Apple’s 2026 Designers of Tomorrow exhibition opened October 20 at Hôtel de Maisons in Paris. Returning for a second year in collaboration with Design Miami, the showcase is curated by Rodman Primack and organized around the theme of presence. A similar exchange between digital exploration and physical production appears in Bünyamin Aydın’s work with iPad Pro for Les Benjamins, where sketches and shared references helped develop a fashion collection.

The featured participants are Salem S. Charabi, Isabel Rower, Yoonjeong Lee, and Parafernalia. Their work provides a starting point for examining a broader creative question: how can an object encourage someone to stop, pay attention, and engage with their surroundings?

For Apple readers, the exhibition also offers a different way to think about creative technology. The finished works invite attention through their materials and use, while digital tools operate behind the scenes.

Four Projects With Different Material Languages

Charabi’s The Eminence of Light combines palm fronds, banana paper, and limestone in a lantern developed with MacBook Pro sketching and 3D modeling.

Rower’s Padded Chair 1860 features a glossy finish and ceramic beaded fringe that makes sound when brushed. Her workflow includes documenting ideas on iPhone and arranging references in Keynote.

Lee’s New Form combines a wooden table and lamp, incorporating worn pencils and approximately 5,000 layers of veneer. She uses iPad Pro and Apple Pencil within a connected workflow.

Parafernalia’s Genki Matsumura and Sebastián Zorrilla created MonoAmbiente 1, a stainless steel piece that opens to reveal stools and a table. Their geographically separated collaboration involves shared sketches and communication apps. Apple describes the projects and their development in its exhibition announcement.

Presence Begins With What an Object Asks You to Notice

Together, the projects suggest several ways to interpret presence. Light can draw attention across a room. An unexpected sound can make a familiar action feel different. A recognizable material placed in an unfamiliar role can prompt a second look.

These are useful distinctions because attention is not always a matter of visual impact. An object may become interesting through movement, proximity, or participation. Looking at a photograph and encountering the work physically can therefore produce different experiences.

That is an editorial reading of the exhibition’s theme, rather than a measured finding about visitors. Whether a particular piece creates that response will depend on the person encountering it and the circumstances of the encounter.

Digital Planning Supports Physical Judgment

The workflows offer a practical reminder that creative work contains several different kinds of decisions. Sketching helps establish a possibility. Modeling can help examine relationships between components. Collecting references provides material for comparison.

None of those steps eliminates the need to make judgments about the finished object. A convincing image cannot, by itself, establish whether a surface feels right, a structure behaves as intended, or an interaction is satisfying.

For someone developing a personal project, the useful question is which stage needs support. A student organizing visual research may need a clearer reference archive. Two collaborators may need a more consistent way to exchange revisions. A maker exploring a complex form may benefit from investigating its geometry before committing materials.

The exhibition illustrates specific workflows, rather than prescribing one equipment list for every creative discipline.

Collaboration Is Part of the Design Process

Working with another person introduces a separate challenge: making an unfinished thought understandable. A sketch that feels obvious to its creator may leave a collaborator unsure about scale, construction, or purpose.

A useful shared reference gives both people something concrete to discuss. It can expose disagreements early, preserve an alternative worth revisiting, or clarify which version is currently under consideration.

For readers, that offers an approachable lesson without requiring a professional studio. Naming revisions clearly, recording why a decision changed, and keeping references accessible can make a small project easier to develop. The value comes from the working habit as much as the software involved.

Read the Showcase as a Set of Creative Examples

Apple positions the exhibition around its products’ role in design. That makes it a curated account with a clear commercial perspective, rather than an independent comparison of creative platforms. The featured experiences show how these designers work; they do not establish that equivalent results require identical devices.

The stronger takeaway is to examine the path from observation to finished object. Each stage needs a purpose: capturing something worth exploring, testing a possibility, communicating a revision, or assessing the physical result. For readers interested in making things, that process is the most useful part of the showcase—and something they can apply to their next project.

The post Apple’s 2026 Designers of Tomorrow Explores Creativity Beyond the Screen appeared first on AppleMagazine.

The panel is back from Macstock and share highlights and lessons from the conference, including creative experimentation, podcast strategy, video production, accessibility, health, digital legacy planning, and useful Mac tools. Chuck Joiner, David Ginsburg, Marty Jencius, Eric Bolden, Brian Flanigan-Arthurs, Jim Rea, Web Bixby, Jeff Gamet, and Mark Fuccio reflect on the friendships and international […]

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The Road to Macstock wraps up with organizer Mike Potter providing last-minute updates for attendees, including his Creator Camp session on why the Mac still matters and a weekend talk about giving old Macs new life. He explains the event’s themed schedule, hotel room situation, late registration options, and what to bring. Mike also teases this year's T-shirt design details, talks about Macstock swag advice, and the value of curiosity, community, and conversation as a highlight of the event. 

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At Flip The Script in San Francisco at WWDC, Vladyslav Hamolia, AI Staff Engineer for MacPaw.discusses the company’s push to become AI-first by rethinking existing products, building internal tools, and supporting third-party vendors. He profiles Eney, the MacPaw AI assistant (available in Setapp) designed to perform tasks, optimize workflows, and improve daily routines. It addresses security while balancing local-first privacy goals with hybrid cloud models. 

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In the CYME booth at NAB in Las Vegas, Matthieu Kopp, Chief Technology Officer, previews the next Peakto release, highlighting support for much larger media libraries, expanded collaboration, guest sharing, and secure on-premise access without cloud uploads. He also discusses other features such as new culling tools, improved ingest control, subclips for video workflows, and […]

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From NAB in Las Vegas, Matt Bach, Senior Product Manager at Puget Systems, explains PugetBench, a free benchmarking tool for end users that tests real applications such as Premiere Pro, Photoshop, After Effects, DaVinci Resolve, and soon Unreal Engine. Matt discusses why real-world benchmarks beat synthetic scores, how software updates can dramatically change performance, and […]

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The Road to Macstock takes us to Dave Hamilton, who previews his Macstock session, “From Curiosity to Capability,” which will focus on practical AI workflows anyone can use. Dave discusses how generative AI has changed coding, project management, proposals, productivity, and procrastination for him. The session will encourage experimentation as a way to get involved as AI tools rapidly evolve.

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In the rapidly evolving realm of artificial intelligence and deep learning, conventional electronic computing architectures are increasingly strained by the demands of large-scale data processing and intricate computational tasks. Optical computing has risen as a compelling alternative, leveraging the inherent physical phenomena of light—such as interference and diffraction—to execute computations fundamentally differently from traditional electronics. By harnessing these optical effects, optical computing systems (OCS) promise unparalleled advantages: notably superior processing speeds, heightened energy efficiency, and a natural aptitude for massive parallelism. These attributes make optical computing especially attractive for intensive applications like image processing, machine learning, and big data analytics, holding transformative potential across these domains.

Yet, despite these promising characteristics, contemporary optical computing platforms grapple with a significant bottleneck rooted in their hardware-centric development workflows. Conventionally, researchers must directly engage with physical OCS hardware to develop, tune, and validate computational tasks. This process entails sequential access to a limited number of costly and often heavily utilized devices. Users must physically occupy the system to load parameters, fine-tune configurations, and iteratively calibrate the hardware—an intrinsically tedious and time-consuming cycle. Each new user or task demands resetting the system state, leading to protracted queues, repetitive adjustments, and redundant calibrations. The resulting workflows impose long hours of equipment occupation and elevated trial-and-error expenses, stifling the possibility for concurrent task development and drastically curtailing research throughput and system agility.

To surmount these entrenched challenges, cutting-edge research has introduced the concept of the Digital Twin Optical Computing System (DT-OCS). This innovative approach establishes a high-fidelity digital replica—a “digital twin”—of the physical OCS that accurately models its input-output behavior under varying configuration parameters. Crucially, the digital twin allows researchers to perform extensive offline simulations, training, and optimization without the constraints of physical hardware availability. Analogous to a digital simulator software that mirrors a complex “real machine,” DT-OCS enables extensive task development and parameter tuning within a virtual environment, dramatically decoupling software development from hardware dependency. Consequently, optimized parameters validated digitally can be seamlessly transferred back to the physical system for deployment, bypassing numerous iterative hardware trials.

The implications of DT-OCS extend well beyond mere time savings. By effectively creating a “digital development toolkit” for optical computing, this framework revolutionizes the research ecosystem—transforming optical computing platforms from isolated, hardware-bound experiments to versatile, shareable research infrastructures. Researchers can now conduct task training, performance benchmarking, and method comparisons entirely within a unified digital realm, fostering collaborative innovation. Distinguished from legacy OCS paradigms, DT-OCS supports parallel task development workflows, allowing multiple computational problems to be advanced simultaneously without hardware conflicts. This capability markedly enhances flexibility, reduces development bottlenecks, and accelerates the maturation and practical adoption of optical computing technologies.

Envisioning the trajectory of optical computing, the integration of digital twin models with physical hardware appears indispensable. Contemporary transportation systems, for instance, benefit from the synergy between tangible road networks and dynamic digital mapping platforms. Similarly, next-generation optical computing platforms should embody dual representations: a robust physical computing infrastructure supplemented by perpetually updated digital twins. This paradigm champions accessibility, reproducibility, and scalability—cornerstones for fostering broad-scale collaboration. It paves the way for unified validation standards and equitable performance comparisons, catalyzing optical computing’s evolution into a general-purpose, widely deployable resource rather than a niche experimental setup.

The DT-OCS framework’s hallmark is its strategic decoupling of task development from hardware constraints. In traditional optical computing, task training and parameter fine-tuning necessitate iterative hardware engagements involving adjustments, diagnostics, and recalibrations. This approach encumbers the process with inefficiencies and precludes simultaneous multi-task development. DT-OCS obviates these impediments by faithfully modeling the physical system’s responses across parameter spectra, enabling exhaustive offline optimization. This digital-first workflow increases research agility, minimizes hardware occupancy, and substantially compresses the development timeline. Consequently, DT-OCS amplifies the throughput and adaptability of optical computing research endeavors.

The real-world efficacy of the DT-OCS framework has been experimentally validated using a cutting-edge high-speed optical computing system incorporating silicon photonic feature-computing chips. These empirical studies demonstrated DT-OCS applications in diverse computational domains, including image classification and sequential decision-making. Notably, configurations optimized within the digital twin translated flawlessly to the physical hardware, with observed task performance closely mirroring digital forecasts. This underscores not only the high fidelity of the DT-OCS model but also its robust transferability across practical scenarios. Moreover, by enabling parallel task development within the digital domain, DT-OCS expedites the overall research workflow, markedly elevating efficiency.

An illustrative application of the DT-OCS system involved offline training and deployment for the Fashion-MNIST image classification task—a benchmark dataset widely used in machine learning. This practical demonstration affirms DT-OCS’s capacity to bridge digital model development with real-world optical computing implementations. Beyond efficiency gains, DT-OCS signifies a conceptual shift toward disentangling computational task design from the idiosyncrasies of physical system constraints. Historically, optical computing research has been hamstrung by its dependence on specific hardware platforms and accessibility limitations, hindering reproducibility and cross-task comparisons. The introduction of an open-source digital twin framework surmounts these barriers by democratizing access and standardizing evaluation protocols.

By openly releasing the DT-OCS framework alongside associated task datasets, the research fosters a community-driven ecosystem where software-based task design, training, and validation can proceed independently of physical system constraints. This openness engenders scalability, reproducibility, and collaborative potential that traditional optical computing architectures cannot easily achieve. Researchers worldwide gain the freedom to explore broader task horizons, perform comprehensive benchmarking, and validate innovative approaches within a consistent computational substrate. Such a paradigm shift propels optical computing from isolated experimental configurations into accessible and extensible platforms, expediting technological advancements and practical deployments.

This pioneering work advocates a transformative application paradigm for optical computing platforms: the symbiotic coexistence of physical hardware and computationally equivalent open-source digital twin models. Only by embracing such dual-system architectures can the field transcend limitations imposed by experimental variability and hardware scarcity. The resultant platforms will be shareable, replicable, and adaptable to a diverse array of research challenges. As optical computing moves towards becoming a mainstream computational resource, the principles embodied by DT-OCS may well establish the foundational infrastructure that underpins scalable, collaborative innovation in photonic information processing.

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Subject of Research: Not applicable

Article Title: Digital twin optical computing system

News Publication Date: 21-Apr-2026

Web References:

• DOI: 10.29026/oea.2026.250254

References:
DOI: 10.29026/oea.2026.250254

Image Credits: Dr. Tingzhao Fu from the National University of Defense Technology, China, and Dr. Hongwei Chen from Tsinghua University, China

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Keywords

Artificial intelligence, Computer science, Photonics, Engineering, Machine learning, Computer modeling, Computer processing, Semiconductors, Optics, Applied optics