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CaribouLite converts your Raspberry Pi into an open source radio

CaribouLite HAT converts your Raspberry Pi into an open source radioTransform your Raspberry Pi into a powerful tool for radio communication, capable of exploring frequencies that stretch up to an impressive 6 GHz. The CaribouLite Raspberry Pi radio HAT makes this possible, offering a dual-channel software-defined radio (SDR) platform that’s both versatile and affordable. This innovative accessory is designed for hobbyists, educators, and researchers who are passionate about radio exploration and innovation.

The CaribouLite stands out with its dual-channel functionality, allowing users to receive and transmit on two different frequencies at the same time. This is particularly useful for complex radio operations, such as managing cross-band repeaters or monitoring multiple bands simultaneously. It’s a significant advantage for those who are serious about their radio projects and are looking for more sophisticated capabilities.

CaribouLite RPi HAT

By leveraging the computational power and connectivity of the Raspberry Pi, the CaribouLite RPi HAT enables a wide range of radio-centric projects. Whether you’re interested in amateur radio, digital signal processing, or any other radio-related endeavor, this SDR platform provides the necessary tools to dive deep into the world of radio communications.

The CaribouLite Raspberry Pi radio HAT offers access to an expansive frequency spectrum. One channel is dedicated to the 30 MHz to 6 GHz range, while the other focuses on the sub-1-GHz ISM band. This broad coverage invites users to investigate and experiment with a wide array of radio frequencies, making it an invaluable asset for both learning and research. The Raspberry Pi hat is now available to purchase directly from the Crowd Supply website priced from $72.

CaribouLite HAT front view

In the spirit of collaboration and innovation, CaribouLite embraces the open-source movement. Users are provided with hardware and software that can be modified and improved upon. This approach not only benefits individual projects but also contributes to a larger community that values shared knowledge and collective advancement in radio technology. It’s important to note that CaribouLite offers versions that are designed to stay within regulatory boundaries. This ensures compliance with ETSI and FCC standards, which is essential for developers and third-party users who are creating products that must adhere to strict radio regulations.

Raspberry Pi software-defined radio (SDR) platform

For those who may be new to radio programming or even seasoned professionals, CaribouLite’s high-level API support simplifies the development process. The platform is compatible with Soapy, GNU Radio, and Python, providing a streamlined interface that allows users to fully utilize the device’s capabilities without unnecessary complexity.

The CaribouLite RPi HAT is equipped with advanced features that will delight radio aficionados. An internal TCXO clock-source guarantees precise frequency stability, a low-noise amplifier enhances signal reception, and robust transmission power increases the operational range. Additionally, PMOD expansion ports are available for those who wish to further customize and enhance the functionality of their device.

Integration with Raspbian is effortless. As soon as you power up your Raspberry Pi with the CaribouLite RPi HAT attached, it is recognized by the operating system, allowing for a smooth start to your radio projects. For advanced users, an EEPROM API is available, enabling customization of the board’s configuration to meet specific needs.

The CaribouLite Raspberry Pi radio HAT  is a remarkable example of how open-source technology can transform the capabilities of a simple Raspberry Pi into a high-performance, dual-channel SDR experience. It’s an exciting development for Raspberry Pi enthusiasts around the world.

Whether you’re a hobbyist looking to explore the airwaves or a researcher in need of a flexible radio platform, the CaribouLite RPi HAT provides the tools and features necessary to take your radio projects to the next level. With this device, the potential for discovery and innovation in the realm of radio communications is vast and waiting to be unlocked. The Raspberry Pi hat is now available to purchase directly from the Crowd Supply website priced from $72.

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OpenGPTs open source alternative to ChatGPT GPT Builder

OpenGPTs open source alternative to ChatGPTs GPT builder

OpenAI recently held its very first developer conference or OpenAI Dev Day. During which  Sam Altman introduce a new way of building custom AI models based on ChatGPT in the form of the of GPT Builder. This innovative feature created to enable users to personalize versions of ChatGPT using a tool called the GPT Builder. The new ChatGPT GPT Builder allows users to combine instructions, additional knowledge, and various skills to tailor the ChatGPT to their specific needs. If you would like to learn more about how to create a custom GPTs we have created a comprehensive step-by-step to get you started.

For instance, the GPT Builder could be used to create a chatbot designed to assist with mathematical problems or provide cooking advice. The process begins when the GPT Builder asks about your intended creation, then collects more information and context as needed to build the chatbot. You can further customize the chatbot by renaming it, adding a detailed description, and defining its capabilities and actions.

Alternative to ChatGPT GPT Builder

In keeping with the spirit of open source software, a similar tool to the OpenAI GPT Builder, called OpenGPTs, has been developed. OpenGPTs offers a range of features, including a sandbox environment for testing and adjusting the software, the ability to create custom actions, and analytics for thorough data usage analysis. An exciting feature, the knowledge file, is currently under development.

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OpenGPT is an open source app specifically designed to create a similar experience as OpenAI’s GPTs. The system builds upon LangChain, LangServe and LangSmith and has been engineered to provide you with more control over the large language model (LLM). You can choose between the 60+ LLMs that LangChain offers, combined with the prompts you use – you can use LangSmith to debug those if required, together with the tools you give it – choosing from LangChain’s 100+ tools, or easily write your own if required.

Creating a custom GPT AI model

OpenGPTs can be accessed in two main ways: through the hosted version or by installing it on your local system. The hosted version is readily available on cloud hosting platforms like Google Cloud, providing a convenient and easily accessible option. On the other hand, local installation offers more control and customization, though it requires a slightly more complex setup process.

To demonstrate the capabilities of OpenGPTs, consider the process of creating a chatbot. You would start by defining the chatbot’s purpose, such as providing information on a specific topic or assisting with a particular task. Then, you would use the GPT Builder or OpenGPTs to customize the chatbot, defining its capabilities and actions. You could then test and refine the chatbot in the sandbox environment, using analytics to understand its performance and make improvements.

These custom AI models can also be used for a variety of other AI development tasks, such as creating and customizing AI models, generating digital images, or interpreting and executing code. As much as possible, the development team behind OpenGPTs are striving to provide similar features to that offered by OpenAI.

Sandbox

  • Provides a versatile environment for importing, testing, and modifying chatbots.
  • Ideal for developers to experiment and refine chatbots before deployment.
  • Chatbots are entirely code-based, allowing for straightforward and flexible editing.

Custom Actions

  • Allows the definition of additional functionalities for your chatbot, tailored to specific needs.
  • Utilizes OpenAPI specifications to seamlessly integrate new features.
  • Enhances chatbot capabilities beyond the default settings, supported through the integration of various tools.

Knowledge Files (Coming Soon)

  • Enables attachment of supplementary files that chatbots can access for additional information.
  • Ideal for providing chatbots with a more extensive knowledge base or specific data sets.
  • Aims to enhance the chatbot’s responses and interactions through external references.

Tools

  • Offers basic utilities for web browsing, image creation, and more.
  • Comes with default tools like DuckDuckGo for search capabilities and Python REPL for executing Python code.
  • Future updates include the addition of an image creation tool, expanding the chatbot’s multimedia capabilities.

Analytics

  • Provides a comprehensive platform to view and analyze chatbot usage data.
  • Powered by LangSmith, offering insights into user interactions, performance metrics, and usage patterns.
  • Essential for understanding the chatbot’s impact and areas for improvement.

Drafts

  • Facilitates the saving and sharing of chatbot drafts in progress.
  • Supports storing various configurations, aiding in iterative development and collaboration.
  • Useful for teams working on chatbots, allowing for version control and shared access.

Publishing

  • Enables the public distribution of completed chatbots.
  • Chatbots can be deployed via LangServe, allowing for broader accessibility and user engagement.
  • A key feature for developers looking to showcase or monetize their chatbot solutions.

Sharing

  • Simplifies the process of setting up and managing how chatbots are shared.
  • Deployment and sharing functionalities are provided through LangServe.
  • Enhances collaborative efforts and broadens the reach of chatbot applications.

Marketplace (Coming Soon)

  • A platform to search and deploy chatbots created by other users.
  • Encourages a community-driven approach by allowing users to share and leverage each other’s work.
  • Anticipated to become a hub for finding specialized chatbots, fostering innovation and exchange within the user community.

These tools can also be easily combined with various APIs, allowing them to interact with other software in a standardized way. For example, you could use the Open API to integrate your chatbot with a web browsing tools, enabling users to search the internet directly from the chatbot.

The introduction of OpenAI’s GPT custom AI model builder and tools like OpenGPTs represent significant advancements in AI development. They provide a flexible and customizable approach to creating and configuring AI models, opening up new possibilities for their application and use. Whether you’re an experienced developer or a beginner in the field, these tools offer a powerful and accessible way to harness the potential of AI. Enabling you to create your very own custom personal AI assistance to do a wide variety of different tasks helping you improve your productivity and remove the need to do repetitive tasks from your daily workflow.

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Deepseek Coder AI open source coding assistant

Deepseek Coder open source AI coding assistant runs online and locally

If you could do with a little assistance when coding or when learning a new coding language you might be interested in a new AI coding assistant in the form of Deepseek Coder. The AI coding assistant has been created using a series of code language models trained on both 87% code and 13% natural language in English and Chinese, with each model pre-trained on 2T tokens.

Various sizes of the Deepseek Coder AI coding assistant are available from 1B to 33B versions. The advanced coding application harnesses the power of artificial intelligence (AI) to streamline the software development process. The Deepseek Coder AI coding tool operates on an extendable framework, enabling scalability and adaptability to a wide array of project requirements. Deepseek is a web-based application, making it accessible via the internet from anywhere. However, the application can also be accessed locally, offering flexibility to developers who prefer or need to work offline.

Deepseek Coder training

Deepseek Coder also offers efficient code generation capabilities using multiple communicative agents to facilitate software communication, thereby enhancing the efficiency of code creation. This feature allows developers to generate code more quickly and accurately, reducing the time and effort required in the development process. One of the key features of Deepseek Coder is its pre-trained model offering AI coding assistant for 80 programming languages, including popular ones like Python and JavaScript. The application also supports project-level code completion and code infilling, further enhancing its utility for developers.

Features of Deepseek Coder AI coding assistant

– Pretrained on 2 Trillion tokens over more than 80 programming languages.
– Various model sizes (1.3B, 5.7B, 6.7B and 33B) to support different requirements.
– A window size of 16K window** size, supporting project-level code completion and infilling.
– State-of-the-Art performance among open code models.
– Open source and free for research and commercial use.

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The AI model is an open-source application, meaning it is free-to-use software that can be modified and distributed by anyone. This open-source nature fosters a collaborative environment where developers can contribute to the application’s improvement. The application can be used for free for research purposes, and it also supports commercial use cases. The capabilities of Deepseek Coder have been tested in various scenarios to ensure its effectiveness and reliability. For instance, it has been used to create a snake game within seconds, demonstrating its potential in game development. The application’s performance has also been compared with other open-source code models such as ChatDev, with promising results.

Benefits of using an AI coding assistant

  • Efficiency Improvement: AI coding assistants can significantly speed up the coding process by suggesting code snippets, completing lines of code, and automating repetitive tasks.
  • Error Reduction: They help in detecting and correcting syntax errors, code smells, and even identifying potential bugs before runtime, which leads to cleaner code and fewer errors.
  • Learning and Upskilling: Coders can learn from AI suggestions, discovering new functions, libraries, and coding patterns that they might not be familiar with.
  • Code Refactoring: AI assistants can suggest improvements and optimizations to existing code, making it more maintainable and performant.
  • Language Agnosticism: Many AI coding assistants support multiple programming languages, allowing developers to switch between projects without losing productivity.
  • Accessibility: They make coding more accessible to beginners by providing inline documentation and explanations, thus lowering the barrier to entry for programming.
  • Integrations: These tools often integrate with IDEs and other development tools, creating a seamless development environment.
  • Documentation Assistance: They can help generate comments and documentation, ensuring that the codebase is understandable and easier to maintain.
  • Resource Optimization: By automating certain tasks, they free up human resources to focus on more complex and creative aspects of software development.
  • 24/7 Availability: Unlike human counterparts, AI coding assistants are available around the clock, providing assistance whenever needed.

Access and availability

Deepseek Coder can be accessed from the official website or can be downloaded from GitHub for local installation. The website provides easy access and start-up, while local installation allows developers to run the application on their own machines. The application can also be run on LM Studio, an AI platform that supports open-source models. To learn more about LM studio check out our previous articles.

Deepseek Coder is an advanced coding application that utilizes AI to streamline the software development process. Its extendable framework, efficient code generation capabilities, and pre-trained model make it a powerful tool for developers. As an open-source application, it fosters collaboration and is accessible to all. With its impressive capabilities, Deepseek Coder is set to make a significant impact in the world of software development.

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AMD acquiring open source AI software Nod.ai

AMD acquiring open source AI software Nod-ai

In a significant move to bolster its AI software capabilities, Advanced Micro Devices (AMD) has announced plans to acquire Nod.ai. This acquisition is set to accelerate the deployment of AI solutions optimized for AMD’s various processors and graphics processing units (GPUs), marking a significant step forward in AMD’s AI growth strategy.

The acquisition of Nod.ai by AMD is a strategic move that aligns with the company’s focus on an open software ecosystem. This approach simplifies entry for customers, making it easier for them to leverage the power of AI in their operations. The acquisition is expected to significantly improve AMD’s ability to provide AI customers with open software for easy deployment of high-performance AI models, according to Vamsi Boppana, SVP, Artificial Intelligence Group at AMD.

Nod.AI is an Artificial Intelligence and Machine Learning systems company that provides key enabling open source technologies for future A.I systems using advanced compiler based approaches (instead of legacy handwritten kernels).  They created the SHARK Machine Learning Distribution that is built on LLVM, MLIR, OpenXLA’s IREE and nod.ai’s tuning.

Open source AI

Nod.ai’s role in this acquisition is pivotal. The team at Nod.ai has developed software technology that accelerates the deployment of AI solutions, making them more efficient and effective when run on AMD’s processors and GPUs. This technology is already widely used in the cloud, at the edge, and across a broad range of endpoint devices, demonstrating its versatility and robustness.

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The acquisition of Nod.ai by AMD also brings with it Nod.ai’s significant contributions to key AI repositories. As a primary maintainer and major contributor to AI repositories like SHARK, Torch-MLIR, and OpenXLA/IREE, Nod.ai has demonstrated its commitment to the open-source AI community. Anush Elangovan, co-founder and CEO of Nod.ai, stated that the company’s role in these repositories will be brought to a wider customer base on a global scale through the partnership with AMD.

AMD acquiring Nod.AI

Nod.ai’s delivery of optimized AI solutions to various clients is another key aspect of this acquisition. The company provides optimized AI solutions to top hyperscalers, enterprises, and startups. Its SHARK software reduces the need for manual optimization and deployment time for high-performance AI models across various platforms powered by AMD architectures. This capability will be a significant asset to AMD as it seeks to expand its open AI software capabilities.

AMD’s acquisition of Nod.ai is a strategic move that aligns with the company’s AI growth strategy. It will enhance AMD’s open AI software capabilities, accelerate the deployment of AI solutions optimized for AMD hardware, and bring Nod.ai’s significant contributions to AI repositories to a wider audience. This acquisition is a clear demonstration of AMD’s commitment to providing high-performance, open software solutions for AI deployment.

“At Nod.ai, we are a team of engineers focused on problem solving — quickly – and moving at pace in an industry of constant change to develop solutions for the next set of problems,” said Anush Elangovan, co-founder and CEO, Nod.ai. “Our journey as a company has cemented our role as the primary maintainer and major contributor to some of the world’s most important AI repositories, including SHARK, Torch-MLIR and OpenXLA/IREE code generation technology. By joining forces with AMD, we will bring this expertise to a broader range of customers on a global scale.”

Source : AMD

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