Discover how Dell’s AI Factory and PowerProtect solutions empower secure AI innovation with a robust cyber-resilient foundation.   ​  ​Discover how Dell’s AI Factory and PowerProtect solutions empower secure AI innovation with a robust cyber-resilient foundation. Cyber Recovery Blog | Dell

Time once spent manually searching through reels of footage is now compressed into seconds.   ​  ​Time once spent manually searching through reels of footage is now compressed into seconds. Media & Entertainment Blog | Dell

Discover how the Dell AI Factory with NVIDIA empowers businesses to scale AI, overcome challenges and drive innovation.   ​  ​Discover how the Dell AI Factory with NVIDIA empowers businesses to scale AI, overcome challenges and drive innovation. AI Solutions Blog | Dell

When the Log4j zero day broke in December 2021, everyone learned the same lesson: One under-resourced library can send shockwaves through the entire software supply chain. Today the average cloud workload includes over 500 dependencies, many of them tended by unpaid volunteers. The need to support and secure this ecosystem has never been more urgent. In response, GitHub launched the GitHub Secure Open Source Fund in November 2024, which provides maintainers with financial support to participate in a three-week program that delivers security education, mentorship, tooling, certification, community of security-minded maintainers,  and more. By linking this funding to programmatic security outcomes, our goal is to increase security impact, reduce risk, and help secure the software supply chain at scale. Already, we’re seeing measurable impact from proactive work. Our first two sessions brought together 125 maintainers from 71 important and fast growing open source projects  Early outcomes include:  Remediated over 1,100 vulnerabilities detected by CodeQL, reducing their risk surfaces. Participants issued more than 50 new Common Vulnerabilities and Exposures (CVEs), informing and protecting their downstream dependents. Prevented 92 new secrets from being leaked and 176 leaked secrets were detected and resolved  Empowered maintainers for long-term success, with 100% saying they left with actionable next steps for the following year’s roadmap.  Accelerated adoption of security best practices, with 80% of projects enabling three or more GitHub-based security features. Prepared projects for the future of development, as 63% said they have a better understanding of AI and MCP security. Maintainers found novel ways to partner with and use AI to accelerate learnings and implement solutions, with many consulting GitHub Copilot to conduct vulnerability scans and security audits, define and implement fuzzing strategies, and more.  These results show direct security impact immediately from the sessions, and the momentum is just beginning. Maintainers have embraced a culture of security, built out security backlogs, and are actively sharing insights with the maintainers in the community, and with their direct project contributors and consumers. As a result, the entire ecosystem benefits — and the security impact will continue to grow. And we’re not done. Session 3 starts in September 2025, and we want to bring more maintainers that work deeper in the dependency tree and those that manage critical dependencies by themselves. To see the immediate impact following Sessions 1 and 2, let’s look at what changed inside the categories of code that power almost everything you build. How the GitHub Secure Open Source Fund works Each session is a three-week sprint and engagement for a total of 12 months. The three-week sprint is designed and curated by the GitHub Security Lab, and delivered by security experts from GitHub and our partners. The training is structured into different focus areas per week. These include:  Foundations of open source security Threat modeling and secure coding Vulnerability management and AI security Throughout this program, each project receives $10,000 USD via GitHub Sponsors (which breaks down to $6,000 USD during the sprint and $2,000 USD at 6- and 12-month security check-ins). Projects are also invited to a new security focused community, and office hours with the GitHub Security Lab, that they can take advantage of during the full 12 months. They also receive security resources to immediately implement in their project and Azure credits for cloud infrastructure. Learn more > AI and ML frameworks / edge-LLM tooling 🤖 Ollama • AutoGPT/Gravitasml • scikit-learn • OpenCV • CodeCarbon •  Zeus • Cognee • CAMEL-AI •  Ruby-OpenAI These projects are the bedrock of the current AI work with LLMs, agents, orchestration layers, and model toolchains. Together they rack up tens of millions of installs and git clone commands each month, and they’re baked into cloud notebooks like Jupyter, Google Collab, AWS SageMaker, and Microsoft Azure ML. A prompt-injection flaw or poisoned weight file here could spill into thousands of downstream apps overnight, and the teams who rely on them often won’t even know which component failed. Why it matters 🤔 Think of an LLM agent that deploys new micro-services automatically; if its dependency graph gets hijacked, the attacker gains instant “remote DevOps” powers. Hardening these projects helps to protect everyone further up the stack. Project spotlight: Ollama  This project makes running large language models locally possible. Ollama is the easiest way to chat and build with open models. They used this opportunity to threat-model every moving part of their system – from their use of GitHub Actions, DNS security, model distribution, how the models are executed in Ollama’s engine, auto-update checker, and more — then they pruned unused dependencies.  The GitHub Secure Open Source Program is a safe space to ask leading experts security questions, and learn how other high-impact projects address similar challenges. Project spotlight: GravitasML by AutoGPT GravitasML is an MIT licensed XML parser for LLMs, built by the team that launched AutoGPT to be simple and secure by design. Fresh out of the sprint, the AutoGPT team wired CodeQL into every pull request across the AutoGPT Platform and GravitasML, and built a lightweight “security agent” that nudges contributors to tighten controls as they code. This helped turn passive checks into continuous coaching. The maintainers overhauled their security policy, stood up a formal incident-response workflow, and mapped out 28 follow-up tasks (from fuzzing their XML parser to completing the OSS Scorecard) to build a durable roadmap for safer LLM agents at large. The AI-agent ecosystem is safer — and will keep getting safer — because of the Secure Open Source Fund. Front-end and full-stack frameworks / UI libraries 📚 Next.js • Nuxt • Svelte • NativeScript • Bootstrap • shadcn/ui • Path-to-RegExp • WebdriverIO These frameworks ship the pixels users touch and often bundle their own server-side routing. Their install bases number in the millions, and improving their security posture closes off potential XSS, template-injection, and supply-chain hop points. The Bootstrap project alone powers nearly 17.5% of the world’s websites, and Next.js drives the frontends for Notion and Adobe, among many others. Why it matters 🤔 A single vulnerable

Dell AI Data Platform, now with NVIDIA & Elastic, accelerates AI workflows, boosts creativity and scales enterprise innovation seamlessly.   ​  ​Dell AI Data Platform, now with NVIDIA & Elastic, accelerates AI workflows, boosts creativity and scales enterprise innovation seamlessly. AI Solutions Blog | Dell

Explore the future of creative tech at the Dell Booth #301 during SIGGRAPH 2025, August 12-14, at the Vancouver Convention Center.   ​  ​Explore the future of creative tech at the Dell Booth #301 during SIGGRAPH 2025, August 12-14, at the Vancouver Convention Center. Events Blog | Dell

AI PCs are revolutionizing collaboration by supporting AI features in tools like Microsoft Teams, Windows Studio Effects and Zoom.   ​  ​AI PCs are revolutionizing collaboration by supporting AI features in tools like Microsoft Teams, Windows Studio Effects and Zoom. AI Solutions Blog | Dell

GitHub Models brings AI into your GitHub Actions workflows, helping you automate triage, summarize, and more — right where your project lives.  Let’s explore three ways to integrate and automate the use of GitHub Models in GitHub Actions workflows, from the most straightforward to the most powerful. But first: Add the right permissions Before you can use GitHub Models in your Actions workflows, you need to grant your workflow access to AI models. Without the correct permissions, any step that tries to call an AI model will fail. Giving permissions to use GitHub Models is one line in your permissions block: permissions: contents: read issues: write models: read These permissions will give your workflow the ability to read repository content; to read, create, or update issues and comments; and, most importantly for this tutorial, to enable access to GitHub Models.  Security tip Before we get to the examples, be aware of the potential for prompt injection attacks and how to mitigate the risk. It’s best practice to give GitHub Actions workflows the minimum permissions that are required to perform the actions. For example, if you only need to read issue content, don’t set the issues permissions to write. This minimizes the chance of a malicious actor opening an issue and instructing a model to do something you don’t want.  Example one: Request more information in bug reports  This example will show you how to use the AI inference action and how to use AI to create branching logic. You can find the full workflow in this repo. One of the most time-consuming and menial parts of our work as developers is triaging new issues that often contain too little information to reproduce.  Instead of having to spend time assessing and responding to these issues, you can use the AI inference action lets you call leading AI models to analyze or generate text as part of your workflow. The workflow below, for example, will automatically check if new bug reports have enough information to be actionable, and respond if they’re not. To set up the workflow, create a new file in your repository’s .github/workflows directory called bug-reproduction-instructions.yml (create the directory if it doesn’t exist). It will trigger whenever a new issue is opened and then fetch the issue’s title and body for future steps.  name: Bug Report Reproduction Check on: issues: types: [opened] permissions: contents: read issues: write models: read jobs: reproduction-steps-check: runs-on: ubuntu-latest steps: – name: Fetch Issue id: issue uses: actions/github-script@v7 with: script: | const issue = await github.rest.issues.get({ owner: context.repo.owner, repo: context.repo.repo, issue_number: context.issue.number }) core.setOutput(‘title’, issue.data.title) core.setOutput(‘body’, issue.data.body) Now that your workflow has the necessary context, create a new step. This step should only execute if the issue is tagged with a bug label. This step will use the AI inference action, configured with a system prompt that outlines the characteristics of effective reproduction instructions, and provide the value from the issue. – name: Analyze Issue For Reproduction if: contains(join(github.event.issue.labels.*.name, ‘,’), ‘bug’) id: analyze-issue uses: actions/ai-inference@v1 with: model: mistral-ai/ministral-3b system-prompt: | Given a bug report title and text for an application, return ‘pass’ if there is enough information to reliably reproduce the issue, meaning the report clearly describes the steps to reproduce the problem, specifies the expected and actual behavior, and includes environment details such as browser and operating system; if any of these elements are missing or unclear, return a brief description of what is missing in a friendly response to the author instead of ‘pass’. Consider the following title and body: prompt: | Title: ${{ steps.issue.outputs.title }} Body: ${{ steps.issue.outputs.body }} This step will either return a pass if there is enough information provided (more on why we’re doing this in a moment), or return a response detailing what is missing.  You can use over 40 AI models available in the GitHub Models catalog. Just swap out the model value with the identifier on each model’s page.  Next, add one final step, which will post the comment only if the value returned was not pass.  – name: Comment On Issue if: contains(join(github.event.issue.labels.*.name, ‘,’), ‘bug’) && steps.analyze-issue.outputs.response != ‘pass’ uses: actions/github-script@v7 env: AI_RESPONSE: steps.analyze-issue.outputs.response with: script: | await github.rest.issues.createComment({ owner: context.repo.owner, repo: context.repo.repo, issue_number: context.issue.number, body: process.env.AI_RESPONSE }) By prompting the AI model to return a fixed string if certain criteria are met (in this case, a good bug report was filed with enough reproduction information), we can create AI-powered conditional logic in our workflows. Example two: Creating release notes from merged pull requests This example will show you how to use the gh CLI with the gh-models extension. You can find the full workflow in this repo. Generating thorough release notes with new versions of a project can take time, between collating what’s changed and finding a succinct way to explain it to users. But you can actually trigger GitHub Actions workflow steps  when pull requests are merged and use the GitHub CLI to gather information and take action, including calling models. The workflow below, for example, will summarize merged pull requests and add them to a release notes issue — showing how you can save time and energy with each pull request. To set up this workflow, create a new label called release, and create one issue with this label called Publish next release changelog. Then, create a new file in your repository’s .github/workflows directory called release-notes.yml. It will trigger whenever a new pull request is closed, and its single job conditionally will run only if its merged status is true.  name: Add to Changelog on: pull_request: types: – closed permissions: pull-requests: read issues: write contents: read models: read jobs: add_to_changelog: if: github.event.pull_request.merged == true runs-on: ubuntu-latest steps: – name: Checkout repository uses: actions/checkout@v4 Install the gh-models extension with a new step, providing your workflow’s token which now has permissions to use GitHub Models: – name: Install gh-models extension run: gh extension install https://github.com/github/gh-models env: GH_TOKEN: ${{ secrets.GITHUB_TOKEN }} The rest of the steps will take place in one step: – name: Summarize

Editor’s note: This piece was originally published in our LinkedIn newsletter, Branching Out_. Sign up now for more career-focused content >  Everyone’s talking about it: AI is changing how we work. And nowhere is that more true than in the field of software engineering. If you’re just getting started as a developer, you might be wondering: is AI ruining my chances of getting a junior-level role? After all, a 2023 study by ServiceNow and Pearson projects that nearly 26% of tasks performed by [current] junior application developers will be augmented or fully automated by 2027.  In a word: No. Quite the contrary. New learners are well positioned to thrive as junior developers because they’re coming into the workforce already savvy with AI tools, which is just what companies need to adapt to the changing ways software is being developed. Our CEO Thomas Domke says: We’re entering an era where interns and junior developers are showing up already fluent in the best tools for AI code-gen on the market. Why? They vibe with AI. They build with it. Fresh talent ➡ better ideas ➡ the best tools. :robot_face: Thanks @GergelyOrosz for the story on how we’re… https://t.co/Sj6KAbq7dz — Thomas Dohmke (@ashtom) May 27, 2025 Hear more from Thomas on The Pragmatic Engineer podcast >  So what does that mean for you? According to Miles Berry, professor of computing education at the University of Roehampton, today’s learners must develop the skills to work with AI rather than worry about being replaced by it. As a junior developer, you need to think critically about the code your AI tool gives you, stay curious when things feel unfamiliar, and collaborate with AI itself in addition to senior team members.  As Berry puts it: “Creativity and curiosity are at the heart of what sets us apart from machines.”  With that in mind, here are five ways to stand out as a junior developer in the AI era: 1. Use AI to learn faster, not just code faster  Most developers use GitHub Copilot for autocomplete. But if you’re just starting out, you can turn it into something more powerful: a coding coach. Get Copilot to tutor you You can set personal instructions so Copilot guides you through concepts instead of handing you full solutions. Here’s how:  In VS Code, open the Command Palette and run: > Chat: New Instructions File Then paste this into the new file: — applyTo: “**” — I am learning to code. You are to act as a tutor; assume I am a beginning coder. Teach me concepts and best practices, but don’t provide full solutions. Help me understand the approach, and always add: “Always check the correctness of AI-generated responses.” This will apply your tutoring instructions to any file you work on. You can manage or update your instructions anytime from the Chat > Instructions view. Ask Copilot questions Open Copilot Chat in VS Code and treat it like your personal coach. Ask it to explain unfamiliar concepts, walk through debugging steps, or break down tricky syntax. You can also prompt it to compare different approaches (“Should I use a for loop or map here?”), explain error messages, or help you write test cases to validate your logic. Every prompt is a learning opportunity and the more specific your question, the better Copilot can guide you. Practice problem solving without autocomplete  When you’re learning to code, it can be tempting to rely on autocomplete suggestions. But turning off inline completions — at least temporarily — can help strengthen your problem-solving and critical thinking skills. You’ll still have access to Copilot Chat, so you can ask questions and get help without seeing full solutions too early. Just keep in mind: This approach slows things down by design. It’s ideal when you’re learning new concepts, not when you’re under time pressure to build or ship something. To disable Copilot code completion for a project (while keeping chat on), create a folder called .vscode in the root of your project, and add a file named settings.json with this content: { “github.copilot.enable”: { “*”: false } } This setting disables completions in your current workspace, giving you space to think through solutions before asking Copilot for help. Read our full guide on how to use Copilot as a tutor > 2. Build public projects that showcase your skills (and your AI savvy) In today’s AI-powered world, highlighting your AI skills can help you stand out to employers. Your side projects are your portfolio and GitHub gives you the tools to sharpen your skills, collaborate, and showcase your work. Here’s how to get started: In VS Code, open Copilot Chat and type: /new Copilot can scaffold a new project inside your editor to help you get started. Once it’s scaffolded, ask Copilot: “Add the MIT license to this project and publish it as a public project on GitHub.” Open a command line in VS Code and send the following prompt to manually push: git init && git add . && git commit -m “Initial commit” && git push Or create a new repo using the GitHub web interface and upload your files. From there, you can: Track progress with issues, commits, and project boards. Document your journey and milestones in the README. Iterate and improve with feedback and AI assistance. Read our full guide on prompting Copilot to create and publish new projects and start building your public portfolio >  3. Level up your developer toolkit with core GitHub workflows Yes, AI is changing the game, but strong fundamentals still win it. If you’re aiming to level up from student to junior dev, these core workflows are your launchpad: Automate with GitHub Actions. Automating builds and deployments is a best practice for all developers. Use GitHub Actions to build, test, and deploy your projects automatically. Contribute to open source. Join the global developer community by contributing to open source. It’s one of the best ways to learn new skills, grow your resume, and build real-world experience.

Since I joined GitHub as a software engineer on the billing team almost three years ago, I’ve had a front row seat to the evolution of AI coding tools including Github Copilot. What started out as code completions has evolved into so much more including agentic workflows and refactoring suggestions. When I first started using Copilot, I was mainly using it in VSCode. As Copilot has grown and expanded, I’ve extended my use cases beyond my code editor and into all parts of my day-to-day work, including pull requests, code reviews, and more.   GitHub Copilot is now available in all parts of the software development life cycle and one place where it can be extremely useful is when you’re creating pull requests and doing code reviews. During my time at GitHub, I’ve discovered some practical ways Copilot can make a difference during the pull request and code review processes. Here are a few things I’ve started doing that have made my workflow smoother and more efficient. Using Copilot suggestions when reviewing code Often, when I’m reviewing a teammate’s pull request, I’ll have an idea for how their code could be improved, or I’ll want to confirm the code is following best practices. However, I don’t always have time to write the suggested refactored code myself. In these cases, I’ll usually click the Copilot icon next to the file I’m reviewing and ask Copilot to suggest a specific improvement about the lines of code I’m currently reviewing. Then I’ll add that suggestion in my review comment along with some explanation of why I think the suggestion would improve the code.   Recently while reviewing a teammate’s code, I noticed some repetitive code in a Ruby file. I clicked the Copilot icon next to the relevant code and prompted it with: > “Can you refactor this Ruby on Rails code to reduce repetition?” Copilot’s suggestion matched what I would have written and saved me several minutes, which allowed me to focus on higher-level feedback. Since I frequently switch context and review code in different languages like Ruby, TypeScript and Go, sometimes I’ll need a reminder on best practices for each language. I recently used this prompt to help me review some Go code: > “Is this code addition following Go best practices for variable assignment? If not, can you suggest improvements?” Copilot quickly generated a suggestion that I reviewed and used as part of my code review comment. It saved me time and made my feedback more actionable. Formatting tables and data for pull requests I like to add images, screen recordings, and occasionally spreadsheets to my pull request descriptions to add context to help reviewers understand my changes. Adding this type of information in Markdown can be tedious! In these cases, I ask Copilot to do the formatting for me.  Recently, I was working on some improvements to help decrease the load time of a React page, and I wanted to show the improvements that my changes were making. I had been keeping a spreadsheet detailing the the page load times before and after my changes, and I wanted to include it in my pull request description, so I gave Copilot this prompt: Format this data into a GitHub flavored markdown table that I can paste into a GitHub pull request description: Load Time Before (in seconds) Load Time After Updates (in seconds) 1.3 1.2 1.2 1.1 1.1 0.885 1.3 1.3 1.2 0.918 Average 1.22 1.0806 And Copilot gave this response:  Here’s your data formatted as a GitHub-flavored markdown table, ready to paste into a pull request description: Markdown | Test Run | Load Time Before (seconds) | Load Time After Updates (seconds) | |———-|—————————|———————————–| | 1 | 1.3 | 1.2 | | 2 | 1.2 | 1.1 | | 3 | 1.1 | 0.885 | | 4 | 1.3 | 1.3 | | 5 | 1.2 | 0.918 | | **Average** | **1.22** | **1.0806** Let me know if you’d like any adjustments! This simple trick has made it much easier for me to clearly share data in my pull request descriptions and comments. Letting Copilot help write pull request summaries I often use Copilot to help me write pull request summaries. We’ve all been there: you finally open your pull request after fixing a bug or introducing a new feature and the last hurdle is writing the pull request description. Copilot can help kickstart this process for you by using the Copilot icon in the pull request description editor bar. Even if I end up editing the text, having a starting point makes it less daunting. Using Copilot for code reviews and understanding unfamiliar code Copilot is very good at reviewing and explaining code and two ways I leverage this in my day-to-day work are requesting initial code reviews from Copilot and asking questions about unfamiliar code.  Before I mark a pull request as ready for review, I’ll use Copilot to do a quick pass over my changes by requesting a code review from Copilot. It often catches things I might have missed or suggests a better way to write something. And don’t forget to add some notes in the custom instructions in your repository on what you want Copilot to focus on when reviewing pull requests. If I’m reviewing someone else’s code and I don’t understand a change, I’ll ask Copilot to explain it. This helps me get context quickly, especially when I’m less familiar with that part of the codebase. This better understanding of the code allows me to provide more thoughtful and thorough code reviews for my teammates and ensures that I fully understand the potential impact of any pull request that I’m approving.  Copilot’s impact on code reviews and pull requests  While Copilot isn’t a replacement for thoughtful, engaged code reviews, it has become an indispensable tool in my daily workflow as a software engineer. From generating smart suggestions and code refactors, to quick Markdown formatting and drafting pull request summaries, Copilot helps streamline the

Dell and Hugging Face are revolutionizing AI deployment with the Dell Enterprise Hub. From pre-validated models to seamless application integration, this partnership simplifies AI adoption for enterprises. Explore how cutting-edge tools like the Application Catalog and optimized containers empower businesses to scale AI effortlessly.   ​  ​Dell and Hugging Face are revolutionizing AI deployment with the Dell Enterprise Hub. From pre-validated models to seamless application integration, this partnership simplifies AI adoption for enterprises. Explore how cutting-edge tools like the Application Catalog and optimized containers empower businesses to scale AI effortlessly. AI Solutions Blog | Dell

If you’ve ever watched The Matrix Reloaded, Astro Boy, or Happy Feet Two, chances are you’ve seen the fingerprints of …   ​  ​If you’ve ever watched The Matrix Reloaded, Astro Boy, or Happy Feet Two, chances are you’ve seen the fingerprints of … AI Solutions Blog | Dell

Experience the creative freedom of real-time animation with NVIDIA RTX 6000 GPUs and Dell Pro Max PCs.   ​  ​Experience the creative freedom of real-time animation with NVIDIA RTX 6000 GPUs and Dell Pro Max PCs. Dell Pro Max Blog | Dell

Award-winning innovation – discover how the Dell AI Data Platform transforms data management and empowers AI workflows.   ​  ​Award-winning innovation – discover how the Dell AI Data Platform transforms data management and empowers AI workflows. PowerScale Blog | Dell

Customers are asking me: “Should I buy a new AI PC now or wait for AI technology to evolve?” My answer is always the same. Buy now. And I’ll tell you why.   ​  ​Customers are asking me: “Should I buy a new AI PC now or wait for AI technology to evolve?” My answer is always the same. Buy now. And I’ll tell you why. AI Solutions Blog | Dell