Best AI tools for< Reference Code >

Continue is an open-source AI code assistant that enhances development by allowing users to connect any models and context to create custom autocomplete and chat experiences inside IDEs like VS Code and JetBrains. It helps developers remain in flow while coding, accelerates development with a plug-and-play system, and enables users to become leaders in AI by evolving their code assistant capabilities. With features like autocompletion, referencing and chatting, highlighting and instructing, Continue streamlines the coding process and boosts productivity.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::8jl6f-1734972164048-5cde5d8983e2) for reference. Users are directed to consult the documentation for further information and troubleshooting.

The website displays a '404: NOT_FOUND' error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::22md2-1720772812453-4893618e160a) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website www.aiv.life seems to be inaccessible, displaying an 'Access Denied' message. It appears that the user does not have permission to access the page. The error message references a server issue with the code 'Reference #18.99fa56b8.1726419605.7509311'. The website may be related to domain hosting or sales, as it mentions 'www.godaddy.com/forsale'. Further investigation or contacting the website owner may be required to resolve the access issue.

UpCodes is a searchable platform that provides access to building codes, assemblies, and building products libraries. It offers a comprehensive database of regulations and standards for construction projects, enabling users to easily search and reference relevant information. With UpCodes, architects, engineers, contractors, and other industry professionals can streamline their workflow, ensure compliance with codes, and enhance the quality of their designs. The platform is designed to simplify the process of accessing and interpreting building codes, saving time and reducing errors in construction projects.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::ns5hh-1735404713635-8c330f39cb89) for reference. Users are directed to consult the documentation for further information and troubleshooting.

Cloudflare is a platform that offers a range of products and services to help individuals and organizations improve their online presence. It provides tools for web analytics, troubleshooting errors, domain registration, and network security. Cloudflare also offers developer products like Workers and Pages, as well as AI products such as Workers AI and AI Gateway. With a focus on security and performance, Cloudflare aims to make the internet faster, more secure, and more reliable for users worldwide.

The website encountered a server error, preventing it from fulfilling the user's request. The error message indicates a 500 Server Error, suggesting an issue on the server-side that is preventing the completion of the request. Users are advised to wait for 30 seconds and try again. This error message typically occurs when there is a problem with the server configuration or processing of the request.

The website appears to be a generic error page from Netlify indicating that the requested page is not found. It suggests troubleshooting tips for site owners encountering a 404 error. The error message provides an internal ID for reference.

MDN Web Docs is a comprehensive web technology reference for developers, offering detailed information on HTML, CSS, JavaScript, HTTP, Web APIs, Web Extensions, and Accessibility. It provides tutorials, documentation, and resources to help developers learn and improve their skills in web development since 2005.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::7crbp-1720289011850-d12041b250e9) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::txj67-1735836932592-eec46f597b11) for reference. Users are directed to check the documentation for more information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::xsfmp-1735923123827-c52a4ded5ea0) for reference. Users are directed to check the documentation for more information and troubleshooting tips.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::9dqr4-1736268911417-fd3e8899e116) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::k7xdt-1736614074909-2dc430118e75) for reference. Users are directed to check the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::6lm4m-1735577528392-54edc73f6949) for reference. Users are directed to consult the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::7rd4m-1725901316906-8c71a7a2cbd7) for reference. Users are directed to check the documentation for further information and troubleshooting.

Eraser is an AI co-pilot for technical design that offers a reference library for creating diagrams and technical design documents. It allows users to create diagrams at the speed of thought, express data flow using diagram-as-code, collaborate on design docs, and create highly consumable visual documentation. Eraser ensures accurate and consistent designs, beautiful default creations, and a minimal tool design to maintain focus. It offers features like cloud architecture visualization, entity relationship modeling, flow charting, and sequence visualization. Eraser also provides version history, performance optimization, workflow integration, markdown formatting, export options, Github integration, and enterprise-grade security features.

The website page displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::4wq5g-1718736845999-777f28b346ca) for reference. Users are advised to consult the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. It provides a code (DEPLOYMENT_NOT_FOUND) and an ID (sin1::8khvr-1735750532589-ae9b68b9e696) for reference. Users are directed to check the documentation for further information and troubleshooting.

ICE-Score is a tool designed to instruct large language models to evaluate code. It provides a minimum viable product (MVP) for evaluating generated code snippets using inputs such as problem, output, task, aspect, and model. Users can also evaluate with reference code and enable zero-shot chain-of-thought evaluation. The tool is built on codegen-metrics and code-bert-score repositories and includes datasets like CoNaLa and HumanEval. ICE-Score has been accepted to EACL 2024.

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.

A set of instructional materials, code samples and Python scripts featuring LLMs (GPT etc) through interfaces like llamaindex, langchain, Chroma (Chromadb), Pinecone etc. Mainly used to store reference code for my LangChain tutorials on YouTube.

Kaizen is an open-source project that helps teams ensure quality in their software delivery by providing a suite of tools for code review, test generation, and end-to-end testing. It integrates with your existing code repositories and workflows, allowing you to streamline your software development process. Kaizen generates comprehensive end-to-end tests, provides UI testing and review, and automates code review with insightful feedback. The file structure includes components for API server, logic, actors, generators, LLM integrations, documentation, and sample code. Getting started involves installing the Kaizen package, generating tests for websites, and executing tests. The tool also runs an API server for GitHub App actions. Contributions are welcome under the AGPL License.

imodelsX is a Scikit-learn friendly library that provides tools for explaining, predicting, and steering text models/data. It also includes a collection of utilities for getting started with text data. **Explainable modeling/steering** | Model | Reference | Output | Description | |---|---|---|---| | Tree-Prompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/tree_prompt) | Explanation + Steering | Generates a tree of prompts to steer an LLM (_Official_) | | iPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/iprompt) | Explanation + Steering | Generates a prompt that explains patterns in data (_Official_) | | AutoPrompt | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/autoprompt) | Explanation + Steering | Find a natural-language prompt using input-gradients (⌛ In progress)| | D3 | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/d3) | Explanation | Explain the difference between two distributions | | SASC | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/sasc) | Explanation | Explain a black-box text module using an LLM (_Official_) | | Aug-Linear | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_linear) | Linear model | Fit better linear model using an LLM to extract embeddings (_Official_) | | Aug-Tree | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/aug_tree) | Decision tree | Fit better decision tree using an LLM to expand features (_Official_) | **General utilities** | Model | Reference | |---|---| | LLM wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/llm) | Easily call different LLMs | | | Dataset wrapper| [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/data) | Download minimially processed huggingface datasets | | | Bag of Ngrams | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/bag_of_ngrams) | Learn a linear model of ngrams | | | Linear Finetune | [Reference](https://github.com/microsoft/AugML/tree/main/imodelsX/linear_finetune) | Finetune a single linear layer on top of LLM embeddings | | **Related work** * [imodels package](https://github.com/microsoft/interpretml/tree/main/imodels) (JOSS 2021) - interpretable ML package for concise, transparent, and accurate predictive modeling (sklearn-compatible). * [Adaptive wavelet distillation](https://arxiv.org/abs/2111.06185) (NeurIPS 2021) - distilling a neural network into a concise wavelet model * [Transformation importance](https://arxiv.org/abs/1912.04938) (ICLR 2020 workshop) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies) * [Hierarchical interpretations](https://arxiv.org/abs/1807.03343) (ICLR 2019) - extends CD to CNNs / arbitrary DNNs, and aggregates explanations into a hierarchy * [Interpretation regularization](https://arxiv.org/abs/2006.14340) (ICML 2020) - penalizes CD / ACD scores during training to make models generalize better * [PDR interpretability framework](https://www.pnas.org/doi/10.1073/pnas.1814225116) (PNAS 2019) - an overarching framewwork for guiding and framing interpretable machine learning

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

Hailo TAPPAS is a set of full application examples that implement pipeline elements and pre-trained AI tasks. It demonstrates Hailo's system integration scenarios on predefined systems, aiming to accelerate time to market, simplify integration with Hailo's runtime SW stack, and provide a starting point for customers to fine-tune their applications. The tool supports both Hailo-15 and Hailo-8, offering various example applications optimized for different common hosts. TAPPAS includes pipelines for single network, two network, and multi-stream processing, as well as high-resolution processing via tiling. It also provides example use case pipelines like License Plate Recognition and Multi-Person Multi-Camera Tracking. The tool is regularly updated with new features, bug fixes, and platform support.

Tools4AI is a Java-based Agentic Framework for building AI agents to integrate with enterprise Java applications. It enables the conversion of natural language prompts into actionable behaviors, streamlining user interactions with complex systems. By leveraging AI capabilities, it enhances productivity and innovation across diverse applications. The framework allows for seamless integration of AI with various systems, such as customer service applications, to interpret user requests, trigger actions, and streamline workflows. Prompt prediction anticipates user actions based on input prompts, enhancing user experience by proactively suggesting relevant actions or services based on context.

Python package for concise, transparent, and accurate predictive modeling. All sklearn-compatible and easy to use. _For interpretability in NLP, check out our new package:imodelsX _

Translate texts in manga/images. Some manga/images will never be translated, therefore this project is born. * Image/Manga Translator * Samples * Online Demo * Disclaimer * Installation * Pip/venv * Poetry * Additional instructions for **Windows** * Docker * Hosting the web server * Using as CLI * Setting Translation Secrets * Using with Nvidia GPU * Building locally * Usage * Batch mode (default) * Demo mode * Web Mode * Api Mode * Related Projects * Docs * Recommended Modules * Tips to improve translation quality * Options * Language Code Reference * Translators Reference * GPT Config Reference * Using Gimp for rendering * Api Documentation * Synchronous mode * Asynchronous mode * Manual translation * Next steps * Support Us * Thanks To All Our Contributors :

DBRX is a large language model trained by Databricks and made available under an open license. It is a Mixture-of-Experts (MoE) model with 132B total parameters and 36B live parameters, using 16 experts, of which 4 are active during training or inference. DBRX was pre-trained for 12T tokens of text and has a context length of 32K tokens. The model is available in two versions: a base model and an Instruct model, which is finetuned for instruction following. DBRX can be used for a variety of tasks, including text generation, question answering, summarization, and translation.

halbot is a Telegram bot that uses ChatGPT, Gemini, Mistral, and other AI engines to provide a variety of services, including text generation, translation, summarization, and question answering. It is easy to use and extend, and it can be integrated into your own projects. halbot is open source and free to use.

Bagatur chess engine is a powerful Java chess engine that can run on Android devices and desktop computers. It supports the UCI protocol and can be easily integrated into chess programs with user interfaces. The engine is available for download on various platforms and has advanced features like SMP (multicore) support and NNUE evaluation function. Bagatur also includes syzygy endgame tablebases and offers various UCI options for customization. The project started as a personal challenge to create a chess program that could defeat a friend, leading to years of development and improvements.

Melodisco is an AI music player that allows users to listen to music and manage playlists. It provides a user-friendly interface for music playback and organization. Users can deploy Melodisco with Vercel or Docker for easy setup. Local development instructions are provided for setting up the project environment. The project credits various tools and libraries used in its development, such as Next.js, Tailwind CSS, and Stripe. Melodisco is a versatile tool for music enthusiasts looking for an AI-powered music player with features like authentication, payment integration, and multi-language support.

Garak is a free tool that checks if a Large Language Model (LLM) can be made to fail in a way that is undesirable. It probes for hallucination, data leakage, prompt injection, misinformation, toxicity generation, jailbreaks, and many other weaknesses. Garak's a free tool. We love developing it and are always interested in adding functionality to support applications.

Garak is a vulnerability scanner designed for LLMs (Large Language Models) that checks for various weaknesses such as hallucination, data leakage, prompt injection, misinformation, toxicity generation, and jailbreaks. It combines static, dynamic, and adaptive probes to explore vulnerabilities in LLMs. Garak is a free tool developed for red-teaming and assessment purposes, focusing on making LLMs or dialog systems fail. It supports various LLM models and can be used to assess their security and robustness.

The STM32 AI model zoo is a collection of reference machine learning models optimized to run on STM32 microcontrollers. It provides a large collection of application-oriented models ready for re-training, scripts for easy retraining from user datasets, pre-trained models on reference datasets, and application code examples generated from user AI models. The project offers training scripts for transfer learning or training custom models from scratch. It includes performances on reference STM32 MCU and MPU for float and quantized models. The project is organized by application, providing step-by-step guides for training and deploying models.

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.

Amadeus Python SDK provides a rich set of APIs for the travel industry. It allows users to make API calls for various travel-related tasks such as flight offers search, hotel bookings, trip purpose prediction, flight delay prediction, airport on-time performance, travel recommendations, and more. The SDK conveniently maps API paths to similar paths, making it easy to interact with the Amadeus APIs. Users can initialize the client with their API key and secret, make API calls, handle responses, and enable logging for debugging purposes. The SDK documentation includes detailed information about each SDK method, arguments, and return types.

I help developers understand and use APIs, referencing a YAML model.

質問するとMQL4リファレンスに基づいて解答してくれます。

Ruby doc searcher for Ruby reference and Rurema

A GPT pre-trained on duneSQL Farcaster tables, complex examples, and duneSQL syntax. Please reference the dune docs or contact @shoni.eth for errors

Expert in providing accurate, up-to-date, and validated responses, cross-references information with reliable web sources and informs users about the confidence level of its responses.

Your documentation guide for OpenAI, loaded with the latest guides and API references.

参考文献格式化和注释体例专家

Formats academic references to specific styles.

Input your work and harvard referencing will be automated for you

A reference tool for UK energy transmission campaigners.

Bible reference assistant with scripture and translation expertise.

I will create personalized reference photos to show your hair stylist. Upload a photo to try.

Expert in article-based reference generation and formatting.

Expert in citation styles and reference generation.

Using various psychological literature as a reference, we provide advice on problems related to work and daily life.

Crafts speeches from PowerPoint slides and reference materials, adding depth and context.

Guitar teacher focused on giving tabular reference material for song writing or music theory education.

Analyze your results based on reference ranges from the most influential longevity doctors and organizations.

Generates APA papers with detailed reference lists.

This tool is only useful if you have added new sources to your reference list and need to ensure that your in-text citations reflect these updates. Paste your essay below to get started.