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New updates to GitHub Copilot make AI coding assistant even smarter

GitHub Copilot is now even smarter thanks to latest updates

The latest latest updates to Visual Studio Code (VS Code) and GitHub Copilot, have been designed to boost your productivity and make coding feel less like a chore, explains Microsoft. The enhancements to GitHub Copilot are particularly noteworthy. It’s now smarter than ever, providing coding suggestions that are precise and relevant to what you’re trying to achieve. This means that Copilot is getting better at understanding your coding goals and offering recommendations that are in line with your intentions. It’s like having a coding partner that’s learning and growing with you.

With the introduction of a new workspace command in VS Code, searching across your entire project has become a breeze. This feature overcomes previous limitations, allowing you to conduct thorough searches quickly and without leaving your coding environment. It’s a simple yet powerful tool that can save you time when you need to find something fast.

Copilot’s ability to understand the context of your project has also seen a significant boost. It now looks beyond the line of code you’re working on and takes into account the whole project. This results in suggestions that are not just relevant but also coherent with the larger scope of your work.

GitHub Copilot AI updates

The integration of ChatGPT-4 with GitHub Copilot introduces a new level of interaction. You can now talk to Copilot as if it were another developer, making the experience more natural and engaging. This conversational capability can help you articulate your coding challenges and find solutions more effectively.

  • For those repetitive tasks that can be so time-consuming, Copilot now offers assistance in iterating code. Whether you’re adjusting a button for different social media platforms or doing any other repetitive work, Copilot can generate variations to meet your specific needs, potentially saving you hours of manual coding.
  • Copilot has also become more proactive in its suggestions. It can anticipate your coding requirements, from fetching URLs from configuration files to proposing the right CSS styles. This forward-thinking approach means you can stay focused on the bigger picture while Copilot handles the details.
  • You can now ask Copilot context-aware queries that are specific to your project. Need information from a README file? Copilot can pull that data for you, providing insights that are immediately applicable to your work.
  • Troubleshooting errors is a part of every developer’s life, but Copilot’s latest update makes it easier. It now offers solutions based on the output of terminal commands, helping you resolve issues more quickly and with less frustration.
  • A new VS Code extension brings voice command capabilities to the table. You can now interact with Copilot using speech-to-text, which is a boon for developers who prefer to talk through their code or for those who want to code hands-free.
  • Finally, Copilot now helps with the often tedious task of version control documentation. It can auto-generate commit messages and pull request descriptions, ensuring that your documentation is consistent and clear, all while saving you time.

The updates to VS Code and GitHub Copilot are making coding more intuitive and less repetitive. With smarter assistance, streamlined project searches, and enhanced conversational interactions, these tools are helping you focus on what you do best: creating amazing software. These improvements are not just about making your current tasks easier; they’re about empowering you to take on new challenges with confidence. Find out more over on the official GitHub website. Here are some other articles you may find of interest on the subject of Microsoft Copilot AI :

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Autogen vs Aider AI coding assistants comparison guide

Autogen vs Aider AI coding assistants compared

When it comes to programming, the tools you choose can make a big difference in how quickly and effectively you can complete your projects. Two tools that have caught the attention of developers are Aider and Microsoft Autogen. These tools are especially useful for those who work with advanced large language models (LLMs) like OpenAI’s GPT-4. Understanding the differences between Autogen vs Aider can help you pick the right one for your needs.

Imagine you’re working on a coding problem. With Aider, you can describe your issue in a simple sentence, type it into the tool, and Aider will turn your words into the code you need. This is great for when you need to work fast and accurately, like when you’re updating a project or dealing with complex code. Aider is all about making things easier and quicker for developers, especially when working on small to medium-sized projects.

On the other side, Autogen by Microsoft is more than just a code generator. It’s a whole system that lets LLMs keep track of conversations. This means that LLMs can work together, share information, and get better at their tasks over time. Autogen is not just for coding; it’s about creating a space where LLMs can work together and share knowledge. This makes Autogen a versatile tool that can be used for many different kinds of projects.

Autogen vs Aider AI coding assistants

Aegis Accounting have created a fantastic overview video providing more information on the differences between Autogen vs Aider.

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Both Aider and Autogen are made to work with OpenAI’s LLMs, which are known for their impressive ability to reason and generate code. However, Aider is made to work closely with the specific LLM it was designed for, which might limit its use with other LLMs. Autogen, however, is built to work with a variety of LLMs, which could make it a more flexible option for your projects.

How well these tools work for you will depend on how you interact with LLMs. The “personalities” of LLMs, or how they understand and respond to what you ask them, can be very different. It’s important to really understand these differences to get the most out of the tool you choose.

You should also think about privacy and cost. If these are big concerns for you, you might want to use LLMs that run on your own computers. Both Aider and Autogen can be set up to work with LLMs on your own hardware, which can keep your data private and might save you money compared to using cloud services.

While Aider and Autogen aren’t like agent-based systems, where independent agents do tasks in an environment, they do share a similar goal: to make things simpler for users and encourage working together.

Aider is known for being easy to use and for generating code quickly. This has made it popular with programmers who want a straightforward way to make quick changes to their code. I’ll be showing off what Aider can do in an upcoming video, and you’ll see why it’s different from other tools, like GitHub Copilot.

AutoGen is a framework that enables development of large language model (LLM) apps using multiple agents that can communicate with each other to solve tasks. AutoGen agents are customizable, conversable, and seamlessly allow human participation. They can operate in various modes that employ combinations of LLMs, human inputs, and tools, explained Microsoft

  • AutoGen enables building next-gen LLM applications based on multi-agent conversations with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
  • It supports diverse conversation patterns for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy, the number of agents, and agent conversation topology.
  • It provides a collection of working systems with different complexities. These systems span a wide range of applications from various domains and complexities. This demonstrates how AutoGen can easily support diverse conversation patterns.
  • AutoGen provides enhanced LLM inference. It offers utilities like API unification and caching, and advanced usage patterns, such as error handling, multi-config inference, context programming, etc.

Pros:

  • Automation of Repetitive Code: Efficiently handles large amounts of repetitive code, reducing manual coding effort.
  • Error Reduction: Minimizes the risk of human error in repetitive coding tasks.
  • Time-Saving: Speeds up the development process by automating routine tasks.
  • Consistency: Ensures uniformity in code patterns and structures.

Cons:

  • Limited Flexibility: Primarily focused on repetitive tasks; may not be suitable for complex, unique coding challenges.
  • Learning Curve: Requires understanding of templates and automation scripts.

Use Cases:

  • Ideal for projects with extensive repetitive coding requirements, such as UI elements or database operations.

Aider is a command line tool that lets you pair program with GPT-3.5/GPT-4, to edit code stored in your local git repository. Download the AI pair programming assistant that can be called from your terminal from the official GitHub repository.

  • Chat with GPT about your code by launching aider from the command line with set of source files to discuss and edit together. Aider lets GPT see and edit the content of those files.
  • GPT can write and edit code in most popular languages: python, javascript, typescript, html, css, etc.
  • Request new features, changes, improvements, or bug fixes to your code. Ask for new test cases, updated documentation or code refactors.
  • Aider will apply the edits suggested by GPT directly to your source files.
  • Aider will automatically commit each changeset to your local git repo with a descriptive commit message. These frequent, automatic commits provide a safety net. It’s easy to undo changes or use standard git workflows to manage longer sequences of changes.
  • You can use aider with multiple source files at once, so GPT can make coordinated code changes across all of them in a single changeset/commit.
  • Aider can give GPT-4 a map of your entire git repo, which helps it understand and modify large codebases.
  • You can also edit files by hand using your editor while chatting with aider. Aider will notice these out-of-band edits and keep GPT up to date with the latest versions of your files. This lets you bounce back and forth between the aider chat and your editor, to collaboratively code with GPT.

Pros:

  • AI-Driven Pair Programming: Offers real-time coding assistance using GPT-3.5/GPT-4, suitable for a variety of coding tasks.
  • Supports Large Codebases: Capable of handling edits in pre-existing, larger codebases.
  • Integration with Git: Automatically commits changes with descriptive messages, enhancing version control.
  • Language Versatility: Supports multiple programming languages like Python, JavaScript, HTML, CSS, etc.
  • Interactive Development: Allows for a conversational approach to coding, potentially improving understanding and efficiency.

Cons:

  • Dependence on AI Accuracy: Quality of code suggestions depends on the AI’s current capabilities and understanding of the context.
  • Requires Active Supervision: Users need to guide the AI, especially in complex scenarios.
  • Potential Learning Curve: Understanding how to effectively communicate with the AI for optimal results.

Use Cases:

  • Suitable for both new projects and modifications to existing ones, offering a more dynamic and interactive coding experience.

Overall Considerations:

  • Scope of Use: Autogen is more focused on automating repetitive code, whereas Aider is geared towards a more interactive, AI-assisted coding experience.
  • User Interaction: Autogen requires initial setup but runs autonomously, while Aider involves continuous interaction with the AI.
  • Complexity Handling: Aider may be better suited for complex, multi-file changes and debugging, leveraging the AI’s capabilities.
  • Project Size and Nature: Autogen is ideal for large projects with repetitive elements, while Aider shines in diverse coding scenarios, from small to large codebases.

The choice between Autogen and Aider depends on the specific needs of the project. Autogen excels in automating repetitive tasks, reducing manual effort, and ensuring consistency. In contrast, Aider offers a more versatile and interactive approach, suitable for a range of coding tasks, including working with large and complex codebases, with the added benefit of AI-driven insights and suggestions.

Aider and Autogen each have their own benefits when it comes to coding with LLMs. Aider is perfect for developers who want a simple way to make fast code changes. Autogen, with its ability to handle complex interactions with LLMs, is better for projects that need a more detailed setup. As LLM technology gets better, the advanced reasoning they provide will make tools like Aider and Autogen even more valuable. These tools are becoming essential for developers who want to use the latest AI in their programming work.

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Deepseek Coder vs CodeLlama vs Claude vs ChatGPT AI coding

Deepseek Coder vs CodeLlama vs Claude vs ChatGPT AI coding assistants compared

If you are looking for an artificial intelligent AI coding assistant you might be interested in learning more about a new AI model which is showing excellent results when compared to others such as CodeLlama. The world of artificial intelligence is changing on a daily basis and Deepseek Coder, an AI model and coding assistant developed by a team of researchers in Beijing, is setting new standards in the field. This model has outshone its competitors, including the well-known CodeLlama, in various benchmarks, showcasing its superior capabilities.

One of the most impressive aspects of the Deepseek Coder is its scalable architecture. It comes in three different sizes, with 1 billion, 7 billion, and 33 billion parameters, making it versatile enough to handle a wide range of applications. The smallest version is perfect for edge devices or quick GPU inference tasks, which is a big step forward for edge computing, where AI needs to be both practical and efficient.

Another area where Deepseek Coder excels is its licensing model. Unlike other AI models that come with restrictive licensing, Deepseek Coder offers a more permissive approach. This means it can be used for both open-source projects and commercial purposes, giving developers and businesses more freedom to innovate and expand their use of AI.

Deepseek Coder vs CodeLlama vs Claude vs ChatGPT

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When it comes to integrating and deploying AI models, the format of the prompts used can make a big difference. Deepseek Coder’s intuitive prompt design makes it easy to call functions and perform context-aware inference. This is especially useful for creating AI chat interfaces that are user-friendly or for integrating with platforms like Runpod.

The model’s GPU inference efficiency is another standout feature. It ensures quick and effective processing, which is essential for commercial AI applications that require real-time interaction and high throughput. Deepseek Coder also excels in handling long context inference. This is crucial for generating coherent and contextually accurate responses during interactions. The model’s ability to manage long contexts is a testament to its advanced design and the thorough analysis by its creators.

For developers looking to integrate AI into their systems, Deepseek Coder’s function calling feature is a game-changer. It simplifies the integration process, improving the overall developer experience. This is complemented by Trellis fine-tuned models, which are optimized for specific tasks and industries, ensuring top-notch performance.

To help with the adoption of Deepseek Coder, there are several resources available. The Hugging Face repository provides pre-trained models and a space for community contributions. For those who prefer a more hands-on approach, Google Colab offers collaborative notebooks that are perfect for experimentation and development.

Runpod’s AI templates are another resource that can be incredibly helpful. They provide a seamless deployment process with cloud computing environments that are ready to use, which is a great advantage for developers who want to get their AI projects off the ground quickly.

The fine-tuning capabilities of Deepseek Coder are comparable to those of Llama models. This allows for customization to suit the specific needs of your datasets and applications, giving you the flexibility to tailor the AI to your requirements.

Overall, the Deepseek Coder is a powerful tool in the world of AI innovation. With its scalable design, flexible licensing, advanced features, and a wealth of resources for developers, it is well-equipped to help professionals in various industries explore and push the boundaries of AI technology.

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Aider AI coding assistant updated with new features

Aider AI coding assistant

If you are searching for an AI coding assistant you might be interested to know that new features and enhancements of been rolled out to the Aider AI pair programming tool. Now upgraded with the GPT-4 Turbo model, these new features add significant advancements to the AI coding assistant and how it can help developers by interacting with their coding environments.

The integration of the GPT-4 Turbo model with the Aider AI coding assistant heralds a significant leap forward in the realm of coding and software development. This model is renowned for its expansive 128k context window, which ushers in an era of unprecedented speed and efficiency for the Aider platform.

Aider AI coding assistant

With this integration, Aider advances past its previous limitations, and is now capable of adeptly managing larger and more complex codebases. The inclusion of the GPT-4 Turbo model also enhances Aider’s ability to automatically commit changes made by GPT models to Git repositories, complete with clear and sensible commit messages. This feature alone stands as a transformative advancement for developers engaged in intricate project management. OpenAI explains a little more about what you can expect from its latest GPT-4 Turbo AI model.

“GPT-4 Turbo is our latest generation model. It’s more capable, has an updated knowledge cutoff of April 2023 and introduces a 128k context window (the equivalent of 300 pages of text in a single prompt). The model is also 3X cheaper for input tokens and 2X cheaper for output tokens compared to the original GPT-4 model. The maximum number of output tokens for this model is 4096.”

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GPT-4 Turbo support

The GPT-4 Turbo model’s impact on coding speed is nothing short of remarkable. Speed benchmarks reveal that it is up to five times faster than the earlier GPT-3.5 Turbo. This increase in speed is paired with a noteworthy 53% success rate in first attempts at code editing, a testament to the model’s improved accuracy and effectiveness. Such metrics underscore Aider’s value as a tool that not only accelerates the coding process but also enhances precision and quality, catering to developers who prioritize both speed and accuracy.

Aider AI coding assistant features

  • Interactive Coding Dialogue: Launch Aider from the command line with a set of source files. Engage in a real-time conversation with GPT about your code, discussing and collaboratively editing the contents of these files.
  • Multilingual Code Support: Aider and GPT can handle a wide range of popular programming languages, including Python, JavaScript, TypeScript, HTML, and CSS, facilitating diverse coding projects.
  • Custom Code Enhancements: Request specific improvements or modifications, such as new features, bug fixes, or performance enhancements. Aider also assists in generating new test cases, updating documentation, or refactoring existing code for better performance and readability.
  • Direct Source File Editing: Aider applies GPT’s suggested edits directly to your source files, streamlining the code revision process.
  • Automated Git Commits: Each change set suggested by GPT is automatically committed to your local git repository, complete with descriptive commit messages. This feature provides a robust safety net, allowing for easy reversion of changes or the use of standard git workflows for managing complex code changes.
  • Multiple File Coordination: Utilize Aider with multiple source files simultaneously. GPT can then execute coordinated code changes across all files in a single changeset or commit, enhancing the consistency and efficiency of multi-file projects.
  • Git Repository Mapping: Provide GPT-4 with a map of your entire git repository. This comprehensive view enables GPT to better understand and modify large and complex codebases.
  • Hybrid Editing Flexibility: Seamlessly switch between coding in Aider and manual editing using your preferred editor. Aider recognizes these out-of-band edits and updates GPT with the latest file versions. This feature allows for a dynamic and collaborative coding experience, combining the strengths of AI assistance with personal coding expertise.

Adopting these advanced capabilities requires a straightforward setup process. Users must have Git installed to clone the Aider repository, Python as the programming language editor, and Visual Studio Code to configure and run the Aider platform. This setup is intentionally designed to be user-friendly, ensuring that developers can quickly start benefiting from the enhanced features without a cumbersome installation process.

The practical applications of Aider’s upgraded capabilities are vast and varied. For instance, tasks like downloading, analyzing, and visualizing US Census data, which once might have been tedious and time-consuming, are now streamlined and efficient. Additionally, Aider adeptly handles complex multi-file changes, including debugging, thereby simplifying what traditionally would be challenging coding tasks. Such examples vividly illustrate how Aider, with the integration of the GPT-4 Turbo model, can transform complicated coding projects into more manageable and less labor-intensive endeavors.

The updated Aider AI coding assistant, now powered by the GPT-4 Turbo model, stands as a shining example of the continuous advancement in AI-assisted coding technologies. It emerges as an indispensable tool for developers who seek to enhance their coding workflows, manage larger and more intricate projects effortlessly, and stay at the forefront of coding efficiency and innovation.

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How to use the Duet AI coding assistant to build your next app

Using Duet AI to build apps on Google Cloud

If you would like a little helping hand when coding your very first app or next generation phone or web application. You might be interested to know that Google’s Duet AI can help you. By providing coding assistance during the creation process and can be integrated into your favorite IDE. Duet AI, an advanced AI-powered coding assistant, is specifically designed to enhance your productivity on Google Cloud.

This state-of-the-art coding assistant is built on Google’s robust foundation models, providing personalized and context-aware assistance while prioritizing data privacy and security. As a fully managed service, Duet AI utilizes the latest AI technology from Google and is constructed on the Vertex AI platform. It strictly follows Google’s AI principles and has earned numerous certifications for information security and privacy, demonstrating its dedication to protecting user data.

Google Duet AI

Duet AI offers a unique blend of AI-powered assistance, designed to expedite the building of applications on Google Cloud. It provides insightful suggestions to address application code challenges, offers guidance in Integrated Development Environments (IDEs), and references sources in Cloud Code. This AI-powered assistance supports a wide range of programming languages, including Go, Java, Javascript, Python, and SQL, catering to a diverse group of developers.

Duet AI is designed to simplify and expedite the application development process. It integrates seamlessly with various IDEs, including industry leaders like Visual Studio Code and JetBrains IntelliJ, to provide real-time code assistance. Combined with Cloud Code extensions, this feature allows you to write, debug, and deploy applications directly from your IDE, significantly reducing development time and boosting productivity.

Beyond code assistance, Duet AI also offers conversational assistance through a user-friendly natural language chat interface. This feature allows you to interact with Duet AI conversationally, making complex coding concepts and solutions easier to understand. The natural language chat interface is powered by advanced Natural Language Processing (NLP) algorithms, enabling Duet AI to understand and respond to your queries effectively and efficiently.

AI Coding Assistance across Multiple IDEs

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Duet AI extends its AI-powered code assistance to multiple IDEs. It uses machine learning models to understand your unique coding style and provide personalized code suggestions. This feature is available in various IDEs, including Visual Studio Code, JetBrains IntelliJ, and other cloud-based development tools like Colab for Enterprise, Cloud Workstations, Cloud Shell Editor, Cloud Spanner, and BigQuery.

Duet AI respects and upholds intellectual property rights, ensuring that user data and inputs are not used for product and model learning. It follows a strict data governance policy, ensuring that your code and data remain private and secure, thereby fostering trust and confidence among its users. As a fully managed service, Duet AI removes the need for you to manage the underlying AI infrastructure. It uses the latest AI technology from Google, including large foundation models and the Vertex AI platform. This allows you to focus on building applications while Duet AI handles the rest, ensuring a smooth and hassle-free development process.

Security and Privacy

Duet AI strictly follows Google’s AI principles, which include being socially beneficial, avoiding creating or reinforcing unfair bias, being built and tested for safety, being accountable to people, incorporating privacy design principles, upholding high standards of scientific excellence, and being made available for uses that accord with these principles.

In addition to adhering to these principles, Duet AI has earned multiple certifications for information security and privacy, including ISO/IEC 27001, 27017, 27018, and 27701. These certifications highlight Duet AI’s commitment to maintaining the highest standards of data security and privacy, further enhancing its credibility.

Duet AI is a powerful tool that can significantly enhance your productivity on Google Cloud. Its AI-powered assistance, conversational assistance in natural language, and AI-powered code assistance in multiple IDEs make it an invaluable resource for any developer. Furthermore, its respect for intellectual property rights, fully managed service, and strict adherence to Google’s AI principles and certifications for security and privacy make it a reliable and trustworthy tool for building applications on Google Cloud. To learn more about how you can use Duet AI to develop an application jump over to the official Google Cloud website.

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Astro Pi Mission Space Lab coding competition launches once again

Astro Pi Mission Space Lab

The team over at Raspberry Pi have once again launched the Astro Pi Mission Space Lab competition enabling young people all over Europe a chance to have their code for a science experiment run in space on the International Space Station (ISS). So if you are think you are up for the challenge this year’s Mission Space Lab, astronauts from the European Space Agency are setting young people a task : to write a computer program that runs on the ISS and calculates the speed at which the ISS is orbiting planet Earth. Participation in Mission Space Lab is completely free.

The Astro Pi Mission Space Lab is a unique initiative that offers young people an unprecedented opportunity to contribute to space exploration. This program invites participants aged up to 19 years to write a Python program for the Astro Pi computers onboard the International Space Station (ISS) with the goal of calculating the speed of the ISS.

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Participants engage in this project in teams of 2 to 6, working under the guidance of mentors who help them navigate through the process. The Mission Space Lab program is designed to be inclusive and accessible to all levels of programming experience. For beginners, a guided project is available to provide a roadmap for the mission. Meanwhile, more experienced teams are encouraged to create their own innovative solutions.

Raspberry Pi Astro Pi

The Astro Pi, the heart of this program, are Raspberry Pi computers equipped with a High Quality Camera, a Sense HAT add-on board with sensors, and a Coral machine learning accelerator. The Astro Pis are not just ordinary computers; they are specifically designed to withstand the harsh conditions of space and function effectively in the ISS environment.

In this program, participants are encouraged to use sensor data from the Astro Pis in creative ways to calculate the ISS’s speed. This requires not only programming skills but also an understanding of physics and mathematics. The combination of these disciplines provides a rich and challenging learning environment for participants.

Certificate of achievement

All participants whose programs successfully run on the ISS will receive a certificate of achievement, adding a noteworthy accomplishment to their portfolio. In addition, they will have the chance to attend a Q&A webinar with an ESA astronaut, a rare opportunity to interact directly with a professional in the field of space exploration. They will also receive data from the ISS based on their programs, providing them with a unique dataset for further analysis and learning.

Astro Pi Guides

To support the teams in their mission, the Astro Pi Mission Space Lab provides a mentor guide and a creator guide. The mentor guide includes tips on structuring the mission and assisting the team in problem-solving. It serves as a valuable resource for mentors to effectively guide their teams. On the other hand, the creator guide is designed to help participants design and create their Python programs. It provides insights into the technical aspects of the mission, guiding participants through the process of program creation.

Furthermore, a project guide is also provided as part of the resources. This guide showcases one way to complete the task by writing a program that calculates the ISS speed using photos taken by the Astro Pi’s camera. This guide serves as an example and inspiration for participants, showing them what is possible with the Astro Pi and Python programming.

The Astro Pi Mission Space Lab is a groundbreaking program that combines space exploration with coding, offering young people a unique opportunity to contribute to real-life space missions. It not only enhances their programming skills but also fosters a deeper understanding of space science, making it a truly enriching experience.

For more details and to apply to enter the new Astro Pi Mission Space Lab  jump over to the official Raspberry Pi Foundation website.

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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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Using Google Duet AI coding assistant to speed up development

Using Google Duet AI to speed up development

If you are looking to improve your coding or development workflow you may be interested in a new artificial intelligent coding assistant created by Google. Duet AI is a chat interface that provides answers to cloud-related queries and offers guidance on best practices. It is specifically trained on Google Cloud content, including documents, sample code, and best practices. This feature allows developers to have immediate access to relevant information, without having to search through numerous resources.

Google Duet AI

The AI offers conversational assistance in Console and Integrated Development Environments (IDEs) such as Visual Studio Code and JetBrains IntelliJ, via the Cloud Code extensions. This interactive assistance can drastically reduce the time spent on debugging and troubleshooting, allowing developers to focus more on actual coding and innovation.

One of the most notable features of Duet AI is its capability for code completion and generation. It can assist with writing apps, calling APIs, or querying data by completing code while you write or generating code blocks based on comments. This feature is available in multiple IDEs, including Visual Studio Code, JetBrains IDEs, Colab for Enterprise, Cloud Workstations, Cloud Shell Editor, Cloud Spanner, and BigQuery.

Duet AI supports over 20 programming languages, including Go, Java, Javascript, Python, and SQL. This wide range of supported languages makes Duet AI a versatile tool that can cater to a broad spectrum of developers, regardless of their preferred coding language.

AI coding assistant

To demonstrate the capabilities of Duet AI, tutorials are available in the video below is a tutorial on integrating the weather API into a Python Flask app can help developers understand how to leverage Duet AI in real-world applications.

Data privacy and Intellectual Property (IP) protection are critical concerns in the digital world. Duet AI ensures that user data, inputs, and recommendations are not used for product and model learning and development. This feature ensures that data and IP remain exclusively owned by the user, providing peace of mind for developers who use the tool.

When Duet AI provides suggestions that directly quote from a source, it includes source citations. This helps users comply with license requirements and promotes transparency. Users also have the option to block these suggestions if desired.

As a fully managed service, Duet AI is regularly and automatically updated and monitored, using the latest-tested AI technology from Google. This means that developers can always have access to the most up-to-date tools and features, without having to worry about maintenance or updates.

Google Workspace AI assistant

No hosting required

Due to Google’s investment in AI infrastructure ensuring performance and scale the service does not require hosting by the user. This feature reduces the burden of hosting and maintenance on the user, allowing them to focus on their core development work. For those looking to build their own custom experiences, Google offers the Vertex AI platform, which Duet AI is built upon. This provides developers with the flexibility to tailor their AI tools to suit their specific needs and preferences.

Duet AI offers a range of features designed to assist developers in their work. From code completion and generation to data privacy and IP protection, Duet AI is a versatile tool that can speed up development and enhance productivity. By integrating Duet AI into their workflow, developers can leverage the power of AI to stay ahead in the fast-paced world of technology.

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10 Best AI Coding Assistant Tools in 2024

As the software development industry continues to advance at an unprecedented pace, many developers are looking for an ideal AI coding tool to streamline workflow, reduce errors, and enhance overall productivity. But with lots of AI coding assistants online, it can be quite tricky to know which is the most useful.

In this post, we’ve reviewed the top 10 best AI coding assistant tools in 2024. From established giants to cutting-edge startups, we’ll introduce you to the top AI-powered tools that are set to revolutionize the way you code.

Top 10 Best AI Coding Assistants

1. Spreading

Topping our list is an industry-leading AI coding assistant, Spreading. The tool offers various features that allow you to create next-generation technical documents and write codes within a few clicks.

With Spreading, you can eliminate fragmented workflows, consolidate tasks, and improve overall efficiency. Besides, the availability of customizable blocks makes it easy for you to arrange your knowledge base content quickly.

Key Features

  • Seamless document management process.
  • Facilitate fast creation of multiple websites.
  • Easily code with AI and switch between different programming languages.
  • Create and publish high-quality technical documentation in just one click.

2. Codium 

Another valueable assest that you can add to your developer toolkit is Codium – a free AI-powered coding assistant that offers best-in-class AI code completion, search, and chat. This AI coding assistant enables you to write better code, save time, and unleash their full coding potential. Besides, it supports over 70+ languages and integrates with your favorite IDEs.

Key Features

  • Utilizes AI to analyze source code and generate meaningful tests that can catch bugs before they reach users.
  • Built-in search feature streamlines coding process and simplifies complex tasks, allowing programmers to code much more efficiently
  • Offers intelligent code suggestions while developers write code, offering snippets or entire functions.
  • Quickly analyze your code, docstring, and comments within seconds.

3. Replit

Replit AI is one of the best AI  for coding that is designed to empower software creators by redefining the software development process. This tool supports  over 16 programming languages, including JavaScript, Python, C, Java, Perl, Ruby, HTML, CSS, SQL, etc., and it’s packed with a wode range of features, including intelligent search, code suggestions and function generation, and meaningful code tests. 

Key Features

  • Boast efficient code completion and assistant features that enables developers to write code faster with fewer errors.
  • Offers excellent collaboration tools to enable devs collaborate in real-time.
  • Supports an efficient ghostwriter that comes with a proactive debugger.
  • It can be accessible via Android and iOS mobile apps.

4. CodeT5

CodeT5 is a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers. This AI coding tool is trained on over 8.35 million instances of code, including user-written comments from publicly available, open-source GitHub repositories. CodeT5 offers features such as text-to-code generation, code-to-code translation, and code summarization, empowering you to bridge the gap between natural language instructions and executable code.

Key Features

  • Unified pre-trained encoder-decoder Transformer model.
  • Offers a code summarization feature that allows developers to summarize code and extract its most important parts.
  • Supports a novel identifier-aware pre-training task that enables the model to distinguish which code tokens are identifiers and to recover them when they are masked.
  • Code-to-code translation allows you to translate code from one programming language to another easily.

5. Amazon CodeWhisperer

Developed by Amazon Web Service (AWS), CodeWhisper is an AI coding assistant that offers users code recommendation in real time. It is trained on billions of lines of code and can generate code suggestions ranging from snippets to full functions based on existing code and comments.

CodeWhisperer supports 15 programming languages, including Python, Java, and JavaScript, and can be used with popular integrated development environments (IDEs) such as VS Code, IntelliJ IDEA, AWS Cloud9, AWS Lambda console, JupyterLab, and Amazon SageMaker Studio.

Key Features

  • Boasts 15 programming languages, including Python, Java, and JavaScript.
  • Supports integration into popular Integrated Development Environments (IDEs).
  • Efficiently scans code, detect hard-to-find vulnerabilities, and offers code suggestions to remediate them smoothly.
  • You can customize CodeWhisper to generate recommendation that suits your requirements by making it aware of your API, internal libraries, etc.

6. CodeGeex

CodeGeeX is an open-source coding AI tool designed assist developers in coding tasks. With its powerful AI capabilities, it offers features like code generation and completion, comment generation, and code translation, and it  supports multiple IDEs and languages, making it suitable for a wide range of development projects. Besides, the underlying CodeGeex2-6B model, available on GitHub, ensures the tool’s accuracy and effectiveness.

Key Features

  • Generate code snippets and provide intelligent suggestions to complete code blocks.
  • Supports code translation between different programming languages, including Javascript, Python, etc.
  • Automatically generate comments for code segments
  • Supports an AI Chatbot that provides you with solutions when you encounter an technical difficulties.

7. AskCodi 

Powered by OpenAI GPT, AskCoding is an AI coding system that provide various applications for code generation, unit test creation, documentation, and code conversions. Although it may fail or suggest code that doesn’t work, this AI coding tool will give you access to helpful features that can help both new and existing devs to be more efficient and free up their time so they can focus on innovative ideas and new product. Besides, it can be used as an extension/plugin for Visual Studio Code, Sublime Text, and the JetBrains’ IDEs.

Key Features

  • Supports AI powered chat to enable developers craft coding conversations.
  • Supports extension that can be used with various platforms like Visual Stuudio Code and Sublime Text.
  • Supports 50+ programming languages.
  • Codi Workbook, allows you to generate code, ask AI to explain code, write documentation, and create unit tests.

8. Codiga

Codiga is another AI  coding assistant that offers a birds-eye view of code quality. It provides a dashboard that reports all important metrics about code quality, including the overall number of code violations, duplicates, long and complex functions. With Codiga, you can detect issues in real-time in the integrated development environment (IDE) and get fixes quickly, and it  supports various IDEs, including VS Code, JetBrains, VisualStudio, GitHub, Gitlab, and Bitbucket.

Key Features

  • Create your own custom code analysis rules from the your browser in less than 5 minutes.
  • Offers Codi Workbook, an interactive environment where developers can generate code, ask AI to explain code, write documentation, and create unit tests.
  • Create and share smart code snippet with your team.
  • Support over 1800 rules for 12+ languages.

9. GitHub Copilot

If you’re looking for an AI pair programmer, GitHub Copilot is one AI coding assistant that can come in handy for your needs. Similar to Amazon CodeWhisper, this tool is trained on billions of lines of code and uses natural language prompts to suggest individual lines and whole functions instantly. Besides, GitHub Copilot is powered by a generative AI model developed by GitHub, OpenAI, and Microsoft, and it is available as an extension for Visual Studio Code, Visual Studio, Neovim, and the JetBrains suite of Integrated Development Environments (IDEs).

Key Features

  • Support for dozens of programming languages.
  • Integration with popular IDEs.
  • Personalization based on user interactions.
  • Analytics on user engagement and content performance.

10. Sourcegraph Cody

Cody is an AI coding assistant developed by Sourcegraph. It is a free and open-source tool that utilizes Sourcegraph’s code graph and Large Language Models (LLMs) to write code and provide answers based on the user’s codebase. This tool is just like a personal dedicated AI coding assistant that has a comprehensive understanding of the user’s entire codebase, vast knowledge of open-source code, and extensive training data for code understanding and problem-solving.

Key Features

  • Personalization based on user interactions.
  • Context retrieval from the user’s broader codebase and Sourcegraph’s code graph
  • It allows you to provide answer to technical questions and write code into your IDE.
  • It is compatible with VSCode, Neovim, Emacs, and IntelliJ.

Conclusion

We have explored the top 10 best AI coding assistant tools available in 2024, their features, and how they can revolutionize the way developers work. From code generation to debugging and security analysis, these AI assistant tools offer a wide range of functionalities, and by  them, you can write better code, improve productivity, and enhance software quality.

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SolidGPT AI coding assistant and learning framework

SolidGPT AI coding assistant and learning framework

SolidGPT is an AI coding and collaboration framework designed to facilitate software development tasks, providing a platform for users to interact with their code repository, ask code-related questions, and discuss requirements. The framework is optimized for software development tools, making it a valuable tool for businesses and individuals alike.

SolidGPT is a smart GPT model that empowers AI to work with humans using already available tools to accomplish business goals as well as code-related tasks. It scans and learns from your code to provide coding advice, deploy code, and generate product requirement documents using the information in the repository. The framework can automate or semi-automate processes from the inception of an idea to the documentation of software development, task division, and task implementation.

How to use SolidGPT AI coding assistant

Other articles we have written that you may find of interest on the subject of coding and AI tools :

AI coding assistants can be beneficial for both novice and experienced coders for a variety of reasons:

For learners:

  • Guided Learning: AI assistants can offer real-time suggestions and corrections, providing immediate feedback that is essential for the learning process.
  • Code Examples: They can instantly generate code snippets, helping learners understand how to implement specific logic or functions.
  • Syntax Help: Beginners often struggle with syntax errors; an AI assistant can automatically detect and suggest fixes for these, reducing frustration.
  • Resource Pointer: They can refer learners to relevant documentation or tutorials, helping them deepen their understanding of the language or concept.
  • Confidence Builder: For learners, getting the code to work is often a confidence booster, and AI coding assistants can facilitate this by helping to quickly resolve issues.

For experienced coders:

  • Productivity: Automated suggestions can speed up the coding process by reducing the amount of manual typing and searching for syntax or library functions.
  • Code Quality: Some advanced AI coding assistants can detect not just syntax errors but also inefficient code structures, suggesting more optimized alternatives.
  • Collaboration: AI can help standardize coding styles and practices across a team, making collaborative projects easier to manage.
  • Time-Saving: Experienced coders often have to work with multiple languages and frameworks; AI assistants can quickly offer context-specific suggestions, saving the developer the time needed to recall or look up specific commands.
  • Automating Repetitive Tasks: Many experienced coders have routine coding tasks. An AI assistant can automate or expedite these, allowing the coder to focus on more complex problems.

AI coding framework

The framework is designed with three key agents: the PM Agent, PE Agent, and SDE Agent. The PM Agent refines ideas into structured PRD documents, the PE Agent creates high-level designs and organizes tasks, and the SDE Agent trains to harness the open-source project, Lowdefy. These agents work together to facilitate a smooth and efficient software development process.

One of the unique features of SolidGPT is its ability to connect private datasets to create a Question and answer chatbot or add LLM prompts for in-context learning. This allows users to add private data and create their own agent workgraph using embedding finetuning and in-context learning. SolidGPT provides a visualized Build Graph UI for easier and quicker graph building, making it user-friendly and accessible.

Furthermore, SolidGPT can automatically generate customized agent skills based on keywords to create a business agents team. Users can build an agents graph where each agent focuses on specific work and passes the work result to the user and other agents. This allows for a collaborative approach with AI, with agent-created content auto-syncing with Notion for user reviews during the LLM workflow.

Installation and setup process

Getting started and installing SolidGPT is a straightforward process requiring a few prerequisites, including having Git, Python, Visual Studio Code, and a functional API key linked to a billing account. Once installed, SolidGPT can be used to generate product requirement documents and get technical solutions for specific problems. Users can onboard their projects, input their requirements, and SolidGPT will generate the necessary code and provide a solution.

SolidGPT can be deployed locally to ensure data privacy and can be used to build commercially used systems, such as a terms of service system. The model allows users to upload their code repository and input their requirements, as well as their OpenAI API key. SolidGPT then formulates the code needed to fulfill these requirements and provides a private solution based on the user’s code repository.

SolidGPT is a human-AI collaboration framework that can be useful for a range of users. From boosting software products to facilitating deep customized agent skills, solid graph, and human-AI deep collaboration, SolidGPT offers an array of features and capabilities. Whether you’re looking to deploy different ranges of software to different products, save time on coding, or need technical solutions for specific problems, SolidGPT could be the tool you need.

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