Best AI tools for< Ruby Programmer >

Supersimple is an AI-native data analytics platform that combines a semantic data modeling layer with the ability to answer ad hoc questions, giving users reliable, consistent data to power their day-to-day work.

Replit is a software creation platform that provides an integrated development environment (IDE), artificial intelligence (AI) assistance, and deployment services. It allows users to build, test, and deploy software projects directly from their browser, without the need for local setup or configuration. Replit offers real-time collaboration, code generation, debugging, and autocompletion features powered by AI. It supports multiple programming languages and frameworks, making it suitable for a wide range of development projects.

Every AI is an AI software that offers over 120 AI models, including ChatGPT from OpenAI and Anthropic/Claude, for a wide range of applications. It provides incredible speeds and access to all models for a subscription fee of $20. The platform aims to simplify AI development at scale by offering developer-friendly solutions with extensive documentation and SDKs for popular programming languages like Ruby and JavaScript.

Chat Blackbox is an AI tool that specializes in AI code generation, code chat, and code search. It provides a platform where users can interact with AI to generate code, discuss code-related topics, and search for specific code snippets. The tool leverages artificial intelligence algorithms to enhance the coding experience and streamline the development process. With Chat Blackbox, users can access a wide range of features to improve their coding skills and efficiency.

Blackbox is an AI-powered code generation, code chat, and code search tool that helps developers write better code faster. With Blackbox, you can generate code snippets, chat with an AI assistant about code, and search for code examples from a massive database.

Sublayer is a model-agnostic AI agent framework in Ruby that offers AI-assisted coding to help users leverage good patterns in their codebase for generation. It provides a Rubygem for quickly building AI agents and other AI-powered automations. The platform showcases featured projects from both the team and the community, all built with the Sublayer gem. Users can join the Discord community to chat with the Sublayer Team and stay updated through their blog to learn more about their approach to AI.

Code & Pepper is an elite software development company specializing in FinTech and HealthTech. They combine human talent with AI tools to deliver efficient solutions. With a focus on specific technologies like React.js, Node.js, Angular, Ruby on Rails, and React Native, they offer custom software products and dedicated software engineers. Their unique talent identification methodology selects the top 1.6% of candidates for exceptional outcomes. Code & Pepper champions human-AI centaur teams, harmonizing creativity with AI precision for superior results.

Hoop.dev is an AI application that provides live AI data masking in Rails console sessions. It offers shield Rails console access, automated employee onboarding & off-boarding, and AI data masking to protect customer data with a plug & play PII filter. The application enables compliant access without disrupting speed, automates HIPAA, SOC 1/2, PCI, GDPR, & other security controls, and reduces Rails Console use by finding repeated operations and turning Ruby scripts into repeatable no-code UIs.

SeaGOAT is a local search tool that leverages vector embeddings to enable you to search your codebase semantically. It is designed to work on Linux, macOS, and Windows and can process files in various formats, including text, Markdown, Python, C, C++, TypeScript, JavaScript, HTML, Go, Java, PHP, and Ruby. SeaGOAT uses a vector database called ChromaDB and a local vector embedding engine to provide fast and accurate search results. It also supports regular expression/keyword-based matches. SeaGOAT is open-source and licensed under an open-source license, and users are welcome to examine the source code, raise concerns, or create pull requests to fix problems.

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)

Gemini AI is a Ruby Gem designed to provide low-level access to Google's generative AI services through Vertex AI, Generative Language API, or AI Studio. It allows users to interact with Gemini to build abstractions on top of it. The Gem provides functionalities for tasks such as generating content, embeddings, predictions, and more. It supports streaming capabilities, server-sent events, safety settings, system instructions, JSON format responses, and tools (functions) calling. The Gem also includes error handling, development setup, publishing to RubyGems, updating the README, and references to resources for further learning.

llama_cpp.rb provides Ruby bindings for the llama.cpp, a library that allows you to use the Llama language model in your Ruby applications. Llama is a large language model that can be used for a variety of natural language processing tasks, such as text generation, translation, and question answering. This gem is still under development and may undergo many changes in the future.

The LiveKit Agent Framework is designed for building real-time, programmable participants that run on servers. Easily tap into LiveKit WebRTC sessions and process or generate audio, video, and data streams. The framework includes plugins for common workflows, such as voice activity detection and speech-to-text. Agents integrates seamlessly with LiveKit server, offloading job queuing and scheduling responsibilities to it. This eliminates the need for additional queuing infrastructure. Agent code developed on your local machine can scale to support thousands of concurrent sessions when deployed to a server in production.

LiveKit Agents for Node.js is a framework designed for building realtime, programmable voice agents that can see, hear, and understand. It includes support for OpenAI Realtime API, allowing for ultra-low latency WebRTC transport between GPT-4o and users' devices. The framework provides concepts like Agents, Workers, and Plugins to create complex tasks. It offers a CLI interface for running agents and a versatile web frontend called 'playground' for building and testing agents. The framework is suitable for developers looking to create conversational voice agents with advanced capabilities.

DevQualityEval is an evaluation benchmark and framework designed to compare and improve the quality of code generation of Language Model Models (LLMs). It provides developers with a standardized benchmark to enhance real-world usage in software development and offers users metrics and comparisons to assess the usefulness of LLMs for their tasks. The tool evaluates LLMs' performance in solving software development tasks and measures the quality of their results through a point-based system. Users can run specific tasks, such as test generation, across different programming languages to evaluate LLMs' language understanding and code generation capabilities.

Airframe is a set of essential building blocks for developing applications in Scala. It includes logging, object serialization using JSON or MessagePack, dependency injection, http server/client with RPC support, functional testing with AirSpec, and more.

Qdrant is a vector similarity search engine and vector database. It is written in Rust, which makes it fast and reliable even under high load. Qdrant can be used for a variety of applications, including: * Semantic search * Image search * Product recommendations * Chatbots * Anomaly detection Qdrant offers a variety of features, including: * Payload storage and filtering * Hybrid search with sparse vectors * Vector quantization and on-disk storage * Distributed deployment * Highlighted features such as query planning, payload indexes, SIMD hardware acceleration, async I/O, and write-ahead logging Qdrant is available as a fully managed cloud service or as an open-source software that can be deployed on-premises.

LangChain is an amazing framework to get LLM projects done in a matter of no time, and the ecosystem is growing fast. Here is an attempt to keep track of the initiatives around LangChain. Subscribe to the newsletter to stay informed about the Awesome LangChain. We send a couple of emails per month about the articles, videos, projects, and tools that grabbed our attention Contributions welcome. Add links through pull requests or create an issue to start a discussion. Please read the contribution guidelines before contributing.

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

Awesome ChatGPT is an artificial intelligence chatbot developed by OpenAI. It offers a wide range of applications, web apps, browser extensions, CLI tools, bots, integrations, and packages for various platforms. Users can interact with ChatGPT through different interfaces and use it for tasks like generating text, creating presentations, summarizing content, and more. The ecosystem around ChatGPT includes tools for developers, writers, researchers, and individuals looking to leverage AI technology for different purposes.

llama.cpp is a C++ implementation of LLaMA, a large language model from Meta. It provides a command-line interface for inference and can be used for a variety of tasks, including text generation, translation, and question answering. llama.cpp is highly optimized for performance and can be run on a variety of hardware, including CPUs, GPUs, and TPUs.

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

This simple package can be used to fix an invalid json string. To know all cases in which this package will work, check out the unit test. Inspired by https://github.com/josdejong/jsonrepair Motivation Some LLMs are a bit iffy when it comes to returning well formed JSON data, sometimes they skip a parentheses and sometimes they add some words in it, because that's what an LLM does. Luckily, the mistakes LLMs make are simple enough to be fixed without destroying the content. I searched for a lightweight python package that was able to reliably fix this problem but couldn't find any. So I wrote one How to use from json_repair import repair_json good_json_string = repair_json(bad_json_string) # If the string was super broken this will return an empty string You can use this library to completely replace `json.loads()`: import json_repair decoded_object = json_repair.loads(json_string) or just import json_repair decoded_object = json_repair.repair_json(json_string, return_objects=True) Read json from a file or file descriptor JSON repair provides also a drop-in replacement for `json.load()`: import json_repair try: file_descriptor = open(fname, 'rb') except OSError: ... with file_descriptor: decoded_object = json_repair.load(file_descriptor) and another method to read from a file: import json_repair try: decoded_object = json_repair.from_file(json_file) except OSError: ... except IOError: ... Keep in mind that the library will not catch any IO-related exception and those will need to be managed by you Performance considerations If you find this library too slow because is using `json.loads()` you can skip that by passing `skip_json_loads=True` to `repair_json`. Like: from json_repair import repair_json good_json_string = repair_json(bad_json_string, skip_json_loads=True) I made a choice of not using any fast json library to avoid having any external dependency, so that anybody can use it regardless of their stack. Some rules of thumb to use: - Setting `return_objects=True` will always be faster because the parser returns an object already and it doesn't have serialize that object to JSON - `skip_json_loads` is faster only if you 100% know that the string is not a valid JSON - If you are having issues with escaping pass the string as **raw** string like: `r"string with escaping\"" Adding to requirements Please pin this library only on the major version! We use TDD and strict semantic versioning, there will be frequent updates and no breaking changes in minor and patch versions. To ensure that you only pin the major version of this library in your `requirements.txt`, specify the package name followed by the major version and a wildcard for minor and patch versions. For example: json_repair==0.* In this example, any version that starts with `0.` will be acceptable, allowing for updates on minor and patch versions. How it works This module will parse the JSON file following the BNF definition: ::= | ::= | | ; Where: ; is a valid real number expressed in one of a number of given formats ; is a string of valid characters enclosed in quotes ; is one of the literal strings 'true', 'false', or 'null' (unquoted) ::=