Latest news with #VisualStudioCode
Techday NZ
18-06-2025
- Business
- Techday NZ
Latent AI unveils platform to speed & secure edge AI rollout
Latent AI has announced the launch of Latent Agent, an edge AI platform designed to simplify the management and security of deploying artificial intelligence models at the edge. Built upon the Latent AI Efficient Inference Platform (LEIP), Latent Agent is designed to automate optimisation and deployment tasks, enabling developers to iterate, deploy, monitor, and secure edge AI models at scale. The company states that the new platform addresses the complexity issues that have made enterprise adoption of edge AI challenging. Complexity of traditional MLOps Traditional machine learning operations (MLOps) force developers to manually optimise models for specific hardware, often without a comprehensive understanding of device constraints. This can create pressure on teams, as optimisation workflows typically demand multiple specialists per hardware pipeline, and the complexity multiplies with each additional hardware target. According to Latent AI, this challenge has extended go-to-market timelines to as much as twelve weeks and led to substantial resource overhead for many organisations, particularly those looking to scale across diverse edge devices such as drones and sensors. "The rapid shift to edge AI has exposed gaps in traditional MLOps, slowing innovation and scalability," said Sek Chai, CTO and Co-founder of Latent AI. "Latent Agent eliminates the model-to-hardware guessing game, replacing weeks-long deployment cycles and scarce expertise with intelligent automation. This is a game-changer for enterprises racing to stay competitive." Platform features Latent Agent aims to streamline the lifecycle of edge AI, spanning exploration, training, development, and deployment across a range of hardware platforms. A key feature is its natural language interface, which lets developers set their AI requirements while receiving model-to-hardware recommendations from Latent AI Recipes. This knowledge base draws on 12TB of telemetry data compiled from over 200,000 device hours. Within the platform, a Visual Studio Code (VS Code) extension has been introduced to incorporate these agentic capabilities into developer workflows, providing an interface for requirement gathering and deployment. Other capabilities highlighted include an adaptive model architecture that can autonomously detect performance drift in deployed models and take remedial actions, such as retraining or over-the-air updates, without human intervention. Latent Agent's Recipes leverages automatically benchmarked model-to-hardware configurations, aiming to enable faster iteration and model deployment. The company states this accelerated approach will remove bottlenecks caused by manual processes and facilitate secure management of AI infrastructure at scale. "The biggest barrier to edge AI at scale has always been the complexity of optimising models for constrained hardware environments," said Dan Twing, President and COO of Enterprise Management Associates, and Principal Analyst for Intelligent Automation. "Latent Agent addresses that challenge head-on. It streamlines the hardest part of edge AI—getting high-performance models running on diverse devices—so teams can move faster and scale confidently." Business focus Latent Agent is being presented as a tool to accelerate development timelines, allow autonomous operations, and support scaling. By reducing the need for deep machine learning or hardware expertise, the company claims deployment times can be shortened from twelve weeks to a matter of hours. The agentic platform's compile-once, deploy-anywhere function is said to support any chip, operating system, or form factor, thereby assisting in the management of thousands of edge devices simultaneously. Furthermore, Latent Agent incorporates security measures such as model encryption, watermarking, and compliance with Department of Defence (DoD) security standards, designed to protect sensitive deployments. "At Latent AI, we've always believed that edge AI should be as simple to deploy as it is powerful to use," said Jags Kandasamy, CEO and Co-founder of Latent AI. "Latent Agent represents the natural evolution of our mission—transforming edge AI from a specialised engineering challenge into an accessible conversation. By combining our proven optimisation expertise with agentic intelligence, we're not just making edge AI faster; we're making it possible for any developer to achieve what previously required a team of ML experts." The new platform is now available to organisations seeking to improve deployment speed, operational autonomy, scalability, and security for edge AI models.
Arabian Post
07-06-2025
- Business
- Arabian Post
Mistral Code Sets New Benchmark for Enterprise AI Development
Mistral AI has unveiled Mistral Code, a comprehensive AI-powered coding assistant designed specifically for enterprise software development. The platform integrates advanced language models with secure, customisable deployment options, aiming to address the stringent compliance and security requirements of large organisations. Mistral Code offers a suite of features tailored for enterprise needs, including in-IDE assistance, on-premise deployment capabilities, and robust enterprise tooling. Built upon the open-source project Continue, it enhances the base with role-based access control, audit logging, and usage analytics. The assistant supports integration with popular development environments such as JetBrains IDEs and Visual Studio Code, currently available in private beta. A standout feature of Mistral Code is its emphasis on customisation. Enterprises can fine-tune the AI models to align with their specific codebases, facilitating more accurate code completions and refactoring suggestions. This level of adaptability is designed to improve developer productivity while maintaining alignment with organisational coding standards. ADVERTISEMENT Security and data sovereignty are central to Mistral Code's design. The platform allows for deployment in various environments, including cloud, reserved capacity, or air-gapped on-premises GPUs, ensuring that sensitive codebases remain within the organisation's control. This flexibility addresses common concerns about data exposure associated with cloud-based AI tools. Mistral AI's approach positions Mistral Code as a direct competitor to existing solutions like GitHub Copilot, particularly in scenarios where data privacy and customisation are paramount. By offering a platform that combines advanced AI capabilities with enterprise-grade security and customisation, Mistral AI aims to meet the complex needs of modern software development teams.
Geeky Gadgets
23-05-2025
- Business
- Geeky Gadgets
Visual Studio Code in No Longer an IDE Editor : VSC Evolution
What if the tools you've relied on for years suddenly became something entirely different? That's the reality developers are waking up to as Visual Studio Code, once heralded as a lightweight and flexible IDE, has evolved into a innovative AI-powered editor. This shift isn't just a minor upgrade—it's a redefinition of what a coding environment can be. By integrating advanced AI tools like GitHub Copilot, Visual Studio Code now offers capabilities that were unimaginable just a few years ago, from auto-generating code to streamlining debugging. But with this transformation comes a wave of unease: Will these tools enhance creativity or erode the foundational skills that define great developers? The answer is far from simple, and the stakes couldn't be higher. In this overview, Ed Andersen explores how Visual Studio Code's AI-driven evolution is reshaping the software development landscape and what it means for you as a developer. You'll discover how tools like GitHub Copilot are redefining productivity, but also why their proprietary nature has sparked debate within the open source community. Along the way, we'll examine the broader implications of Microsoft's strategic shifts, including its recent layoffs and the uncertain future of cross-platform development frameworks like .NET MAUI. Whether you see AI as a innovative ally or a disruptive force, one thing is clear: the role of the developer is changing, and understanding these shifts is essential for navigating what lies ahead. AI's Impact on Development Microsoft Layoffs and Strategic Shifts Microsoft's recent layoffs have raised important questions about the company's priorities and their potential impact on the developer ecosystem. Among those affected were senior engineers from the .NET MAUI and .NET on Android teams, both of which are critical to Microsoft's cross-platform development efforts. Tools like MAUI have been pivotal in allowing you to create applications for multiple platforms using a single codebase. The layoffs also extended to AI leadership roles, adding further complexity to Microsoft's evolving AI strategy. For developers, this restructuring introduces uncertainty about the future of tools and frameworks you may depend on. Speculation has grown about whether Microsoft might reduce its focus on certain cross-platform initiatives, potentially turning to acquisitions like Avalonia or Uno Platform to address gaps. These developments emphasize the need for you to remain vigilant as the tools and technologies you rely on continue to change. Understanding these shifts will help you adapt to the evolving landscape and make informed decisions about the frameworks and platforms you choose to work with. Visual Studio Code's AI-Powered Transformation The transformation of Visual Studio Code into an AI-driven editor represents a pivotal moment in software development. With the seamless integration of GitHub Copilot, AI assistance has become an essential part of the coding process. This integration allows you to generate code snippets, debug issues, and refactor projects with enhanced efficiency. However, the closed-source nature of GitHub Copilot has drawn criticism, particularly from advocates of open source principles who value transparency and community-driven innovation. While AI tools like GitHub Copilot can significantly boost your productivity, they also present challenges. Over-reliance on these tools could lead to a decline in foundational programming skills, as developers may become more dependent on AI-generated solutions. Furthermore, the proprietary nature of such tools could limit your ability to innovate independently, potentially conflicting with the collaborative ethos that has long been a cornerstone of the development community. As you integrate these tools into your workflow, it is essential to strike a balance between using AI for efficiency and maintaining your technical expertise. Visual Studio Code IDE, Becomes An AI Editor Watch this video on YouTube. Take a look at other insightful guides from our broad collection that might capture your interest in AI-Powered Editors. What's New in .NET 10 Preview 4? Amid these fantastic changes, .NET 10 Preview 4 introduces updates aimed at improving your workflow. One notable addition is the asynchronous method for creating zip files, `CreateFromDirectoryAsync`. This feature addresses longstanding community feedback by providing a more efficient way to handle file compression tasks. Such updates demonstrate Microsoft's continued commitment to refining its tools, even as its broader focus shifts toward AI-driven solutions. For developers working with .NET, these enhancements can simplify routine tasks, allowing you to focus on solving more complex problems. However, the uncertainty surrounding Microsoft's cross-platform development strategy may temper your enthusiasm for these incremental improvements. As you evaluate these updates, it is important to consider how they fit into the larger context of Microsoft's evolving priorities and the potential implications for your projects. Implications for the Developer Community The evolution of Visual Studio Code and the updates to .NET 10 Preview 4 reflect broader trends that are reshaping the software development industry. As AI tools become increasingly integrated into your workflows, the role of the developer is undergoing a transformation. This raises critical questions about the future of software development, including the potential commoditization of developer roles and the shifting ethos of platforms like GitHub. For instance, as reliance on AI-driven tools grows, there may be less emphasis on foundational programming skills. This could change how new developers enter the field and how experienced developers maintain their expertise. Additionally, the changing nature of platforms like GitHub, which has historically been a hub for open source collaboration, may challenge your ability to contribute to and benefit from community-driven projects. These shifts underscore the importance of staying informed and adaptable as the industry continues to evolve. The integration of AI into development tools also presents opportunities for increased efficiency and innovation. However, it is crucial to approach these changes with a critical perspective, considering both the benefits and the potential risks. By actively engaging with these developments, you can position yourself to thrive in a dynamic and rapidly changing environment while contributing to the ongoing evolution of the developer community. Media Credit: Ed Andersen Filed Under: AI, Toys Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
Techday NZ
20-05-2025
- Business
- Techday NZ
GitHub debuts Copilot agent to boost coding with secure oversight
GitHub has announced that its Copilot software now includes an asynchronous coding agent, directly integrated into GitHub and accessible from Visual Studio Code (VS Code). The new Copilot coding agent operates within GitHub's native control layer and is powered by GitHub Actions, enabling developers to delegate both routine and specialised coding tasks. It is designed to require human approval before any continuous integration or continuous deployment (CI/CD) workflows proceed, ensuring that developers retain oversight of code changes. The coding agent, previously referred to as Project Padawan, is intended to help developers work more quickly and at greater scale, while supporting secure code commitments. The Copilot coding agent is now available to all Copilot Enterprise and Copilot Pro+ users, with organisations including CommBank, Carvana, and EY among those already recognising its usage. GitHub Chief Executive Officer Thomas Dohmke said, "GitHub is where the world's developers work on their projects. Now, it's becoming the place where they collaborate with agents in a configurable, steerable, and verifiable way. It's vital that organisations and developers are ready to embrace these agents without compromising their security posture." He added, "Built around an integrated, secure, and fully customisable development environment powered by GitHub Actions, the Copilot coding agent is the most enterprise-ready of its kind—amplifying human developers with trust by design. And these protections aren't just for us: as the new home of AI agents, we're making the same primitives available to partners to ensure an open ecosystem for agentic peer programming." The Copilot coding agent integrates with the software development life cycle. Developers can activate the agent by assigning a GitHub issue or initiating a session through Copilot Chat in VS Code. Commits generated by the agent are pushed to a draft pull request and can be tracked through session logs. Developers can review the agent's work, provide feedback, and request further iterations via regular pull request reviews. The agent is designed to maintain security through features such as branch protections and managed internet access. Additionally, any pull requests created by the agent require manual approval before triggering any CI/CD activities, establishing an additional layer of control over build and deployment processes. With the Model Context Protocol (MCP), developers are able to provide the agent with access to external data and additional capabilities. Configuration of MCP servers is available within repository settings, offering flexibility in how the agent interacts with software and data. The Copilot coding agent's computing environment is underpinned by GitHub Actions, leveraging a platform with more than 25,000 actions available in the GitHub Marketplace. Every weekday, over 40 million tasks are handled by GitHub-hosted and self-hosted runners. The agent thus operates within an established infrastructure that supports a broad range of development workflows at both open source and large enterprise scale. During private preview testing, the agent demonstrated effectiveness at completing low-to-medium complexity tasks in established codebases. These tasks include adding features, resolving bugs, extending tests, refactoring code, and improving documentation. Rodrigo Castillo, Chief Technology Officer at CommBank, commented, "We believe agentic AI is the next phase of evolution for software engineering. Together with GitHub, we have tightened DevSecOps cycles, while using existing security controls without modification. We're also automating common tasks, such as tech debt management, giving our engineering teams more time for creative flow: designing better products and experiences for our customers, and better platforms for our other engineers." Alex Devkar, Senior Vice President, Engineering and Analytics at Carvana, said, "The GitHub Copilot coding agent fits into our existing workflow and converts specifications to production code in minutes. This increases our velocity and enables our team to channel their energy toward higher-level creative work." James Zabinski, DevEx Lead at EY, added, "The Copilot coding agent is opening up doors for human developers to have their own agent-driven team, all working in parallel to amplify their work. We're now able to assign tasks that would typically detract from deeper, more complex work—allowing developers to focus on high-value coding tasks." Copilot Enterprise and Copilot Pro+ users can access the agent via consumption of Copilot premium requests and GitHub Actions minutes, both of which are included as part of their subscription entitlements. Kate Holterhoff, senior analyst at RedMonk, observed, "With its autonomous coding agent, GitHub is looking to shift Copilot from an in-editor assistant to a genuine collaborator in the development process. This evolution aims to enable teams to delegate implementation tasks and thereby achieve a more efficient allocation of developer resources across the software lifecycle." GitHub has also announced new artificial intelligence-related features intended to increase flexibility for software developers. Next month, GitHub will begin open sourcing the Copilot Chat extension for VS Code under the MIT licence, allowing developers to inspect and extend the AI's functionality. Over time, key AI features will be integrated directly into the VS Code core to further support developer control and transparency. All GitHub users now have access to the Models tab within repositories, where they can build, test, and manage AI features. This update includes support for prompt management, lightweight evaluations, and enterprise controls for secure and governed AI usage on the platform. GitHub Models has expanded to include support for Grok 3 from xAI, broadening the choice of models available to developers seeking different AI capabilities in their projects. Agent mode functionality is being extended with availability in JetBrains, Eclipse, and Xcode integrated development environments, allowing Copilot users to work with agents in a wider range of programming tools.
Geeky Gadgets
15-05-2025
- Geeky Gadgets
How Remote Agents Are Changing AI Assisted Programming in 2025
What if coding wasn't just about writing lines of code but orchestrating a symphony of tasks executed seamlessly in the background? Enter the era of remote agents, a new development in AI-assisted programming that's poised to redefine how developers approach their craft. Imagine delegating tedious debugging or repetitive code refactoring to an AI system that works asynchronously, freeing you to focus on the creative and strategic aspects of your project. This isn't just a futuristic vision—it's happening now, and it's reshaping the very fabric of software development. But as with any paradigm shift, this transformation raises questions: How do these agents work? What challenges do they present? And, most importantly, how will they impact the role of developers in the long run? This overview by GosuCoder explores the profound implications of remote agents, from their ability to boost productivity through parallel workflows to the new skills developers must master to thrive in this evolving landscape. You'll uncover how these tools are not only automating routine tasks but also driving innovation in ways that traditional coding workflows never could. Yet, the story isn't without its complexities—remote agents excel at small, well-defined tasks but stumble when faced with nuanced, iterative problems. As we delve into this fantastic technology, you'll gain a deeper understanding of its potential, its limitations, and the ways it's set to change programming forever. The question is no longer whether remote agents will transform software development, but how prepared we are to embrace this shift. Remote Agents in Programming Understanding Remote Agents Remote agents are specialized AI systems designed to automate coding tasks in the background, freeing developers from the need to wait for real-time responses. These agents integrate seamlessly with widely used development environments such as Visual Studio Code and task management platforms like Jira. By automating repetitive or time-consuming tasks, remote agents enable developers to focus on creative problem-solving and higher-level programming challenges. For example, a developer working on a complex application can assign routine tasks—such as debugging or minor code adjustments—to a remote agent. This delegation not only saves time but also ensures that the developer's attention remains on critical aspects of the project. By streamlining workflows, remote agents are becoming an essential tool for modern software development. How Remote Agents Operate Remote agents are particularly effective for small, well-defined tasks that can be completed independently. Examples of such tasks include: Fixing bugs in existing code in existing code Implementing minor UI changes to improve user experience to improve user experience Refactoring code to enhance readability and maintainability These tasks are executed in isolated, containerized environments, making sure that ongoing work remains unaffected. Developers can continue their primary coding tasks while remote agents handle the assigned work asynchronously. Once the tasks are completed, the results are returned for review and iteration. This workflow minimizes downtime and ensures continuous progress, making remote agents a valuable asset for optimizing productivity. However, the effectiveness of remote agents depends on the clarity of the instructions provided. Developers must define tasks with precision to ensure successful execution. This requirement underscores the importance of clear communication and task planning in using the full potential of remote agents. How AI Remote Agents Are Redefining Software Development Watch this video on YouTube. Advance your skills in AI agents by reading more of our detailed content. Challenges and Constraints Despite their numerous advantages, remote agents are not without limitations. They face significant challenges in handling: Complex, large-scale tasks that require iterative feedback and nuanced decision-making that require iterative feedback and nuanced decision-making Tasks involving subjective judgment, where human expertise is indispensable To use remote agents effectively, developers must invest time in creating detailed task descriptions and explicit instructions. This preparatory work can be time-consuming, particularly for projects with intricate requirements. Additionally, managing multiple parallel tasks can increase cognitive load, requiring developers to juggle various streams of work simultaneously. These challenges highlight the need for careful task orchestration and oversight to maximize the benefits of remote agents. The Evolution of AI-Assisted Programming The journey of AI-assisted programming has been marked by significant milestones, starting with basic autocomplete tools like GitHub Copilot. Over time, advancements in AI have led to the development of more sophisticated solutions, such as ChatGPT, Claude, and Klein, which offer advanced coding assistance. Remote agents represent the next major leap in this evolution, allowing asynchronous, multi-tasking workflows that redefine how developers interact with AI systems. By automating routine tasks and facilitating parallel workflows, remote agents are pushing the boundaries of what AI can achieve in software development. This evolution is not only enhancing productivity but also reshaping the role of developers, who must now adapt to new ways of working and thinking. Future Implications and Opportunities The rise of remote agents is expected to drive profound changes in the software development landscape. By allowing parallel task execution, these tools promise to: Boost productivity by automating time-intensive tasks by automating time-intensive tasks Reduce development timelines , accelerating project delivery , accelerating project delivery Encourage innovation in AI-assisted programming As both open source and proprietary tools adopt similar features, competition in the AI-assisted programming space is likely to intensify. This increased competition is expected to spur further innovation, leading to the development of even more advanced tools and capabilities. However, the adoption of remote agents also requires developers to adapt to new workflows. Instead of focusing solely on direct task execution, developers must learn to manage and oversee multiple streams of work. This shift in focus demands a new set of skills, including task orchestration, prioritization, and effective communication. Key Players Driving Remote Agent Technology Several tools are at the forefront of remote agent technology, each contributing to the growing ecosystem of AI-assisted programming solutions. Notable examples include: Augment Code and Cursor : Early pioneers in background agent functionality and : Early pioneers in background agent functionality R Code and Root Code: Emerging competitors exploring innovative capabilities These tools are shaping the future of software development by offering unique features designed to enhance developer workflows. As the technology matures, more players are likely to enter the market, further enriching the ecosystem and driving advancements in AI-assisted programming. Impact on Developers and the Industry For developers, the adoption of remote agents presents both opportunities and challenges. On the positive side, these tools: Reduce downtime by automating routine and repetitive tasks by automating routine and repetitive tasks Maximize output through parallel workflows, allowing faster project completion However, remote agents also introduce new complexities. Developers must adapt to managing the mental demands of parallel task execution and embrace a more dynamic coding environment. This shift requires a departure from traditional coding practices, with a greater emphasis on task orchestration and strategic oversight. For the broader industry, the rise of remote agents signals a move toward more efficient and innovative software development practices. By allowing developers to work smarter, not harder, these tools are poised to become a cornerstone of modern programming, driving progress and redefining the role of developers in the process. Media Credit: GosuCoder Filed Under: AI, Top News Latest Geeky Gadgets Deals Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.
