#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

#typescript #ai #generative_ai #html_css_javascript #tailwindcss

OpenUI is a tool that makes building user interfaces easy, fast, and fun by letting you describe your design ideas and see them appear live on screen. It supports multiple frameworks like React, Svelte, and Web Components, so you can quickly create and test UI components without complex coding. OpenUI is open source, encouraging collaboration and continuous improvement from developers worldwide. It also integrates with many AI models to help prototype smarter applications. This means you can save time, reduce hassle, and bring your creative UI ideas to life more efficiently and flexibly.

https://github.com/wandb/openui

#python #asr #deeplearning #generative_ai #large_language_models #machine_translation #multimodal #neural_networks #speaker_diariazation #speaker_recognition #speech_synthesis #speech_translation #tts

NVIDIA NeMo is a powerful, easy-to-use platform for building, customizing, and deploying generative AI models like large language models (LLMs), vision language models, and speech AI. It lets you quickly train and fine-tune models using pre-built code and checkpoints, supports the latest model architectures, and works on cloud, data center, or edge environments. NeMo 2.0 is even more flexible and scalable, with Python-based configuration and modular design, making it simple to experiment and scale up. The main benefit is that you can create advanced AI applications faster, with less effort, and at lower cost, while getting high performance and easy deployment options[1][2][3].

https://github.com/NVIDIA/NeMo

#rust #ai #ai_engineering #anthropic #artificial_intelligence #deep_learning #genai #generative_ai #gpt #large_language_models #llama #llm #llmops #llms #machine_learning #ml #ml_engineering #mlops #openai #python #rust

TensorZero is a free, open-source tool that helps you build and improve large language model (LLM) applications by using real-world data and feedback. It gives you one simple API to connect with all major LLM providers, collects data from your app’s use, and lets you easily test and improve prompts, models, and strategies. You can see how your LLMs perform, compare different options, and make them smarter, faster, and cheaper over time—all while keeping your data private and under your control. This means you get better results with less effort and cost, and your apps keep improving as you use them[1][2][3].

https://github.com/tensorzero/tensorzero

#typescript #ai_gateway #gateway #generative_ai #hacktoberfest #langchain #llama_index #llmops #llms #openai #prompt_engineering #router

The AI Gateway by Portkey lets you connect to over 1600 AI models quickly and securely through one simple API, making it easy to integrate any language, vision, or audio AI model in under two minutes. It ensures fast responses with less than 1ms latency, automatic retries, load balancing, and fallback options to keep your AI apps reliable and scalable. It also offers strong security with role-based access, guardrails, and compliance with standards like SOC2 and GDPR. You can save costs with smart caching and optimize usage without changing your code. This helps you build powerful, cost-effective, and secure AI applications faster and with less hassle.

https://github.com/Portkey-AI/gateway

#python #agents #generative_ai_tools #llamacpp #llm #onnx #openvino #parsing #retrieval_augmented_generation #small_specialized_models

llmware is a powerful, easy-to-use platform that helps you build AI applications using small, specialized language models designed for business tasks like question-answering, summarization, and data extraction. It supports private, secure deployment on your own machines without needing expensive GPUs, making it cost-effective and safe for enterprise use. You can organize and search your documents, run smart queries, and combine knowledge with AI to get accurate answers quickly. It also offers many ready-to-use models and examples, plus tools for building chatbots and agents that automate complex workflows. This helps you save time, improve accuracy, and securely leverage AI for your business needs[1][3][5].

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

#python #agents #ai #api_gateway #asyncio #authentication_middleware #devops #docker #fastapi #federation #gateway #generative_ai #jwt #kubernetes #llm_agents #mcp #model_context_protocol #observability #prompt_engineering #python #tools

The MCP Gateway is a powerful tool that unifies different AI service protocols like REST and MCP into one easy-to-use endpoint. It helps you manage multiple AI tools and services securely with features like authentication, retries, rate-limiting, and real-time monitoring through an admin UI. You can run it locally or in scalable cloud environments using Docker or Kubernetes. It supports various communication methods (HTTP, WebSocket, SSE, stdio) and offers observability with OpenTelemetry for tracking AI tool usage and performance. This gateway simplifies connecting AI clients to diverse services, making development and management more efficient and secure.

https://github.com/IBM/mcp-context-forge

#python #artificial_intelligence #cybersecurity #generative_ai #llm #pentesting

Cybersecurity AI (CAI) is an open-source, lightweight framework that helps you build AI agents to find and fix security vulnerabilities efficiently. It supports many AI models and tools, works on multiple operating systems, and allows human control during tasks. CAI automates complex security testing steps like scanning, exploiting, and validating bugs, making bug bounty hunting easier and faster. It also logs detailed traces for better analysis and supports teamwork among AI agents. Using CAI can boost your cybersecurity skills, save time, and improve your ability to protect systems from attacks by combining AI power with your expertise.

https://github.com/aliasrobotics/cai

#javascript #ai #anthropic #chatbots #chatgpt #claude #gemini #generative_ai #google_deepmind #large_language_models #llm #openai #prompt_engineering #prompt_injection #prompts

There is a collection of system prompts used by popular chatbots like ChatGPT and others. These prompts are instructions that guide how chatbots respond. They are now available publicly on GitHub, which can be very helpful for users. By seeing these prompts, users can understand how chatbots work and even learn how to create their own AI tools. This can help developers build better AI applications and improve their understanding of AI technology.

https://github.com/asgeirtj/system_prompts_leaks

#kotlin #agentframework #agentic_ai #agents #ai #aiagentframework #android_ai #anthropic #generative_ai #java #jvm #kotlin #ktor #llm #mcp #ollama #openai #spring

Koog is a Kotlin-based open-source framework that helps you build AI agents fully in Kotlin, making it easy to create smart assistants that can use tools, manage complex tasks, and remember past interactions. It supports multiple AI models like OpenAI and Google, runs on many platforms (JVM, JavaScript, iOS), and offers features like real-time streaming, custom tools, and efficient memory use. Koog also provides debugging tools, flexible workflows, and scales from simple chatbots to enterprise systems. Using Koog lets you develop powerful, maintainable AI agents quickly and naturally within the Kotlin ecosystem, benefiting your projects with speed, flexibility, and strong integration options.

https://github.com/JetBrains/koog

#typescript #agent #agent_platform #ai_plugins #chatbot #chatbot_framework #coze #coze_platform #generative_ai #go #kouzi #low_code_ai #multimodel_ai #no_code #rag #studio #typescript #workflow

Coze Studio is an easy-to-use, all-in-one platform for building AI agents and apps without needing much coding. It offers visual tools to design, debug, and deploy AI projects quickly using drag-and-drop workflows, plugins, and large language models like GPT-4. You can create smart assistants, chatbots, or custom AI apps with ready templates and manage models, knowledge bases, and plugins in one place. It supports no-code and low-code development, making AI accessible to both beginners and professionals, saving you time and effort in building powerful AI solutions tailored to your needs. It also supports multi-model integration and easy deployment.

https://github.com/coze-dev/coze-studio

#go #agent #agentic #ai #chatbot #chatbots #embeddings #evaluation #generative_ai #golang #knowledge_base #llm #multi_tenant #multimodel #ollama #openai #question_answering #rag #reranking #semantic_search #vector_search

WeKnora is a powerful tool that helps you understand and find answers in complex documents like PDFs and Word files. It uses advanced AI to read documents, understand what they mean, and answer your questions in a simple way. This tool is useful for businesses and researchers because it can quickly find information from many documents, making it easier to manage knowledge and make decisions. It also supports multiple languages and can be used privately, ensuring your data stays safe.

https://github.com/Tencent/WeKnora

#python #agents #artificial_intelligence #cybersecurity #generative_ai #llm #penetration_testing

Strix is a free, open-source tool that uses AI agents to automatically find and fix security problems in your apps by acting like real hackers—running your code, hunting for vulnerabilities, and proving they’re real by actually exploiting them, not just guessing[1][2]. It works fast, gives clear reports, and can even suggest fixes or create pull requests to help you secure your code quickly. You can run it on your own computer, in your development pipeline, or use a cloud version for easier setup. The main benefit is that you get thorough, real-world security testing without the slow pace and high cost of manual checks, helping you catch and fix issues before they become serious problems.

https://github.com/usestrix/strix

#python #agents #gcp #gemini #genai_agents #generative_ai #llmops #mlops #observability

You can quickly create and deploy AI agents using the Agent Starter Pack, a Python package with ready-made templates and full infrastructure on Google Cloud. It handles everything except your agent’s logic, including deployment, monitoring, security, and CI/CD pipelines. You can start a project in just one minute, customize agents for tasks like document search or real-time chat, and extend them as needed. This saves you time and effort by providing production-ready tools and integration with Google Cloud services, letting you focus on building smart AI agents without worrying about backend setup or deployment details.

https://github.com/GoogleCloudPlatform/agent-starter-pack