Best AI tools for< Find Code Examples >

MidJourneySref.com is a platform dedicated to exploring and discovering the best sref codes for MidJourney, a tool that generates unique and consistent styles of images. The website offers a curated selection of sref codes that produce visually appealing and high-quality images, illustrative examples for each code, categorized search experience, easy marking of favorite codes, and free access to a wide variety of sref codes. By leveraging these resources, users can enhance their creative projects and achieve stunning visual results.

**bloop** is an AI-powered legacy code modernization tool that helps teams modernize, write, and understand their legacy code. With bloop, you can automatically convert COBOL and Java programs to readable Java code that can be immediately modified and maintained. bloop also provides an AI pair programmer that can help you write COBOL code faster and more efficiently. Additionally, bloop offers a variety of other features to help you understand your codebase, including natural language code navigation, multi-repo search, and regex matching.

Safurai is an AI-powered coding assistant that helps developers write code faster, safer, and better. It offers a range of features, including a textbox for asking questions and getting code suggestions, shortcuts for code optimization and unit testing, the ability to train the assistant on specific projects, and a natural language search for finding code. Safurai is compatible with various IDEs, including Visual Studio Code, IntelliJ, and PyCharm.

The website page 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::fd55k-1727629228031-7a0d0ffffbbb'. Users are directed to refer to the documentation for further information and troubleshooting.

The website displays a 404 error message indicating that the deployment cannot be found. Users encountering this error are advised to refer to the documentation for more information and troubleshooting.

WhyHive is an AI-powered data analysis tool that helps users find and code key themes in their data, then visualize their findings with beautiful, shareable charts. It is designed to be easy to use, even for those with no prior experience with data analysis. WhyHive can analyze thousands of rows of data in minutes, saving users hours of manual coding time. It can also be used to analyze both quantitative and qualitative data, making it a versatile tool for a variety of research projects.

Wand is an AI-powered tool that helps you find and fix errors in your code. It uses machine learning to identify potential problems and provides suggestions for how to resolve them. Wand can be used with a variety of programming languages, including Python, Java, JavaScript, and C++.

AI Code Reviewer is a tool that uses artificial intelligence to review code. It can help you find bugs, improve code quality, and enforce coding standards.

SourceAI is an AI-powered code generator that allows users to generate code in any programming language. It is easy to use, even for non-developers, and has a clear and intuitive interface. SourceAI is powered by GPT-3 and Codex, the most advanced AI technology available. It can be used to generate code for a variety of tasks, including calculating the factorial of a number, finding the roots of a polynomial, and translating text from one language to another.

Code Snippets AI is an AI-powered code snippets library for teams. It helps developers master their codebase with contextually-rich AI chats, integrated with a secure code snippets library. Developers can build new features, fix bugs, add comments, and understand their codebase with the help of Code Snippets AI. The tool is trusted by the best development teams and helps developers code smarter than ever. With Code Snippets AI, developers can leverage the power of a codebase aware assistant, helping them write clean, performance optimized code. They can also create documentation, refactor, debug and generate code with full codebase context. This helps developers spend more time creating code and less time debugging errors.

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::bfgnm-1727111577504-fc9106f5c26a'. Users are directed to refer to the documentation for further information and troubleshooting.

Wondershare Help Center provides comprehensive support for Wondershare products, including video editing, video creation, diagramming, PDF solutions, and data management. It offers a wide range of resources such as tutorials, FAQs, troubleshooting guides, and access to customer support.

Nerdynav is a website that provides reviews of AI tools and software for businesses. The website is run by Nav, a software developer and online entrepreneur who has tested over 100 tools to help businesses find the best solutions for their needs. Nerdynav's reviews are data-backed and provide insights into the features, advantages, and disadvantages of each tool. The website also includes articles on how to use AI to improve business processes and increase productivity.

This website is a job board specifically for prompt engineering jobs. It provides a list of the latest prompt engineering jobs, as well as resources for prompt engineering. The website is designed to help people find jobs in the field of prompt engineering and to learn more about the field.

CodiumAI is an AI-powered tool that helps developers write better code by generating meaningful tests, finding edge cases and suspicious behaviors, and suggesting improvements. It integrates with popular IDEs and Git platforms, and supports a wide range of programming languages. CodiumAI is designed to help developers save time, improve code quality, and stay confident in their code.

Korbit is an AI-powered code review tool that helps developers write better code, faster. It integrates directly into your GitHub PR workflow and provides instant feedback on your code, identifying issues and providing actionable recommendations. Korbit also provides valuable insights into code quality, project status, and developer performance, helping you to boost your productivity and elevate your code.

ILoveMyQA is an AI-powered QA testing service that provides comprehensive, well-documented bug reports. The service is affordable, easy to get started with, and requires no time-zapping chats. ILoveMyQA's team of Rockstar QAs is dedicated to helping businesses find and fix bugs before their customers do, so they can enjoy the results and benefits of having a QA team without the cost, management, and headaches.

GPTfy is a website that helps users find the best GPTs (Generative Pre-trained Transformers) for their needs. GPTs are AI-powered language models that can be used for a variety of tasks, such as writing, translating, and coding. GPTfy provides a directory of GPTs, as well as reviews and comparisons to help users choose the right GPT for their project.

AICorr.com is a website offering free coding tutorials with a focus on artificial intelligence, data science, machine learning, and statistics. Users can learn and practice coding in Python and SQL, explore projects with real data, and access a wealth of information in an easy-to-understand format. The website aims to provide up-to-date and relevant information to a global audience, ensuring a seamless learning experience for all.

Locus is a free browser extension that uses natural language processing to help users quickly find information on any web page. It allows users to search for specific terms or concepts using natural language queries, and then instantly jumps to the relevant section of the page. Locus also integrates with AI-powered tools such as GPT-3.5 to provide additional functionality, such as summarizing text and generating code. With Locus, users can save time and improve their productivity when reading and researching online.

Enterprise h2oGPTe - GenAI RAG is a repository containing code examples, notebooks, and benchmarks for the enterprise version of h2oGPTe, a powerful AI tool for generating text based on the RAG (Retrieval-Augmented Generation) architecture. The repository provides resources for leveraging h2oGPTe in enterprise settings, including implementation guides, performance evaluations, and best practices. Users can explore various applications of h2oGPTe in natural language processing tasks, such as text generation, content creation, and conversational AI.

The Modular Accelerated Xecution (MAX) platform is an integrated suite of AI libraries, tools, and technologies that unifies commonly fragmented AI deployment workflows. MAX accelerates time to market for the latest innovations by giving AI developers a single toolchain that unlocks full programmability, unparalleled performance, and seamless hardware portability.

Hands-On Large Language Models is a repository containing code examples from the book 'The Illustrated LLM Book' by Jay Alammar and Maarten Grootendorst. The repository provides practical tools and concepts for using Large Language Models with over 250 custom-made figures. It covers topics such as language model introduction, tokens and embeddings, transformer LLMs, text classification, text clustering, prompt engineering, text generation techniques, semantic search, multimodal LLMs, text embedding models, fine-tuning representation models, and fine-tuning generation models. The examples are designed to be run on Google Colab with T4 GPU support, but can be adapted to other cloud platforms as well.

The ChatGPT R package provides a set of features to assist in R coding. It includes addins like Ask ChatGPT, Comment selected code, Complete selected code, Create unit tests, Create variable name, Document code, Explain selected code, Find issues in the selected code, Optimize selected code, and Refactor selected code. Users can interact with ChatGPT to get code suggestions, explanations, and optimizations. The package helps in improving coding efficiency and quality by providing AI-powered assistance within the RStudio environment.

The Java-AI-Book-Code repository contains code examples for the 2020 edition of 'Practical Artificial Intelligence With Java'. It is a comprehensive update of the previous 2013 edition, featuring new content on deep learning, knowledge graphs, anomaly detection, linked data, genetic algorithms, search algorithms, and more. The repository serves as a valuable resource for Java developers interested in AI applications and provides practical implementations of various AI techniques and algorithms.

This course has 18 lessons. Each lesson covers its own topic so start wherever you like! Lessons are labeled either "Learn" lessons explaining a Generative AI concept or "Build" lessons that explain a concept and code examples in both **Python** and **TypeScript** when possible. Each lesson also includes a "Keep Learning" section with additional learning tools. **What You Need** * Access to the Azure OpenAI Service **OR** OpenAI API - _Only required to complete coding lessons_ * Basic knowledge of Python or Typescript is helpful - *For absolute beginners check out these Python and TypeScript courses. * A Github account to fork this entire repo to your own GitHub account We have created a **Course Setup** lesson to help you with setting up your development environment. Don't forget to star (🌟) this repo to find it easier later. ## 🧠 Ready to Deploy? If you are looking for more advanced code samples, check out our collection of Generative AI Code Samples in both **Python** and **TypeScript**. ## 🗣️ Meet Other Learners, Get Support Join our official AI Discord server to meet and network with other learners taking this course and get support. ## 🚀 Building a Startup? Sign up for Microsoft for Startups Founders Hub to receive **free OpenAI credits** and up to **$150k towards Azure credits to access OpenAI models through Azure OpenAI Services**. ## 🙏 Want to help? Do you have suggestions or found spelling or code errors? Raise an issue or Create a pull request ## 📂 Each lesson includes: * A short video introduction to the topic * A written lesson located in the README * Python and TypeScript code samples supporting Azure OpenAI and OpenAI API * Links to extra resources to continue your learning ## 🗃️ Lessons | | Lesson Link | Description | Additional Learning | | :-: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------: | ------------------------------------------------------------------------------ | | 00 | Course Setup | **Learn:** How to Setup Your Development Environment | Learn More | | 01 | Introduction to Generative AI and LLMs | **Learn:** Understanding what Generative AI is and how Large Language Models (LLMs) work. | Learn More | | 02 | Exploring and comparing different LLMs | **Learn:** How to select the right model for your use case | Learn More | | 03 | Using Generative AI Responsibly | **Learn:** How to build Generative AI Applications responsibly | Learn More | | 04 | Understanding Prompt Engineering Fundamentals | **Learn:** Hands-on Prompt Engineering Best Practices | Learn More | | 05 | Creating Advanced Prompts | **Learn:** How to apply prompt engineering techniques that improve the outcome of your prompts. | Learn More | | 06 | Building Text Generation Applications | **Build:** A text generation app using Azure OpenAI | Learn More | | 07 | Building Chat Applications | **Build:** Techniques for efficiently building and integrating chat applications. | Learn More | | 08 | Building Search Apps Vector Databases | **Build:** A search application that uses Embeddings to search for data. | Learn More | | 09 | Building Image Generation Applications | **Build:** A image generation application | Learn More | | 10 | Building Low Code AI Applications | **Build:** A Generative AI application using Low Code tools | Learn More | | 11 | Integrating External Applications with Function Calling | **Build:** What is function calling and its use cases for applications | Learn More | | 12 | Designing UX for AI Applications | **Learn:** How to apply UX design principles when developing Generative AI Applications | Learn More | | 13 | Securing Your Generative AI Applications | **Learn:** The threats and risks to AI systems and methods to secure these systems. | Learn More | | 14 | The Generative AI Application Lifecycle | **Learn:** The tools and metrics to manage the LLM Lifecycle and LLMOps | Learn More | | 15 | Retrieval Augmented Generation (RAG) and Vector Databases | **Build:** An application using a RAG Framework to retrieve embeddings from a Vector Databases | Learn More | | 16 | Open Source Models and Hugging Face | **Build:** An application using open source models available on Hugging Face | Learn More | | 17 | AI Agents | **Build:** An application using an AI Agent Framework | Learn More | | 18 | Fine-Tuning LLMs | **Learn:** The what, why and how of fine-tuning LLMs | Learn More |

Generative AI Indepth Basic to Advance is a repository focused on providing tutorials and resources related to generative artificial intelligence. The repository covers a wide range of topics from basic concepts to advanced techniques in the field of generative AI. Users can find detailed explanations, code examples, and practical demonstrations to help them understand and implement generative AI algorithms. The goal of this repository is to help beginners get started with generative AI and to provide valuable insights for more experienced practitioners.

The Oracle Cloud Infrastructure Data Science and AI services Examples repository provides demos, tutorials, and code examples showcasing various features of the OCI Data Science service and AI services. It offers tools for data scientists to develop and deploy machine learning models efficiently, with features like Accelerated Data Science SDK, distributed training, batch processing, and machine learning pipelines. Whether you're a beginner or an experienced practitioner, OCI Data Science Services provide the resources needed to build, train, and deploy models easily.

LearnOpenCV is a repository containing code for Computer Vision, Deep learning, and AI research articles shared on the blog LearnOpenCV.com. It serves as a resource for individuals looking to enhance their expertise in AI through various courses offered by OpenCV. The repository includes a wide range of topics such as image inpainting, instance segmentation, robotics, deep learning models, and more, providing practical implementations and code examples for readers to explore and learn from.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

This repository is for the book 'Building AI Applications with ChatGPT APIs' published by Packt. It provides code examples and instructions for mastering ChatGPT, Whisper, and DALL-E APIs through building innovative AI projects. Readers will learn to develop AI applications using ChatGPT APIs, integrate them with frameworks like Flask and Django, create AI-generated art with DALL-E APIs, and optimize ChatGPT models through fine-tuning.

The AI Workshop repository provides a comprehensive guide to utilizing OpenAI's APIs, including Chat Completion, Embedding, and Assistant APIs. It offers hands-on demonstrations and code examples to help users understand the capabilities of these APIs. The workshop covers topics such as creating interactive chatbots, performing semantic search using text embeddings, and building custom assistants with specific data and context. Users can enhance their understanding of AI applications in education, research, and other domains through practical examples and usage notes.

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Amadeus Java SDK provides a rich set of APIs for the travel industry, allowing developers to access various functionalities such as flight search, booking, airport information, and more. The SDK simplifies interaction with the Amadeus API by providing self-contained code examples and detailed documentation. Developers can easily make API calls, handle responses, and utilize features like pagination and logging. The SDK supports various endpoints for tasks like flight search, booking management, airport information retrieval, and travel analytics. It also offers functionalities for hotel search, booking, and sentiment analysis. Overall, the Amadeus Java SDK is a comprehensive tool for integrating Amadeus APIs into Java applications.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

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.

Nous is an open-source TypeScript platform for autonomous AI agents and LLM based workflows. It aims to automate processes, support requests, review code, assist with refactorings, and more. The platform supports various integrations, multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It offers advanced features like reasoning/planning, memory and function call history, hierarchical task decomposition, and control-loop function calling options. Nous is designed to be a flexible platform for the TypeScript community to expand and support different use cases and integrations.

Sophia is an open-source TypeScript platform designed for autonomous AI agents and LLM based workflows. It aims to automate processes, review code, assist with refactorings, and support various integrations. The platform offers features like advanced autonomous agents, reasoning/planning inspired by Google's Self-Discover paper, memory and function call history, adaptive iterative planning, and more. Sophia supports multiple LLMs/services, CLI and web interface, human-in-the-loop interactions, flexible deployment options, observability with OpenTelemetry tracing, and specific agents for code editing, software engineering, and code review. It provides a flexible platform for the TypeScript community to expand and support various use cases and integrations.

FuseMedML is a Python framework designed to accelerate machine learning-based discovery in the medical field by promoting code reuse. It provides a flexible design concept where data is stored in a nested dictionary, allowing easy handling of multi-modality information. The framework includes components for creating custom models, loss functions, metrics, and data processing operators. Additionally, FuseMedML offers 'batteries included' key components such as fuse.data for data processing, fuse.eval for model evaluation, and fuse.dl for reusable deep learning components. It supports PyTorch and PyTorch Lightning libraries and encourages the creation of domain extensions for specific medical domains.

An Open-Source Engineering Guide for Prompt-in-context-learning from EgoAlpha Lab. 📝 Papers | ⚡️ Playground | 🛠 Prompt Engineering | 🌍 ChatGPT Prompt | ⛳ LLMs Usage Guide > **⭐️ Shining ⭐️:** This is fresh, daily-updated resources for in-context learning and prompt engineering. As Artificial General Intelligence (AGI) is approaching, let’s take action and become a super learner so as to position ourselves at the forefront of this exciting era and strive for personal and professional greatness. The resources include: _🎉Papers🎉_: The latest papers about _In-Context Learning_ , _Prompt Engineering_ , _Agent_ , and _Foundation Models_. _🎉Playground🎉_: Large language models（LLMs）that enable prompt experimentation. _🎉Prompt Engineering🎉_: Prompt techniques for leveraging large language models. _🎉ChatGPT Prompt🎉_: Prompt examples that can be applied in our work and daily lives. _🎉LLMs Usage Guide🎉_: The method for quickly getting started with large language models by using LangChain. In the future, there will likely be two types of people on Earth (perhaps even on Mars, but that's a question for Musk): - Those who enhance their abilities through the use of AIGC; - Those whose jobs are replaced by AI automation. 💎EgoAlpha: Hello! human👤, are you ready?

Assists with Java design by providing code examples, and best practices.

专注匹配github各类代码,加以合成，支持用户API

Assists with R programming by providing code examples, debugging tips, and best practices.

Assists with Ruby programming by providing code examples, debugging tips, and best practices.

Your front-end web dev assistant giving you code examples and advice from MDN Web Docs, Web.dev, and CSS Tricks.

Assists with React framework queries, offering solutions, debugging tips, and code examples.

Ask Robert Sösemann, a Salesforce MVP and inventor of PMD for Salesforce, about Salesforce Development, Clean Code and PMD

Your Langchain codebase assistant.

Specializes in searching MQL codebase for EAs, indicators, and scripts.

Your go-to source for in-depth US zip code demographics and statistics, with easy-to-download data tables.

Checks through Embedded System Datasheet and Suggest Source Codes

I find and fix missing parentheses in code.

Find the lowest Gas Prices quickly and easily based on Zip Code or City and State.

I find the best-priced Christmas gifts locally or online. Upload or Paste your family and friends Christmas list and your zip code.

Providing usable promo codes for various products

Your friend who always shows up with active discount codes

Finds GitHub projects for solo devs focused on profit.

Helps with learning a programming language by recommending projects for its unique strengths and use-cases. Provide the name of language only as the prompt.

Love is in the AI - Your exclusive dating advisor for modern romance!

SAP UI5 and Open UI5 expert, guiding in code review and best practices.