Best AI tools for< Rename Functions >

Bulk Rename Utility is a free online file renaming tool that combines AI-powered and rule-based operations to efficiently rename multiple files or folders. Users can choose between AI Mode, where they describe renaming needs to the AI, and Rule Mode, which offers customizable renaming methods. The tool supports various file operations, diverse renaming rules, and ensures user privacy by performing local operations and secure browsing. Bulk Rename Utility stands out for its user-friendly interface, advanced features, browser compatibility, and platform support, making it a versatile solution for batch file renaming tasks.

Figma Autoname is an AI-powered plugin designed to simplify the process of renaming layers in Figma designs. By leveraging artificial intelligence, the plugin automates the task of renaming layers, saving designers valuable time and effort. With features like blazing fast renaming, smart detection of components names, and community-driven development, Figma Autoname enhances the efficiency and productivity of designers. The plugin is free to use and open-source, allowing users to contribute to its development. Figma Autoname is praised by designers for its convenience and time-saving capabilities.

AI Renamer is an application that utilizes artificial intelligence to automatically rename files based on their content. It offers powerful features such as smart recognition, batch processing, and support for various file types. Users can also integrate their own AI models for enhanced flexibility and privacy. The application provides credit-based pricing options and supports both Mac and Windows platforms.

Diagram is a suite of AI-powered design tools that help designers create beautiful and effective designs. With Diagram, you can generate SVG icons, create magical visuals, write and edit text, rename layers, and more. Diagram also offers a variety of features that make it easy to collaborate with other designers and share your work.

heyCLI is a command-line interface (CLI) tool that allows users to interact with their Linux systems using natural language. It is designed to make it easier for users to perform common tasks without having to memorize complex commands. heyCLI is still in its early stages of development, but it has the potential to be a valuable tool for both new and experienced Linux users.

The RevEng.AI Ghidra Plugin by RevEng.ai allows users to interact with their API within Ghidra for Binary Code Similarity analysis to aid in Reverse Engineering stripped binaries. Users can upload binaries, rename functions above a confidence threshold, and view similar functions for a selected function.

RevEng.AI IDA Pro Plugin is a tool that integrates with the RevEng.AI platform to provide various features such as uploading binaries for analysis, downloading analysis logs, renaming function names, generating AI summaries, synchronizing functions between local analysis and the platform, and configuring plugin settings. Users can upload files for analysis, synchronize function names, rename functions, generate block summaries, and explain function behavior using this plugin. The tool requires IDA Pro v8.0 or later with Python 3.9 and higher. It relies on the 'reait' package for functionality.

GhidrOllama is a script that interacts with Ollama's API to perform various reverse engineering tasks within Ghidra. It supports both local and remote instances of Ollama, providing functionalities like explaining functions, suggesting names, rewriting functions, finding bugs, and automating analysis of specific functions in binaries. Users can ask questions about functions, find vulnerabilities, and receive explanations of assembly instructions. The script bridges the gap between Ghidra and Ollama models, enhancing reverse engineering capabilities.

Temporal AI Agent is a demo showcasing a multi-turn conversation with an AI agent running inside a Temporal workflow. The agent collects information towards a goal using a simple DSL input. It is currently set up to search for events, book flights around those events, and create an invoice for those flights. The AI agent responds with clarifications and prompts for missing information. Users can configure the agent to use ChatGPT 4o or a local LLM via Ollama. The tool requires Rapidapi key for sky-scrapper to find flights and a Stripe key for creating invoices. Users can customize the agent by modifying tool and goal definitions in the codebase.

Databend is an open-source cloud data warehouse built in Rust, offering fast query execution and data ingestion for complex analysis of large datasets. It integrates with major cloud platforms, provides high performance with AI-powered analytics, supports multiple data formats, ensures data integrity with ACID transactions, offers flexible indexing options, and features community-driven development. Users can try Databend through a serverless cloud or Docker installation, and perform tasks such as data import/export, querying semi-structured data, managing users/databases/tables, and utilizing AI functions.

DAILA is a unified interface for AI systems in decompilers, supporting various decompilers and AI systems. It allows users to utilize local and remote LLMs, like ChatGPT and Claude, and local models such as VarBERT. DAILA can be used as a decompiler plugin with GUI or as a scripting library. It also provides a Docker container for offline installations and supports tasks like summarizing functions and renaming variables in decompilation.

LocalVocal is a live-streaming AI assistant plugin for OBS that allows you to transcribe audio speech into text and perform various language processing functions on the text using AI / LLMs (Large Language Models). It's privacy-first, with all data staying on your machine, and requires no GPU, cloud costs, network, or downtime.

Discord-AI-Selfbot is a Python-based Discord selfbot that uses the `discord.py-self` library to automatically respond to messages mentioning its trigger word using Groq API's Llama-3 model. It functions as a normal Discord bot on a real Discord account, enabling interactions in DMs, servers, and group chats without needing to invite a bot. The selfbot comes with features like custom AI instructions, free LLM model usage, mention and reply recognition, message handling, channel-specific responses, and a psychoanalysis command to analyze user messages for insights on personality.

LangChain-Udemy-Course is a comprehensive course directory focusing on LangChain, a framework for generative AI applications. The course covers various aspects such as OpenAI API usage, prompt templates, Chains exploration, callback functions, memory techniques, RAG implementation, autonomous agents, hybrid search, LangSmith utilization, microservice architecture, and LangChain Expression Language. Learners gain theoretical knowledge and practical insights to understand and apply LangChain effectively in generative AI scenarios.

This repository contains a minimal, clean code implementation of the Byte Pair Encoding (BPE) algorithm, commonly used in LLM tokenization. The BPE algorithm is "byte-level" because it runs on UTF-8 encoded strings. This algorithm was popularized for LLMs by the GPT-2 paper and the associated GPT-2 code release from OpenAI. Sennrich et al. 2015 is cited as the original reference for the use of BPE in NLP applications. Today, all modern LLMs (e.g. GPT, Llama, Mistral) use this algorithm to train their tokenizers. There are two Tokenizers in this repository, both of which can perform the 3 primary functions of a Tokenizer: 1) train the tokenizer vocabulary and merges on a given text, 2) encode from text to tokens, 3) decode from tokens to text. The files of the repo are as follows: 1. minbpe/base.py: Implements the `Tokenizer` class, which is the base class. It contains the `train`, `encode`, and `decode` stubs, save/load functionality, and there are also a few common utility functions. This class is not meant to be used directly, but rather to be inherited from. 2. minbpe/basic.py: Implements the `BasicTokenizer`, the simplest implementation of the BPE algorithm that runs directly on text. 3. minbpe/regex.py: Implements the `RegexTokenizer` that further splits the input text by a regex pattern, which is a preprocessing stage that splits up the input text by categories (think: letters, numbers, punctuation) before tokenization. This ensures that no merges will happen across category boundaries. This was introduced in the GPT-2 paper and continues to be in use as of GPT-4. This class also handles special tokens, if any. 4. minbpe/gpt4.py: Implements the `GPT4Tokenizer`. This class is a light wrapper around the `RegexTokenizer` (2, above) that exactly reproduces the tokenization of GPT-4 in the tiktoken library. The wrapping handles some details around recovering the exact merges in the tokenizer, and the handling of some unfortunate (and likely historical?) 1-byte token permutations. Finally, the script train.py trains the two major tokenizers on the input text tests/taylorswift.txt (this is the Wikipedia entry for her kek) and saves the vocab to disk for visualization. This script runs in about 25 seconds on my (M1) MacBook. All of the files above are very short and thoroughly commented, and also contain a usage example on the bottom of the file.

Databend is an open-source cloud data warehouse that serves as a cost-effective alternative to Snowflake. With its focus on fast query execution and data ingestion, it's designed for complex analysis of the world's largest datasets.

This project provides a proof of concept for deploying a scalable, serverless LLM Generative AI inference engine on AWS Lambda. It leverages the llama.cpp project to enable the usage of more accessible CPU and RAM configurations instead of limited and expensive GPU capabilities. By deploying a container with the llama.cpp converted models onto AWS Lambda, this project offers the advantages of scale, minimizing cost, and maximizing compute availability. The project includes AWS CDK code to create and deploy a Lambda function leveraging your model of choice, with a FastAPI frontend accessible from a Lambda URL. It is important to note that you will need ggml quantized versions of your model and model sizes under 6GB, as your inference RAM requirements cannot exceed 9GB or your Lambda function will fail.

ChatterUI is a mobile app that allows users to manage chat files and character cards, and to interact with Large Language Models (LLMs). It supports multiple backends, including local, koboldcpp, text-generation-webui, Generic Text Completions, AI Horde, Mancer, Open Router, and OpenAI. ChatterUI provides a mobile-friendly interface for interacting with LLMs, making it easy to use them for a variety of tasks, such as generating text, translating languages, writing code, and answering questions.

K8sGateway is a feature-rich, fast, and flexible Kubernetes-native API gateway built on Envoy proxy and Kubernetes Gateway API. It excels in function-level routing, supports legacy apps, microservices, and serverless. It offers robust discovery capabilities, seamless integration with open-source projects, and supports hybrid applications with various technologies, architectures, protocols, and clouds.

Riona-AI-Agent is a versatile AI chatbot designed to assist users in various tasks. It utilizes natural language processing and machine learning algorithms to understand user queries and provide accurate responses. The chatbot can be integrated into websites, applications, and messaging platforms to enhance user experience and streamline communication. With its customizable features and easy deployment, Riona-AI-Agent is suitable for businesses, developers, and individuals looking to automate customer support, provide information, and engage with users in a conversational manner.

Kgateway is a feature-rich, fast, and flexible Kubernetes-native API gateway built on top of Envoy proxy and the Kubernetes Gateway API. It excels in function-level routing, supports legacy apps, microservices, and serverless, offers robust discovery capabilities, integrates seamlessly with open-source projects, and is designed to support hybrid applications with various technologies, architectures, protocols, and clouds.

SwarmZero SDK is a library that simplifies the creation and execution of AI Agents and Swarms of Agents. It supports various LLM Providers such as OpenAI, Azure OpenAI, Anthropic, MistralAI, Gemini, Nebius, and Ollama. Users can easily install the library using pip or poetry, set up the environment and configuration, create and run Agents, collaborate with Swarms, add tools for complex tasks, and utilize retriever tools for semantic information retrieval. Sample prompts are provided to help users explore the capabilities of the agents and swarms. The SDK also includes detailed examples and documentation for reference.

llamafile is a tool that enables users to distribute and run Large Language Models (LLMs) with a single file. It combines llama.cpp with Cosmopolitan Libc to create a framework that simplifies the complexity of LLMs into a single-file executable called a 'llamafile'. Users can run these executable files locally on most computers without the need for installation, making open LLMs more accessible to developers and end users. llamafile also provides example llamafiles for various LLM models, allowing users to try out different LLMs locally. The tool supports multiple CPU microarchitectures, CPU architectures, and operating systems, making it versatile and easy to use.

TTS Generation WebUI is a comprehensive tool that provides a user-friendly interface for text-to-speech and voice cloning tasks. It integrates various AI models such as Bark, MusicGen, AudioGen, Tortoise, RVC, Vocos, Demucs, SeamlessM4T, and MAGNeT. The tool offers one-click installers, Google Colab demo, videos for guidance, and extra voices for Bark. Users can generate audio outputs, manage models, caches, and system space for AI projects. The project is open-source and emphasizes ethical and responsible use of AI technology.

Creates short, concise filenames from titles