Have you ever seen a toy and said “That wants to be a deck”? [Attoparsec] did, when his eyes fell upon the Little Talking Scholar, a punch card driven toy …read more

anomalie majeure, SpaceX, Elon Musk, explosion Starship, échec technique, fusée Starship, technologie spatiale, sécurité spatiale, innovations, échec SpaceX L'univers des vols spatiaux a toujours été parsemé de défis techniques et de catastrophes tragiques, et l'explosion récente du Starship de SpaceX n'est pas seulement une autre anomalie dans la longue liste des échecs. Non, c'est un cri...

And I seem to be the only one.

Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerial (or satellite) image. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-View (BEV) but are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the vertical (height) dimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoF (x, y, and yaw) pose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models

“The best way to predict the future is to invent it,” the famed computer scientist Alan Kay once said. Uttered more out of exasperation than as inspiration, his remark has nevertheless attained gospel-like status among Silicon Valley entrepreneurs, in particular a handful of tech billionaires who fancy themselves the chief architects of humanity’s future.  Sam Altman, Jeff Bezos, Elon Musk, and others may have slightly different goals and ambitions in the near term, but their grand visions for the next decade and beyond are remarkably similar. Framed less as technological objectives and more as existential imperatives, they include aligning AI with the interests of humanity; creating an artificial superintelligence that will solve all the world’s most pressing problems; merging with that superintelligence to achieve immortality (or something close to it); establishing a permanent, self-­sustaining colony on Mars; and, ultimately, spreading out across the cosmos. While there’s a sprawling patchwork of ideas and philosophies powering these visions, three features play a central role, says Adam Becker, a science writer and astrophysicist: an unshakable certainty that technology can solve any problem, a belief in the necessity of perpetual growth, and a quasi-religious obsession with transcending our physical and biological limits. In his timely new book, More Everything Forever: AI Overlords, Space Empires, and Silicon Valley’s Crusade to Control the Fate of Humanity, Becker calls this triumvirate of beliefs the “ideology of technological salvation” and warns that tech titans are using it to steer humanity in a dangerous direction.  “In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress.” “The credence that tech billionaires give to these specific science-fictional futures validates their pursuit of more—to portray the growth of their businesses as a moral imperative, to reduce the complex problems of the world to simple questions of technology, [and] to justify nearly any action they might want to take,” he writes. Becker argues that the only way to break free of these visions is to see them for what they are: a convenient excuse to continue destroying the environment, skirt regulations, amass more power and control, and dismiss the very real problems of today to focus on the imagined ones of tomorrow.  A lot of critics, academics, and journalists have tried to define or distill the Silicon Valley ethos over the years. There was the “Californian Ideology” in the mid-’90s, the “Move fast and break things” era of the early 2000s, and more recently the “Libertarianism for me, feudalism for thee”  or “techno-­authoritarian” views. How do you see the “ideology of technological salvation” fitting in?  I’d say it’s very much of a piece with those earlier attempts to describe the Silicon Valley mindset. I mean, you can draw a pretty straight line from Max More’s principles of transhumanism in the ’90s to the Californian Ideology [a mashup of countercultural, libertarian, and neoliberal values] and through to what I call the ideology of technological salvation. The fact is, many of the ideas that define or animate Silicon Valley thinking have never been much of a ­mystery—libertarianism, an antipathy toward the government and regulation, the boundless faith in technology, the obsession with optimization.  What can be difficult is to parse where all these ideas come from and how they fit together—or if they fit together at all. I came up with the ideology of technological salvation as a way to name and give shape to a group of interrelated concepts and philosophies that can seem sprawling and ill-defined at first, but that actually sit at the center of a worldview shared by venture capitalists, executives, and other thought leaders in the tech industry.  Readers will likely be familiar with the tech billionaires featured in your book and at least some of their ambitions. I’m guessing they’ll be less familiar with the various “isms” that you argue have influenced or guided their thinking. Effective altruism, rationalism, long­termism, extropianism, effective accelerationism, futurism, singularitarianism, ­transhumanism—there are a lot of them. Is there something that they all share?  They’re definitely connected. In a sense, you could say they’re all versions or instantiations of the ideology of technological salvation, but there are also some very deep historical connections between the people in these groups and their aims and beliefs. The Extropians in the late ’80s believed in self-­transformation through technology and freedom from limitations of any kind—ideas that Ray Kurzweil eventually helped popularize and legitimize for a larger audience with the Singularity.  In most of these isms you’ll find the idea of escape and transcendence, as well as the promise of an amazing future, full of unimaginable wonders—so long as we don’t get in the way of technological progress. I should say that AI researcher Timnit Gebru and philosopher Émile Torres have also done a lot of great work linking these ideologies to one another and showing how they all have ties to racism, misogyny, and eugenics. You argue that the Singularity is the purest expression of the ideology of technological salvation. How so? Well, for one thing, it’s just this very simple, straightforward idea—the Singularity is coming and will occur when we merge our brains with the cloud and expand our intelligence a millionfold. This will then deepen our awareness and consciousness and everything will be amazing. In many ways, it’s a fantastical vision of a perfect technological utopia. We’re all going to live as long as we want in an eternal paradise, watched over by machines of loving grace, and everything will just get exponentially better forever. The end. The other isms I talk about in the book have a little more … heft isn’t the right word—they just have more stuff going on. There’s more to them, right? The rationalists and the effective altruists and the longtermists—they think that something like a singularity will happen, or could happen, but that there’s this really big danger between where we are now and that potential event. We have to address the fact that an all-powerful AI might destroy humanity—the so-called alignment problem—before any singularity can happen.  Then you’ve got the effective accelerationists, who are more like Kurzweil, but they’ve got more of a tech-bro spin on things. They’ve taken some of the older transhumanist ideas from the Singularity and updated them for startup culture. Marc Andreessen’s “Techno-Optimist Manifesto” [from 2023] is a good example. You could argue that all of these other philosophies that have gained purchase in Silicon Valley are just twists on Kurzweil’s Singularity, each one building on top of the core ideas of transcendence, techno­-optimism, and exponential growth.  Early on in the book you take aim at that idea of exponential growth—specifically, Kurzweil’s “Law of Accelerating Returns.” Could you explain what that is and why you think it’s flawed? Kurzweil thinks there’s this immutable “Law of Accelerating Returns” at work in the affairs of the universe, especially when it comes to technology. It’s the idea that technological progress isn’t linear but exponential. Advancements in one technology fuel even more rapid advancements in the future, which in turn lead to greater complexity and greater technological power, and on and on. This is just a mistake. Kurzweil uses the Law of Accelerating Returns to explain why the Singularity is inevitable, but to be clear, he’s far from the only one who believes in this so-called law. “I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear.” My sense is that it’s an idea that comes from staring at Moore’s Law for too long. Moore’s Law is of course the famous prediction that the number of transistors on a chip will double roughly every two years, with a minimal increase in cost. Now, that has in fact happened for the last 50 years or so, but not because of some fundamental law in the universe. It’s because the tech industry made a choice and some very sizable investments to make it happen. Moore’s Law was ultimately this really interesting observation or projection of a historical trend, but even Gordon Moore [who first articulated it] knew that it wouldn’t and couldn’t last forever. In fact, some think it’s already over.  These ideologies take inspiration from some pretty unsavory characters. Transhumanism, you say, was first popularized by the eugenicist Julian Huxley in a speech in 1951. Marc Andreessen’s “Techno-Optimist Manifesto” name-checks the noted fascist Filippo Tommaso Marinetti and his futurist manifesto. Did you get the sense while researching the book that the tech titans who champion these ideas understand their dangerous origins? You’re assuming in the framing of that question that there’s any rigorous thought going on here at all. As I say in the book, Andreessen’s manifesto runs almost entirely on vibes, not logic. I think someone may have told him about the futurist manifesto at some point, and he just sort of liked the general vibe, which is why he paraphrases a part of it. Maybe he learned something about Marinetti and forgot it. Maybe he didn’t care.  I really believe that when you get as rich as some of these guys are, you can just do things that seem like thinking and no one is really going to correct you or tell you things you don’t want to hear. For many of these billionaires, the vibes of fascism, authoritarianism, and colonialism are attractive because they’re fundamentally about creating a fantasy of control.  You argue that these visions of the future are being used to hasten environmental destruction, increase authoritarianism, and exacerbate inequalities. You also admit that they appeal to lots of people who aren’t billionaires. Why do you think that is?  I think a lot of us are also attracted to these ideas for the same reasons the tech billionaires are—they offer this fantasy of knowing what the future holds, of transcending death, and a sense that someone or something out there is in control. It’s hard to overstate how comforting a simple, coherent narrative can be in an increasingly complex and fast-moving world. This is of course what religion offers for many of us, and I don’t think it’s an accident that a sizable number of people in the rationalist and effective altruist communities are actually ex-evangelicals. More than any one specific technology, it seems like the most consequential thing these billionaires have invented is a sense of inevitability—that their visions for the future are somehow predestined. How does one fight against that? It’s a difficult question. For me, the answer was to write this book. I guess I’d also say this: Silicon Valley enjoyed well over a decade with little to no pushback on anything. That’s definitely a big part of how we ended up in this mess. There was no regulation, very little critical coverage in the press, and a lot of self-mythologizing going on. Things have started to change, especially as the social and environmental damage that tech companies and industry leaders have helped facilitate has become more clear. That understanding is an essential part of deflating the power of these tech billionaires and breaking free of their visions. When we understand that these dreams of the future are actually nightmares for the rest of us, I think you’ll see that senseof inevitability vanish pretty fast.  This interview was edited for length and clarity. Bryan Gardiner is a writer based in Oakland, California. 

The Secure Government Email (SGE) Common Implementation Framework New Zealand’s government is introducing a comprehensive email security framework designed to protect official communications from phishing and domain spoofing. This new framework, which will be mandatory for all government agencies by October 2025, establishes clear technical standards to enhance email security and retire the outdated SEEMail service.  Key Takeaways All NZ government agencies must comply with new email security requirements by October 2025. The new framework strengthens trust and security in government communications by preventing spoofing and phishing. The framework mandates TLS 1.2+, SPF, DKIM, DMARC with p=reject, MTA-STS, and DLP controls. EasyDMARC simplifies compliance with our guided setup, monitoring, and automated reporting. Start a Free Trial What is the Secure Government Email Common Implementation Framework? The Secure Government Email (SGE) Common Implementation Framework is a new government-led initiative in New Zealand designed to standardize email security across all government agencies. Its main goal is to secure external email communication, reduce domain spoofing in phishing attacks, and replace the legacy SEEMail service. Why is New Zealand Implementing New Government Email Security Standards? The framework was developed by New Zealand’s Department of Internal Affairs (DIA) as part of its role in managing ICT Common Capabilities. It leverages modern email security controls via the Domain Name System (DNS) to enable the retirement of the legacy SEEMail service and provide: Encryption for transmission security Digital signing for message integrity Basic non-repudiation (by allowing only authorized senders) Domain spoofing protection These improvements apply to all emails, not just those routed through SEEMail, offering broader protection across agency communications. What Email Security Technologies Are Required by the New NZ SGE Framework? The SGE Framework outlines the following key technologies that agencies must implement: TLS 1.2 or higher with implicit TLS enforced TLS-RPT (TLS Reporting) SPF (Sender Policy Framework) DKIM (DomainKeys Identified Mail) DMARC (Domain-based Message Authentication, Reporting, and Conformance) with reporting MTA-STS (Mail Transfer Agent Strict Transport Security) Data Loss Prevention controls These technologies work together to ensure encrypted email transmission, validate sender identity, prevent unauthorized use of domains, and reduce the risk of sensitive data leaks. Get in touch When Do NZ Government Agencies Need to Comply with this Framework? All New Zealand government agencies are expected to fully implement the Secure Government Email (SGE) Common Implementation Framework by October 2025. Agencies should begin their planning and deployment now to ensure full compliance by the deadline. The All of Government Secure Email Common Implementation Framework v1.0 What are the Mandated Requirements for Domains? Below are the exact requirements for all email-enabled domains under the new framework. ControlExact RequirementTLSMinimum TLS 1.2. TLS 1.1, 1.0, SSL, or clear-text not permitted.TLS-RPTAll email-sending domains must have TLS reporting enabled.SPFMust exist and end with -all.DKIMAll outbound email from every sending service must be DKIM-signed at the final hop.DMARCPolicy of p=reject on all email-enabled domains. adkim=s is recommended when not bulk-sending.MTA-STSEnabled and set to enforce.Implicit TLSMust be configured and enforced for every connection.Data Loss PreventionEnforce in line with the New Zealand Information Security Manual (NZISM) and Protective Security Requirements (PSR). Compliance Monitoring and Reporting The All of Government Service Delivery (AoGSD) team will be monitoring compliance with the framework. Monitoring will initially cover SPF, DMARC, and MTA-STS settings and will be expanded to include DKIM. Changes to these settings will be monitored, enabling reporting on email security compliance across all government agencies. Ongoing monitoring will highlight changes to domains, ensure new domains are set up with security in place, and monitor the implementation of future email security technologies.  Should compliance changes occur, such as an agency’s SPF record being changed from -all to ~all, this will be captured so that the AoGSD Security Team can investigate. They will then communicate directly with the agency to determine if an issue exists or if an error has occurred, reviewing each case individually. Deployment Checklist for NZ Government Compliance Enforce TLS 1.2 minimum, implicit TLS, MTA-STS & TLS-RPT SPF with -all DKIM on all outbound email DMARC p=reject  adkim=s where suitable For non-email/parked domains: SPF -all, empty DKIM, DMARC reject strict Compliance dashboard Inbound DMARC evaluation enforced DLP aligned with NZISM Start a Free Trial How EasyDMARC Can Help Government Agencies Comply EasyDMARC provides a comprehensive email security solution that simplifies the deployment and ongoing management of DNS-based email security protocols like SPF, DKIM, and DMARC with reporting. Our platform offers automated checks, real-time monitoring, and a guided setup to help government organizations quickly reach compliance. 1. TLS-RPT / MTA-STS audit EasyDMARC enables you to enable the Managed MTA-STS and TLS-RPT option with a single click. We provide the required DNS records and continuously monitor them for issues, delivering reports on TLS negotiation problems. This helps agencies ensure secure email transmission and quickly detect delivery or encryption failures. Note: In this screenshot, you can see how to deploy MTA-STS and TLS Reporting by adding just three CNAME records provided by EasyDMARC. It’s recommended to start in “testing” mode, evaluate the TLS-RPT reports, and then gradually switch your MTA-STS policy to “enforce”. The process is simple and takes just a few clicks. As shown above, EasyDMARC parses incoming TLS reports into a centralized dashboard, giving you clear visibility into delivery and encryption issues across all sending sources. 2. SPF with “-all”In the EasyDARC platform, you can run the SPF Record Generator to create a compliant record. Publish your v=spf1 record with “-all” to enforce a hard fail for unauthorized senders and prevent spoofed emails from passing SPF checks. This strengthens your domain’s protection against impersonation. Note: It is highly recommended to start adjusting your SPF record only after you begin receiving DMARC reports and identifying your legitimate email sources. As we’ll explain in more detail below, both SPF and DKIM should be adjusted after you gain visibility through reports. Making changes without proper visibility can lead to false positives, misconfigurations, and potential loss of legitimate emails. That’s why the first step should always be setting DMARC to p=none, receiving reports, analyzing them, and then gradually fixing any SPF or DKIM issues. 3. DKIM on all outbound email DKIM must be configured for all email sources sending emails on behalf of your domain. This is critical, as DKIM plays a bigger role than SPF when it comes to building domain reputation, surviving auto-forwarding, mailing lists, and other edge cases. As mentioned above, DMARC reports provide visibility into your email sources, allowing you to implement DKIM accordingly (see first screenshot). If you’re using third-party services like Google Workspace, Microsoft 365, or Mimecast, you’ll need to retrieve the public DKIM key from your provider’s admin interface (see second screenshot). EasyDMARC maintains a backend directory of over 1,400 email sources. We also give you detailed guidance on how to configure SPF and DKIM correctly for major ESPs.  Note: At the end of this article, you’ll find configuration links for well-known ESPs like Google Workspace, Microsoft 365, Zoho Mail, Amazon SES, and SendGrid – helping you avoid common misconfigurations and get aligned with SGE requirements. If you’re using a dedicated MTA (e.g., Postfix), DKIM must be implemented manually. EasyDMARC’s DKIM Record Generator lets you generate both public and private keys for your server. The private key is stored on your MTA, while the public key must be published in your DNS (see third and fourth screenshots). 4. DMARC p=reject rollout As mentioned in previous points, DMARC reporting is the first and most important step on your DMARC enforcement journey. Always start with a p=none policy and configure RUA reports to be sent to EasyDMARC. Use the report insights to identify and fix SPF and DKIM alignment issues, then gradually move to p=quarantine and finally p=reject once all legitimate email sources have been authenticated.  This phased approach ensures full protection against domain spoofing without risking legitimate email delivery. 5. adkim Strict Alignment Check This strict alignment check is not always applicable, especially if you’re using third-party bulk ESPs, such as Sendgrid, that require you to set DKIM on a subdomain level. You can set adkim=s in your DMARC TXT record, or simply enable strict mode in EasyDMARC’s Managed DMARC settings. This ensures that only emails with a DKIM signature that exactly match your domain pass alignment, adding an extra layer of protection against domain spoofing. But only do this if you are NOT a bulk sender. 6. Securing Non-Email Enabled Domains The purpose of deploying email security to non-email-enabled domains, or parked domains, is to prevent messages being spoofed from that domain. This requirement remains even if the root-level domain has SP=reject set within its DMARC record. Under this new framework, you must bulk import and mark parked domains as “Parked.” Crucially, this requires adjusting SPF settings to an empty record, setting DMARC to p=reject, and ensuring an empty DKIM record is in place: • SPF record: “v=spf1 -all”. • Wildcard DKIM record with empty public key.• DMARC record: “v=DMARC1;p=reject;adkim=s;aspf=s;rua=mailto:…”. EasyDMARC allows you to add and label parked domains for free. This is important because it helps you monitor any activity from these domains and ensure they remain protected with a strict DMARC policy of p=reject. 7. Compliance Dashboard Use EasyDMARC’s Domain Scanner to assess the security posture of each domain with a clear compliance score and risk level. The dashboard highlights configuration gaps and guides remediation steps, helping government agencies stay on track toward full compliance with the SGE Framework. 8. Inbound DMARC Evaluation Enforced You don’t need to apply any changes if you’re using Google Workspace, Microsoft 365, or other major mailbox providers. Most of them already enforce DMARC evaluation on incoming emails. However, some legacy Microsoft 365 setups may still quarantine emails that fail DMARC checks, even when the sending domain has a p=reject policy, instead of rejecting them. This behavior can be adjusted directly from your Microsoft Defender portal. Read more about this in our step-by-step guide on how to set up SPF, DKIM, and DMARC from Microsoft Defender. If you’re using a third-party mail provider that doesn’t enforce having a DMARC policy for incoming emails, which is rare, you’ll need to contact their support to request a configuration change. 9. Data Loss Prevention Aligned with NZISM The New Zealand Information Security Manual (NZISM) is the New Zealand Government’s manual on information assurance and information systems security. It includes guidance on data loss prevention (DLP), which must be followed to be aligned with the SEG. Need Help Setting up SPF and DKIM for your Email Provider? Setting up SPF and DKIM for different ESPs often requires specific configurations. Some providers require you to publish SPF and DKIM on a subdomain, while others only require DKIM, or have different formatting rules. We’ve simplified all these steps to help you avoid misconfigurations that could delay your DMARC enforcement, or worse, block legitimate emails from reaching your recipients. Below you’ll find comprehensive setup guides for Google Workspace, Microsoft 365, Zoho Mail, Amazon SES, and SendGrid. You can also explore our full blog section that covers setup instructions for many other well-known ESPs. Remember, all this information is reflected in your DMARC aggregate reports. These reports give you live visibility into your outgoing email ecosystem, helping you analyze and fix any issues specific to a given provider. Here are our step-by-step guides for the most common platforms: Google Workspace Microsoft 365 These guides will help ensure your DNS records are configured correctly as part of the Secure Government Email (SGE) Framework rollout. Meet New Government Email Security Standards With EasyDMARC New Zealand’s SEG Framework sets a clear path for government agencies to enhance their email security by October 2025. With EasyDMARC, you can meet these technical requirements efficiently and with confidence. From protocol setup to continuous monitoring and compliance tracking, EasyDMARC streamlines the entire process, ensuring strong protection against spoofing, phishing, and data loss while simplifying your transition from SEEMail.

In a previous article, we looked at some practical examples of how to code SVG by hand. In that guide, we covered the basics of the SVG elements rect, circle, ellipse, line, polyline, and polygon (and also g). This time around, we are going to tackle a more advanced topic, the absolute powerhouse of SVG elements: path. Don’t get me wrong; I still stand by my point that image paths are better drawn in vector programs than coded (unless you’re the type of creative who makes non-logical visual art in code — then go forth and create awe-inspiring wonders; you’re probably not the audience of this article). But when it comes to technical drawings and data visualizations, the path element unlocks a wide array of possibilities and opens up the world of hand-coded SVGs. The path syntax can be really complex. We’re going to tackle it in two separate parts. In this first installment, we’re learning all about straight and angular paths. In the second part, we’ll make lines bend, twist, and turn. Required Knowledge And Guide Structure Note: If you are unfamiliar with the basics of SVG, such as the subject of viewBox and the basic syntax of the simple elements (rect, line, g, and so on), I recommend reading my guide before diving into this one. You should also familiarize yourself with <text> if you want to understand each line of code in the examples. Before we get started, I want to quickly recap how I code SVG using JavaScript. I don’t like dealing with numbers and math, and reading SVG Code with numbers filled into every attribute makes me lose all understanding of it. By giving coordinates names and having all my math easy to parse and write out, I have a much better time with this type of code, and I think you will, too. The goal of this article is more about understanding path syntax than it is about doing placement or how to leverage loops and other more basic things. So, I will not run you through the entire setup of each example. I’ll instead share snippets of the code, but they may be slightly adjusted from the CodePen or simplified to make this article easier to read. However, if there are specific questions about code that are not part of the text in the CodePen demos, the comment section is open. To keep this all framework-agnostic, the code is written in vanilla JavaScript (though, really, TypeScript is your friend the more complicated your SVG becomes, and I missed it when writing some of these). Setting Up For Success As the path element relies on our understanding of some of the coordinates we plug into the commands, I think it is a lot easier if we have a bit of visual orientation. So, all of the examples will be coded on top of a visual representation of a traditional viewBox setup with the origin in the top-left corner (so, values in the shape of 0 0 ${width} ${height}. I added text labels as well to make it easier to point you to specific areas within the grid. Please note that I recommend being careful when adding text within the <text> element in SVG if you want your text to be accessible. If the graphic relies on text scaling like the rest of your website, it would be better to have it rendered through HTML. But for our examples here, it should be sufficient. So, this is what we’ll be plotting on top of: See the Pen SVG Viewbox Grid Visual [forked] by Myriam. Alright, we now have a ViewBox Visualizing Grid. I think we’re ready for our first session with the beast. Enter path And The All-Powerful d Attribute The <path> element has a d attribute, which speaks its own language. So, within d, you’re talking in terms of “commands”. When I think of non-path versus path elements, I like to think that the reason why we have to write much more complex drawing instructions is this: All non-path elements are just dumber paths. In the background, they have one pre-drawn path shape that they will always render based on a few parameters you pass in. But path has no default shape. The shape logic has to be exposed to you, while it can be neatly hidden away for all other elements. Let’s learn about those commands. Where It All Begins: M The first, which is where each path begins, is the M command, which moves the pen to a point. This command places your starting point, but it does not draw a single thing. A path with just an M command is an auto-delete when cleaning up SVG files. It takes two arguments: the x and y coordinates of your start position. const uselessPathCommand = `M${start.x} ${start.y}`; Basic Line Commands: M , L, H, V These are fun and easy: L, H, and V, all draw a line from the current point to the point specified. L takes two arguments, the x and y positions of the point you want to draw to. const pathCommandL = `M${start.x} ${start.y} L${end.x} ${end.y}`; H and V, on the other hand, only take one argument because they are only drawing a line in one direction. For H, you specify the x position, and for V, you specify the y position. The other value is implied. const pathCommandH = `M${start.x} ${start.y} H${end.x}`; const pathCommandV = `M${start.x} ${start.y} V${end.y}`; To visualize how this works, I created a function that draws the path, as well as points with labels on them, so we can see what happens. See the Pen Simple Lines with path [forked] by Myriam. We have three lines in that image. The L command is used for the red path. It starts with M at (10,10), then moves diagonally down to (100,100). The command is: M10 10 L100 100. The blue line is horizontal. It starts at (10,55) and should end at (100, 55). We could use the L command, but we’d have to write 55 again. So, instead, we write M10 55 H100, and then SVG knows to look back at the y value of M for the y value of H. It’s the same thing for the green line, but when we use the V command, SVG knows to refer back to the x value of M for the x value of V. If we compare the resulting horizontal path with the same implementation in a <line> element, we may Notice how much more efficient path can be, and Remove quite a bit of meaning for anyone who doesn’t speak path. Because, as we look at these strings, one of them is called “line”. And while the rest doesn’t mean anything out of context, the line definitely conjures a specific image in our heads. <path d="M 10 55 H 100" /> <line x1="10" y1="55" x2="100" y2="55" /> Making Polygons And Polylines With Z In the previous section, we learned how path can behave like <line>, which is pretty cool. But it can do more. It can also act like polyline and polygon. Remember, how those two basically work the same, but polygon connects the first and last point, while polyline does not? The path element can do the same thing. There is a separate command to close the path with a line, which is the Z command. const polyline2Points = M${start.x} ${start.y} L${p1.x} ${p1.y} L${p2.x} ${p2.y}; const polygon2Points = M${start.x} ${start.y} L${p1.x} ${p1.y} L${p2.x} ${p2.y} Z; So, let’s see this in action and create a repeating triangle shape. Every odd time, it’s open, and every even time, it’s closed. Pretty neat! See the Pen Alternating Triangles [forked] by Myriam. When it comes to comparing path versus polygon and polyline, the other tags tell us about their names, but I would argue that fewer people know what a polygon is versus what a line is (and probably even fewer know what a polyline is. Heck, even the program I’m writing this article in tells me polyline is not a valid word). The argument to use these two tags over path for legibility is weak, in my opinion, and I guess you’d probably agree that this looks like equal levels of meaningless string given to an SVG element. <path d="M0 0 L86.6 50 L0 100 Z" /> <polygon points="0,0 86.6,50 0,100" /> <path d="M0 0 L86.6 50 L0 100" /> <polyline points="0,0 86.6,50 0,100" /> Relative Commands: m, l, h, v All of the line commands exist in absolute and relative versions. The difference is that the relative commands are lowercase, e.g., m, l, h, and v. The relative commands are always relative to the last point, so instead of declaring an x value, you’re declaring a dx value, saying this is how many units you’re moving. Before we look at the example visually, I want you to look at the following three-line commands. Try not to look at the CodePen beforehand. const lines = [ { d: `M10 10 L 10 30 L 30 30`, color: "var(--_red)" }, { d: `M40 10 l 0 20 l 20 0`, color: "var(--_blue)" }, { d: `M70 10 l 0 20 L 90 30`, color: "var(--_green)" } ]; As I mentioned, I hate looking at numbers without meaning, but there is one number whose meaning is pretty constant in most contexts: 0. Seeing a 0 in combination with a command I just learned means relative manages to instantly tell me that nothing is happening. Seeing l 0 20 by itself tells me that this line only moves along one axis instead of two. And looking at that entire blue path command, the repeated 20 value gives me a sense that the shape might have some regularity to it. The first path does a bit of that by repeating 10 and 30. But the third? As someone who can’t do math in my head, that third string gives me nothing. Now, you might be surprised, but they all draw the same shape, just in different places. See the Pen SVG Compound Paths [forked] by Myriam. So, how valuable is it that we can recognize the regularity in the blue path? Not very, in my opinion. In some cases, going with the relative value is easier than an absolute one. In other cases, the absolute is king. Neither is better nor worse. And, in all cases, that previous example would be much more efficient if it were set up with a variable for the gap, a variable for the shape size, and a function to generate the path definition that’s called from within a loop so it can take in the index to properly calculate the start point. Jumping Points: How To Make Compound Paths Another very useful thing is something you don’t see visually in the previous CodePen, but it relates to the grid and its code. I snuck in a grid drawing update. With the method used in earlier examples, using line to draw the grid, the above CodePen would’ve rendered the grid with 14 separate elements. If you go and inspect the final code of that last CodePen, you’ll notice that there is just a single path element within the .grid group. It looks like this, which is not fun to look at but holds the secret to how it’s possible: <path d="M0 0 H110 M0 10 H110 M0 20 H110 M0 30 H110 M0 0 V45 M10 0 V45 M20 0 V45 M30 0 V45 M40 0 V45 M50 0 V45 M60 0 V45 M70 0 V45 M80 0 V45 M90 0 V45" stroke="currentColor" stroke-width="0.2" fill="none"></path> If we take a close look, we may notice that there are multiple M commands. This is the magic of compound paths. Since the M/m commands don’t actually draw and just place the cursor, a path can have jumps. So, whenever we have multiple paths that share common styling and don’t need to have separate interactions, we can just chain them together to make our code shorter. Coming Up Next Armed with this knowledge, we’re now able to replace line, polyline, and polygon with path commands and combine them in compound paths. But there is so much more to uncover because path doesn’t just offer foreign-language versions of lines but also gives us the option to code circles and ellipses that have open space and can sometimes also bend, twist, and turn. We’ll refer to those as curves and arcs, and discuss them more explicitly in the next article. Further Reading On SmashingMag “Mastering SVG Arcs,” Akshay Gupta “Accessible SVGs: Perfect Patterns For Screen Reader Users,” Carie Fisher “Easy SVG Customization And Animation: A Practical Guide,” Adrian Bece “Magical SVG Techniques,” Cosima Mielke