Author name: ITMAITY

How PowerStore’s latest release prepares you for what’s next.   ​  ​How PowerStore’s latest release prepares you for what’s next. PowerStore Blog | Dell

Unlocking new levels of flexibility, efficiency and scale to meet the demands of the modern AI data center.   ​  ​Unlocking new levels of flexibility, efficiency and scale to meet the demands of the modern AI data center. Storage Blog | Dell

Strengthening infrastructure to safeguard innovation and enable growth.   ​  ​Strengthening infrastructure to safeguard innovation and enable growth. PowerProtect Blog | Dell

IR Kinetics revolutionized real-time tracking with advanced AI technology and expert guidance from Dell Professional Services.   ​  ​IR Kinetics revolutionized real-time tracking with advanced AI technology and expert guidance from Dell Professional Services. Customer Blog | Dell

Allow us to reintroduce ourselves: GitHub Copilot is getting a whole lot more agentic with increased context of your tools and services, powered by the world’s leading models, starting today. 👏 We are excited to roll out agent mode in Visual Studio Code to all users, now complete with MCP support that unlocks access to any context or capabilities you want. What’s more, we are thrilled to release a new open source and local GitHub MCP server, giving you the ability to add GitHub functionality to any LLM tool that supports MCP. 🤖 In keeping with our commitment to offer multi-model choice, we’re making Anthropic Claude 3.5, 3.7 Sonnet, 3.7 Sonnet Thinking, Google Gemini 2.0 Flash, and OpenAI o3-mini generally available via premium requests, included in all paid Copilot tiers. These premium requests are in addition to unlimited requests for agent mode, context-driven chat, and code completions that all paid plans have when using our base model (👀 more below). With the new Pro+ tier, individual developers get the most out of the latest models with Copilot. The agent awakening doesn’t stop there. We are also announcing the general availability of the Copilot code review agent, which in just over a month in preview has been used by over 1 million developers on GitHub. Plus, the general availability of next edit suggestions so you can tab tab tab your way to coding glory. 🏆 Agent mode in VS Code Agent mode is progressively rolling out to VS Code users in stable, as we aim for full availability to all users in the coming weeks. You can also manually enable it now. Compared to chat or multi-file edits, which allow you to propose code changes across multiple files in your workspace, agent mode is fundamentally capable of taking action to translate your ideas into code. With simple prompts, agent mode takes Copilot beyond answering a question, instead completing all necessary subtasks across automatically identified or generated files to ensure your primary goal is achieved. Agent mode can suggest terminal commands or tool calls and ask you to execute them. It also analyzes run-time errors with self-healing capabilities. Since the launch to VS Code Insiders in February, developers have been using agent mode for a variety of tasks: from autofixing code gen errors, to building webapps, to yeeting commits – whatever that means. 🙂 https://x.com/xthree/status/1902748372022264142 Agent mode is powered by your choice of Claude 3.5 and 3.7 Sonnet, Google Gemini 2.0 Flash, and OpenAI GPT-4o. Currently, agent mode achieves a pass rate of 56.0% on SWE-bench Verified with Claude 3.7 Sonnet. We anticipate agent mode to grow more capable as chain of thought reasoning models continue to advance. To activate agent mode, upgrade to the latest VS Code and enable in settings. Model Context Protocol (MCP) is now available in public preview Developers spend their days conducting a wide array of tasks to get the job done, from research, to navigating telemetry, to infrastructure management, to coding and debugging. And they use many tools for this, the so-called engineering stack. MCP allows you to equip agent mode with the context and capabilities it needs to help you, like a USB port for intelligence. When you enter a chat prompt in agent mode within VS Code, the model can use different tools to handle tasks like understanding database schema or querying the web. This setup allows for more interactive and context-sensitive coding support. For example, with a prompt to “Update my GitHub profile to include the title of the PR that was assigned to me yesterday,” agent mode would take that request, combined with the list of all available MCP tools, and ask an LLM what to do next. Over time, the agent would continue calling tools iteratively, until the task is complete. Already, GitHub is home to a massive and growing MCP ecosystem that you can discover and use today. Here is a great repository that acts as a community inventory with some of the best MCP servers to use. The GitHub local MCP server equips agent mode with compelling capabilities such as searching across repositories and code, managing issues and creating PRs – turning agent mode into a powerful user of the GitHub platform. Get started by setting up local and remote MCP servers and using tools with agent mode in Visual Studio Code. To get started with the GitHub local MCP server, visit the repository, now supported natively in VS Code. Premium model requests Since GitHub Universe, we introduced a number of new models for chat, multi-file edits, and now agent mode. With the general availability of these models, we are introducing a new premium request type. Premium requests are in addition to the unlimited requests for agent mode, context-driven chat, and code completions in all paid plans for our base model (currently: OpenAI GPT-4o). Customers with Copilot Pro will receive 300 monthly premium requests, beginning on May 5, 2025. Customers with Copilot Business and Copilot Enterprise will receive 300 and 1000 monthly premium requests respectively, starting between May 12 and May 19, 2025. Until then, use of these premium models is unlimited. We are also introducing a new Pro+ plan for individuals with 1500 monthly premium requests and access to the best models, like GPT-4.5, for $39 per month. Copilot paid users1 will also have the ability to pay-as-you-go for additional premium request usage. Individuals and organizations can choose to opt-in to use additional requests beyond their included amount, in addition to setting spending limits on requests to control costs with ease. GitHub Copilot Business and Enterprise administrators can manage requests via their Copilot Admin Billing Settings. Additional premium requests start at $0.04 per request. Each premium model will consume a specific number of premium requests, allowing you to use a more powerful or efficient model when you need it, all while you have continued, unlimited access to Copilot’s base model. You can learn more about how seat-based premium requests will work in our documentation. Happy 50th birthday,

Discover how the City of Durham, NC is using AI to revolutionize city planning, safety and sustainability for its residents.   ​  ​Discover how the City of Durham, NC is using AI to revolutionize city planning, safety and sustainability for its residents. AI Solutions Blog | Dell

At GitHub Security Lab, one of the most common vulnerability types we find relates to the cross-origin resource sharing (CORS) mechanism. CORS allows a server to instruct a browser to permit loading resources from specified origins other than its own, such as a different domain or port. Many developers change their CORS rules because users want to connect to third party sites, such as payment or social media sites. However, developers often don’t fully understand the dangers of changing the same-origin policy, and they use unnecessarily broad rules or faulty logic to prevent users from filing further issues. In this blog post, we’ll examine some case studies of how a broad or faulty CORS policy led to dangerous vulnerabilities in open source software. We’ll also discuss DNS rebinding, an attack with similar effects to a CORS misconfiguration that’s not as well known among developers. What is CORS and how does it work? CORS is a way to allow websites to communicate with each other directly by bypassing the same-origin policy, a security measure that restricts websites from making requests to a different domain than the one that served the web page. Understanding the Access-Control-Allow-Origin and Access-Control-Allow-Credentials response headers is crucial for correct and secure CORS implementation. Access-Control-Allow-Origin is the list of origins that are allowed to make cross site requests and read the response from the webserver. If the Access-Control-Allow-Credentials header is set, the browser is also allowed to send credentials (cookies, http authentication) if the origin requests it. Some requests are considered simple requests and do not need a CORS header in order to be sent cross-site. This includes the GET, POST, and HEAD requests with content types restricted to application/x-www-form-urlencoded, multipart/form-data, and text/plain. When a third-party website needs access to account data from your website, adding a concise CORS policy is often one of the best ways to facilitate such communication. To implement CORS, developers can either manually set the Access-Control-Allow-Origin header, or they can utilize a CORS framework, such as RSCors, that will do it for them. If you choose to use a framework, make sure to read the documentation—don’t assume the framework is safe by default. For example, if you tell the CORS library you choose to reflect all origins, does it send back the response with a blanket pattern matching star (*) or a response with the actual domain name (e.g., stripe.com)? Alternatively, you can create a custom function or middleware that checks the origin to see whether or not to send the Access-Control-Allow-Origin header. The problem is, you can make some security mistakes when rolling your own code that well-known libraries usually mitigate. Common mistakes when implementing CORS For example, when comparing the origin header with the allowed list of domains, developers may use the string comparison function equivalents of startsWith, exactMatch, and endsWith functions for their language of choice. The safest function is exactMatch where the domain must match the allow list exactly. However, what if payment.stripe.com wants to make a request to our backend instead of stripe.com? To get around this, we’d have to add every subdomain to the allow list. This would inevitably cause users frustration when third-party websites change their APIs. Alternatively, we can use the endsWith function. If we want connections from Stripe, let’s just add stripe.com to the allowlist and use endsWith to validate and call it a day. Not so fast, since the domain attackerstripe.com is now also valid. We can tell the user to only add full urls to the allowlist, such as https://stripe.com, but then we have the same problem as exactMatch. We occasionally see developers using the startsWith function in order to validate domains. This also doesn’t work. If the allowlist includes https://stripe.com then we can just do https://stripe.com.attacker.com. For any origin with subdomains, we must use .stripe.com (notice the extra period) in order to ensure that we are looking at a subdomain. If we combine exactMatch for second level domains and endsWith for subdomains, we can make a secure validator for cross site requests. Lastly, there’s one edge case found in CORS: the null origin should never be added to allowed domains. The null origin can be hardcoded into the code or added by the user to the allowlist, and it’s used when requests come from a file or from a privacy-sensitive context, such as a redirect. However, it can also come from a sandboxed iframe, which an attacker can include in their website. For more practice attacking a website with null origin, check out this CORS vulnerability with trusted null origin exercise in the Portswigger Security Academy. How can attackers exploit a CORS misconfiguration? CORS issues allow an attacker to make actions on behalf of the user when a web application uses cookies (with SameSite None) or HTTP basic authentication, since the browser must send those requests with the required authentication. Fortunately for users, Chrome has defaulted cookies with no Samesite to SameSite Lax, which has made CORS misconfiguration useless in most scenarios. However, Firefox and Safari are still vulnerable to these issues using bypass techniques found by PTSecurity, whose research we highly recommend reading for knowing how someone can exploit CORS issues. What impact can a CORS misconfiguration have? CORS issues can give a user the power of an administrator of a web application, so the usefulness depends on the application. In many cases, administrators have the ability to execute scripts or binaries on the server’s host. These relaxed security restrictions allow attackers to get remote code execution (RCE) capabilities on the server host by convincing administrators to visit an attacker-owned website. CORS issues can also be chained with other vulnerabilities to increase their impact. Since an attacker now has the permissions of an administrator, they are able to access a broader range of services and activities, making it more likely they’ll find something vulnerable. Attackers often focus on vulnerabilities that affect the host system, such as arbitrary file write or RCE. Real-world examples A CORS misconfiguration allows for RCE

In a recent study, TechTarget’s Enterprise Strategy Group (ESG) set out to validate the benefits of adopting Dell thin client …   ​  ​In a recent study, TechTarget’s Enterprise Strategy Group (ESG) set out to validate the benefits of adopting Dell thin client … Customer Blog | Dell

Dell Pro Docks simplify IT with secure and manageable docking solutions for the AI PC. Featuring four new docks & powerful management tools.   ​  ​Dell Pro Docks simplify IT with secure and manageable docking solutions for the AI PC. Featuring four new docks & powerful management tools. Client Peripherals Blog | Dell

Welcome back to season two of GitHub for Beginners, a series designed to help you navigate GitHub more confidently! So far, we’ve explored how to use GitHub Copilot and some of its essential features. Today, we will be learning all about large language models (LLMs) and the basics of prompt engineering. LLMs are powerful, and the way we interact with them via prompts matters. For example, have you ever tried asking an LLM a question, but it can’t really figure out what you’re trying to ask? Understanding the power of prompts (and the limitations that come with them) can help you become even more productive. In this post, we’ll explore: How LLMs work and how prompts are processed. How to engineer the most effective prompts. How to troubleshoot prompts when we don’t get the outcomes we want. For all demos in this series, we use GitHub Copilot in Visual Studio Code. Copilot is available in other IDEs, but the available functionality may vary depending on your environment. Learn more about supported IDEs > Let’s get started! What’s an LLM? Large language models are a type of AI that are trained on a large (hence the name) amount of text data to understand and generate human-like language. By predicting the next word in a sentence based on the context of the words that came before it, LLMs respond to humans in a way that is relevant and coherent. Sort of like an ultra-smart autocomplete! When it comes to using LLMs, there are three important things to understand: Context: This is the surrounding information that helps an LLM understand what you’re talking about. Just like when you have a conversation with a friend, the more context you offer, the more likely the conversation will make sense. Tokens: For LLMs, text is broken down into units of tokens. This could be a word, part of a word, or even just one single letter. AI models process tokens to generate responses, so the number of tokens you use with an LLM can impact its response. Too few tokens can lead to a lack of context, but too many could overwhelm the AI model or run into its built-in token limits. Limitations: LLMs are powerful, but not all-powerful. Instead of understanding language like humans, LLMs rely on patterns and probabilities from training data. Taking a deeper dive into training data is beyond the scope of this post, but as a general rule, the ideal data set is diverse and broad. Models are never perfect—sometimes they can hallucinate, provide incorrect answers, or give nonsensical responses. What is a prompt? A prompt is a natural language request that asks an LLM to perform a specific task or action. A prompt gives the model context via tokens, and works around the model’s potential limitations, so that the model can give you a response. For example, if you prompt an LLM with “Write a JavaScript function to calculate the factorial of a number,” it will use its training data to give you a function that accomplishes that task. Depending on how a specific model was trained, it might process your prompt differently, and present different code. Even the same model can produce different outputs. These models are nondeterministic, which means you can prompt it the same way three times and get three different results. This is why you may receive different outputs from various models out in the world, like OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini. Now that we know what a prompt is, how do we use prompts to get the outputs we want? What is prompt engineering? Imagine that a friend is helping you complete a task. It’s important to give them clear and concise instructions if there’s a specific way the task needs to be done. The same is true for LLMs: a well-crafted prompt can help the model understand and deliver exactly what you’re looking for. The act of crafting these prompts is prompt engineering. That’s why crafting the right prompt is so important: when this is done well, prompt engineering can drastically improve the quality and relevance of the outputs you get from an LLM. Here are a few key components of effective prompting: An effective prompt is clear and precise, because ambiguity can confuse the model. It’s also important to provide enough context, but not too much detail, since this can overwhelm the LLM. If you don’t get the answer you’re expecting, don’t forget to iterate and refine your prompts! Learn all about prompt engineering for Copilot Chat Discover simple strategies in our documentation to write better prompts and improve your Copilot results. How to engineer better Copilot Chat prompts > Let’s try it out! Example: How to refine prompts to be more effective Imagine you’re using GitHub Copilot and say: Write a function that will square numbers in a list in a new file with no prior code to offer Copilot context. At first, this seems like a straightforward and effective prompt. But there are a lot of factors that aren’t clear: What language should the function be written in? Do you want to include negative numbers? Will the input ever have non-numbers? Should it affect the given list or return a new list? How could we refine this prompt to be more effective? Let’s change it to: Write a Python function that takes a list of integers and returns a new list where each number is squared, excluding any negative numbers. This new prompt is clear and specific about what language we want to use, what the function should do, what constraints there are, and the expected input type. When we give GitHub Copilot more context, the output will be better aligned with what we want from it! Just like coding, prompt engineering is about effective communication. By crafting your prompts thoughtfully, you can more effectively use tools like GitHub Copilot to make your workflows smoother and more efficient. That being said, working with LLMs means there will still

If you know where to look, exposed secrets are easy to find. Secrets are supposed to prevent unauthorized access, but in the wrong hands, they can be—and typically are—exploited in seconds. To give you an idea of the scope of the problem, more than 39 million secrets were leaked across GitHub in 2024 alone.1 Every minute GitHub blocks several secrets with push protection.2 Still, secret leaks remain one of the most common—and preventable—causes of security incidents. As we develop code faster than ever previously imaginable, we’re leaking secrets faster than ever, too. That’s why, at GitHub, we’re working to prevent breaches caused by leaked tokens, credentials, and other secrets–making protection against secret exposures accurate, built-in, and accessible to every developer. Today, we’re launching the next evolution of GitHub Advanced Security, aligning with our ongoing mission to keep your secrets…secret. Secret Protection and Code Security, now available as standalone products Advanced Security for GitHub Team organizations A free, organization-wide secret scan to help teams identify and reduce exposure.3 Here’s how secrets leak, what we’re doing to stop it, and what you can do to protect your code. Let’s jump in. How do secret leaks happen? Most software today depends on secrets—credentials, API keys, tokens—that developers handle dozens of times a day. These secrets are often accidentally exposed, which makes sense. Less intuitively, a large number of breaches come from well-meaning developers who purposely expose a secret. Developers also often underestimate the risk of private exposures, committing, sharing, or storing these secrets in ways that feel convenient in the moment, but which introduce risk over time. Unfortunately, these seemingly innocuous secret exposures are small threads to pull for an attacker looking to unravel a whole system. Bad actors are extremely skilled at using a foothold provided by “low risk” secrets for lateral movement to higher-value assets. Even without the risk of insider threats, persisting any secret in git history (or elsewhere) makes us vulnerable to future mistakes. Research shows that accidental mistakes (like inadvertently making a repository public) were higher in 2024 than ever before. If you’re interested in learning more about secret leaks and how to protect yourself, check out this great video from my colleague Chris Reddington: What is GitHub doing about it? We care deeply about protecting the developer community from the risk of exposed secrets. A few years ago, we formally launched our industry partnership program, which has now grown to hundreds of token issuers like AWS, Google Cloud Platform, Meta, and OpenAI—all fully committed to protecting the developer community from leaked secrets. 💡 Did you know? GitHub partners with providers to build detectors for their secrets behind-the-scenes. This improves our ability to detect secrets accurately and quickly, and to work together to mitigate risk in the case of a publicly leaked secret. In the case of a public leak, GitHub not only notifies you with a secret scanning alert, but also immediately notifies the secret issuer (if they participate in the GitHub secret scanning partnership program). The issuer can then take action depending on their policy, like quarantining, revoking, or further notifying involved parties. Last year, we rolled out push protection by default for public repositories, which has since blocked millions of secrets for the open source community. And finally, as of today, we’re rolling out additional changes to our feature availability, aligning with our ongoing goal to help organizations of all sizes protect themselves from the risk of exposed secrets: a new point-in-time scan, free for organizations; a new pricing plan, to make our paid security tooling more affordable; and the release of Secret Protection and Code Security to GitHub Team plans. What you can do to protect yourself from exposed secrets The easiest way to protect yourself from leaked secrets is not to have any in the first place. Push protection, our built-in solution, is the simplest way to block secrets from accidental exposure. It leverages the same detectors that we created through our partnership program with cloud providers, ensuring secrets are caught quickly and accurately with the lowest rate of false positives possible. Get started Push protection prevents secret leaks–without compromising the developer experience–by scanning for secrets before they are pushed. You can enable push protection immediately with a couple clicks from your repository, organization, and enterprise settings. Studies have shown that GitHub Secret Protection is the only secret scanning tool—proprietary or open source—that can claim an over one in two true positive rate across all findings4. GitHub received a precision score of 75% (compared to the next best, 46% precision). Compared to alternatives like open source scanning solutions, it’s not that GitHub is finding fewer secrets, it’s that we’re finding real ones, so that you can spend your time worrying less about false positives, and more about what matters–shipping. 💡 Did you know? GitHub leverages GitHub Copilot in order to also detect unstructured secrets like passwords with extremely low false positive rates. My colleagues Ashwin Mohan and Courtney Claessens just wrote a great piece, which goes into depth on how we built Copilot secret scanning. Long-lived credentials are some of the most common and dangerous types of secrets to leak, as they often persist unnoticed for months–or years–and give bad actors extended access. That’s why managing secrets through their full lifecycle is critical. Beyond push protection, you can protect yourself from leaks by following security best practices to ensure secrets are securely managed from creation to revocation: Creation: follow the principle of least privilege and make sure secrets are securely generated. Rotation: outside of user credentials, secrets should be regularly rotated. Revocation: restrict access when no longer needed–or when compromised. Throughout the lifecycle of a secret, you should eliminate human interaction and automate secret management whenever possible. In addition, you should adopt a continuous monitoring solution for detecting exposures, so you can react quickly. Like push protection, GitHub’s built-in solution for secret scanning is the simplest way to triage previously leaked secrets. Starting today, investing in GitHub’s built-in security tooling is more

Learn how traditional, generative and agentic AI shape enterprise strategies, so you can deploy AI effectively.   ​  ​Learn how traditional, generative and agentic AI shape enterprise strategies, so you can deploy AI effectively. Artificial Intelligence Blog | Dell

Ever find yourself staring at an AI coding assistant, wondering why it’s not quite nailing what you need? Maybe it’s spitting out code that’s close but not quite right, or you’re stuck wrestling with a problem that spans multiple files, wishing it could just get the bigger picture. Often, when developers hit these snags, it’s less about the tool, and more about knowing how to use it. So here’s the key question you should ask yourself: “Do I need a quick answer or a thoughtful conversation?” That’s the secret to unlocking AI coding tools like GitHub Copilot. Because different aspects of Copilot serve different needs, and we’re here to help you discern when is the best time for agent mode and when you should be using Copilot Edits instead. Both are powerhouse Copilot features, built to supercharge your coding, but they shine in different scenarios. Copilot Edits is your go-to for fast, precise tweaks—think refactoring a function, squashing a bug, or applying consistent changes across files without losing your flow. Agent mode, on the other hand, steps up as your AI collaborator for thornier, multi-file challenges—analyzing your codebase, proposing architectural fixes, and even running terminal commands while you steer the ship and approve each move. Figuring out which one fits your needs doesn’t just save time, it turns your AI assistant into a seamless extension of your own coding instincts. Copilot command center: Your chat window Before learning more about agent mode and Copilot Edits, it’s essential to understand the Copilot chat window in VS Code—your central hub for AI interactions. This is where you can: Ask coding questions like “How do I implement JWT authentication in Node.js?” Use /explain to understand complex code blocks Debug issues with /fix Generate tests with /tests Access both Edits and agent mode features With the chat window giving you a solid grasp of your code’s context, you’re in the driver’s seat to pick the perfect Copilot tool: Edits or agent mode, for whatever’s next. Let me take you through a real-world example of how I’ve been using GitHub Copilot to evolve my personal website. It started with a simple idea: create an interactive terminal-style easter egg that showcases my skills in a developer-friendly way. I began with a basic command processing function that handled a few simple commands: function processCommand(command) { try { if (!command?.trim()) { return ‘Please enter a command. Type “more” for available commands.’; } const sanitizedCommand = command.toLowerCase().trim(); switch (sanitizedCommand) { case ‘more’: return commands.more; case ‘about’: return commands.about; case ‘skills’: return commands.skills; case ‘projects’: return commands.projects; case ‘contact’: return commands.contact; default: return `Command not found: “${command}”. Type ‘more’ for available commands.`; } } catch (error) { console.error(‘[Terminal] Error processing command:’, error); return ‘An error occurred while processing the command.’; } } Initially, I used Copilot Edits to quickly add new features. For instance, I wanted to add a ‘github’ command to showcase my repositories: Copilot Edits made targeted changes to the switch statement in the processCommand function and added the new command to the commands object—a perfect example of quick, focused modifications to existing code. As my terminal interface matured, I wanted to implement several interactive features that would require more comprehensive improvements. To get animation effects, keyboard navigation controls, and an extensible theming system that spanned multiple files to work together seamlessly, I turned to agent mode. The agent mode advantage When we designed agent mode, we didn’t want to create a tool that’s a simple AI feature: We wanted to create an AI that could pair with you! Rather than working on specific, limited changes where you might be reinventing the wheel, you’re now providing higher-level direction while the AI tackles the implementation details across multiple files or systems. That being said, with agent mode, you’re still in control of the process. The AI has more latitude to explore your codebase and suggest comprehensive solutions, but you always review and approve the changes before they’re applied. It’s not about surrendering control, it’s about effectively delegating implementation details while you focus on the bigger picture. What makes agent mode special: Codebase search: It searches your codebase to find relevant files without you explicitly specifying them. Self-iteration: It can iterate on its own output to complete your entire request in one go. Error identification and repair: It automatically recognizes and fixes errors in suggested code. Terminal command execution: It suggests and runs terminal commands with your permission. Build and run capabilities: It can build and run your application to check if changes work correctly. I reach for agent mode when: Building complete features: “Add analytics tracking throughout the app” Navigating unfamiliar codebases: “Help me understand how authentication works in this project” Writing and verifying tests: “Write tests for the UserService and make sure they pass” Implementing terminal-heavy tasks: “Set up a new React project with TypeScript, Redux, and styled-components” Doing complex refactoring: “Refactor our API calls to use the new error handling pattern” Continuing with my terminal easter egg, let’s say I want to implement a much more extensive upgrade with multiple features, like adding a typing animation effect for responses, command history navigation with up/down arrows, and tab completion for commands. At the same time, I want to create a new TerminalThemes.css file with different color scheme options that users can switch between with a ‘theme’ command. This is where agent mode truly shines. The task spans multiple files, requires an understanding of the existing codebase, and involves creating entirely new functionality. Here, agent mode would: Search through the codebase to understand the terminal implementation. Create the new CSS file for themes. Add typing animation functionality to terminal responses. Implement keyboard handlers for history navigation. Build the tab completion system. Add the theme command to the switch statement. Test the implementation to ensure everything works correctly. This is the beauty of agent mode: it has a complex understanding of patterns and relationships in different parts of the codebase! Agent mode can ensure that the typing animation did

From their studio in Finland, Cineshare is developing new approaches to character animation and virtual production that balance visual quality with performance constraints.   ​  ​From their studio in Finland, Cineshare is developing new approaches to character animation and virtual production that balance visual quality with performance constraints. Precision Blog | Dell

Discover how AMD empowers creators with AI, real-time rendering and virtual production tools, redefining the future of storytelling.   ​  ​Discover how AMD empowers creators with AI, real-time rendering and virtual production tools, redefining the future of storytelling. Media & Entertainment Blog | Dell