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Cody Visual Studio Code AI coding assistant Copilot alternative

Cody Visual Studio Code AI coding assistant Copilot alternative

Imagine you’re working on a complex coding project, and you hit a snag. You’re deep in the trenches of Visual Studio Code (VS Code), and you wish you had a partner to help you navigate through the maze of your codebase. What if there was an AI assistant that could understand your project’s unique context and provide real-time, context-aware suggestions? Enter Cody, the AI coding assistant that’s transforming the way developers work in their Integrated Development Environment (IDE). Cody is more than just an autocomplete tool; it’s a collaborative partner that boosts your coding efficiency.

Cody’s most impressive feature is its ability to grasp the context of your codebase. This deep understanding allows Cody to offer finely-tuned recommendations that align with your project’s specific needs. Whether you’re trying to solve a complex bug or writing unit tests, Cody is there to assist. It’s not just about autocompletion; Cody’s AI-driven chat feature lets you interact with your code as if you were working with another developer. You can ask questions and receive insightful guidance, making it feel like a two-way conversation.

But Cody’s capabilities don’t stop there. It comes with a suite of tools that extend beyond generating code snippets and answering technical questions. Cody can help you understand complicated code, automate the creation of documentation, and identify code smells—those subtle issues that could affect the quality or performance of your code. These powerful features are easily accessible through a user-friendly sidebar, putting Cody’s diverse functions at your fingertips.

Cody Visual Studio Code AI coding assistant

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One of the appealing aspects of Cody is its broad accessibility. It offers a free tier with a generous rate limit, making it available to a wide range of developers. Moreover, Cody is versatile, compatible with various IDEs, not just VS Code. This flexibility means you can integrate Cody into your preferred coding environment effortlessly.

Cody takes a holistic approach to application development. It doesn’t just provide code suggestions; it offers advice on tech stacks, UI components, and even adds a touch of gamification to make coding more engaging. With Cody, you can insert code snippets directly into your project and get troubleshooting support, streamlining your development workflow.

For developers who are keen on refining their code, Cody is a valuable resource. It can help with style editing and implementing functionality through chat commands. Cody can also generate documentation and unit tests, as well as conduct code smell analysis. This ensures that your code is not only functional but also adheres to industry best practices.

Cody is more than a typical AI coding assistant. It’s a comprehensive tool that enhances the coding experience within VS Code and other IDEs. By understanding the context of your codebase and delivering personalized recommendations, Cody enables you to create better code more quickly. With its wide range of features, from autocomplete to code smell detection, and its accessible free tier, Cody is an indispensable resource for developers looking to improve their productivity and code quality. Cody is now generally available, ready to integrate into your development workflow and elevate your coding skills.

Image Credit : Sourcegraph

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Using the Gemini Pro API to build AI apps in Google AI Studio

how to use the Google Gemini API

Google has recently introduced a powerful new tool for developers and AI enthusiasts alike: providing access to the Gemini Pro API. This tool is now a part of Google AI Studio, and it’s making waves in the tech community due to its advanced capabilities in processing both text and images using it’s a vision capabilities. This guide provides a quick overview of how you can use the  Gemini Pro API for free to test it out.

The Gemini Pro API is a multimodal platform and particularly notable for its ability to merge text and vision, which significantly enhances how users interact with AI. Google AI Studio is offering free access to the API, with a limit of 60 queries per minute. This generous offer is an invitation for both beginners and experienced developers to dive into AI development without worrying about initial costs.

Using the Gemini Pro API

For those with more complex requirements, the API can be used to construct RAG pipelines, which are instrumental in refining AI applications. By providing additional context during the generation process, these pipelines contribute to more accurate and informed AI responses.

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The platform that hosts the Gemini Pro API, Google AI Studio, was previously known as Maker Suite. The new name signifies Google’s commitment to enhancing the user experience and the continuous advancement of AI tools. When you decide to incorporate the Gemini Pro API into your projects, you’ll be working with the Python SDK, which is a mainstay in the tech industry. This SDK simplifies the integration process, and the use of API keys adds a layer of security. Google AI Studio also places a high priority on safety, offering settings to control the content produced by the API to ensure it meets the objectives of your project.

One of the standout features of the API is its vision model, which goes beyond text processing. It enables the interpretation of images and the generation of corresponding text. This feature is particularly useful for projects that require an understanding of visual elements, such as image recognition and tagging systems.

To support users in harnessing the full power of the Gemini Pro API, Google provides extensive documentation and a collection of prompts. These resources are designed to be accessible to users of all skill levels, offering both instructional material and practical use cases.

The Gemini Pro API, along with the vision capabilities offered by Google AI Studio, equips developers with a comprehensive suite of tools for AI project development. With its no-cost entry point, sophisticated integration options, and robust support system, Google is enabling innovators to take the lead in the tech world. Whether the task at hand involves text generation, real-time responses, or image analysis, the Gemini Pro API is a vital resource for unlocking the vast potential of artificial intelligence.

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How to build custom Copilots with Azure AI Studio

How to build custom Copilots with Azure AI Studio

Azure AI Studio offers users a comprehensive platform for developing and deploying generative AI applications. Providing a single-platform approach to building and deploying AI custom Copilots. As well as  offering a number of different AI models, including those from Azure OpenAI, Meta, NVIDIA, and Microsoft Research. But it’s not just about the variety; it’s the seamless integration these models offer that truly enhances your development experience.

The platform allows the integration of your own data through OneLake in Microsoft Fabric. This feature ensures that your models are grounded in real-world data, enhancing their relevance and accuracy. Azure AI Studio is not just about starting a project; it’s about nurturing it through every stage.

“Accelerate decision-making and enhance efficiency across your enterprise using powerful AI tools and machine learning models. Explore the pricing options for access to our new Azure AI Studio. During preview, there’s no additional charge for using Azure AI Studio. Azure AI services, Azure Machine Learning, and other Azure resources used inside of Azure AI Studio will be billed at their existing rates. Pricing is subject to change when Azure AI Studio is generally available” explains Microsoft.

From prompt engineering to multi-modal applications and rigorous quality and safety testing, this platform supports the full lifecycle of AI application development. You’ll find the Playground feature particularly intriguing for prompt experimentation, alongside a prompt flow tool for custom orchestration.

In an era where ethical considerations are paramount, Azure AI Studio prioritizes responsible AI practices. The platform includes built-in evaluation tools to assess AI applications before they go into production. Moreover, content classifications ensure the safety and appropriateness of responses.

Build custom Copilots with Azure

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The platform’s support for multi-modality allows the incorporation of diverse functionalities like language, vision, speech, and search. This versatility opens up a world of possibilities in application development, catering to a wide array of use cases.

Advanced User Features

  • Azure AI Studio provides a single platform for building and deploying AI copilots.
  • It offers access to a wide range of AI models from Azure OpenAI, Meta, NVIDIA, and Microsoft Research, as well as open-source options.
  • Developers can integrate their own data using OneLake in Microsoft Fabric for model grounding.
  • The platform supports full lifecycle development, including prompt engineering, multi-modal applications, and quality and safety testing.
  • Azure AI Studio features a Playground for prompt experimentation and a prompt flow tool for custom orchestration.
  • It includes built-in evaluation tools to assess AI applications before production.
  • Responsible AI content classifications are available to ensure the safety of responses.
  • Azure AI Studio supports multi-modality, allowing the incorporation of language, vision, speech, and search functionalities.
  • The platform provides options for fine-tuning large language models (LLMs) for advanced users with data science expertise.
  • Users can access additional resources through QuickStart guides.

For those with a deeper understanding of data science, Azure AI Studio doesn’t disappoint. It offers options for fine-tuning large language models (LLMs), granting advanced users more control over their applications.

Accessible and Resourceful

Getting started with Azure AI Studio is a breeze. Accessible at ai.azure.com, the platform also provides QuickStart guides to help you hit the ground running. Whether you’re building a copilot app using Azure AI Studio or exploring its advanced capabilities, these resources are invaluable.

Azure OpenAI Service Power your apps with large-scale AI models. Learn More
Azure AI Search Enterprise scale search for app development. Learn More
Azure AI Content Safety Use AI to monitor text and image content for safety. Learn More
Azure AI Document Intelligence Accelerate information extraction from documents. Learn More
Azure AI Speech Transcribe, translate and generate spoken audio. Learn More
Azure AI Language Identify, analyze and summarize text with natural language processing. Learn More
Azure AI Translator Real-time machine translation for documents and text. Learn More
Azure AI Vision Analyze, extract and categorize information from images. Learn More

Additional Insights

Microsoft’s integration of copilots in its popular workloads, such as Bing, Microsoft 365, and GitHub, underscores the platform’s versatility. Azure AI Studio not only supports the creation of dynamic applications incorporating images, text, speech, and videos but also offers comprehensive control over the orchestration of these elements.

The ability to filter out harmful content through Azure AI Content Classifications and the provision to fine-tune LLMs for customized behavior further enhance the platform’s utility. Azure AI Studio empowers you to build, test, deploy, and monitor generative AI apps at scale, ensuring a robust and efficient development process.

Embark on Your AI Journey

As you embark on your journey with Azure AI Studio, remember that you are not just developing applications; you are shaping the future of AI technology. The platform’s blend of accessibility, versatility, and responsibility makes it an ideal choice for both budding and seasoned developers. With Azure AI Studio, the possibilities are limitless.

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Beginners guide to using Microsoft Copilot Studio

using Microsoft Copilot Studio to create GPT

In today’s fast-paced digital world, businesses and individuals are constantly on the lookout for tools that can help them manage their online content more effectively. Microsoft has introduced a new solution that is capturing the attention of many AI enthusiasts and developers in the form of Microsoft Copilot Studio. This platform allows users to create a personalized digital assistant, in a similar way to how OpenAI made available custom GPTs. You can create, manage, and publish GPTs from Copilot Studio to Copilot for Microsoft 365, although the services currently in a public preview development stage.

Studio acts as a Copilot to navigate the complexities of digital content management enabling you to create complex AI models extremely easily. What makes this tool particularly appealing is its availability to those who do not have a full Microsoft 365 Copilot license, offering advanced support at a more accessible price point. This beginners guide will help you get started in just a few minutes creating your own custom Copilot AI models. Microsoft has made bot building even simpler with AI-powered capabilities in Microsoft Copilot Studio. Whether you’re new to conversational AI or a seasoned developer.

Creating  custom GPTs  using Copilot Studio

To get started once you our at the Microsoft Copilot Studio webpage in the navigation menu, select Copilots to open the Copilots page and view all the bots you have access to in this environment. On the page, you can see metadata about these copilots, including the owner, when it was last published, and when it was last modified.

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The process of setting up your own digital Copilot is straightforward. You begin by giving it a unique name, which it will use to interact with users. Next, you select the language that your Copilot will operate in, ensuring it communicates effectively with your target audience. The setup is completed by linking your Copilot to various content sources, such as internal documents and external websites. This step is crucial as it arms your assistant with a vast array of knowledge to draw upon when assisting users.

Once your Copilot is connected to these information sources, you can test its capabilities within the Copilot Studio to fine-tune its responses. This ensures that your digital assistant is not only functional but also provides high-quality support to your users. As your business evolves, Copilot Studio offers the flexibility to add new content sources and modify moderation settings, keeping your Copilot up-to-date with your company’s changing needs.

When you’re ready to launch, your Copilot can be integrated into various platforms, such as Microsoft Teams, your company’s website, or even mobile apps. It uses sophisticated language models to offer assistance that is both natural and contextually relevant, making interactions seamless for users.

Security is a top concern for any business, and Microsoft Copilot Studio does not take this lightly. Your data is securely stored in Dataverse, ensuring that your information is protected within your own tenant. This focus on security allows you to use the Copilot with confidence, knowing that your data is safe.

Microsoft Copilot Studio is more than just a tool; it’s a strategic asset that brings advanced artificial intelligence and workflow management to a wider audience. Its ease of setup, ability to scale, and secure framework enable businesses to enhance their digital interactions and efficiency. With Microsoft Copilot Studio, companies are well-equipped to face the future of digital content management.

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How to make your first Microsoft Copilot Studio AI assistant

How to make a Microsoft Copilot Studio AI assitstant

During its Ignite 2023 event earlier this month, Microsoft unveiled a wealth of new AI tools it had implemented into its range of services, with one such service being Microsoft Copilot Studio. Similar to how OpenAI has recently rolled out the ability for users to create GPTs users of Studio can also create AI assistants in just a few clicks. Imagine having the power to create a personal AI assistant that can handle tasks, answer questions, and interact with users just like a human would.

With Microsoft Copilot Studio, this is not just a possibility, it’s a reality that’s within your reach. This guide will walk you through the process of building your own AI assistant, step by step, without the need for complex coding. Whether you’re looking to enhance customer service, streamline internal processes, or simply explore the capabilities of AI, this journey will help you achieve your goals.

Microsoft Copilot Studio overview

Microsoft Copilot Studio lets you create powerful AI-powered copilots for a range of requests—from providing simple answers to common questions to resolving issues requiring complex conversations. Engage with customers and employees in multiple languages across websites, mobile apps, Facebook, Microsoft Teams, or any channel supported by the Azure Bot Framework.

Microsoft Copilot Studio is available as both a standalone web app, and as a discrete app within Microsoft Teams. Most of the functionality between the two is the same. However, there might be different reasons to choose one version or the other based on the ways you want to use Microsoft Copilot Studio.

Building a Copilot Studio AI assistant

To begin, you’ll need to open Microsoft Copilot Studio in a web browser that supports it such as Microsoft’s own Edge. Once there, sign in with your Microsoft account work email. This step is crucial as it ensures that your AI assistant will be able to integrate seamlessly with other Microsoft services, which can be incredibly beneficial for your project, especially if it is work-related.

After you’ve logged in, you’ll set up your Power Apps environment. This is where your AI assistant will live and operate. You can go with the default setting or customize it to better fit your organization’s needs. Think of this environment as the foundation of your AI assistant’s capabilities, so it’s important to get it right.

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Next, you’ll move on to personalizing your AI assistant. On the App Homepage, you can give your assistant a name, choose the language it will use, and select an icon that represents your brand. You can also preload it with content to give it a base level of knowledge. This is where you start to see your assistant come to life, as it begins to reflect your brand’s identity and values.

The initial setup of your AI assistant should take about 15 minutes. During this time, you can dive into more customization options to enhance your assistant’s abilities. Once it’s ready, you’ll be taken to an overview page where you can further refine the user interface, topics, and testing features.

One of the most exciting parts of creating your AI assistant is developing custom topics and dialog trees. This is where your assistant becomes truly unique. You’ll create topics that are relevant to your users and set up dialog trees that guide the conversation based on user input. By saving these topics and incorporating elements like variables, entities, and Power Automate flows, you can create complex and meaningful interactions.

Microsoft’s Copilot Studio demonstration

With your dialog trees and custom topics in place, it’s time to test your AI assistant. This step is crucial to ensure that it performs as expected. Test it thoroughly, and once you’re satisfied with how it’s functioning, you can deploy it for user interaction. Keep a close eye on its performance and use AI analysis tools to make continuous improvements.

By following this guide, you’ve successfully navigated the creation of a customizable AI assistant using Microsoft Copilot Studio. From gaining access to the platform and setting up your environment, to personalizing, testing, and deploying your assistant, you now have the tools to introduce an AI solution that can enhance user engagement. Remember, the key to success is ongoing performance analysis, which will help you evolve and perfect your assistant’s capabilities, ensuring it becomes an invaluable part of your operations. For more information on the new Microsoft Copilot Studio and Microsoft 365 integrations jump over to the official Microsoft website.

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New Google Product Studio features to help merchants

New Google Product Studio features to help merchants this holiday season

As the holiday season approaches, businesses are looking for new ways to stand out, attract customers, and increase their revenue. To help small businesses with this cause Google is offering new features to help merchants enhance their visibility and customer engagement during this critical period.

Allowing businesses to increase holiday season sales thanks to the rollout of Search and Google Maps to Product Studio for managing product imagery. These updates are designed to provide businesses with the tools they need to thrive in the competitive holiday marketplace, enticing shoppers and making it easier to close sales.

Google Search and Google Maps

One of the exciting innovations is the introduction of a small business attribute on Google Search and Google Maps. This new feature allows merchants to identify themselves as small businesses, and products sold by businesses with this attribute will bear a “small business” label.

This makes it easier for shoppers to find and support these businesses, which can be a significant advantage during the holiday season when consumers are actively seeking out unique and personalized gifts. Google’s initiative to support small businesses extends to those using Merchant Center or Business Profile in the U.S., including those using the Google & YouTube app on Shopify. The company is also partnering with Etsy to include eligible sellers, further expanding the reach of this initiative.

Increase your sales this holiday season

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In terms of the mechanics of the small business attribute, Google plans to automatically add it to some merchants’ listings based on specific factors. These factors could include the number of products they offer, the number of locations they have, or their web traffic. This automatic addition could help many small businesses gain visibility without having to navigate the complexities of digital marketing.

Google Product Studio

Another significant update is the rollout of Product Studio, a suite of AI tools designed to help merchants create and manage product imagery. One of the standout features of Product Studio is an AI-powered scene generation feature. This tool can help merchants place products into any creative scene, making their product images more appealing and engaging for potential customers.

In addition to this, Product Studio offers prompt ideas, including holiday-themed scenes, to spark inspiration. It also allows merchants to improve resolution or remove distracting backgrounds from their product images, ensuring that their products are displayed in the best possible light.

Shopping Graph

Google is also expanding the type of information shoppers see when they look for businesses on Search. The knowledge panel that appears on certain retailer searches will start showing other helpful shopping information, such as current deals, shipping and return policies, customer service information, and ratings and reviews. This feature can help build trust with customers, as they can see at a glance the key information about a business and its offerings.

Google is making updates to deals and promotions on the Shopping Graph. The Shopping Graph, which understands more than 35 billion listings from across the web, is a vital tool in ensuring shoppers see the latest information about holiday promotions. To take advantage of this, merchants are advised to add their promotions to Merchant Center and review their settings.

How to increase sales during the holiday season

  • Targeted Marketing and Promotions: Understanding customer preferences and past buying patterns can guide targeted marketing efforts. For example, offering holiday-themed discounts or bundles can attract both new and returning customers. Tailoring email campaigns and social media content to reflect seasonal themes and special offers can also be effective.
  • Leveraging Social Media and Online Presence: The holiday season is a prime time to engage with customers on social media. Sharing gift ideas, promoting limited-time offers, and using holiday-themed hashtags can increase visibility. Additionally, optimizing the business’s website for holiday shopping, ensuring it’s mobile-friendly, and easy to navigate can enhance customer experience and drive sales.
  • Personalization and Customer Experience: Offering personalized services or products can distinguish a small business from larger competitors. This could involve personalized gift recommendations, custom packaging, or thoughtful touches that resonate with the holiday spirit. Exceptional customer service, including flexible return policies and efficient problem resolution, also plays a crucial role.
  • Local Community Engagement: Small businesses can leverage their local community ties by participating in or sponsoring local events, collaborating with other local businesses for cross-promotions, or contributing to local causes. This not only builds goodwill but can also increase brand visibility and loyalty.
  • Inventory and Supply Chain Management: Ensuring that popular items are in stock and can be delivered promptly is crucial. This might involve early planning for inventory based on predictive analysis of holiday trends. Effective supply chain management ensures that businesses can meet customer demand without overstocking.
  • Creating a Festive Atmosphere: In physical stores, creating a festive atmosphere through decorations, music, and holiday-themed displays can enhance the shopping experience. This festive environment can be replicated online through website design and interactive elements.

The new features released by Google to help small businesses increase their sales during the holiday season offer a range of opportunities for merchants to enhance their visibility and customer engagement. By making the most of these tools, businesses can increase their holiday season revenue and finish the year on a high note.

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LM Studio makes it easy to run AI models locally on your PC, Mac

LM Studio makes it easy to run AI models locally on your PC Mac and Linux

If you are interested in trying out the latest AI models and large language models that have been trained in different ways. Or would simply like one of the open source AI models running locally on your home network. Assisting you with daily tasks. You will be pleased to know that it is really easy to run LLM and hence AI Agents on your local computer without the need for third-party servers. Obviously the more powerful your laptop or desktop computer have the better, but as long as you have 8GB of RAM as a minimum you should be able to run at least one or two smaller AI models such as Mistral and others.

Running AI models locally opens up opportunities for individuals and small businesses to experiment and innovate with AI without the need for expensive servers or cloud-based solutions. Whether you’re a student, an AI enthusiast, or a professional researcher, you can now easily run AI models on your PC, Mac, or Linux machine.

One of the most user-friendly tools for this purpose is LM Studio, a software that allows you to install and use a variety of AI models. With a straightforward installation process, you can have LM Studio set up on your computer in no time. It supports a wide range of operating systems, including Windows, macOS, and Linux, making it accessible to a broad spectrum of users.

The user interface of LM Studio is designed with both beginners and advanced users in mind. The advanced features are neatly tucked away, so they don’t overwhelm new users but are easily accessible for those who need them. For instance, you can customize options and presets to tailor the software to your specific needs.

LM Studio dashboard

LM Studio dashboard chat box

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LM Studio supports several AI models, including large language models. It even allows for running quantized models in GF format, providing a more efficient way to run these models on your computer. The flexibility to download and add different models is another key feature. Whether you’re interested in NLP, image recognition, or any other AI application, you can find a model that suits your needs.

Search for  AI models and LLMs

LM Studio dashboard

AVX2 support required

Your computer will need to support AVX2 here are a few ways to check what CPU or system is running. Once you know you can then do a quick search to see if the specifications list support for AVX2. You can also ask ChatGPT once you know your CPU.  obviously CPUs after it’s OpenAI’s cut-off date are most likely to support AVX2.

Windows:

  1. Open the Command Prompt.
  2. Run the command systeminfo.
  3. Look for your CPU model in the displayed information, then search for that specific CPU model online to find its specifications.

macOS:

  1. Go to the Apple Menu -> About This Mac -> System Report.
  2. Under “Hardware,” find the “Total Number of Cores” and “Processor Name.”
  3. Search for that specific CPU model online to check its specifications.

Linux:

  1. Open the Terminal.
  2. Run the command lscpu or cat /proc/cpuinfo.
  3. Check for the flag avx2 in the output.

Software Utility:

You can use third-party software like CPU-Z (Windows) or iStat Menus (macOS) to see detailed specifications of your CPU, including AVX2 support.

Vendor Websites:

Visit the CPU manufacturer’s website and look up your CPU model. Detailed specifications should list supported instruction sets.

Direct Hardware Check:

If you have the skill and comfort level to do so, you can directly check the CPU’s markings and then refer to vendor specifications.

For Windows users with an M2 drive, LM Studio can be run on this high-speed storage device, providing enhanced performance. However, as mentioned before, regardless of your operating system, one crucial factor to consider is the RAM requirement. As a rule of thumb, a minimum of 8 GB of RAM is recommended to run smaller AI models such as Mistral. Larger models may require more memory, so it’s important to check the specifications of the models you’re interested in using.

In terms of model configuration and inference parameters, LM Studio offers a range of options. You can tweak these settings to optimize the performance of your models, depending on your specific use case. This level of control allows you to get the most out of your AI models, even when running them on a personal computer.

One of the most powerful features of LM Studio is the ability to create a local host and serve your model through an API. This means you can integrate your model into other applications or services, providing a way to operationalize your AI models. This feature transforms LM Studio from a mere tool for running AI models locally into a platform for building and deploying AI-powered applications.

Running AI models locally on your PC, Mac, or Linux machine is now easier than ever. With tools like LM Studio, you can experiment with different models, customize your settings, and even serve your models through an API. Whether you’re a beginner or a seasoned AI professional, these capabilities open up a world of possibilities for innovation and exploration in the field of AI.

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Build a personal AI assistant running on your laptop with LM Studio

build a custom personal AI assistant on your laptop

If you are interested in learning more about how you can easily create your very own personal AI assistant running it locally from your laptop or desktop PC. You might be interested in a new program and framework called LM Studio. LM Studio is a lightweight program designed to make it easy to install and use of local language models on personal computers rather than third-party servers. One of the key features of LM Studio is its user-friendly interface making it easy to manage a variety of different AI models depending on your needs all from one interface

Thanks to its minimalist UI and chatbot interface LM Studio has been specifically designed to provide users with an efficient and easy-to-use platform for running language models. This feature is particularly beneficial for users who are new to the world of large language models, as it simplifies the process of running these models locally. Which until a few months ago was quite a tricky undertaking to do but has now been simplified thanks to the likes of LM Studio and other framework such as Ollama and others.

How to run personal AI assistance locally on your laptop

One of the standout features of LM Studio is the ability for users to start their own inference server with just a few clicks. This feature offers users the ability to play around with their inferences, providing them with a deeper understanding of how these models work. Additionally, LM Studio provides a guide for choosing the right model based on the user’s RAM, further enhancing the user experience.

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Benefits of running LLM is locally

The benefits of running large language models on your laptop or desktop PC locally :

  • Hands-On Experience: Working directly with the model code allows you to understand the architecture, data preprocessing, and other technical aspects in detail.
  • Customization: You have the freedom to tweak parameters, modify the architecture, or even integrate the model with other systems to see how it performs under different conditions.
  • Debugging and Profiling: Running models locally makes it easier to debug issues, profile computational performance, and optimize code. You can get a clear picture of how resources like memory and CPU are utilized.
  • Data Privacy: You can experiment with sensitive or proprietary datasets without sending the data over the network, thus maintaining data privacy.
  • Cost-Efficiency: There’s no need to pay for cloud-based machine time for experimentation, although the upfront hardware cost and electricity can be significant.
  • Offline Availability: Once downloaded and set up, the model can be run without an internet connection, allowing you to work on AI projects anywhere.
  • End-to-End Understanding: Managing the entire pipeline, from data ingestion to model inference, provides a holistic view of AI systems.
  • Skill Development: The experience of setting up, running, and maintaining a large-scale model can be a valuable skill set for both academic and industrial applications.

Another significant feature of LM Studio is its compatibility with any ggml Llama, MPT, and StarCoder model on Hugging Face. This includes models such as Llama 2, Orca, Vicuna, Nous Hermes, WizardCoder, MPT, among others. This wide range of compatibility allows users to explore different models, expanding their knowledge and experience in the field of large language models.

LM Studio also allows users to discover, download, and run local LMS within the application. This feature simplifies the process of finding and using different models, eliminating the need for multiple platforms or programs. Users can search for and download models that are best suited for their computer, enhancing the efficiency and effectiveness of their work.

Ensuring privacy and security is a key focus of LM Studio. The program is 100% private, using an encryption method and providing a clear statement that explains how it uses HTTP requests. This feature provides users with the assurance that their data and information are secure.

User feedback and continuous improvement are key components of LM Studio’s approach. The program has a feedback tab where users can provide constructive feedback and request features. This feature ensures that LM Studio continues to evolve and improve based on user needs and preferences. Furthermore, LM Studio has a Discord where users can get more information, provide feedback, and request features.

LM Studio is a comprehensive platform for experimenting with local and open-source Large Language Models. Its user-friendly interface, wide range of compatibility, and focus on privacy and security make it an ideal choice for users looking to explore the world of large language models. Whether you’re a seasoned professional or a beginner in the field, LM Studio offers a platform that caters to your needs.

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How to create the perfect (fake) studio backgrounds using AI

How to make fake studio set backgrounds for video creation

Unfortunately we can’t always film our streams, tutorials or vlogs in amazing real-life locations. However thanks to the explosion in AI tools and technologies over the last 12 months it is now possible to make the perfect fake studio background for your stream.  Using a variety of different software applications it is possible to create studio backgrounds using images and even drop them in after you have filmed your video. Making it very easy to change locations, props and layouts.

Creating AI-enhanced video backgrounds for  is a relatively new AI technique that has transformed the world of video production. It allows creators to design visually appealing backgrounds without the need for expensive equipment or elaborate sets. This quick guide show you how to create your very own studio backgrounds. Taking you through the process of modelling the perfect studio settings for your streams using AI, from shooting the video to exporting the final product.

The first step in creating AI-enhanced video backgrounds is shooting the video with consistent lighting and a steady camera. This is crucial as it ensures that the footage is clear and of high quality, which will make the editing process easier. It is recommended to film in front of a blank setup, as this will allow for greater flexibility when it comes to adding or changing elements in the scene.

Designing the perfect (fake) studio set and backgrounds

Once the footage has been shot, it can be imported into an editing software such as Premiere Pro. Here, a still frame is taken from the footage and moved into Adobe Firefly, a powerful editing tool that allows users to make desired changes to the snapshot. Adobe Firefly’s ‘generative fill’ tool is particularly useful for this process, as it allows users to add or change elements in the scene. This tool offers three different options for the new elements, allowing for a range of realistic or exaggerated effects.

However, it’s important to note that the technology is not perfect and may require some trial and error to achieve the desired results. To make the process smoother, it is recommended to select individual sections and adjust them one at a time. Once the desired changes have been made, the edited image can be exported as a JPEG or brought back into Premiere Pro. Watch the video below to learn more about how this can be done.

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The next step is to add the new background to the video using an online tool such as vidmix.app. This involves integrating the image with the original footage by placing it over the top and using a mask to blend the two together. To make the scene look more natural, a subtle noise layer or film grade overlay can be added on top of everything. This technique can also be used to extend video backgrounds, creating a wider shot or allowing for a longer zoom.

The final step in creating AI-enhanced video backgrounds is to color grade the entire scene to ensure everything blends together seamlessly. This involves adjusting the color balance, contrast, and saturation of the scene to create a cohesive look. The process involves extending the canvas size in Adobe Firefly and using the generative fill tool to create the extended background. The new background is then blended with the original footage in Premiere Pro, creating a seamless, wider shot.

Using Adobe firefly to create fake studio backgrounds

Benefits of fake AI generated studio sets and backgrounds

  • Cost-Effectiveness: Building or renting physical sets can be expensive and time-consuming. AI-generated backgrounds can be far more cost-effective.
  • Flexibility: The ability to switch backgrounds allows for a more dynamic setting or adapting to different themes/topics without the need for physical changes.
  • Professionalism: A clean, well-designed digital background can give the video a more polished and professional look, which could attract more viewers or sponsors.
  • Space Efficiency: You don’t need to allocate physical space for different sets, allowing you to operate even from a small room.
  • Resource Optimization: Digital sets can be reused across multiple projects without additional costs, and can be easily updated or customized.
  • Creative Control: AI-generated backgrounds allow for almost unlimited creativity, which might be impractical or impossible in a physical setting. For instance, one could simulate a newsroom, outer space, or a historical setting with ease.
  • Focus on Content: With the setting taken care of, content creators can focus on the content itself—be it the dialogue, the performance, or other aspects of the video.
  • Quick Iteration: If you’re split-testing viewer engagement with different backgrounds, AI makes it easy to swap settings quickly.
  • Consistency: If you’re running a series of videos, maintaining a consistent setting is much easier when it’s digital.
  • Technical Advantages: Some advanced digital backgrounds can integrate interactive elements or special effects directly, which would require post-production work otherwise.

 

Creating AI-enhanced video studio or set background is a process that involves shooting high-quality footage, editing a snapshot from the footage, adding a new background, and blending the new background with the original footage. While the technology is not perfect and may require some trial and error, the end result is a visually appealing video background that can enhance any stream or video production. Don’t forget this is what can be created now and new AI enhancements are being rolled out on a monthly basis. So as always we will keep you up to speed and make sure we provide you with the latest AI technologies, tools and techniques.

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Google Project IDX vs Visual Code Studio code editors compared

Google Project IDX vs Visual Code Studio

In the ever-evolving landscape of web development, Google has recently unveiled a new initiative, Project IDX. This browser-based development experience is built on Google Cloud and is powered by Codey, a foundational AI model trained on code and built on PaLM 2. The primary aim of Project IDX is to simplify the process of building, managing, and deploying full-stack web and multiplatform applications. It supports a wide range of popular frameworks and languages, making it a versatile tool for developers.

Project IDX is also built on Code OSS, which means it should feel familiar to developers, regardless of the project they are working on. This familiarity is a key aspect of Project IDX, as it allows developers to seamlessly transition between different projects and frameworks.

In the broader context, Project IDX represents Google’s attempt to compete with Microsoft in the developer ecosystem. By offering a versatile, cloud-based development environment, Google is positioning itself to try and regain popularity among developers.

Google Project IDX vs Visual Code Studio

One of the standout features of Project IDX is its integration with GitHub. Developers can import their existing projects from GitHub, allowing them to continue their work without interruption. Additionally, Project IDX offers pre-baked templates for popular frameworks such as Angular, Flutter, Next.js, React, Svelte, Vue, and languages like JavaScript, Dart, and more. Google has also announced that support for Python, Go, and other languages is on the horizon.

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Google recognizes that developers spend a significant amount of time writing code, and with recent advances in AI, there are substantial opportunities to make this time more productive. To this end, Project IDX incorporates Google’s innovations in AI, including the Codey and PaLM 2 models. These AI models, which also power Studio Bot in Android Studio and Duet in Google Cloud, can help developers write code faster and improve the quality of their code.

Currently, Project IDX offers smart code completion, an assistive chatbot, and contextual code actions like “add comments” and “explain this code”. Google acknowledges that these AI capabilities are in their early stages, but they are committed to enhancing IDX AI to better assist developers in their work.

Google Project IDX

Project IDX is hosted on Google Cloud Server, providing a cloud-based development environment. It uses a VS Code-like editor and runs computations on a server, eliminating the need for a high-performance computer on the user’s end. This feature sets Project IDX apart from traditional development environments and aligns it with existing cloud-based environments like Replit and Microsoft’s Visual Studio Code Spaces.

The deep integration of Project IDX with Google’s development offerings, such as Flutter and Firebase, is another noteworthy aspect. This integration allows developers to leverage Google’s robust development tools, further enhancing the capabilities of Project IDX.

Project IDX is a promising new initiative from Google. Its cloud-based nature, integration with popular frameworks and languages, and AI-powered features make it a compelling tool for developers. As Google continues to refine and expand Project IDX, it will be interesting to see how it shapes the future of web and multiplatform app development. Project IDX is a promising new initiative from Google. Its cloud-based nature, integration with popular frameworks and languages, and AI-powered features make it a compelling tool for developers. As Google continues to refine and expand Project IDX, it will be interesting to see how it shapes the future of web and multiplatform app development.

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