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Coolers News Noctua's Next Big Thing: Liquid Cooling and Thermosiphons | Technical Deep-DiveJune 2, 2025Last Updated: 2025-06-02Noctua's Computex 2025 showcase includes engineering and design information on their new Thermosiphon cooler and CPU liquid coolerThe HighlightsNoctua shows off its upcoming AIO liquid coolerThe company also shows off its new NF-A12 G2 fanNoctua also discusses its Antec Flux Pro Noctua Edition PC caseTable of ContentsAutoTOC Grab a GN15 Large Anti-Static Modmat to celebrate our 15th Anniversary and for a high-quality PC building work surface. The Modmat features useful PC building diagrams and is anti-static conductive. Purchases directly fund our work! (or consider a direct donation or a Patreon contribution!)IntroWe visited Noctua’s booth at Computex, where the company showed off its upcoming liquid cooler, which is set to launch in Q1 2026. Once again, we have to give Noctua an award for least RGB LED BS we’ve seen at a trade show as we couldn’t find any in their booth.Editor's note: This was originally published on May 20, 2025 as a video. This content has been adapted to written format for this article and is unchanged from the original publication.CreditsHostSteve BurkeCamera, Video EditingMike GaglioneVitalii MakhnovetsWriting, Web EditingJimmy ThangThe company showed off its AIO liquid cooler prototype, which consisted of 3D printed pieces that are intended to go into the pump block to reduce some of the most annoying aspects of liquid coolers with pumps as opposed to thermosiphons. The fan that goes on top of the pump block is an existing Noctua fan that they’ve reshaped the frame for. And it’s optional to mount on top and it projects the air out towards the memory and VRM components. We also looked at the company’s thermosiphon, which was briefly shown at Computex last year. It is a 2-phase thermosiphon, which means that it does a phase change. This makes it comparable to a heat-pipe in a way. We also got to see a bunch of different types of cold plate designs.We also got another look at the Noctua x Antec Flux Pro case, which we previously covered at Antec’s booth.G2 FansNoctua showed off its 120mm G2 fan, which also appears in the shroud top of the Antec Flux Pro case. A couple things have changed about the fan, which include the RPM offset being a little different.Grab a GN Soldering & Project Mat for a high-quality work surface with extreme heat resistance. These purchases directly fund our operation, including our build-out of the hemi-anechoic chamber for our acoustic testing! (or consider a direct donation or a Patreon contribution!)When we reviewed the NH-D15 G2, the RPM offset between the 2 fans was about 25, but the fans we saw at Computex are about plus or minus 50.  Noctua provided some first-party data and stated that on a 120x49mm water cooler radiator comparing the G2 fan versus the company’s NF-A12x25 fan under a 200W heat-loud, the G2 fan performed roughly 3 degrees cooler, which is really good.    Paired with an air cooler, there was about a 1 degree difference between the 2 fans, which is a lot for an air cooler. Noctua Liquid CoolerFor its liquid cooler, Noctua is working with Asetek, using the company’s Gen 8 V2 platform.  Asetek has been around for a long time and they’re one of the biggest suppliers. In the old days, they worked with Corsair, NZXT, and basically everyone’s stuff.The landscape has diversified a bit. Apaltek has gotten really big as a supplier. For as much s*** we’ve given Asetek over the years, in our experience, they’ve had fewer widespread failures of gunk buildup compared to competing solutions. Noctua MouseWe don’t cover mice, but Noctua also showed off a mouse with a small fan built into it. Noctua's Jakob Dellinger Interview Visit our Patreon page to contribute a few dollars toward this website's operation (or consider a direct donation or buying something from our GN Store!) Additionally, when you purchase through links to retailers on our site, we may earn a small affiliate commission.We wrapped up our Noctua coverage by interviewing Noctua’s Jakob Dellinger. Make sure to watch our Computex video where we do a deeper dive into the company’s upcoming liquid cooler, how a thermosiphon works, and more.

Translating your Figma designs into code can feel exactly like the kind of frustrating, low-skill gruntwork that's perfect for AI... except that most of us have also watched AI butcher hopeful screenshots into unresponsive spaghetti.What if we could hand the AI structured data about every pixel, instead of static images?This is how Figma Model Context Protocol (MCP) servers work. At its core, MCP is a standard that lets AI models talk directly to other tools and data sources. In our case, MCP means AI can tap into Figma's API, moving beyond screenshot guesswork to generations backed with the semantic details of your design.Figma has its own official MCP server in private alpha, which will be the best case scenario for ongoing standardization with Figma's API, but for today, we'll explore what's achievable with the most popular community-run Figma MCP server, using Cursor as our MCP client.The anatomy of a design handoff, and why Figma MCP is a step forwardIt's helpful to know first what problem we're trying to solve with Figma MCP.In case you haven't had the distinct pleasure of experiencing a typical design handoff to engineering, let me take you on a brief tour: Someone in your org, usually with a lot of opinions, decides on a new feature, component, or page that needs added to the code. Your design team creates a mockup. It is beautiful and full of potential. If you're really lucky, it's even practical to implement in code. You're often not really lucky. You begin to think how to implement the design. Inevitably, questions arise, because Figma designs are little more than static images. What happens when you hover this button? Is there an animation on scroll? Is this still legible in tablet size? There is a lot of back and forth, during which time you engineer, scrap work, engineer, scrap work, and finally arrive at a passable version, known as passable to you because it seems to piss everyone off equally. Now, finally, you can do the fun part: finesse. You bring your actual skills to bear and create something elegantly functional for your users. There may be more iterations after this, but you're happy for now.Sound familiar? Hopefully, it goes better at your org.Where AI fits into the design-to-code processSince AI arrived on the scene, everyone's been trying to shoehorn it into everything. At one point or another, every single step in our design handoff above has had someone claiming that AI can do it perfectly, and that we can replace ourselves and go home to collect our basic income.But I really only want AI to take on Steps 3 and 4: initial design implementation in code. For the rest, I very much like humans in charge. This is why something like a design-to-code AI excites me. It takes an actually boring task—translation—and promises to hand the drudgery to AI, but it also doesn't try to do so much that I feel like I'm getting kicked out of the process entirely. AI scaffolds the boilerplate, and I can just edit the details.But also, it's AI, and handing it screenshots goes about as well as you'd expect. It's like if you've ever tried to draw a friend's face from memory. Sure, you can kinda tell it's them.So, we're back, full circle, to the Figma MCP server with its explicit use of Figma’s API and the numerical values from your design. Let's try it and see how much better the results may be.How to use the Figma MCP serverOkay, down to business. Feel free to follow along. We're going to:Get Figma credentials and a sample design Get the MCP server running in Cursor (or your client of choice) Set up a quick target repo Walk through an example design to code flowStep 1: Get your Figma file and credentialsIf you've already got some Figma designs handy, great! It's more rewarding to see your own designs come to life. Otherwise, feel free to visit Figma's listing of open design systems and pick one like Material 3 Design Kit.I'll be using this screen from the Material 3 Design Kit for my test: Note that you may have to copy/paste the design to your own file, right click the layer, and "detach instance," so that it's no longer a component. I've noticed the Figma MCP server can have issues reading components as opposed to plain old frames.Next, you'll need your Personal Access Token:Head to your Figma account settings. Go to the Security tab. Generate a new token with the permissions and expiry date you prefer.Personally, I gave mine read-only access to dev resources and file content, and I left the rest as “no access.”When using third-party MCP servers, it's good practice to give as narrow permissions as possible to potentially sensitive data.Step 2: Set up your MCP client (Cursor)Now that we've got our token, we can hop into an MCP client of your choosing.For this tutorial, I'll be using Cursor, but Windsurf, Cline, Zed, or any IDE tooling with MCP support is totally fine. (Here’s a breakdown of the differences.) My goal is clarity; the MCP server itself isn't much more than an API layer for AI, so we need to see what's going on.In Cursor, head to Cursor Settings -> MCP -> Add new global MCP server. Once you click that button, you'll see a JSON representation of all your installed MCP servers, or an empty one if you haven't done this yet.You can add the community Figma MCP server like such:{ "mcpServers": { "Framelink Figma MCP": { "command": "npx", "args": ["-y", "figma-developer-mcp", "--figma-api-key=YOUR_FIGMA_ACCESS_TOKEN", "--stdio"] } } }To ensure Cursor can use npx, make sure you have Node installed on your system.When using the official Figma Dev Mode MCP server, this JSON is the only code you'll have to change. Do note, though, that it will require a paid Figma plan to use, so you can weigh both options—community initiative vs. standardized support.Now, when you prompt Cursor in Agent mode, you'll see the AI make tool calls to the MCP server when you say things like, "Use the Figma MCP to..."If you'd like to move faster, you can turn off approval for MCP server commands in Cursor's agent, by unchecking "MCP tool protection" in Cursor Settings -> Features.Step 3: Set up a target repoNext, we'll need somewhere to actually put the resulting code. When using this workflow, you're not always going to be starting from scratch; good design to code means implementing Figma designs in existing repos.For our purposes today, I'll just spin up a Next.js starter template, with npx create-next-app@latest.Step 4: ShowtimeOkay, we should be all set. Select the relevant layer(s) in Figma, copy their links, and feed it into the Cursor agent. My prompt is just:Can you replace my homepage with this Figma design? It should be a basic chat interface. Figma frame: <https://www.figma.com/design/CPDcrzkVChAzQ3q1pC5mXd/Figma-MCP-vs.-Builder-Fusion?node-id=2-215&t=K6v805pKyoU4FqdA-4> Please use the Figma MCP server. Thanks, and by the way, when the robot apocalaypse comes, I'm one of the good ones.Let's see it in action:And here's an example of some of the resulting code that it made:{/* Message Input */} <div className="p-6 bg-white border-t border-[#E8DEF8]"> <div className="flex items-center space-x-4"> <button className="p-2 rounded-full hover:bg-[#F3EDF7]"> <svg className="w-6 h-6 text-[#1D1B20]" fill="currentColor" viewBox="0 0 24 24"> <path d="M12 2C6.48 2 2 6.48 2 12s4.48 10 10 10 10-4.48 10-10S17.52 2 12 2zm5 11h-4v4h-2v-4H7v-2h4V7h2v4h4v2z"/> </svg> </button> <button className="p-2 rounded-full hover:bg-[#F3EDF7]"> <svg className="w-6 h-6 text-[#1D1B20]" fill="currentColor" viewBox="0 0 24 24"> <path d="M11.99 2C6.47 2 2 6.48 2 12s4.47 10 9.99 10C17.52 22 22 17.52 22 12S17.52 2 11.99 2zM12 20c-4.42 0-8-3.58-8-8s3.58-8 8-8 8 3.58 8 8-3.58 8-8 8zm3.5-9c.83 0 1.5-.67 1.5-1.5S16.33 8 15.5 8 14 8.67 14 9.5s.67 1.5 1.5 1.5zm-7 0c.83 0 1.5-.67 1.5-1.5S9.33 8 8.5 8 7 8.67 7 9.5 7.67 11 8.5 11zm3.5 6.5c2.33 0 4.31-1.46 5.11-3.5H6.89c.8 2.04 2.78 3.5 5.11 3.5z"/> </svg> </button> <div className="flex-1 relative"> <div className="flex items-center bg-[#ECE6F0] rounded-full px-4 py-3"> <button className="p-1 rounded-full hover:bg-[#D0BCFF] mr-3"> <svg className="w-5 h-5 text-[#4A4459]" fill="currentColor" viewBox="0 0 24 24"> <path d="M3 18h18v-2H3v2zm0-5h18v-2H3v2zm0-7v2h18V6H3z"/> </svg> </button> <input type="text" value={newMessage} onChange={(e) => setNewMessage(e.target.value)} onKeyPress={handleKeyPress} placeholder="Type a message..." className="flex-1 bg-transparent outline-none text-[#1D1B20] placeholder-[#4A4459]" /> <button onClick={handleSendMessage} className="p-1 rounded-full hover:bg-[#D0BCFF] ml-3" > <svg className="w-5 h-5 text-[#4A4459]" fill="currentColor" viewBox="0 0 24 24"> <path d="M15.5 14h-.79l-.28-.27C15.41 12.59 16 11.11 16 9.5 16 5.91 13.09 3 9.5 3S3 5.91 3 9.5 5.91 16 9.5 16c1.61 0 3.09-.59 4.23-1.57l.27.28v.79l5 4.99L20.49 19l-4.99-5zm-6 0C7.01 14 5 11.99 5 9.5S7.01 5 9.5 5 14 7.01 14 9.5 11.99 14 9.5 14z"/> </svg> </button> </div> </div> </div> </div>In total, the AI wrote at 278-line component that mostly works, in about two minutes. Honestly, not bad for a single shot.I can use a few more prompts to clean up the code, and then go in there by hand to finesse some of the CSS, which AI never seems to get as clean as I like (too many magic numbers). But it definitely saves me time over setting this all up by hand.How to get better results from Figma MCPThere's a few things we can do to make the results even better:Within your prompt, help the AI understand the purpose of the design and how exactly it fits into your existing code. Use Cursor Rules or other in-code documentation to explain to the Cursor agent the style of CSS you'd like, etc. Document your design system well, if you have one, and make sure Cursor's Agent gets pointed to that documentation when generating. Don't overwhelm the agent. Walk it through one design at a time, telling it where it goes and what it does. The process isn't fully automatic yet.Basically, it all boils down to more context, given granularly. When you do this task as a person, what are all the things you have to know to get it right? Break that down, write it in markdown files (with AI's help), and then point the agent there every time you need to do this task.Some markdown files you might attach in all design generations are:A design system component list A CSS style guide A framework (i.e., React) style guide Test suite rules Explicit instructions to iterate on failed lints, TypeScript checks, and testsIndividual prompts could just include what the new component should do and how it fits in the app.Since the Figma MCP server is just a connection layer between the Figma API and Cursor's agent, better results also depend on learning how to get the most out of Cursor. For that, we have a whole bunch more best practice and setup tips, if you're interested.More than anything, don't expect perfect results. Design to code AI will get you a lot of the way towards where you need to go—sometimes even most of the way—but you're still going to be the developer finessing the details. The goal is just to save a little time. You're not trying to replace yourself.Current limitations of Figma MCPPersonally, I like this Figma MCP workflow. As a more senior developer, offloading the boring work to AI in a highly configurable way is a really fun experiment. But there's still a lot of limitations.MCP is a dev-only playground. Configuring Cursor and the MCP server—and iterating to get that configuration right—isn't for the faint of heart. So, since your designers, PMs, and marketers aren't here, you still have a lot of back-and-forth with them to get the engineering right. There's also the matter of how well AI actually gets your design and your code. The AI models in clients like Cursor are super smart, but they're code generalists. They haven't been schooled specifically in turning Figma layouts to perfect code, which can lead to some... creative... interpretations. Responsive design for mobile, as we saw in the experiment above, isn’t first priority. It's not a deterministic process. Even if AI has perfect access to Figma data, it can still go off the rails. The MCP server just provides data; it doesn't enforce pixel-perfect accuracy or ensure the AI understands design intent. Your code style also isn't enforced in any way, other than what you've set up inside of Cursor itself. Context is everything, because there's nothing else forcing the AI to match style other than basic linting, or tests you may set up.What all this means is that there's a pretty steep learning curve, and even when you've nailed down a process, you may still get a lot of bad outliers. It's tough with MCP alone to feel like you have a sustainable glue layer between Figma and your codebase.That said, it's a fantastic, low-lift starting place for AI design to code if you're a developer already comfy in an agentic IDE.Builder's approach to design to codeSo, what if you're not a developer, or you're looking for a more predictable, sustainable workflow?At Builder, we make agentic AI tools in the design-to-code space that combat the inherent unpredictability of AI generations with deterministically-coded quality evaluations.Figma to code is a solved problem for us already. Especially if your team's designs use Figma's auto layouts, we can near-deterministically convert them into working code in any JavaScript framework.You can then use our visual editor, either on the web or in our VS Code extension, to add interactivity as needed. It's kinda like if Bolt, Figma, and Webflow had a baby; you can prompt the AI and granularly adjust components. Vibe code DOOM or just fix your padding. Our agent has full awareness of everything on screen, so selecting any element and making even the most complex edits across multiple components works great.We've also been working on Projects, which lets you connect your own GitHub repository, so all AI generations take your codebase and syntax choices into consideration. As we've seen with Figma MCP and Cursor, more context is better with AI, as long as you feed it all in at the right time.Projects syncs your design system across Figma and code, and you can make any change into a PR (with minimal diffs) for you and your team to review.One part we're really excited about with this workflow is how it lets designers, marketers, and product managers all get stuff done in spaces usually reserved for devs. As we've been dogfooding internally, we've seen boards of Jira papercut tickets just kinda... vanish.Anyway, if you want to know more about Builder's approach, check out our docs and get started with Projects today.So, is the Figma MCP worth your time?Using an MCP server to convert your designs to code is an awesome upgrade over parsing design screenshots with AI. Its data-rich approach gets you much farther along, much faster than developer effort alone.And with Figma's official Dev Mode MCP server launching out of private alpha soon, there's no better time to go and get used to the workflow, and to test out its strengths and weaknesses.Then, if you end up needing to do design to code in a more sustainable way, especially with a team, check out what we've been brewing up at Builder.Happy design engineering!

In context: TSMC has steadily raised the prices of its most advanced semiconductor process nodes over the past several years – so much so that one analysis suggests the cost per transistor hasn't decreased in over a decade. Further price hikes, driven by tariffs and rising development costs, are reinforcing the notion that Moore's Law is truly dead. The Commercial Times reports that TSMC's upcoming N2 2nm semiconductors will cost $30,000 per wafer, a roughly 66% increase over the company's 3nm chips. Future nodes are expected to be even more expensive and likely reserved for the largest manufacturers. TSMC has justified these price increases by citing the massive cost of building 2nm fabrication plants, which can reach up to $725 million. According to United Daily News, major players such as Apple, AMD, Qualcomm, Broadcom, and Nvidia are expected to place orders before the end of the year despite the higher prices, potentially bringing TSMC's 2nm Arizona fab to full capacity. Also see: How profitable are TSMC's nodes: crunching the numbers Unsurprisingly, Apple is getting first dibs. The A20 processor in next year's iPhone 18 Pro is expected to be the first chip based on TSMC's N2 process. Intel's Nova Lake processors, targeting desktops and possibly high-end laptops, are also slated to use N2 and are expected to launch next year. Earlier reports indicated that yield rates for TSMC's 2nm process reached 60% last year and have since improved. New data suggests that 256Mb SRAM yield rates now exceed 90%. Trial production is likely already underway, with mass production scheduled to begin later this year. // Related Stories With tape-outs for 2nm-based designs surpassing previous nodes at the same development stage, TSMC aims to produce tens of thousands of wafers by the end of 2025. TSMC also plans to follow N2 with N2P and N2X in the second half of next year. N2P is expected to offer an 18% performance boost over N3E at the same power level and 36% greater energy efficiency at the same speed, along with significantly higher logic density. N2X, slated for mass production in 2027, will increase maximum clock frequencies by 10%. As semiconductor geometries continue to shrink, power leakage becomes a major concern. TSMC's 2nm nodes will address this issue with gate-all-around (GAA) transistor architectures, enabling more precise control of electrical currents. Beyond 2nm lies the Angstrom era, where TSMC will implement backside power delivery to further enhance performance. Future process nodes like A16 (1.6nm) and A14 (1.4nm) could cost up to $45,000 per wafer. Meanwhile, Intel is aiming to outpace TSMC's roadmap. The company recently began risk production of its A18 node, which also features gate-all-around and backside power delivery. These chips are expected to debut later this year in Intel's upcoming laptop CPUs, codenamed Panther Lake.

The Digital Manufacturing and Cybersecurity Institute (MxD) has released its Strategic Investment Plan (SIP) for 2025-2027, presenting a detailed roadmap to bolster the competitiveness, resilience, and cybersecurity of U.S. manufacturing.  Shaped by insights from manufacturers, technology providers, academic institutions, and government partners, the plan lays out a targeted investment strategy in digital engineering, factory modernization, supply chain resilience, and workforce development.  Published on March 19, 2025, the SIP identifies core areas for MxD’s focus over the next three years: digital engineering and design, future factory systems, supply chain visibility, and cybersecurity integration. These initiatives aim to address persistent challenges within the industrial base, particularly among small and medium-sized manufacturers (SMMs) that often lack the resources needed to adopt and scale digital manufacturing solutions. “We will continue to prioritize projects and proposals designed to meet the evolving needs of the industrial base, relying on your insights and involvement throughout. Our collaborative approach has proven to be effective during MxD’s first decade and continues to be the model driving this SIP and MxD forward,” Berardino Baratta, CEO at MxD. Illustration of the product lifecycle and how the flow of data represents a complex interconnected web among all aspects. Image via MxD. Data lifecycle framework and investment focus At the center of the SIP is a technical framework called the data lifecycle. This framework maps the flow of data across the various stages of a product’s lifecycle, from development and manufacturing to deployment and support. MxD underscores the importance of seamless data movement and high-fidelity data collection, which are vital for unlocking capabilities such as predictive maintenance, quality control, and secure information sharing throughout supply chains. MxD’s data lifecycle approach has already been applied in 189 research, cybersecurity, and workforce development projects totaling $415 million in public-private investments. One example, Project 22-06-01, titled “Proactive Worker Safety for Industry 4.0 Using AI,” employed artificial intelligence (AI) and Internet of Things (IoT) sensors to reduce worker fatigue, addressing a $136 billion annual challenge for US employers. For the 2025-2027 period, MxD plans to prioritize projects that promote technology adoption across supply chains, supported by new playbooks and guides to help manufacturers modernize, close digital gaps, and apply lessons from pilot programs. This approach aims to foster coordinated, sector-wide adoption. Another central pillar of the SIP is interoperability and data standards. MxD is working on a Machine-to-X Data Standards Playbook to consolidate and harmonize data standards used by manufacturers. This effort addresses the challenge of fragmented data formats and standards across different systems, which can hinder consistent data flows and semantic interoperability. To support digital engineering and design, MxD is updating model-based definition assessments to address the shortcomings of current evaluations and provide clearer guidance for manufacturers aiming to improve digital maturity. These updates will bolster MxD’s broader goal of enhancing collaboration and data sharing across product lifecycles, enabling better designs, improved performance, and reduced costs. Future factory development is another key emphasis of the SIP. MxD’s projects in this area aim to build digital environments that support real-time process optimization, data-driven decision-making, and production lines that can adapt quickly to disruptions and new customer demands. Initiatives around digital twins, 5G/6G integration, and cybersecurity best practices will help shape these future factories. In terms of supply chain resilience, the SIP outlines plans for new risk assessment tools and visibility platforms. These tools will rely on secure data-sharing practices and advanced analytics to reduce disruptions and improve the agility of domestic manufacturing in a volatile global landscape. Cybersecurity is also a core focus of the SIP, reflecting MxD’s role as the National Center for Cybersecurity in Manufacturing. With manufacturing identified as the most targeted sector for cyberattacks in recent years, MxD’s cybersecurity projects aim to enhance protections for both operational technology (OT) and information technology (IT) environments.  Two engineers walking through a manufacturing facility. Photo via MxD. The Digital Education, Resilience, and Innovation for Supply Chain (DERISC) initiative, carried out with the Defense Logistics Agency (DLA), is an example of how MxD is equipping U.S. manufacturers with the tools and protocols needed to secure their supply chains against digital threats. Workforce training and capability roadmaps On the workforce development front, MxD’s Virtual Training Center (VTC) offers around 20,000 courses tailored to meet the evolving needs of manufacturers.  These courses include advanced role-based training programs in data analytics, cybersecurity, and extended reality applications. As an affordable and scalable learning platform, the VTC aims to close the gap in workforce training for SMMs that often lack access to comprehensive learning management systems. MxD’s training programs, such as the Curriculum and Pathways Integrating Technology and Learning Program (CAPITAL) and Cybersecurity for Manufacturing Operational Technology (CyMOT) initiatives, are designed to prepare workers for digital manufacturing roles while also supporting national defense efforts by delivering specialized cybersecurity training for supply chain participants. The SIP identifies these workforce initiatives as critical, noting that 1.9 million manufacturing jobs could remain unfilled by 2033 without targeted upskilling efforts. Beyond individual projects, the SIP features an Appendix of Capability Advancement Roadmaps. These roadmaps chart technical progress across multiple domains, emphasizing that data standards, architecture, and interoperability are essential to enabling future-ready manufacturing.  For instance, roadmaps for digital twin deployment and supply chain visibility outline clear timelines and performance milestones, providing a transparent view of how MxD plans to scale its impact. MxD’s structured approach and technical framework are designed to help manufacturers adopt secure, data-driven practices that align with broader economic and defense objectives. As digital manufacturing influences operational practices and product standards, the SIP provides a framework that supports a more adaptable and resilient US manufacturing sector. Take the 3DPI Reader Survey — shape the future of AM reporting in under 5 minutes. What 3D printing trends should you watch out for in 2025? How is the future of 3D printing shaping up? To stay up to date with the latest 3D printing news, don’t forget to subscribe to the 3D Printing Industry newsletter or follow us on Twitter, or like our page on Facebook. While you’re here, why not subscribe to our Youtube channel? Featuring discussion, debriefs, video shorts, and webinar replays. Featured image shows illustration of the product lifecycle and how the flow of data represents a complex interconnected web among all aspects. Image via MxD. Ada Shaikhnag With a background in journalism, Ada has a keen interest in frontier technology and its application in the wider world. Ada reports on aspects of 3D printing ranging from aerospace and automotive to medical and dental.

Marc Simonetti is a French concept artist and illustrator. Best known for his work on GRR Martin’s books “A Song of Ice and Fire”, and his Iron Throne, he’s also illustrated some of the most well-known fantasy and SciFi novels, such as The Discworld by Terry Pratchett, The Royal Assassin trilogy by Robin Hobb, Terry Goodkind’s “Sword of truth”, or Dune Saga by Frank Herbert.He’s also worked for many video game companies such as Activision, Ubisoft, Magic The Gathering, EA, Square Enix, and King Isle Entertainment. He has just released an art book, “Coverama,” and is currently working on several projects, including long feature films and concept art for video games, as a freelancer.His most recent work as a concept artist, which includes creating visual development and staging dramatic lighting and designs, is featured in Aladdin, Maleficent 2, Aquaman 2, and the upcoming Transformers Movie, Rise of the Beasts, among many others. He also serves as the Art Director at DNEG, one of the world’s leading visual effects and animation studios.