#python #chatgpt #generative_ai #large_language_models #react_flow

Langflow is a tool that helps you build AI applications easily, even if you're not an expert. It's based on Python and works with any model, API, or database. You can use a visual interface to drag and drop elements to build your application, test it immediately, and manage conversations between multiple agents. Langflow offers a free cloud service so you can start quickly without any setup, and it also provides enterprise-grade security and scalability. This makes it easy to create and deploy AI applications, saving you time and effort.

https://github.com/langflow-ai/langflow

#typescript #ai #ai_assistant #anthropic #chatbot #chatgpt #claude #developer_tools #development_tools #devtools #gemini #generative_ai #gpt #javascript #js #llm #nodejs #openai #typescript

Repomix is a tool that packs your entire code repository into a single file, making it easy for AI tools like ChatGPT and Claude to understand and analyze your code. It formats your code in a way that AI can process efficiently, provides token counts, and is simple to use with just one command. You can customize what to include or exclude, and it respects your `.gitignore` files. Repomix also includes security checks to prevent sensitive information from being included. To use it, simply install Repomix with `npm install -g repomix` and run `repomix` in your project directory. This helps you get comprehensive code reviews, generate documentation, and more, all while ensuring your code is secure and optimized for AI analysis.

https://github.com/yamadashy/repomix

#python #auto_regressive_model #autoregressive_models #diffusion_models #generative_ai #generative_model #gpt #gpt_2 #image_generation #large_language_models #neurips #transformers #vision_transformer

VAR (Visual Autoregressive Modeling) is a new way to generate images that improves upon existing methods. It uses a "next-scale prediction" approach, which means it generates images from coarse to fine details, unlike the traditional method of predicting pixel by pixel. This makes VAR models better than diffusion models for the first time. You can try VAR on a demo website and generate images interactively, which is fun and easy. VAR also follows power-law scaling laws, making it efficient and scalable. The benefit to you is that you can create high-quality images quickly and easily, and even explore technical details through provided scripts and models.

https://github.com/FoundationVision/VAR

#rust #agent #ai #artificial_intelligence #automation #generative_ai #large_language_model #llm #llmops #rust #scalable_ai

Rig is a Rust library that helps you build apps using Large Language Models (LLMs) like OpenAI and Cohere. It makes it easy to integrate these models into your application with minimal code. Rig supports various vector stores like MongoDB and Neo4j, and it provides simple but powerful tools to work with LLMs. To get started, you can add Rig to your project using `cargo add rig-core` and follow the examples provided. This library is constantly improving, so your feedback is valuable. Using Rig can save you time and effort by providing a straightforward way to use LLMs in your projects.

https://github.com/0xPlaygrounds/rig

#mdx #chatgpt #deep_learning #generative_ai #language_model #openai #prompt_engineering

Prompt engineering helps you use language models more effectively by designing better prompts. This skill is useful for various tasks like question answering, arithmetic reasoning, and coding. With prompt engineering, you can improve how language models perform and understand their capabilities and limitations. There are resources available, such as guides, courses, and tools, to help you learn and apply prompt engineering techniques. These resources include detailed guides, video lectures, and self-paced courses that can enhance your skills and make you more efficient in using language models.

https://github.com/dair-ai/Prompt-Engineering-Guide

#python #cloud_native #cncf #deep_learning #docker #fastapi #framework #generative_ai #grpc #jaeger #kubernetes #llmops #machine_learning #microservice #mlops #multimodal #neural_search #opentelemetry #orchestration #pipeline #prometheus

Jina-serve is a tool that helps you build and deploy AI services easily. It supports major machine learning frameworks and allows you to scale your services from local development to production quickly. You can use it to create AI services that communicate via gRPC, HTTP, and WebSockets. It has features like built-in Docker integration, one-click cloud deployment, and support for Kubernetes and Docker Compose, making it easy to manage and scale your AI applications. This makes it simpler for you to focus on the core logic of your AI projects without worrying about the technical details of deployment and scaling.

https://github.com/jina-ai/serve

#jupyter_notebook #amazon_bedrock #amazon_titan #bedrock #embeddings #generative_ai #knowledge_base #langchain #rag

This repository provides pre-built examples to help you get started with Amazon Bedrock, a service for working with generative AI. You can learn the basics of Bedrock, how to craft effective prompts, implement AI agents, import custom models, and more. It also includes guides on responsible AI use, productionizing workloads, and improving model observability. To use these examples, ensure you have the necessary AWS IAM permissions and follow the detailed instructions in each folder. This resource helps you quickly and effectively use Amazon Bedrock for various AI tasks, making it easier to integrate generative AI into your projects.

https://github.com/aws-samples/amazon-bedrock-samples

#python #agents #ai_agents #ai_agents_framework #artificial_intelligence #developer_tools #devtools #generative_ai #knowledge_graph #memory #rag

Potpie is an open-source platform that helps you automate code analysis, testing, and development tasks. It creates AI agents that understand your codebase deeply, allowing them to assist with debugging, feature development, and more. You can use pre-built agents for common tasks like debugging and testing, or create custom agents to handle specific needs. Potpie integrates seamlessly into your existing development workflow and works with codebases of any size or language. This makes it easier for developers to understand the codebase quickly, review code changes, and generate tests, saving time and improving efficiency.

https://github.com/potpie-ai/potpie

#jupyter_notebook #agents #artificial_intelligence #generative_ai #llms #rag

This repository helps you learn and build Generative AI applications using MongoDB. It includes many examples and sample apps for different AI tasks, such as Retrieval-Augmented Generation (RAG) and AI Agents. You can find Jupyter notebooks, JavaScript and Python apps, and contributions from AI partners. To get started, you need to create a free MongoDB Atlas account, set up a database cluster, and get the connection string. This resource benefits you by providing step-by-step guides and support, making it easier to integrate MongoDB into your AI projects and learn from community resources.

https://github.com/mongodb-developer/GenAI-Showcase

#jupyter_notebook #agentic_ai #agentic_framework #agentic_rag #ai_agents #ai_agents_framework #autogen #generative_ai #semantic_kernel

This course helps you learn about AI Agents from the basics to advanced levels. AI Agents are systems that use large language models to perform tasks by accessing tools and knowledge. The course includes 10 lessons covering topics like agent fundamentals, frameworks, and use cases. It provides code examples and supports multiple languages. By completing this course, you can build your own AI Agents and apply them in various applications, such as customer support or event planning, making complex tasks easier and more efficient.

https://github.com/microsoft/ai-agents-for-beginners