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I Built a Cinematic Developer Portfolio Instead of a Traditional One — Here’s What I Learned Most developer portfolios follow the same structure: About. Skills. Projects. Contact. There is nothing wrong with that. But when I started rebuilding mine, I wanted it to feel less like a collection of sections and more like an experience . So what started as a simple portfolio redesign slowly turned into a cinematic, interactive developer portfolio built around my journey as a Software Engineer and AI Developer . 🌐 Live Portfolio: https://pavan-sai-portfolio-xi.vercel.app The Idea I wanted the visitor to feel like they were entering a story rather than opening another resume website. The final experience includes: A cinematic opening sequence A custom soundtrack Full-screen scene-based navigation Animated video backgrounds Project showcases and case studies A personal journey timeline Responsive mobile layouts A cinematic closing scene Smooth transitions between sections The overall visual direction is built around: Black + Champagne Gold + Cinematic Lighting The Portfolio Flow The experience follows a sequence: Intro → Pavan Sai → Hero → About → Skills → Projects → Journey → Contact → Closing Credits Instead of normal vertical scrolling, the main portfolio behaves more like a sequence of scenes. Visitors move through the experience using navigation controls. Project case studies are separate and can scroll normally because they contain more detailed technical information. The Projects Some of the projects featured in the portfolio include: DevPilot AI An AI-powered DevOps incident recovery platform focused on detecting failures, diagnosing issues and helping recover production systems. ROCmPorter Agent An AI-assisted CUDA-to-AMD ROCm migration tool that analyzes repositories, identifies CUDA dependencies and helps generate migration changes. HunarHub A local skilled-worker discovery and service marketplace platform. I also showcase other engineering, AI and full-stack pro

WordPress sitelerinde hız problemi yaşandığında genellikle ilk refleks bir cache eklentisi kurmak oluyor. Bazen gerçekten fark yaratıyor, bazen de PageSpeed puanı biraz yükselmesine rağmen site hâlâ yavaş hissettiriyor. Bunun nedeni WordPress performansının tek bir ayardan oluşmaması. Hosting, tema, eklentiler, görseller, JavaScript dosyaları ve veritabanı aynı anda sayfanın yüklenme süresini etkileyebiliyor. Bu nedenle bir siteyi hızlandırmaya başlamadan önce asıl problemin nerede olduğunu bulmak gerekiyor. WordPress projelerinde sık karşılaştığım 7 performans hatasını aşağıda topladım. Gereğinden büyük görseller kullanmak En sık karşılaştığım problemlerden biri bu. Sayfada 700 piksel genişliğinde görüntülenecek bir görselin 4000-5000 piksel olarak yüklenmesi oldukça yaygın. Özellikle yüksek çözünürlüklü stok fotoğraflar doğrudan WordPress'e yüklendiğinde tek bir görsel birkaç megabayta ulaşabiliyor. Bu da özellikle mobil bağlantılarda ciddi yük oluşturuyor. Görselleri yüklemeden önce kullanılacağı alana uygun boyuta getirmek, sıkıştırmak ve mümkün olduğunda WebP veya AVIF gibi modern formatları tercih etmek önemli. Ancak yalnızca dosya formatını değiştirmek yeterli değil. 4000 piksel genişliğindeki bir görseli WebP'ye çevirmek, görselin gereğinden büyük olduğu gerçeğini değiştirmiyor. Her problemi cache eklentisiyle çözmeye çalışmak Cache WordPress performansında önemli bir yere sahip. Fakat cache eklentisi kurmak her hız problemini ortadan kaldırmaz. Sunucu geç cevap veriyorsa, çok fazla JavaScript çalışıyorsa veya veritabanında ağır sorgular varsa cache yalnızca problemin bir bölümünü gizleyebilir. Ayrıca aynı anda birden fazla optimizasyon eklentisi kullanmak da başka sorunlara yol açabiliyor. Örneğin bir eklentide JavaScript erteleme, başka bir eklentide tekrar JavaScript optimizasyonu ve hosting panelinde üçüncü bir optimizasyon sistemi açıldığında hangi ayarın ne yaptığını takip etmek zorlaşıyor. Ben mümkün olduğunca tek bir ana cache sistemi üzerinden ilerl

JetBrains has described how it began centralising AI usage after development-related spending increased roughly tenfold in six months. Rather than restricting engineers to a small set of approved tools, the company built a shared access and accounting layer intended to preserve tool choice while giving teams greater visibility and control over consumption. By Matt Foster

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. Introduction: The...

This is a submission for Frontend Challenge - Comfort Food Edition, CSS Art . Inspiration In an Indian tech office, no product release, critical bug fix, or late-night deployment is complete without a 5-minute pantry chai break. A steaming clay Kulhad of Ginger Adrak Chai paired with crisp Garma-Garam Samosas is the ultimate comfort food that powers developers through endless coding sprints. This authentic workplace culture and rich street-food nostalgia inspired me to build a pure-CSS interactive art and corporate pantry scene. Demo Live Interactive Demo : Corporate Chai & Samosa Experience CodePen Embed : codepen.io GitHub Repository : Sayista-Yazdani/corporate-chai What I Built Adrak Chai & Samosa is an interactive web experience featuring: Pure CSS Hero Artwork : Handcrafted clay-textured Kulhad Chai cup with a shimmering tea surface, malai rim, and layered rising steam animations. Golden-brown samosas with crisp crimped edges, served on a traditional plate alongside mint and tamarind chutneys. Interactive Corporate Pantry Corner : Fully detailed tea kitchen equipped with a glowing gas stove, boiling tea saucepan with foam, spice jars ( Adrak , Elaichi , Chai Patti ), and stacked clay cups. 4 Distinct Characters : Rohan (Frontend Dev), Amit (Tech Lead), Priya (Product Manager), and Kaka (Pantry Specialist). Web Speech API & Audio Narration : Real voice speech synthesis with gender-matched voice profiles for each character. Character Gaze & Mouth Choreography : Characters automatically look toward whoever is currently speaking with dynamic gaze shifting, listening poses, and animated mouth movements. Journey & Tech Stack Technical Implementation CSS Artwork : Built entirely with CSS gradient meshes, polygon shapes, keyframe animations, and layered pseudo-elements ( ::before / ::after ). Audio Engine : Powered by native window.speechSynthesis with dynamic voice selection and text cleaning. State Management : Reactive data-speaker HTML attributes driving multi-char

I used to think financial apps were designed mostly for people who already understood finance. You open an app, see dozens of charts, numbers, and settings, and sometimes your first thought is: “Okay… where do I even start?” AI is slowly changing that experience. The most useful AI applications in fintech are not always about making predictions. In many cases, they are about removing friction: organizing information, automating repetitive tasks, and helping users understand complicated processes. We can already see this trend in different areas. Companies like Stripe are using technology to simplify online payments, while platforms like Plaid help connect financial data between different services. The same idea is appearing in other parts of finance too. Some digital financial platforms are exploring automation tools that help users create more structured workflows instead of manually managing every step. For example, platforms like BYDFi have introduced automated tools that allow users to use predefined strategies rather than constantly monitoring every market movement. Of course, automation does not replace human decisions. A smarter tool does not mean a person can ignore research or risk management. For developers, the interesting challenge is not building the most complicated system. It is building something that makes complicated things easier for real users. Maybe the future of fintech is not about adding more buttons. Maybe it is about helping people need fewer buttons.

A few years ago, a technical interview mostly tested one thing: can you write correct code, from memory, under pressure. That bar has quietly shifted — and a lot of developers preparing for interviews right now haven't fully clocked it. AI coding assistants are part of daily work at most companies now, from big IT services firms to small product teams. Interview panels have adjusted to that reality faster than most prep guides have. What's actually different now Interviewers care less about whether you can produce a function from scratch, and more about whether you understand what code is doing and why. It's increasingly common to be handed a piece of AI-generated code and asked to find the bug, justify a design decision, or optimize it — instead of writing something from zero on a whiteboard. Some companies go further and let you use AI tools during the technical round, then evaluate how well you direct the tool, verify its output, and catch its mistakes. The skill being tested has moved from "can you write code" to "can you reason clearly with code as your material." Three things I keep seeing candidates get wrong Treating a finished course or degree as the finish line. Completing a syllabus tells an employer you were exposed to concepts. It doesn't tell them you can apply those concepts to a messy, real-world problem — which is exactly what open-ended interview scenarios are designed to expose. Leaning on AI tools without understanding the output. Using an AI assistant while practicing at home is fine. The problem shows up when that habit surfaces in a live interview as an inability to explain your own solution. If you can't walk through why a piece of code works, a couple of follow-up questions will make that obvious fast. Underrating communication and debugging skills. As AI tools take on more initial code-writing, the human value shifts toward reviewing, debugging, and explaining decisions to teammates. Candidates who only practiced writing code — and never pr

Spent the week redesigning my portfolio’s blog layout and nuking thousands of stale notes in my Obsidian vault. Between the UI polish and some deep dives into libp2p DHT de-flaking, I pushed 36 commits and managed to delete almost 16,000 lines of clutter. TL;DR I’ve always believed that your digital space needs a good pruning every now and then to stay healthy. This week was the embodiment of that philosophy. I pushed 36 commits across four primary projects, resulting in over 23,000 additions and nearly 16,000 deletions. Most of that churn came from a massive redesign of my portfolio's blog and a long-overdue "fresh start" for my Obsidian vault. On the open-source side, I spent some quality time in the weeds of py-libp2p , chasing down flaky DHT tests and proposing better subnet diversity limits. What I Built Portfolio Redesign: The Typography Pivot My main focus this week was my portfolio. I’ve been feeling like the blog layout was getting a bit cluttered, so I opened and merged PR #15, which was all about "typographic hierarchy instead of decoration." I’m moving away from unnecessary borders and boxes and letting the type do the heavy lifting. I spent a lot of time in components/blog and app/blog refining the layout. I implemented borderless filter pills and full-width rows to give the content more room to breathe. One of the bigger technical shifts was moving the blog list to be fully server-rendered. It feels snappier, and it allowed me to implement more "honest" dates and better hover states on the rows. I also added a real focus ring for accessibility (because we’ve all been frustrated by keyboard navigation that feels like a guessing game). By the time I was done, I’d touched over 200 files in that repo alone. The Obsidian Purge I also took a metaphorical chainsaw to my obsidian-vault . I nuked nearly 10,000 lines of stale content. I removed entire directories for "Projects," "Rust," and "Backend" notes that were just gathering digital dust. It’s easy to let

If you love the simplicity of Docker Swarm (native Compose files, lightweight single binary) but miss the advanced capabilities of Kubernetes (targeted label placement, SRE-grade observability, built-in DNS service discovery, and zero-trust ingress), meet Gubernator (gbnt) . We are excited to release Gubernator v2.13.0 , introducing three massive feature suites natively integrated into a single binary and a modern Material Design 3 Flutter Web Dashboard: Google SRE Multi-Burn-Rate SLO Engine & Interactive Suite CoreDNS 4-Tab Management Suite & Interactive Dig Playground Caddy Ingress & Zero-Trust Reverse Proxy Suite Fun Fact: The entirety of Gubernator's codebase, multi-node deployment pipelines, and SRE features were designed, built, and pair-programmed using **Google Antigravity (AGY) , Google DeepMind's agentic AI coding assistant! Let's dive into what's new and how you can level up your self-hosted or production container clusters! 1. Google SRE Multi-Burn-Rate SLO Engine & Web Suite Defining Service Level Objectives (SLOs) and tracking Error Budgets is the gold standard of Site Reliability Engineering. Until now, implementing SLOs meant running heavy Kubernetes CRDs (via tools like Sloth or Pyrra) or using costly SaaS platforms. Gubernator v2.13.0 brings Google SRE Workbook (Chapter 5) compliant multi-burn-rate alerting straight to simple docker-compose.yml services: version : " 3.8" services : payment-api : image : hashicorp/http-echo:latest labels : gbnt.slo.enable : " true" gbnt.slo.target : " 99.9" gbnt.slo.window : " 30d" gbnt.slo.template : " caddy-http" gbnt.slo.journey : " Checkout Flow" What makes Gubernator's SLO Suite unique? Google Multi-Burn-Rate Alerting : Automatically generates standard 4-window Prometheus recording and alert rules ( Critical Page 1h/6h & Warning Ticket 3d/14d ). Dynamic "No-Code" Management : Click "+ Configure / Add SLO" in the Web UI or call POST /v1/slo/edit to create, edit, or disable SLOs on the fly without editing Compose

Most "AI gift finders" are a search box with a chatbot glued on. I wanted to build something different — a quiz-driven gift recommender that ranks real Amazon products by who the recipient actually is , not just keywords. I call it GiftHive . In this post I'll walk through the architecture, the conversion tricks I learned shipping it, and the bits I'm proudest of. The Problem Picking gifts is emotionally expensive. You scroll Amazon for an hour, second-guess every option, and end up buying a gift card. Existing tools don't help because they optimize for keyword match , not recipient fit . GiftHive flips the input: instead of "show me gifts under $50", you answer a 30-second quiz about the person (relationship, interests, occasion, budget) and get a ranked shortlist with explanations of why each gift fits. Stack Next.js (App Router) — SSR for fast first paint, RSC for product data Tailwind CSS — design system + dark mode via CSS variables Cloudflare Pages — edge-deployed, free tier covers the traffic Amazon Associates — affiliate revenue model The Funnel The whole site is a 3-step conversion funnel: Landing page — exit-intent modal + social proof toasts prime the visitor Quiz — 30-second, one-question-per-screen flow, no login Results — ranked products with countdown bar and "X people found gifts this week" social proof Every step has a single primary CTA. The exit-intent modal is route-aware — it only fires on / and stays silent on /quiz and /results so it never interrupts the funnel mid-flow. That bug cost me ~15% of quiz completions before I caught it. Personalization Logic Each quiz answer maps to a vector of attributes (interests, style, budget, relationship). Products in the catalog have matching tags. Ranking is a weighted score: score = tag_overlap * w1 + budget_match * w2 + occasion_match * w3 No ML model needed — a few hundred products and clean tagging is enough to feel personal. Amazon Affiliate Integration Every product link runs through getAmazonUrl() w

I was watching The Mandalorian the other day when it struck me that I don't know Pedro Pascal, which...

Say "AI" in a room full of developers and a pecking order forms on its own. Python sits at the top,...

The concrete problem Running two or three coding-agent sessions is easy. Knowing when their work is safe to combine is not. One session changes an API while another writes regression tests against the old shape. A third investigates a production failure and quietly edits the same configuration file. Git worktrees prevent immediate filesystem collisions, but they do not explain task dependencies, transfer assumptions, or warn that two agents are solving incompatible versions of the problem. The developer becomes a human message bus: checking terminals, copying commit IDs, repeating context, and deciding which session should wait. The more capable each agent becomes, the less useful a wall of terminal panes is as a coordination interface. The current signal Claude Code now supports messaging between sessions on the same machine. Its documentation describes session discovery, plain-text messages, and a local messaging socket. Agent view separately exposes background-session state, worktrees, pull-request status, and a JSON listing suitable for scripts. Hooks can observe tool input and block a tool call before execution. That does not prove demand for a new product. It does create a concrete implementation moment: the primitives for handoffs and visibility exist, while dependency ownership and conflict negotiation remain a workflow problem. In RayTally's bounded Hacker News snapshot at August 9, 00:33 UTC, the cross-session messaging discussion had 50 points and 26 comments and ranked 18th. Those numbers describe that historical observation only; they are not user counts, market validation, or a prediction of lasting interest. A product direction: a control desk for handoffs The useful product is not another chat window. It is a small local control desk that makes each session declare four things: its goal, worktree, files it expects to touch, and the result another session is waiting for. When the API session finishes, the testing session should receive a compact hando

🚀 Shipping a major update to my portfolio After weeks of designing, developing, and refining, I'm excited to share the latest version of my personal portfolio. Rather than building another static portfolio, I wanted to treat it like a real product—focusing on performance, interaction design, accessibility, analytics, and user experience. 🌐 Live: https://www.alanbabychan.online What I built 🟩 Matrix Theme A cyberpunk-inspired dark mode featuring animated binary effects, glowing UI elements, and an immersive developer experience. ⬜ Paper Theme A clean, modern light mode designed with readability, visual hierarchy, and clarity in mind. 🎵 Interactive Audio System •Background music •UI sound effects •Dedicated settings panel •Adjustable volume controls •Built using the Web Audio API 🖱️ Interactive Cursor A custom mouse-follow glow and subtle cursor interactions that enhance the browsing experience without becoming distracting. ✨ Micro-interactions Hover states, smooth page transitions, animated UI components, and responsive visual feedback to make every interaction feel intentional. 📖 UX & Accessibility Built around clear typography, intuitive navigation, responsive layouts, and accessibility-focused design to provide a consistent experience across devices. 📊 Performance & Analytics Built with Next.js and optimized for speed, SEO, and scalability. Implemented a complete Google Analytics 4 setup including: •SPA page tracking •Google Consent Mode v2 •Custom event tracking •User interaction analytics Tech Stack: Next.js • React • Tailwind CSS • Framer Motion • Web Audio API • Google Analytics 4 • Microsoft Clarity Coming Soon... 👀 I'm currently building a personal AI assistant that will allow visitors to interact with my portfolio, ask questions about my projects, experience, and skills, and explore everything conversationally. What I learned This project pushed me to dive deeper into: •Theme architecture •Frontend performance optimization •Animation systems •Custom UI inte

As developers, we use package managers almost every day. Whether we are working with Node.js, React, Next.js, TypeScript, Express, Prisma, or other technologies in the JavaScript ecosystem, choosing the right package manager can have a meaningful impact on our development workflow. Recently, I spent some time comparing the most popular package managers: npm, Yarn, pnpm, and Bun. After looking at them from the perspective of performance, dependency management, disk efficiency, ecosystem compatibility, and developer productivity, my current preference is pnpm. Why pnpm? For me, pnpm provides one of the best overall balances between speed, disk efficiency, reliability, dependency management, and developer experience. One of the key differences is how pnpm handles dependencies. It uses a content-addressable store and links packages into projects instead of unnecessarily keeping separate copies of the same packages for every project. This can reduce disk usage and make package installation more efficient, especially when working on multiple JavaScript or TypeScript projects. Another advantage is pnpm's stricter dependency management. It encourages projects to explicitly declare the packages they actually depend on, which can help prevent accidental reliance on transitive dependencies. This becomes particularly useful when working on larger applications, monorepos, or team-based projects. What about Bun? Bun is extremely interesting because it is much more than a package manager. It provides a JavaScript/TypeScript runtime, package manager, test runner, and bundler. Its performance is impressive, especially when it comes to package installation and certain development workflows. However, I don't think raw speed should be the only factor when choosing a technology for production. Compatibility, ecosystem maturity, team familiarity, tooling support, and long-term maintainability are equally important. That is why I see Bun as an excellent and promising tool, but I would not

Classic Machine Learning Through the Eyes of an SRE — Part 4 When a computation is too hard, don't compute harder. Change coordinates until it becomes easy. Every engineer has made this move. Pick the right data structure and the impossible query goes O(1). Re-index the table and the report that took an hour takes a second. Move the problem into a space where it's trivial, solve it there, come back. That's the kernel trick. SVM's famous move isn't building a curvy model — it's finding a FLAT cut in a transformed space, which corresponds to a curved boundary back in your original features. The separator stays linear in the transformed space. The space did the work. And here's the part that makes it a trick rather than just a projection: the data never actually goes up there. The optimization only ever needs inner products between pairs of points, and a kernel function computes what that inner product would be in the high-dimensional space, directly from the original coordinates. You get the geometry of a space you never built. Some kernels correspond to infinitely many dimensions, which would otherwise be an awkward amount of memory to allocate. The bet it makes SVM bets that the most ROBUST boundary is the one with the widest margin — maximum distance from the nearest points on each side. And here's the part that rewired me: only those nearest points matter. They're the support vectors. The non-support-vector points don't directly determine the final boundary at all. Compare that to the forest, which averages over EVERYTHING. SVM is the opposite extreme: the borderline cases that become support vectors define the decision boundary. In delivery-risk terms — the projects that teach you where the line is aren't the disasters or the easy wins. They're the borderline ones that barely breached and barely survived. SVM formalizes that. Everything old returns After trees and forests threw away gradient descent, SVM brings some of the regression toolkit back: an explicit los

Qarinah compiles a compact, cited project-memory pack instead of asking every new coding-agent session to replay the entire available history. The published estimate Across six committed software-task fixtures, the full-history baseline contained 442,113 portable estimated input-context tokens . The Qarinah path used 5,682 . Every required target was still directly covered in the top five results. That is: 436,431 fewer estimated input-context tokens; 98.71% less repeated context; and a 77.81:1 baseline-to-pack ratio. The ratio is not a claim that every provider bill drops by 98.71%, or that an agent session lasts 77.81 times longer. It measures the compared input-context volume in the published six-fixture estimate. What the same token rate would cost The table applies four flat, uncached input-token rates to the same two token estimates. It is arithmetic, not a provider invoice. Flat uncached input rate Full-history baseline Qarinah pack Estimated saving $1 / million tokens $0.442113 $0.005682 $0.436431 $3 / million tokens $1.326339 $0.017046 $1.309293 $5 / million tokens $2.210565 $0.028410 $2.182155 $15 / million tokens $6.631695 $0.085230 $6.546465 The calculation is: estimated tokens / 1,000,000 x flat input rate It deliberately excludes provider-native tokenization, caching, output tokens, reasoning tokens, tool calls, retrieval, hosting, and fixed fees. Real cost depends on the provider, model, cache behavior, context composition, and how often the same history would otherwise be resent. Why the pack remains useful Compression only matters if the next task can still find its evidence. The benchmark checks both volume and retrieval coverage: every required target had to be directly present in the top five. Qarinah preserves the source event ID and content hash for selected context, so a later agent receives a bounded handoff that can be inspected instead of an opaque story. Qarinah also passed 380 of 380 deterministic file-specific exact and typo-tolerant que

If you've ever needed to build a serious data table in React, you've probably run into this problem....

Almost every cost discussion about AI agents opens with a model price per million tokens, which is the one number that tells you the least. The bill you actually receive is a stack of four things: API calls, infrastructure, the one time build, and the recurring costs nobody put in the estimate. Here is how the stack usually breaks down and which layer is worth attacking first. Where The Money Actually Goes For a typical business agent, a support bot or an internal automation running on a managed platform, monthly operating cost lands between 200 and 1,000 dollars. API calls are 40 to 60 percent of that. Hosting, a vector database for memory, and monitoring share the rest. The spread on either side is wide: a solo developer on open source models and a small VPS can stay under 50 dollars a month, while an enterprise running multi agent systems on frontier models regularly spends 5,000 to 13,000 a month before anyone counts the build. Infrastructure has its own shape. Serverless is the cheapest entry, and a moderate agent handling 10,000 to 20,000 interactions a month usually runs 50 to 200 dollars in compute with no idle charge. Containers on ECS, Cloud Run or Kubernetes cost 100 to 500 and buy persistent connections and steady latency. Self hosted GPU starts around 200 a month for a T4 class instance and passes 1,000 for A100 or H100 class, which only pays off at volumes high enough to amortize it. Vector storage adds 20 to 500, and pgvector on a Postgres you already run removes that line entirely. Model Choice Is A Routing Decision The price spread between tiers is large enough that treating model selection as one global choice is the expensive mistake. Frontier reasoning models sit at the top of the range, mid tier models cost a fraction of that, and the lightweight tier is cheaper again by roughly an order of magnitude. An agent that sends every step to the top tier is paying reasoning prices for string formatting. The fix is routing per step rather than per agent

The deadline nobody missed Every expiry story we've written here has the same shape. A certificate lapses, nobody was watching, something breaks, and everyone is surprised. A Splunk license at a federal agency. Microsoft's own network connectivity tool. Same plot, different logo. This one is the opposite, and that's what makes it worth reading. On October 19, 2026, roughly ten weeks from this writing, the Microsoft Windows Production PCA 2011 certificate expires. It sits under the trust chain for the Windows boot process on essentially every PC shipped in the last fifteen years. Nobody forgot it. The expiry date has been printed inside the certificate since 2011. Microsoft has been publishing guidance for over two years, shipping replacement certificates through Windows Update since 2024, running OEM briefings, and pushing an automatic rollout that requires most users to do nothing at all. It is August. It still isn't done. What's actually expiring Three certificates, four replacements, three dates, all in 2026: Certificate Expires Replaced by Microsoft Corporation KEK CA 2011 June 24, 2026 Microsoft Corporation KEK 2K CA 2023 Microsoft Corporation UEFI CA 2011 June 27, 2026 Microsoft UEFI CA 2023 Microsoft Corporation UEFI CA 2011 June 27, 2026 Microsoft Option ROM UEFI CA 2023 Microsoft Windows Production PCA 2011 October 19, 2026 Windows UEFI CA 2023 Both June dates have already passed. October is the one that matters most, because that's the certificate used to sign the Windows Boot Manager itself. Devices that don't pick up the 2023 certificates keep booting and keep taking normal Windows updates. What they lose is the ability to receive new protections for the early boot path: updates to Boot Manager, Secure Boot database changes, revocation lists, and mitigations for bootkit vulnerabilities discovered from here on. In other words, the machine doesn't fail. It just quietly stops being patchable in the one layer that sits below your antivirus, your EDR agent, a