How to Optimize your Codespaces: Pro-tips for managing quotas
Now that GitHub Codespaces is free for anyone, you might be surprised to see how fast you can hit the free quota. Here are four things you can do to make the most out of the 90 hours you get every month (and 180 hours if you are a student).Use AI for Free with GitHub Models and TypeScript! 💸💸💸
Learn how to use AI for free with GitHub Models! Test models like GPT-4o without paying for APIs or setting up infrastructure. This step-by-step guide shows how to integrate GitHub Models with TypeScript in the Microblog AI Remix project. Start exploring AI for free today!Construyendo una Aplicación Web con Inteligencia Artificial usando Python
En la segunda sesión del GitHub Copilot Bootcamp LATAM, organizado por Microsoft Reactor, el ingeniero Manuel Ortiz, Embajador de Microsoft Learn y líder comunitario en GitHub, guió a desarrolladores en la creación de una aplicación web con capacidades de inteligencia artificial. Este taller práctico combinó fundamentos de desarrollo backend en Python con técnicas avanzadas de integración de modelos de lenguaje de Azure OpenAI. Introducción a Azure Open AI Azure Open AI es una colaboración entre Microsoft y OpenAI que permite a los desarrolladores integrar modelos avanzados de inteligencia artificial en sus aplicaciones utilizando la infraestructura de Azure. Esto ofrece acceso a modelos poderosos como GPT-4, que pueden ser utilizados para una variedad de tareas, desde procesamiento de lenguaje natural hasta generación de texto. Configuración de Azure Open AI Para comenzar a usar Azure Open AI, debes seguir algunos pasos básicos: Crear una Cuenta en Azure: Si aún no tienes una cuenta, puedes crear una en el portal de Azure. Los estudiantes pueden solicitar créditos gratuitos para usar los servicios de Azure. Crear un Servicio Azure Open AI: Accede al portal de Azure y busca "Azure Open AI". Haz clic en "Crear" y selecciona tu suscripción y grupo de recursos. Elige la región y configura el nombre del servicio, que debe ser alfanumérico y sin caracteres especiales. Selecciona el plan de precios adecuado y finaliza la creación del servicio. Obtener las Credenciales: Después de crear el servicio, necesitarás las credenciales (clave de API y endpoint) para autenticar tus solicitudes. Estas credenciales se pueden encontrar en la sección de "Claves y Endpoints" del servicio creado. Integración con Python y Flask Python es uno de los lenguajes de programación más populares para el desarrollo de aplicaciones de inteligencia artificial debido a su simplicidad y vasta biblioteca de herramientas. Durante la configuración, puedes usar varias bibliotecas y herramientas que facilitan el desarrollo de IA con Python, incluyendo: TensorFlow: Una biblioteca de código abierto para aprendizaje automático. Keras: Una API de alto nivel para redes neuronales, que funciona sobre TensorFlow. Scikit-learn: Una biblioteca para aprendizaje automático en Python. Flask: Un microframework para desarrollo de aplicaciones web. Una vez configurado el servicio Azure Open AI, puedes integrarlo en tus aplicaciones Python usando Flask. Aquí tienes un ejemplo de cómo hacerlo: Instalación de las Bibliotecas Necesarias: Crea un entorno virtual e instala las bibliotecas necesarias, como flask y openai. Configuración del Proyecto: Crea un archivo .env para almacenar tus credenciales de forma segura. Configura tu aplicación Flask para cargar estas credenciales y conectarse al servicio Azure Open AI. Creación del Modelo de IA: Utiliza la biblioteca openai para enviar prompts al modelo y recibir respuestas. Integra estas respuestas en tu aplicación web para proporcionar funcionalidades de IA a los usuarios. Ejemplo de Código Aquí tienes un ejemplo simplificado de cómo configurar y usar Azure Open AI en una aplicación Flask: from flask import Flask, request, render_template import openai import os app = Flask(__name__) # Cargar las credenciales del archivo .env openai.api_key = os.getenv("AZURE_OPENAI_API_KEY") openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") @app.route("/", methods=["GET", "POST"]) def index(): response_text = "" if request.method == "POST": prompt = request.form["prompt"] response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=100 ) response_text = response.choices.text.strip() return render_template("index.html", response_text=response_text) if __name__ == "__main__": app.run(debug=True) Beneficios de Azure Open AI Acceso a Modelos Avanzados: Utiliza los modelos más recientes y poderosos de OpenAI. Escalabilidad: La infraestructura de Azure permite escalar tus aplicaciones según sea necesario. Seguridad y Conformidad: Benefíciate de las robustas medidas de seguridad y conformidad de Azure. Sigue aprendiendo Si deseas aprender más sobre estas técnicas, mira las grabaciones del GitHub Copilot Bootcamp, comienza a utilizar el GitHub Copilot gratuito y descubre cómo transformar tu manera de programar utilizando inteligencia artificial.Use IA de Graça com GitHub Models e TypeScript! 💸💸💸
Descubra como usar IA sem custos com GitHub Models! Teste modelos como GPT-4o sem precisar pagar por APIs ou configurar infraestrutura. Aprenda passo a passo a integrar GitHub Models com TypeScript no Microblog AI Remix e comece a explorar IA gratuitamente!GitHub Copilot 2025년 3월 업데이트
Visual Studio Code 2025년 2월 릴리스에 오신 것을 환영합니다. 이번 버전에서는 다양한 기능을 추가했으며, 특히 GitHub Copilot과 관련한 주요 업데이트는 다음과 같습니다: Next Edit Suggestions (미리보기) - Copilot이 다음으로 수정할 가능성이 높은 코드를 예측합니다. Agent 모드 (미리보기) - Copilot이 자동으로 작업을 완료합니다. Copilot Edits의 노트북 지원 - 노트북 파일의 편집을 쉽게 할 수 있습니다. 코드 검색 - Copilot이 채팅 프롬프트와 관련된 파일을 검색합니다. 사용자 지정 지침 GA - Copilot을 사용자의 요구에 맞게 설정할 수 있습니다.The Future of AI: Reduce AI Provisioning Effort - Jumpstart your solutions with AI App Templates
In the previous post, we introduced Contoso Chat – an open-source RAG-based retail chat sample for Azure AI Foundry, that serves as both an AI App template (for builders) and the basis for a hands-on workshop (for learners). And we briefly talked about five stages in the developer workflow (provision, setup, ideate, evaluate, deploy) that take them from the initial prompt to a deployed product. But how can that sample help you build your app? The answer lies in developer tools and AI App templates that jumpstart productivity by giving you a fast start and a solid foundation to build on. In this post, we answer that question with a closer look at Azure AI App templates - what they are, and how we can jumpstart our productivity with a reuse-and-extend approach that builds on open-source samples for core application architectures.Exploring Azure OpenAI Assistants and Azure AI Agent Services: Benefits and Opportunities
In the rapidly evolving landscape of artificial intelligence, businesses are increasingly turning to cloud-based solutions to harness the power of AI. Microsoft Azure offers two prominent services in this domain: Azure OpenAI Assistants and Azure AI Agent Services. While both services aim to enhance user experiences and streamline operations, they cater to different needs and use cases. This blog post will delve into the details of each service, their benefits, and the opportunities they present for businesses. Understanding Azure OpenAI Assistants What Are Azure OpenAI Assistants? Azure OpenAI Assistants are designed to leverage the capabilities of OpenAI's models, such as GPT-3 and its successors. These assistants are tailored for applications that require advanced natural language processing (NLP) and understanding, making them ideal for conversational agents, chatbots, and other interactive applications. Key Features Pre-trained Models: Azure OpenAI Assistants utilize pre-trained models from OpenAI, which means they come with a wealth of knowledge and language understanding out of the box. This reduces the time and effort required for training models from scratch. Customizability: While the models are pre-trained, developers can fine-tune them to meet specific business needs. This allows for the creation of personalized experiences that resonate with users. Integration with Azure Ecosystem: Azure OpenAI Assistants seamlessly integrate with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Cognitive Services. This enables businesses to build comprehensive solutions that leverage multiple Azure capabilities. Benefits of Azure OpenAI Assistants Enhanced User Experience: By utilizing advanced NLP capabilities, Azure OpenAI Assistants can provide more natural and engaging interactions. This leads to improved customer satisfaction and loyalty. Rapid Deployment: The availability of pre-trained models allows businesses to deploy AI solutions quickly. This is particularly beneficial for organizations looking to implement AI without extensive development time. Scalability: Azure's cloud infrastructure ensures that applications built with OpenAI Assistants can scale to meet growing user demands without compromising performance. Understanding Azure AI Agent Services What Are Azure AI Agent Services? Azure AI Agent Services provide a more flexible framework for building AI-driven applications. Unlike Azure OpenAI Assistants, which are limited to OpenAI models, Azure AI Agent Services allow developers to utilize a variety of AI models, including those from other providers or custom-built models. Key Features Model Agnosticism: Developers can choose from a wide range of AI models, enabling them to select the best fit for their specific use case. This flexibility encourages innovation and experimentation. Custom Agent Development: Azure AI Agent Services support the creation of custom agents that can perform a variety of tasks, from simple queries to complex decision-making processes. Integration with Other AI Services: Like OpenAI Assistants, Azure AI Agent Services can integrate with other Azure services, allowing for the creation of sophisticated AI solutions that leverage multiple technologies. Benefits of Azure AI Agent Services Diverse Use Cases: The ability to use any AI model opens a world of possibilities for businesses. Whether it's a specialized model for sentiment analysis or a custom-built model for a niche application, organizations can tailor their solutions to meet specific needs. Enhanced Automation: AI agents can automate repetitive tasks, freeing up human resources for more strategic activities. This leads to increased efficiency and productivity. Cost-Effectiveness: By allowing the use of various models, businesses can choose cost-effective solutions that align with their budget and performance requirements. Opportunities for Businesses Improved Customer Engagement Both Azure OpenAI Assistants and Azure AI Agent Services can significantly enhance customer engagement. By providing personalized and context-aware interactions, businesses can create a more satisfying user experience. For example, a retail company can use an AI assistant to provide tailored product recommendations based on customer preferences and past purchases. Data-Driven Decision Making AI agents can analyze vast amounts of data and provide actionable insights. This capability enables organizations to make informed decisions based on real-time data analysis. For instance, a financial institution can deploy an AI agent to monitor market trends and provide investment recommendations to clients. Streamlined Operations By automating routine tasks, businesses can streamline their operations and reduce operational costs. For example, a customer support team can use AI agents to handle common inquiries, allowing human agents to focus on more complex issues. Innovation and Experimentation The flexibility of Azure AI Agent Services encourages innovation. Developers can experiment with different models and approaches to find the most effective solutions for their specific challenges. This culture of experimentation can lead to breakthroughs in product development and service delivery. Enhanced Analytics and Insights Integrating AI agents with analytics tools can provide businesses with deeper insights into customer behavior and preferences. This data can inform marketing strategies, product development, and customer service improvements. For example, a company can analyze interactions with an AI assistant to identify common customer pain points, allowing them to address these issues proactively. Conclusion In summary, both Azure OpenAI Assistants and Azure AI Agent Services offer unique advantages that can significantly benefit businesses looking to leverage AI technology. Azure OpenAI Assistants provide a robust framework for building conversational agents using advanced OpenAI models, making them ideal for applications that require sophisticated natural language understanding and generation. Their ease of integration, rapid deployment, and enhanced user experience make them a compelling choice for businesses focused on customer engagement. Azure AI Agent Services, on the other hand, offer unparalleled flexibility by allowing developers to utilize a variety of AI models. This model-agnostic approach encourages innovation and experimentation, enabling businesses to tailor solutions to their specific needs. The ability to automate tasks and streamline operations can lead to significant cost savings and increased efficiency. Additional Resources To further explore Azure OpenAI Assistants and Azure AI Agent Services, consider the following resources: Agent Service on Microsoft Learn Docs Watch On-Demand Sessions Streamlining Customer Service with AI-Powered Agents: Building Intelligent Multi-Agent Systems with Azure AI Microsoft learn Develop AI agents on Azure - Training | Microsoft Learn Community and Announcements Tech Community Announcement: Introducing Azure AI Agent Service Bonus Blog Post: Announcing the Public Preview of Azure AI Agent Service AI Agents for Beginners 10 Lesson Course https://aka.ms/ai-agents-beginnersIngeniería de prompts con GitHub Copilot: Potenciando el desarrollo de software con IA
GitHub Copilot está revolucionando el desarrollo con inteligencia artificial. 🚀 En el Bootcamp de GitHub Copilot LATAM, exploramos cómo usarlo para escribir mejor código, refactorizar proyectos y automatizar tareas. Desde prompts estratégicos hasta pruebas unitarias y trabajo con grandes bases de código, esta sesión cubrió herramientas clave para maximizar la productividad. ¿Listo para llevar tu desarrollo al siguiente nivel? Únete al desafío, accede a recursos exclusivos y mejora tus habilidades con GitHub Copilot. 🔥 #GitHubCopilot #IA #DesarrolloLondon Reactor Meetup Pre-AI Tour
Thanks for joining the London Reactor Meetup today. Here you can find the resources that have been shared during the meetup and the speakers contact details. Resources Semantic Kernel GitHub Repository Copilot custom agents - Blog Agents with graph connectors - Blog Generative AI with JavaScript Generative AI with JavaScript YoutTube Series Upcoming You can find all upcoming Reactor events HERE Speaker contact and resources: Chris Noring, Senior Cloud Advocate @ Microsoft LinkedIn Håkan Silfvernagel, Senior AI Architect (MVP) @ Sopra Steria LinkedIn Alex Pearce, Cheif Strategist (MVP) @ Softcat LinkedIn Lewis Baybutt, Consultant (MVP) @ Avanade LinkedIn Liam Hampton, Senior Cloud Advocate & Software Engineer @ Microsoft LinkedIn
