Best AI tools for< Prepare Code >

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.

Codei is an AI-powered platform designed to help individuals land their dream software engineering job. It offers features such as application tracking, question generation, and code evaluation to assist users in honing their technical skills and preparing for interviews. Codei aims to provide personalized support and insights to help users succeed in the tech industry.

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AI Slide Maker is an innovative tool that utilizes artificial intelligence to create visually appealing and professional presentations in a matter of minutes. With its advanced algorithms, the application can analyze content, suggest relevant design templates, and generate slides automatically. Users can simply input their text and images, and the AI Slide Maker will take care of the rest, saving time and effort. Whether for business meetings, educational purposes, or personal projects, this tool streamlines the presentation creation process and ensures high-quality results.

Caktus AI is an AI assistant designed to help students with their studies. It can answer questions, provide explanations, and generate content. Caktus AI is powered by a large language model, which allows it to understand and respond to complex questions in a natural way. Caktus AI is still under development, but it has already shown great promise as a tool for helping students learn.

OpExams is an AI-powered platform designed to assist educators in creating, administering, grading, and analyzing exams. It offers features such as online exams, quizzes, paper exams, code assignments, interactive lessons, AI questions generator, and AI quiz generator. OpExams aims to streamline the exam preparation process for teachers by providing tools from the future, making it effortless to manage various types of assessments. With a global community of educators using OpExams, the platform is revolutionizing the traditional exam procedures and enhancing the teaching and learning experience worldwide.

Leetcode Wizard is a desktop application designed to assist users in coding interviews by providing solutions for any Leetcode problem, known or unknown. It aims to help users achieve success in coding interviews, particularly at top tech companies like FAANG. The application uses AI to analyze coding problems, suggest algorithms, generate code output, and provide a complexity analysis. Leetcode Wizard is built with a privacy-first mindset, ensuring undetectable operation during coding interviews.

Interview Solver is a desktop application that acts as your copilot during coding interviews, providing instant solutions to LeetCode problems and system design questions. It features screengrabbing capabilities, one-shot solutions, query selected text functionality, global hotkeys, and syntax highlighting for all major languages. Interview Solver is designed to give you an AI advantage during live interviews, helping you land your dream job.

RTutor is an AI tool that leverages OpenAI's large language models to translate natural language into R or Python code for data analysis. Users can upload data in various formats, ask questions, and receive results in seconds. The tool allows users to analyze traditional statistics data, where rows are observations and columns are variables. RTutor provides comprehensive Exploratory Data Analysis (EDA) reports and supports various functions for data visualization and summary. It offers a user-friendly interface for users to interact with the tool in plain English, making data analysis accessible to a wide range of users.

GIPY is an intelligent assistant that allows you to use ChatGPT on any Windows application. With GIPY, you can easily write emails, take notes, prepare reports and presentations, and even program software. To use GIPY, simply type >>> in any Windows application and start typing your prompt. GIPY will then use ChatGPT to generate text and code for you.

GetSelected.ai is a personal AI-powered interviewer platform that helps users enhance their interview skills through AI technology. The platform offers features such as mock interviews, personalized feedback, job position customization, AI-driven quizzes, resume optimization, and code compiler for IT roles. Users can practice interview scenarios, improve communication skills, and prepare for recruitment processes with the help of AI tools. GetSelected.ai aims to provide a comprehensive and customizable experience to meet unique career goals and stand out in the competitive job market.

Exabeam is a cybersecurity and compliance platform that offers Security Information and Event Management (SIEM) solutions. The platform provides flexible choices for threat detection, investigation, and response, whether through cloud-based AI-driven solutions or on-premises SIEM deployments. Exabeam's AI-driven Security Operations Platform combines advanced threat detection capabilities with automation to deliver faster and more accurate TDIR. With features like UEBA, SOAR, and insider threat detection, Exabeam helps organizations improve security posture and optimize investments. The platform supports various industries and use cases, offering pre-built content, behavioral analytics, and context enrichment for enhanced threat coverage and compliance.

Testmint.ai is an online mock test platform designed to help users prepare for competitive exams. It offers a wide range of practice tests and study materials to enhance exam readiness. The platform is user-friendly and provides a simulated exam environment to improve test-taking skills. Testmint.ai aims to assist students and professionals in achieving their academic and career goals by offering a comprehensive and effective exam preparation solution.

The Software Engineer Interview Questions Generator is an AI-powered tool designed to help software engineers prepare for job interviews by generating a wide range of technical questions based on different programming languages, frameworks, and technologies. Users can select specific topics and the number of questions they want to generate, making it a valuable resource for interview preparation. The tool leverages AI technology to provide relevant and challenging questions that cover various aspects of software development, enabling users to enhance their knowledge and practice their problem-solving skills.

Questmate is a web-based platform that offers a variety of tools and resources to help users prepare for quests and adventures. It provides a range of features such as quest planning, resource management, and interactive maps to enhance the user experience. With Questmate, users can easily organize their quests, track their progress, and collaborate with other adventurers. The platform aims to streamline the quest preparation process and make it more engaging and efficient for users.

JobInterview.guru is an AI-powered platform designed to provide personalized interview training for job seekers. Leveraging advanced AI technology, the platform offers realistic job interview simulations, detailed insights into interview questions, and personalized feedback to help users prepare effectively. With a focus on efficiency and cost-effectiveness, JobInterview.guru aims to empower users to confidently navigate their job interviews and land their dream jobs.

Validator by Yazero is a platform that helps users validate their startup ideas using AI. It provides a community where users can share their ideas, get feedback, and find collaborators. Validator also offers a variety of features to help users improve their ideas, such as idea validation, market research, and financial planning.

Huru is the #1 AI-Powered Interview Prep App designed to help users ace their job interviews. It offers unlimited mock interviews with realistic, job-specific questions and provides instant, personalized feedback on answers, body language, and vocal delivery. With powerful features and integrations from popular job boards, Huru helps users build confidence, improve communication skills, and stand out in any interview scenario. The application covers diverse industries and roles, offering tailored interview questions and expert AI feedback to enhance interview performance. Huru is available on multiple platforms and languages, making it a comprehensive tool for job seekers at any career stage.

Preps is an AI-powered mock interview simulation platform designed to help users prepare for technical interviews. It offers realistic interview scenarios that mimic real-world technical interviews conducted at top tech companies. Users can practice with AI interviewers in real-time, receive personalized feedback, and improve their interview skills. With Preps, users can simulate various interview scenarios, practice unexpected questions, and refine their answers to increase their chances of success in technical interviews.

PrepMasterAI is an AI-powered platform designed to help individuals ace their job interviews by providing personalized practice questions, real-time feedback, and performance tracking. Users can unlock their full potential through tailored practice questions and insights to improve their interview skills. The platform aims to assist job seekers in identifying and enhancing their strengths and weaknesses to increase their chances of landing their dream job.

Codellm-devkit (CLDK) is a Python library that serves as a multilingual program analysis framework bridging traditional static analysis tools and Large Language Models (LLMs) specialized for code (CodeLLMs). It simplifies the process of analyzing codebases across multiple programming languages, enabling the extraction of meaningful insights and facilitating LLM-based code analysis. The library provides a unified interface for integrating outputs from various analysis tools and preparing them for effective use by CodeLLMs. Codellm-devkit aims to enable the development and experimentation of robust analysis pipelines that combine traditional program analysis tools and CodeLLMs, reducing friction in multi-language code analysis and ensuring compatibility across different tools and LLM platforms. It is designed to seamlessly integrate with popular analysis tools like WALA, Tree-sitter, LLVM, and CodeQL, acting as a crucial intermediary layer for efficient communication between these tools and CodeLLMs. The project is continuously evolving to include new tools and frameworks, maintaining its versatility for code analysis and LLM integration.

ai-digest is a CLI tool designed to aggregate your codebase into a single Markdown file for use with Claude Projects or custom ChatGPTs. It aggregates all files in the specified directory and subdirectories, ignores common build artifacts and configuration files, and provides options for whitespace removal and custom ignore patterns. The tool is useful for preparing codebases for AI analysis and assistance.

Code2Prompt is a powerful command-line tool that generates comprehensive prompts from codebases, designed to streamline interactions between developers and Large Language Models (LLMs) for code analysis, documentation, and improvement tasks. It bridges the gap between codebases and LLMs by converting projects into AI-friendly prompts, enabling users to leverage AI for various software development tasks. The tool offers features like holistic codebase representation, intelligent source tree generation, customizable prompt templates, smart token management, Gitignore integration, flexible file handling, clipboard-ready output, multiple output options, and enhanced code readability.

K2 (GeoLLaMA) is a large language model for geoscience, trained on geoscience literature and fine-tuned with knowledge-intensive instruction data. It outperforms baseline models on objective and subjective tasks. The repository provides K2 weights, core data of GeoSignal, GeoBench benchmark, and code for further pretraining and instruction tuning. The model is available on Hugging Face for use. The project aims to create larger and more powerful geoscience language models in the future.

CoLLM is a novel method that integrates collaborative information into Large Language Models (LLMs) for recommendation. It converts recommendation data into language prompts, encodes them with both textual and collaborative information, and uses a two-step tuning method to train the model. The method incorporates user/item ID fields in prompts and employs a conventional collaborative model to generate user/item representations. CoLLM is built upon MiniGPT-4 and utilizes pretrained Vicuna weights for training.

ROSGPT_Vision is a new robotic framework designed to command robots using only two prompts: a Visual Prompt for visual semantic features and an LLM Prompt to regulate robotic reactions. It is based on the Prompting Robotic Modalities (PRM) design pattern and is used to develop CarMate, a robotic application for monitoring driver distractions and providing real-time vocal notifications. The framework leverages state-of-the-art language models to facilitate advanced reasoning about image data and offers a unified platform for robots to perceive, interpret, and interact with visual data through natural language. LangChain is used for easy customization of prompts, and the implementation includes the CarMate application for driver monitoring and assistance.

This repository contains the code to prepare the data for the Amber 7B language model. The final training data comes from three sources: RedPajama V1, RefinedWeb, and StarCoderData. The data preparation involves downloading untokenized data, tokenizing the data using the Huggingface tokenizer, concatenating tokens into 2048 token sequences, merging datasets, and splitting the merged dataset into 360 chunks. Each tokenized data chunk is a jsonl file containing samples with 2049 tokens. The repository provides scripts for downloading datasets, tokenizing and concatenating sequences, validating data, and merging subsets into chunks.

CodeFuse-muAgent is a Multi-Agent framework designed to streamline Standard Operating Procedure (SOP) orchestration for agents. It integrates toolkits, code libraries, knowledge bases, and sandbox environments for rapid construction of complex Multi-Agent interactive applications. The framework enables efficient execution and handling of multi-layered and multi-dimensional tasks.

ScreenAgent is a project focused on creating an environment for Visual Language Model agents (VLM Agent) to interact with real computer screens. The project includes designing an automatic control process for agents to interact with the environment and complete multi-step tasks. It also involves building the ScreenAgent dataset, which collects screenshots and action sequences for various daily computer tasks. The project provides a controller client code, configuration files, and model training code to enable users to control a desktop with a large model.

AIO Launcher Scripts is a collection of Lua scripts that can be used with AIO Launcher to enhance its functionality. These scripts can be used to create widget scripts, search scripts, and side menu scripts. They provide various functions such as displaying text, buttons, progress bars, charts, and interacting with app widgets. The scripts can be used to customize the appearance and behavior of the launcher, add new features, and interact with external services.

CodeGPT is a CLI tool written in Go that helps you write git commit messages or do a code review brief using ChatGPT AI (gpt-3.5-turbo, gpt-4 model) and automatically installs a git prepare-commit-msg hook. It supports Azure OpenAI Service or OpenAI API, conventional commits specification, Git prepare-commit-msg Hook, customizing the number of lines of context in diffs, excluding files from the git diff command, translating commit messages into different languages, using socks or custom network HTTP proxies, specifying model lists, and doing brief code reviews.

xFasterTransformer is an optimized solution for Large Language Models (LLMs) on the X86 platform, providing high performance and scalability for inference on mainstream LLM models. It offers C++ and Python APIs for easy integration, along with example codes and benchmark scripts. Users can prepare models in a different format, convert them, and use the APIs for tasks like encoding input prompts, generating token ids, and serving inference requests. The tool supports various data types and models, and can run in single or multi-rank modes using MPI. A web demo based on Gradio is available for popular LLM models like ChatGLM and Llama2. Benchmark scripts help evaluate model inference performance quickly, and MLServer enables serving with REST and gRPC interfaces.

ML-Bench is a tool designed to evaluate large language models and agents for machine learning tasks on repository-level code. It provides functionalities for data preparation, environment setup, usage, API calling, open source model fine-tuning, and inference. Users can clone the repository, load datasets, run ML-LLM-Bench, prepare data, fine-tune models, and perform inference tasks. The tool aims to facilitate the evaluation of language models and agents in the context of machine learning tasks on code repositories.

Can AI Code is a self-evaluating interview tool for AI coding models. It includes interview questions written by humans and tests taken by AI, inference scripts for common API providers and CUDA-enabled quantization runtimes, a Docker-based sandbox environment for validating untrusted Python and NodeJS code, and the ability to evaluate the impact of prompting techniques and sampling parameters on large language model (LLM) coding performance. Users can also assess LLM coding performance degradation due to quantization. The tool provides test suites for evaluating LLM coding performance, a webapp for exploring results, and comparison scripts for evaluations. It supports multiple interviewers for API and CUDA runtimes, with detailed instructions on running the tool in different environments. The repository structure includes folders for interviews, prompts, parameters, evaluation scripts, comparison scripts, and more.

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.

PmHub is a smart project management system based on SpringCloud, SpringCloud Alibaba, and LLM. It aims to help students quickly grasp the architecture design and development process of microservices/distributed projects. PmHub provides a platform for students to experience the transformation from monolithic to microservices architecture, understand the pros and cons of both architectures, and prepare for job interviews. It offers popular technologies like SpringCloud-Gateway, Nacos, Sentinel, and provides high-quality code, continuous integration, product design documents, and an enterprise workflow system. PmHub is suitable for beginners and advanced learners who want to master core knowledge of microservices/distributed projects.

This repository contains custom recipes for H2O Driverless AI, which is an Automatic Machine Learning platform for the Enterprise. Custom recipes are Python code snippets that can be uploaded into Driverless AI at runtime to automate feature engineering, model building, visualization, and interpretability. Users can gain control over the optimization choices made by Driverless AI by providing their own custom recipes. The repository includes recipes for various tasks such as data manipulation, data preprocessing, feature selection, data augmentation, model building, scoring, and more. Best practices for creating and using recipes are also provided, including security considerations, performance tips, and safety measures.

This repository contains the code for the paper 'LESS: Selecting Influential Data for Targeted Instruction Tuning'. The work proposes a data selection method to choose influential data for inducing a target capability. It includes steps for warmup training, building the gradient datastore, selecting data for a task, and training with the selected data. The repository provides tools for data preparation, data selection pipeline, and evaluation of the model trained on the selected data.

Instruct2Act is a framework that utilizes Large Language Models to map multi-modal instructions to sequential actions for robotic manipulation tasks. It generates Python programs using the LLM model for perception, planning, and action. The framework leverages foundation models like SAM and CLIP to convert high-level instructions into policy codes, accommodating various instruction modalities and task demands. Instruct2Act has been validated on robotic tasks in tabletop manipulation domains, outperforming learning-based policies in several tasks.

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