AI tools for Bito AI CodeMate
Related Tools:
Bito AI
Bito AI is an AI-powered code review tool that helps developers write better code faster. It provides real-time feedback on code quality, security, and performance, and can also generate test cases and documentation. Bito AI is trusted by developers across the world, and has been shown to reduce review time by 50%.
Getbito
Getbito.com is a domain parking service provided by GoDaddy.com. It allows users to temporarily park their domain names without setting up a website. The service is free and managed by GoDaddy, a well-known domain registrar and web hosting company. Users can reserve their desired domain names for future use or resale. Getbito.com is a convenient solution for individuals or businesses looking to secure domain names without the need for immediate website development.
Power Platform Helper
Trained on learn.microsoft.com content including Azure Functions, Logic Apps, DAX, Dynamics365, Microsoft 365, Compliance, ODATA, Power Agents, Apps, Automate, BI, Pages, Query, Power Platform Administration, Developer, Guidance
PowerBI GPT
A PowerBI Expert assisting with debugging, dashboard ideas, and PowerBI service guidance.
Power BI Wizard
Your Power BI assistant for dataset creation, DAX, report review, design, and more... [Updated version].
Power Query Assistant
Expert in Power Query and DAX for Power BI, offering in-depth guidance and insights
CLI
Bito CLI provides a command line interface to the Bito AI chat functionality, allowing users to interact with the AI through commands. It supports complex automation and workflows, with features like long prompts and slash commands. Users can install Bito CLI on Mac, Linux, and Windows systems using various methods. The tool also offers configuration options for AI model type, access key management, and output language customization. Bito CLI is designed to enhance user experience in querying AI models and automating tasks through the command line interface.
Awesome-LLM-Prune
This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.