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Build a Chimera OS Linux mini gaming PC with Steam Big Picture access

how to build a Chimera OS Linux mini gaming PC with Steam Big Picture access

Gamers seeking a compact yet powerful Chimera OS powered Linux gaming PC with access to Valve’s Steam Big Picture and the ability to start playing your favorite games on your large screen TV or favorite monitor. May find the MINISFORUM MS01 to be an attractive option. This mini PC has been making waves in the gaming community, not only for its small footprint but also for its robust performance capabilities. The MS01 is designed to cater to those who want a high-quality gaming experience without the bulk of a traditional tower.

At the heart of the MS01 is an Intel Core i9-13900H processor, which, when combined with 32 GB of DDR5 RAM, ensures that games run smoothly and without lag. The inclusion of a Radeon RX 6400 GPU means that even graphically intensive games are displayed with clarity and precision perfect for running Chimera OS. Gamers will appreciate the ample storage options, as the MS01 comes equipped with three M.2 slots, allowing for a potential 24 TB of storage—more than enough to house a large library of games and media.

One of the standout features of the MS01 is its full-size PCIe x16 slot, which provides users with the opportunity to upgrade their GPU as technology advances. This level of upgradeability is particularly appealing to gamers who aim to maintain a state-of-the-art gaming rig. The MS01’s future-proof design ensures that it can keep pace with the latest gaming trends and hardware releases.

Steam Big Picture Chimera OS gaming PC

ChimeraOS is an operating system that provides an out of the box couch gaming experience. After installation, boot directly into Steam Big Picture and start playing your favorite games.

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Connectivity is another area where the MS01 shines. With Wi-Fi 6, Bluetooth 5.2, and multiple Ethernet ports, the mini PC offers a variety of options for connecting to the internet and other devices. This ensures that whether you’re downloading games, streaming content, or engaging in online multiplayer battles, your connection will be both stable and fast.

Chimera OS the perfect gaming companion

The MS01 also supports Chimera OS, a Linux-based operating system that provides a seamless gaming experience similar to that of the popular Steam Deck. Installing Chimera OS on the MS01 is straightforward, and the hardware is fully compatible with the operating system, ensuring a hassle-free setup. Once installed, Chimera OS offers a user-friendly interface and access to a vast selection of games.

ChimeraOS is an innovative operating system designed specifically for enhancing the gaming experience, particularly in a living room setup. It distinguishes itself by offering a seamless couch gaming experience, booting directly into Steam Big Picture mode. This feature underscores its primary function: to transform a traditional computer system into a dedicated gaming console-like environment.

  • Installation of ChimeraOS is designed to be straightforward, enabling users to quickly set up their new gaming system. This ease of installation is a significant advantage for gamers who prefer a plug-and-play experience without the complexities often associated with setting up gaming environments on traditional operating systems.
  • A notable feature of ChimeraOS is its powerful built-in web app. This app allows users to install and manage games from any device, offering a level of convenience and flexibility not commonly found in standard gaming consoles. This functionality reflects the operating system’s focus on user-centric design, prioritizing accessibility and ease of use.
  • ChimeraOS is characterized by its minimalistic design. It provides only the essential components needed for gaming, eliminating unnecessary software or features that could detract from the gaming experience. This minimalism ensures that the system resources are primarily dedicated to gaming performance.
  • The operating system promises an “out of the box” experience, with zero configuration needed for supported games. This feature is particularly appealing to gamers who want to avoid the often tedious process of tweaking settings and configurations before playing a game.
  • Keeping the system up to date is another key aspect of ChimeraOS. It offers regular updates, ensuring that users have the latest drivers and software. These updates are designed to be fully automatic and run in the background, minimizing disruptions to gameplay. This approach to updates is crucial in maintaining optimal performance and security without compromising the gaming experience.
  • Controller compatibility is a central element of ChimeraOS. The interface is fully compatible with controllers, highlighting its living room gaming focus. Additionally, it supports a wide range of controllers, including Xbox, PlayStation, and Steam controllers, among others. This broad compatibility ensures that gamers can use their preferred controllers without compatibility concerns.

Performance tests of the MS01 have shown that it can handle the latest gaming titles with ease. Benchmarks for games like “Spider-Man Miles Morales” and “Cyberpunk 2077” demonstrate that the MS01 delivers high-quality gameplay and consistent frame rates, providing a clear indication of the level of performance gamers can expect from this mini PC.

Overall, the MINISFORUM MS01 is a versatile and powerful gaming machine that excels in both performance and the ability to be upgraded. It’s well-suited for a range of games, from blockbuster AAA titles to independent releases, offering a comprehensive gaming experience on Chimera OS. For gamers who prioritize a system that can adapt and grow with their gaming needs, the MS01 presents itself as a wise investment.

Image Credit : COS

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How to build a drone tracking radar

How to build a drone tracking radar from scratch

If you are looking for a new project to keep you busy for the next few months all weekend you might be interested in building your very own drone tracking radar. If you are interested you’ll be pleased to know that this guide and tutorial series created by John Kraft will help you start your project.

Imagine the enjoy of creating a drone tracking radar from scratch, a device that can pinpoint the location of drones in the sky. This is not just a theoretical exercise; it’s a practical project that you can undertake with the guidance of John Kraft, an expert in the field. This series of articles will take you through the intricate world of radar technology, giving you the tools and knowledge to build a fully functional radar system. You’ll learn about the emission and reflection of radio waves, and how these principles enable us to track objects in motion.

Radar technology is fascinating because it allows us to detect and locate objects using radio waves. As we embark on this journey, we’ll start by unraveling the core principles of radar operation. Understanding these principles is crucial for tracking drones, and you’ll gain valuable insights into how they work. The series will guide you through the complexities of radar, ensuring that you grasp the fundamental concepts before moving on to more advanced topics.

DIY drone tracking radar

Building a radar system requires careful selection and assembly of hardware. This guide will provide instructions on choosing the right components, with a focus on the Analog Devices hardware used in our demonstrations. However, we will also suggest alternative options to accommodate different budgets and resources. You’ll be taken through the assembly process step by step, learning about the role of each component in the radar system.

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The ultimate goal of the series create by John Kraft  is to enable you to track a drone using a radar system that you’ve put together yourself. You’ll dive into the workings of Continuous Wave (CW) radar, which is capable of transmitting a constant signal for real-time tracking. You’ll also learn about modulation techniques that enhance the radar’s precision and enable it to differentiate between targets.

One of the challenges in radar technology is distinguishing the target from other objects, often referred to as clutter. This series will provide you with strategies for target recognition and clutter reduction. These techniques are essential for ensuring that your radar can focus on the drone, even when there are other objects in the vicinity.

As you become more proficient, you’ll learn about range-Doppler plots, which are crucial for tracking the position and speed of multiple targets simultaneously. This knowledge is vital for scenarios where you need to track several drones or navigate environments with numerous moving objects.

A comprehensive overview of the components that make up a radar system will give you a deeper understanding of the mechanics behind your build. You’ll learn about the differences between pulsed and CW radar, and discuss their respective advantages and applications.

For those embarking on this DIY project, the “Phaser” kit has been selected for its functionality and ease of use. You’ll receive a detailed explanation of the kit’s components and how they work together to create a functioning radar system.

Initially, the series will focus on non-beamforming radar techniques. However, it will also lay the groundwork for future discussions on beamforming, a method that can significantly improve the radar’s tracking capabilities by directing radio wave energy with precision.

This educational series is designed to encourage community learning and hands-on involvement. Whether you’re a hobbyist, a student, or an industry professional, you’ll gain valuable expertise in radar systems. You’ll enjoy the practical experience of constructing your own drone tracking radar. Prepare to dive into the fascinating world of radar technology as we guide you through this enlightening series.

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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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Build a custom AI large language model GPU server (LLM) to sell

Setup a custom AI large language model (LLM) GPU server to sell

Deploying a custom language model (LLM) can be a complex task that requires careful planning and execution. For those looking to serve a broad user base, the infrastructure you choose is critical. This guide will walk you through the process of setting up a GPU server, selecting the right API software for text generation, and ensuring that communication is managed effectively. We aim to provide a clear and concise overview that balances simplicity with the necessary technical details.

When embarking on this journey, the first thing you need to do is select a suitable GPU server. This choice is crucial as it will determine the performance and efficiency of your language model. You can either purchase or lease a server from platforms like RunPod or Vast AI, which offer a range of options. It’s important to consider factors such as GPU memory size, computational speed, and memory bandwidth. These elements will have a direct impact on how well your model performs. You must weigh the cost against the specific requirements of your LLM to find a solution that is both effective and economical.

After securing your server, the next step is to deploy API software that will operate your model and handle requests. Hugging Face and VM are two popular platforms that support text generation inference. These platforms are designed to help you manage API calls and organize the flow of messages, which is essential for maintaining a smooth operation.

How to set up a GPU servers for AI models

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Efficient communication management is another critical aspect of deploying your LLM. You should choose software that can handle function calls effectively and offers the flexibility of creating custom endpoints to meet unique customer needs. This approach will ensure that your operations run without a hitch and that your users enjoy a seamless experience.

As you delve into the options for GPU servers and API software, it’s important to consider both the initial setup costs and the potential for long-term performance benefits. Depending on your situation, you may need to employ advanced inference techniques and quantization methods. These are particularly useful when working with larger models or when your GPU resources are limited.

Quantization techniques can help you fit larger models onto smaller GPUs. Methods like on-the-fly quantization or using pre-quantized models allow you to reduce the size of your model without significantly impacting its performance. This underscores the importance of understanding the capabilities of your GPU and how to make the most of them.

For those seeking a simpler deployment process, consider using Docker images and one-click templates. These tools can greatly simplify the process of getting your custom LLM up and running.

Another key metric to keep an eye on is your server’s ability to handle multiple API calls concurrently. A well-configured server should be able to process several requests at the same time without any delay. Custom endpoints can also help you fine-tune your system’s handling of function calls, allowing you to cater to specific tasks or customer requirements.

Things to consider when setting up a GPU server for AI models

  • Choice of Hardware (GPU Server):
    • Specialized hardware like GPUs or TPUs is often used for faster performance.
    • Consider factors like GPU memory size, computational speed, and memory bandwidth.
    • Cloud providers offer scalable GPU options for running LLMs.
    • Cost-effective cloud servers include Lambda, CoreWeave, and Runpod.
    • Larger models may need to be split across multiple multi-GPU servers​​.
  • Performance Optimization:
    • The LLM processing should fit into the GPU VRAM.
    • NVIDIA GPUs offer scalable options in terms of Tensor cores and GPU VRAM​​.
  • Server Configuration:
    • GPU servers can be configured for various applications including LLMs and Natural Language Recognition​​.
  • Challenges with Large Models:
    • GPU memory capacity can be a limitation for large models.
    • Large models often require multiple GPUs or multi-GPU servers​​.
  • Cost Considerations:
    • Costs include GPU servers and management head nodes (CPU servers to coordinate all the GPU servers).
    • Using lower precision in models can reduce the space they take up in GPU memory​​.
  • Deployment Strategy:
    • Decide between cloud-based or local server deployment.
    • Consider scalability, cost efficiency, ease of use, and data privacy.
    • Cloud platforms offer scalability, cost efficiency, and ease of use but may have limitations in terms of control and privacy​​​​.
  • Pros and Cons of Cloud vs. Local Deployment:
    • Cloud Deployment:
      • Offers scalability, cost efficiency, ease of use, managed services, and access to pre-trained models.
      • May have issues with control, privacy, and vendor lock-in​​.
    • Local Deployment:
      • Offers more control, potentially lower costs, reduced latency, and greater privacy.
      • Challenges include higher upfront costs, complexity, limited scalability, availability, and access to pre-trained models​​.
  • Additional Factors to Consider:
    • Scalability needs: Number of users and models to run.
    • Data privacy and security requirements.
    • Budget constraints.
    • Technical skill level and team size.
    • Need for latest models and predictability of costs.
    • Vendor lock-in issues and network latency tolerance​​.

Setting up a custom LLM involves a series of strategic decisions regarding GPU servers, API management, and communication software. By focusing on these choices and considering advanced techniques and quantization options, you can create a setup that is optimized for both cost efficiency and high performance. With the right tools and a solid understanding of the technical aspects, you’ll be well-prepared to deliver your custom LLM to a diverse range of users.

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How to build a ChatBot with ChatGPT and Swift

This video serves as a foundational blueprint, offering a structured framework to guide you through the intricate process. Each step in this journey necessitates a detailed and methodical implementation, calling upon a robust set of programming skills in Swift. These skills are not just limited to writing code; they extend to a deep understanding of iOS app development practices, encompassing aspects such as UI design, handling user interactions, and ensuring seamless performance across various iOS devices.

Furthermore, a pivotal element of this venture is the integration of ChatGPT through its API. This integration is not merely about establishing a connection but about mastering the nuances of network programming within the Swift environment. It involves understanding how to craft and send HTTP requests, process incoming data, and handle potential network-related issues. Additionally, given the nature of network operations and their potential impact on the user experience, a keen focus on asynchronous operations in Swift is paramount. This means you’ll need to adeptly manage tasks that run in the background, ensuring that the app remains responsive and efficient while waiting for or processing data from the ChatGPT API.

In essence, this expanded overview underscores the importance of a holistic approach, where your Swift programming prowess is harmoniously blended with a strategic understanding of iOS app development and the technical specifics of integrating an advanced AI model like ChatGPT. Each component, from the initial setup to the final stages of implementation, must be approached with precision, ensuring that the end product is not only functional but also aligns with the high standards of modern iOS applications.

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Build web apps using AI prompts with GPT-Engineer.app

Build complete apps using a single AI prompt and GPT-Engineer.app

The world of web application development could be witnessing a significant shift with the introduction of GPT-Engineer.app. This innovative tool is making waves by simplifying the process of creating and deploying web apps. It’s designed to understand simple English instructions and convert them into fully operational applications, which is a big deal for both developers and those without a technical background.  The core of this advancement is the original GPT-Engineer, which was a pioneer in the realm of automated code generation has been developed using the open source code project gpt-engineer.

How to use GPT-Engineer.app

  • Specify what to build
  • AI creates a website and display it
  • Edit using natural language
  • One-click deploy

Building on this foundation, GPT-Engineer.APP takes a step further by concentrating on rapid prototyping. This advancement allows for the quick production and refinement of web applications, making it possible to meet specific needs with ease and flexibility.

One of the key aspects of GPT-Engineer.app is its governance model, which ensures that the platform runs smoothly. A portion of the revenue generated by the platform is allocated to support the open-source community and sustain the infrastructure that underpins it. This demonstrates a commitment to continuous development and to nurturing a positive relationship with both users and contributors.

Build interactive web apps with prompts

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Looking ahead, GPT-Engineer.app is preparing to introduce user-friendly editing tools and broaden its scope to include full-stack development. The roadmap includes the integration of APIs that will facilitate database interactions and user authentication. These enhancements are aimed at further simplifying the web app development process.

Currently, there is a high demand for GPT-Engineer.APP, with many potential users eagerly waiting to get their hands on it. It’s important to stay tuned for updates on availability and new features, as the team behind the project is committed to keeping the community informed about the latest progress.

In short, GPT-Engineer.app is on track to transform the coding landscape by removing common obstacles and enabling the rapid creation of effective web applications. Leveraging the success of its predecessor, the app is focused on ongoing improvement and empowering its users. Keep an eye out for more developments as this exciting project continues to unfold.

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Build an artistic sand art drawing machine using LEGO

Build an artistic sand art drawing machine using LEGO

The Japanese practice of drawing patterns in sand, known as “karesansui” or “dry landscape” gardening, is often associated with Zen Buddhism. These gardens are designed to embody a sense of tranquility and minimalism. In karesansui, white sand or gravel is raked into various patterns, representing elements like water, mountains, or islands, despite the absence of these physical elements. Imagine the possibility of creating mesmerizing patterns in sand, right from the comfort of your home.

With the clever use of LEGO bricks and magnets, you can now construct your very own sand art machine, a device that marries the precision of engineering with the beauty of artistic expression. These machines, once complex and inaccessible, have been transformed into a DIY project that invites you to explore the art of pattern-making through a playful and innovative lens.

The concept of sand art machines is not new; it is a product of the continuous interplay between artistic expression and technological innovation. Over time, these devices have evolved from rudimentary manual tools to sophisticated, programmable machines capable of producing intricate and consistent designs. At the core of their operation is the strategic use of magnets, which direct a metal ball bearing across a sandy canvas, etching delicate patterns with each pass.

LEGO sand art drawing machine

if you are looking for a project to keep you busy this weekend and have enough LEGO bricks spare or are looking for inspiration for your next LEGO  project check out the amazing build created by the team over at Brick Machines.

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By utilizing LEGO bricks to build your machine, you unlock a world of customization and accessibility. The modular design of LEGO allows for endless possibilities, enabling you to construct a machine that reflects your personal vision, whether it be simple or complex. This approach not only fosters creativity but also makes the art form more approachable for enthusiasts of all skill levels.

To embark on this creative journey, begin by assembling a sturdy LEGO base that will serve as the foundation for your sand canvas. Incorporate ball bearings to facilitate smooth movement of the metal ball. Next, strategically position magnets within the LEGO framework to guide the ball’s trajectory through the sand.

Once your machine is activated, the ball comes to life, tracing its path through the sand, influenced by the hidden magnets below. The resulting patterns can range from sharply defined geometric figures to more organic, abstract shapes. The beauty of the design emerges slowly, as the ball’s trail intertwines to form complex and captivating art.

The true magic of using LEGO bricks lies in the ease with which you can modify your machine. You can experiment with different magnet placements, ball sizes, or even program the ball’s route to produce an ever-changing piece of art. This adaptability allows for a dynamic and interactive display, demonstrating how simple elements can come together to create extraordinary visual experiences.

Embarking on the project of building a sand art machine with LEGO and magnets is more than just a hobby; it’s an invitation to immerse yourself in a world where creativity and innovation converge. Whether you’re an artist seeking a unique medium, a hobbyist looking for a fresh challenge, or simply someone who appreciates the elegance of design, this endeavor offers an opportunity to produce an array of stunning sand patterns limited only by your imagination.

Image Credit :  Brick Machines

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Build websites using Midjourney, Chat-GPT and Figma

Build websites using Midjourney, Chat-GPT and Figma

Imagine you’re about to create a new website. You might feel overwhelmed by all the decisions and technical stuff you need to figure out. But what if I told you that artificial intelligence (AI) could make it a whole lot easier? AI is changing the way we build websites, making it simpler and more accessible. Let’s dive into how AI can help you from the moment you come up with an idea for your site until the day you launch it.

When you’re starting out, you need to pick a name for your website. It’s got to be something that sticks in people’s minds and fits what your site is all about. There are AI tools out there that can help you with this. They look at data and trends to suggest names that are just right for your content and who you want to reach. Just type in some keywords that relate to your site, and these AI tools will give you a list of names to choose from. This can save you a lot of time and give you some great ideas.

  • Logo Generation: AI-driven graphic design tools use machine learning algorithms to create logos. Users often input basic information such as company name, industry, and preferred style or color scheme. The AI then analyzes this data, referencing a vast database of design elements and principles, to generate logo options. These tools can quickly produce a variety of designs, allowing for rapid iteration and customization.
  • Imagery Creation: Tools like DALL-E, an AI program developed by OpenAI, can generate unique images based on textual descriptions. This is particularly useful for creating customized graphics, illustrations, and even product prototypes. The AI interprets the text input, understanding the context and desired elements, and then constructs an image that aligns with the request.
  • Business Name Ideas: AI can assist in the brainstorming process for business names by using language models trained on a vast corpus of data, including existing business names, domain availability, and linguistic patterns. Users can input keywords or concepts related to their business, and the AI suggests potential names, often also checking domain availability in real-time.
  • Web Content Creation: AI can generate text for websites, including product descriptions, blog posts, and marketing copy. Using natural language processing (NLP), these AI tools can write coherent, contextually relevant, and engaging content. Users often provide a brief, keywords, or outlines, and the AI fills in with appropriately styled text. This can significantly speed up content creation and help maintain a consistent voice across various types of web content.

Designing and creating websites using AI

Once you’ve got a name, you need to create content that will make people want to stay on your site. AI can help you come up with catchy headlines and descriptions. It uses something called natural language processing to write content that’s both interesting and useful. You’ll get custom suggestions for different parts of your site, which you can tweak to make sure they sound like your brand.

Logo and branding

Your website’s logo and icons are important too. They’re like the face of your brand. AI can design these graphics for you, based on the style you want. Tell the AI what you’re looking for, and it will come up with unique designs that stand out. This is a fast way to get graphics that fit your brand perfectly.

AI art generators are a great way to generate inspiration, images and website elements :

Figma stands as an extremely useful tool during the product development cycle, offering a suite of tools designed to enhance collaborative efforts in design and prototyping. It provides a unique environment where teams can explore various design possibilities, create detailed prototypes, and effectively transition their designs into usable code. This all-encompassing approach to product development fosters a co-creative atmosphere, making it easier for teams to work together and share ideas dynamically.

Featured images and artwork

The hero image is the big picture that people see first on your homepage. It’s got to be really eye-catching. AI tools can create hero images that are just right for your site’s theme. They look through tons of images to find one that matches what you need, so you don’t have to spend hours searching and editing pictures yourself.

If you’re using Figma for web design, AI can make it even better. You can take all the graphics and text that AI made for you and put them into Figma. Then you can use Figma’s tools to make a layout that looks good and is easy to use. You can add things like a menu, a big title, a smaller subtitle, and buttons that tell your visitors what to do next.

AI website design process

Central to Figma’s appeal is its blend of powerful design tools and an emphasis on multiplayer collaboration. Teams can delve into the creative process together, benefiting from the ability to provide and receive quality feedback in real-time or asynchronously. This feature-rich environment ensures that ideas are not only explored but also refined with input from various team members, enhancing the final product’s quality and relevance.

Creating text and icons

Your website’s feature sections should clearly show what you’re offering. AI can help you set up these sections with text and icons that quickly tell people about your services or products. When everything is laid out right, it helps guide visitors through your site in a way that makes sense. After you’ve finished your design in Figma, you’ll want to turn it into a real website. There’s a plugin that can take your Figma design and turn it into a WordPress site. This is an important step because it changes your design from just a picture into a website that people can interact with.

Building websites using AI

 

Moreover, Figma revolutionizes the way designers bring their concepts to life. With its sophisticated prototyping capabilities, users can create highly realistic, code-free interactions right within the platform. This seamless integration of design and prototyping tools within a single tool allows for detailed fine-tuning of every aspect of the user experience. Iterations and testing become more efficient and effective, enabling designers to achieve a higher standard of user experience with less effort.

Coding your website using Figma

Additionally, Figma introduces Dev Mode, a dedicated workspace catering specifically to developers. This feature bridges the gap between design and development, allowing developers to access necessary details to translate designs into code within the same file. By integrating these workflows, Figma effectively eliminates the need for context switching, streamlining the development process and fostering a more cohesive product development cycle.

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FigJam complements Figma’s offerings as an online whiteboard platform. It serves as a versatile space where teams involved in product development can collaborate effectively. From initial kickoffs to regular stand-ups, and even through various team rituals and retrospectives, FigJam provides an inclusive environment for team members to brainstorm, plan, and execute their ideas cohesively. This platform not only enhances teamwork but also maintains workflow efficiency by offering visibility and interactive tools that cater to every team member’s needs.

Launching your new website designed using AI

Finally, when everything else is done, it’s time to launch your website. Choose a domain name that’s like the one AI helped you pick, and before you know it, your site will be up and running. AI has made every step easier, from coming up with the design to getting your site out there. Now you can launch a website that looks great without all the stress.

So, AI isn’t just a buzzword; it’s a set of tools that can really help when you’re building a website. It can help with everything from picking a name and writing content to designing your site and making it work. By using AI, you can put together and launch a website that will really make an impact online.

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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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Apple quietly releases MLX AI framework to build foundation AI models

Apple quietly releases MLX AI framework

Apple’s machine learning research team has quietly introduced and released a new machine learning framework called MLX, designed to optimize the development of machine learning models on Apple Silicon. The new framework has been specifically designed and engineered to enhance the way developers engage with machine learning on their devices and has been inspired by frameworks such as PyTorch, Jax, and ArrayFire.

The difference from these frameworks and MLX is the unified memory model. Arrays in MLX live in shared memory. Operations on MLX arrays can be performed on any of the supported device types without performing data copies. Currently supported device types are the CPU and GPU.

What is Apple MLX?

MLX is a NumPy-like array framework designed for efficient and flexible machine learning on Apple silicon, brought to you by Apple machine learning research. The Python API closely follows NumPy with a few exceptions. MLX also has a fully featured C++ API which closely follows the Python API. The main differences between MLX and NumPy are:

  • Composable function transformations: MLX has composable function transformations for automatic differentiation, automatic vectorization, and computation graph optimization.
  • Lazy computation: Computations in MLX are lazy. Arrays are only materialized when needed.
  • Multi-device: Operations can run on any of the supported devices (CPU, GPU, …)

The MLX framework is a significant advancement, especially for those working with Apple’s M-series chips, which are known for their powerful performance in AI tasks. This new framework is not only a step forward for Apple but also for the broader AI community, as it is now available as open-source, marking a shift from Apple’s typically closed-off software development practices. MLX is available on PyPI. All you have to do to use MLX with your own Apple silicon computer is  : pip install mlx

Apple MLX AI framework

The MLX framework is designed to work in harmony with the M-series chips, including the advanced M3 chip, which boasts a specialized neural engine for AI operations. This synergy between hardware and software leads to improved efficiency and speed in machine learning tasks, such as processing text, generating images, and recognizing speech. The framework’s ability to work with popular machine learning platforms like PyTorch and JAX is a testament to its versatility. This is made possible by the MLX data package, which eases the process of managing data and integrating it into existing workflows.

Developers can access MLX through a Python API, which is as user-friendly as NumPy, making it accessible to a wide range of users. For those looking for even faster performance, there is also a C++ API that takes advantage of the speed that comes with lower-level programming. The framework’s innovative features, such as composable function transformation and lazy computation, lead to code that is not only more efficient but also easier to maintain. Additionally, MLX’s support for multiple devices and a unified memory model ensures that resources are optimized across different Apple devices.

Apple MLX

Apple is committed to supporting developers who are interested in using MLX. They have provided a GitHub repository that contains sample code and comprehensive documentation. This is an invaluable resource for those who want to explore the capabilities of MLX and integrate it into their machine learning projects.

The introduction of the MLX framework is a clear indication of Apple’s commitment to advancing machine learning technology. Its compatibility with the M-series chips, open-source nature, and ability to support a variety of machine learning tasks make it a potent tool for developers. The MLX data package’s compatibility with other frameworks, coupled with the availability of both Python and C++ APIs, positions MLX to become a staple in the machine learning community.

The Apple MLX framework’s additional features, such as composable function transformation, lazy computation, multi-device support, and a unified memory model, further enhance its appeal. As developers begin to utilize the resources provided on GitHub, we can expect to see innovative machine learning applications that fully leverage the capabilities of Apple Silicon. Here are some other articles you may find of interest on the subject of AI models :

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