers, students, or peers.

No Code Camp is an online learning platform that teaches people how to use artificial intelligence (AI) and no-code tools to automate their work and build applications. The platform offers a live, 5-week cohort-based course that covers the essentials of no-code development, including data architecture, interface design, AI scaling, and no-code automation. The course is designed for people with no prior coding experience and is taught by experienced instructors who have built and scaled digital products using no-code tools.

VBA Code Generator is an AI-powered tool that allows users to generate VBA code quickly and efficiently. By inputting requirements, users can instantly generate complex VBA code using simple text instructions with the help of AI. The tool is designed for both beginners and experienced users, offering a versatile application that can handle various VBA tasks, from Excel automation to Access database management. With a focus on saving time and streamlining workflows, VBA Code Generator simplifies the coding process and provides accurate formulas for users' specific needs.

Quadratic is an infinite spreadsheet with Python, SQL, and AI. It combines the familiarity of a spreadsheet with the power of code, allowing users to analyze data, write code, and create visualizations in a single environment. With built-in Python library support, users can bring open source tools directly to their spreadsheets. Quadratic also features real-time collaboration, allowing multiple users to work on the same spreadsheet simultaneously. Additionally, Quadratic is built for speed and performance, utilizing Web Assembly and WebGL to deliver a smooth and responsive experience.

Papers With Code is an AI tool that provides access to the latest research papers in the field of Machine Learning, along with corresponding code implementations. It offers a platform for researchers and enthusiasts to stay updated on state-of-the-art datasets, methods, and trends in the ML domain. Users can explore a wide range of topics such as language modeling, image generation, virtual try-on, and more through the collection of papers and code available on the website.

Code to Flowchart is an AI-powered tool that helps users visualize and understand program logic instantly. It allows users to convert code into interactive flowcharts with the help of AI analysis. The tool supports all major programming languages, identifies code paths and logic flows, and offers multiple visualization options like flowcharts, sequence diagrams, and class diagrams. Users can export diagrams in various formats and customize color schemes and themes. Code to Flowchart aims to simplify complex code structures and enhance collaboration among developers.

Code Snippets AI is an AI-powered code snippets library for teams. It helps developers master their codebase with contextually-rich AI chats, integrated with a secure code snippets library. Developers can build new features, fix bugs, add comments, and understand their codebase with the help of Code Snippets AI. The tool is trusted by the best development teams and helps developers code smarter than ever. With Code Snippets AI, developers can leverage the power of a codebase aware assistant, helping them write clean, performance optimized code. They can also create documentation, refactor, debug and generate code with full codebase context. This helps developers spend more time creating code and less time debugging errors.

This repository provides an original implementation of Assessing the Brittleness of Safety Alignment via Pruning and Low-Rank Modifications. It includes tools for neuron-level pruning, pruning based on set difference, Wanda/SNIP score dumping, rank-level pruning, and rank removal with orthogonal projection. Users can specify parameters like prune method, datasets, sparsity ratio, model, and save location to evaluate and modify neural networks for safety alignment.

LLM Datasets is a repository containing high-quality datasets, tools, and concepts for LLM fine-tuning. It provides datasets with characteristics like accuracy, diversity, and complexity to train large language models for various tasks. The repository includes datasets for general-purpose, math & logic, code, conversation & role-play, and agent & function calling domains. It also offers guidance on creating high-quality datasets through data deduplication, data quality assessment, data exploration, and data generation techniques.

HuggingFists is a low-code data flow tool that enables convenient use of LLM and HuggingFace models. It provides functionalities similar to Langchain, allowing users to design, debug, and manage data processing workflows, create and schedule workflow jobs, manage resources environment, and handle various data artifact resources. The tool also offers account management for users, allowing centralized management of data source accounts and API accounts. Users can access Hugging Face models through the Inference API or locally deployed models, as well as datasets on Hugging Face. HuggingFists supports breakpoint debugging, branch selection, function calls, workflow variables, and more to assist users in developing complex data processing workflows.

Thread is an AI-powered Jupyter alternative that integrates an AI copilot into your editing experience. It offers a familiar Jupyter Notebook editing experience with features like natural language code edits, generating cells to answer questions, context-aware chat sidebar, and automatic error explanations or fixes. The tool aims to enhance code editing and data exploration by providing a more interactive and intuitive experience for users. Thread can be used for free with Ollama or your own API key, and it runs locally for convenience and privacy.

Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.

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

A massive list including a huge amount of products and services that are completely free! ⭐ Star on GitHub • 🤝 Contribute # Table of Contents * APIs, Data & ML * Artificial Intelligence * BaaS * Code Editors * Code Generation * DNS * Databases * Design & UI * Domains * Email * Font * For Students * Forms * Linux Distributions * Messaging & Streaming * PaaS * Payments & Billing * SSL

CodeLLM Paper repository provides a curated list of research papers focused on Large Language Models (LLMs) for code. It aims to facilitate researchers and practitioners in exploring the rapidly growing body of literature on this topic. The papers are systematically collected from various top-tier venues, categorized, and labeled for easier navigation. The selection strategy involves abstract extraction, keyword matching, relevance check using LLMs, and manual labeling. The papers are categorized based on Application, Principle, and Research Paradigm dimensions. Contributions to expand the repository are welcome through PR submission, issue submission, or request for batch updates. The repository is intended solely for research purposes, with raw data sourced from publicly available information on ACM, IEEE, and corresponding conference websites.

Ezdata is a data processing and task scheduling system developed based on Python backend and Vue3 frontend. It supports managing multiple data sources, abstracting various data sources into a unified data model, integrating chatgpt for data question and answer functionality, enabling low-code data integration and visualization processing, scheduling single and dag tasks, and integrating a low-code data visualization dashboard system.

RTL-Coder is a tool designed to outperform GPT-3.5 in RTL code generation by providing a fully open-source dataset and a lightweight solution. It targets Verilog code generation and offers an automated flow to generate a large labeled dataset with over 27,000 diverse Verilog design problems and answers. The tool addresses the data availability challenge in IC design-related tasks and can be used for various applications beyond LLMs. The tool includes four RTL code generation models available on the HuggingFace platform, each with specific features and performance characteristics. Additionally, RTL-Coder introduces a new LLM training scheme based on code quality feedback to further enhance model performance and reduce GPU memory consumption.

Reflection-Tuning is a project focused on improving the quality of instruction-tuning data through a reflection-based method. It introduces Selective Reflection-Tuning, where the student model can decide whether to accept the improvements made by the teacher model. The project aims to generate high-quality instruction-response pairs by defining specific criteria for the oracle model to follow and respond to. It also evaluates the efficacy and relevance of instruction-response pairs using the r-IFD metric. The project provides code for reflection and selection processes, along with data and model weights for both V1 and V2 methods.

Data-Juicer is a one-stop data processing system to make data higher-quality, juicier, and more digestible for LLMs. It is a systematic & reusable library of 80+ core OPs, 20+ reusable config recipes, and 20+ feature-rich dedicated toolkits, designed to function independently of specific LLM datasets and processing pipelines. Data-Juicer allows detailed data analyses with an automated report generation feature for a deeper understanding of your dataset. Coupled with multi-dimension automatic evaluation capabilities, it supports a timely feedback loop at multiple stages in the LLM development process. Data-Juicer offers tens of pre-built data processing recipes for pre-training, fine-tuning, en, zh, and more scenarios. It provides a speedy data processing pipeline requiring less memory and CPU usage, optimized for maximum productivity. Data-Juicer is flexible & extensible, accommodating most types of data formats and allowing flexible combinations of OPs. It is designed for simplicity, with comprehensive documentation, easy start guides and demo configs, and intuitive configuration with simple adding/removing OPs from existing configs.

Sonic 3 A.I.R. is a non-profit fan game project that provides source code including dependencies for "Sonic 3 - Angel Island Revisited," a fan-made remaster of Sonic 3 & Knuckles. The project is split into several different projects, including external dependencies, librmx libraries, Lemonscript language library, Oxygen Engine, and S3AIR-specific C++ code. To build for different platforms, refer to the readme files in the respective subdirectories of "Oxygen/sonic3air/build." External libraries and code used in this project include SDL2, libogg & libvorbis, zlib, libcurl, jsoncpp, GLEW, Sound chip emulation related code from Genesis Plus GX, Discord Game SDK, xBRZ upscaler shader code, and Hqx upscaler shader code & data files.

This repository presents the findings of the paper "Compression Represents Intelligence Linearly". The study reveals a strong linear correlation between the intelligence of LLMs, as measured by benchmark scores, and their ability to compress external text corpora. Compression efficiency, derived from raw text corpora, serves as a reliable evaluation metric that is linearly associated with model capabilities. The repository includes the compression corpora used in the paper, code for computing compression efficiency, and data collection and processing pipelines.

The repository 'Awesome-LLM4Cybersecurity' provides a comprehensive overview of the applications of Large Language Models (LLMs) in cybersecurity. It includes a systematic literature review covering topics such as constructing cybersecurity-oriented domain LLMs, potential applications of LLMs in cybersecurity, and research directions in the field. The repository analyzes various benchmarks, datasets, and applications of LLMs in cybersecurity tasks like threat intelligence, fuzzing, vulnerabilities detection, insecure code generation, program repair, anomaly detection, and LLM-assisted attacks.

**Curated collection of papers and resources on how to unlock the reasoning ability of LLMs and MLLMs.** **Description in less than 400 words, no line breaks and quotation marks.** Large Language Models (LLMs) have revolutionized the NLP landscape, showing improved performance and sample efficiency over smaller models. However, increasing model size alone has not proved sufficient for high performance on challenging reasoning tasks, such as solving arithmetic or commonsense problems. This curated collection of papers and resources presents the latest advancements in unlocking the reasoning abilities of LLMs and Multimodal LLMs (MLLMs). It covers various techniques, benchmarks, and applications, providing a comprehensive overview of the field. **5 jobs suitable for this tool, in lowercase letters.** - content writer - researcher - data analyst - software engineer - product manager **Keywords of the tool, in lowercase letters.** - llm - reasoning - multimodal - chain-of-thought - prompt engineering **5 specific tasks user can use this tool to do, in less than 3 words, Verb + noun form, in daily spoken language.** - write a story - answer a question - translate a language - generate code - summarize a document

DeepFlow is an open-source project that provides deep observability for complex cloud-native and AI applications. It offers Zero Code data collection with eBPF for metrics, distributed tracing, request logs, and function profiling. DeepFlow is integrated with SmartEncoding to achieve Full Stack correlation and efficient access to all observability data. With DeepFlow, cloud-native and AI applications automatically gain deep observability, removing the burden of developers continually instrumenting code and providing monitoring and diagnostic capabilities covering everything from code to infrastructure for DevOps/SRE teams.

This repository contains the code for coding, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch). In _Build a Large Language Model (From Scratch)_, you'll discover how LLMs work from the inside out. In this book, I'll guide you step by step through creating your own LLM, explaining each stage with clear text, diagrams, and examples. The method described in this book for training and developing your own small-but-functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT.

Cherry Data Selection project introduces a self-guided methodology for LLMs to autonomously discern and select cherry samples from open-source datasets, minimizing manual curation and cost for instruction tuning. The project focuses on selecting impactful training samples ('cherry data') to enhance LLM instruction tuning by estimating instruction-following difficulty. The method involves phases like 'Learning from Brief Experience', 'Evaluating Based on Experience', and 'Retraining from Self-Guided Experience' to improve LLM performance.

Your go-to source for in-depth US zip code demographics and statistics, with easy-to-download data tables.

Your digital design & verification guide. The conversation data will not be used for training.

Diagram creation: flowcharts, mindmaps, UML, chart, PlotUML, workflow, sequence, ERD, database & architecture visualization for code, presentations and documentation. [New] Add a logo or any image to graph diagrams. Easy Download & Edit

Refactor python code. Python expert with proficiency in data science, machine learning (including LLM apps), and both OOP and functional programming.

Generates Vega-Lite JSON code for Power BI visuals from data and descriptions.

Connect and chat with your databases without writing SQL code - Supports MySQL, PostgreSQL, MongoDB, SQL Server. by AskYourDatabase.

Expert in generating and debugging Neo4j code, with explanations on graph database principles.

Assists with NoSQL programming by providing code examples, debugging tips, and best practices.

ML/DL expert focused on mathematical modeling, Kaggle competitions, and advanced ML models.

Experto en código R y documentación Roxygen2, preciso en argumentos

R coding expert minimizing errors in R programming

Assists with R programming by providing code examples, debugging tips, and best practices.

A detailed expert trained on all 5,588 pages of Apple CoreData, offering complete coding solutions. Saving time? https://www.buymeacoffee.com/parkerrex ☕️❤️

A maestro generates, troubleshoots, and analyzes R programming code.

Extracts JSON from source code using regex.

A math problem solver that creates Julia code.

A detailed expert trained on all 5,671 pages of Apple CloudKit, offering complete coding solutions. Saving time? https://www.buymeacoffee.com/parkerrex ☕️❤️

Master Class - Teaching and creating code examples.

Python pro and anime otaku!

Expert in Hugging Face platform tools and resources