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In retrospect: How AI4Life collaborated with Industry

In retrospect: How AI4Life collaborated with Industry

Interactions and exchanges with industry have been important aspects of the AI4Life project. By actively engaging with stakeholders across microscopy, pharmaceuticals, and technology, AI4Life ensured that its tools, standards, and infrastructure were not only scientifically robust but also relevant and usable in real-world industrial environments.

Engaging industry from the start

Throughout its project lifetime, AI4Life maintained a proactive approach to industry collaboration. The project held dedicated meetings with key stakeholders – including, for instance, the Euro-BioImaging Industry Board, Bayer, Zeiss, and Leica – to exchange ideas, align on technical needs, and explore integration opportunities. Company representatives also participated in AI4Life hackathons, contributing to open discussions and co-development efforts with academic and infrastructure partners. This open-door strategy helped foster trust and mutual understanding between sectors.

Bringing AI models into commercial microscopy platforms

One of the most tangible outcomes of AI4Life’s industry engagement was its collaboration with Leica Microsystems. Together, they successfully integrated deep-learning models from the BioImage Model Zoo (BMZ) into Leica’s AIVIA software, a commercial AI-powered image analysis platform. Leica converted BMZ models into its own repository format, enabling interoperability with AIVIA. This milestone demonstrated that AI4Life’s standards and infrastructure were mature enough for commercial deployment. Updates from this collaboration were shared openly on the AI4Life website.

ZEISS also broadened its collaboration with AI4Life. Their Arivis platform started supporting pre-trained AI models, custom-trained models, and external technologies such as CAREamics, an AI4Life community partner. Since CAREamics models follow the AI4Life model specifications, this opens a new path for BMZ models to be used directly within commercial workflows.

Supporting innovation through Open Calls

AI4Life’s Open Calls for AI-based image analysis support were open to both academic and industry participants, reinforcing the project’s inclusive philosophy. Among the selected projects was one from RD Néphrologie, a French SME, which received support to study the impact of Chronic Kidney Disease on collagen density in tissues such as the heart and kidney. This example highlighted how AI4Life’s infrastructure could be applied to pressing biomedical challenges in both academic and commercial contexts.

Working with technology leaders

AI4Life partners also participated in NVIDIA’s Early Access Program to test the Triton Inference Server. These evaluations provided valuable insights into the scalability of the BioEngine model testing service and informed improvements to the platform’s technical performance.

Supporting sustainability through innovation

The Hypha platform, the core component used and refined in AI4Life for serving AI models, became the foundation for the spin-off company Amun AI. Amun AI’s plug-and-play version of Hypha builds on AI4Life technology, includes confidential computing, and is designed for large-scale data management and AI models serving research institutes and industrial users. This transition from research prototype to commercial product exemplifies AI4Life’s commitment to sustainable innovation and long-term impact.

Why it matters

These collaborations contributed to the early development of AI adoption in bioimaging within industry. By aligning technical development with industry needs, enabling model interoperability, supporting SMEs through Open Calls, testing AI models in commercial environments, and promoting spin-offs, AI4Life laid the foundation for an active ecosystem and long-term impact. Its open, inclusive, and standards-driven approach ensures that the tools created during the project will continue to benefit both academic and industrial users well beyond the official project conclusion.

Stay connected

Community governance is open and transparent. Anyone can join our weekly Wednesday meetings at 16:00 CE(S)T:

  • First week of the month: Community Board (with current and prospective Community Partners).
  • Other weeks: Technical Board (with Task Forces spun up as needed).

The same Zoom link will be used for all meetings, and agendas are kept in HackMD, and meeting notes are posted to GitHub for wider visibility (details here).

Follow Euro-BioImaging and our partners to hear more about upcoming developments, community opportunities, and how you can get involved in shaping the BioImage Model Zoo. 

And don’t forget to subscribe to the Euro-BioImaging Newsletter!

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In retrospect: Open Science in AI4Life

In retrospect: Open Science in AI4Life

From the beginning, AI4Life has treated Open Science as a foundation for how research should be shared, reused, and built upon. It has ensured that its results are accessible to everyone, not just those inside the consortium.

Sharing early, sharing widely

To make knowledge travel faster, AI4Life partners share their manuscripts and technical reports as preprints long before formal publication on platforms like bioRxiv, arXiv, and Zenodo. This way, project partners have invited feedback from the global community and prevented valuable work from staying behind paywalls or review delays.

AI4Life also opened its infrastructure and services to the outside world through Open Calls and Challenges, allowing researchers and developers everywhere to both contribute to and benefit from solutions to real imaging problems.

Open tools, open infrastructure

A significant contribution of AI4Life to Open Science is the development of tools and platforms that make AI models for image analysis easy to find, run, and reuse:

  • BioImage Model Zoo: a public repository of AI models for microscopy and bioimaging, where each model is openly licensed, versioned, and citable.

  • BioEngine: a web-based environment that lets users run AI models and workflows in the browser or on cloud resources.

  • Integration with community tools: Platforms like DL4MicEverywhere, BiaPy, CAREamics, deepImageJ, ilastik, ImJoy, SpotMAX, and ZeroCostDL4Mic bring these AI4Life tools directly into the daily workflows of life scientists.

Open by design

AI4Life ensures openness by:

  • Publishing data, code, and models under open licenses (MIT, BSD, Creative Commons).
  • Assigning DOIs and permanent identifiers so each model version can be cited and traced.
  • Storing resources openly in Zenodo, the BioImage Model Zoo (BioImage.IO), and the BioImage Archive.
  • Requiring reproducibility tests for every model in the BioImage Model Zoo, models must include test inputs/outputs and pass automated checks.
  • Using containerised environments (e.g. DL4MicEverywhere) allows tools to run the same way on any computer, cluster, or cloud system.

A community effort – Working together, not alone

AI4Life has actively fostered open engagement with both the imaging and AI development communities, recognising that building impactful infrastructure requires collective effort. Through workshops, hackathons, and other collaborative activities, the project has invited external researchers, tool developers, and domain experts to co-create solutions, share insights, and shape the direction of the AI4Life services and infrastructure. This inclusive approach ensures that the tools and standards developed are not only technically sound but also aligned with the real-world needs of diverse scientific communities.

To that end, AI4Life also works closely with European Research Infrastructures (Euro-BioImaging ERIC, EMBRC, EU-OPENSCREEN, Instruct ERIC, EMPHASIS) and a growing network of Community Partners. These organisations help embed AI4Life tools into their daily activities and disseminate their results.

By combining early sharing, open infrastructure, community collaboration, and automated reproducibility checks, AI4Life has built trust, transparency, and long-term impact, ensuring that the tools created today remain useful for researchers tomorrow.

Stay connected

Community governance is open and transparent. Anyone can join our weekly Wednesday meetings at 16:00 CE(S)T:

  • First week of the month: Community Board (with current and prospective Community Partners).
  • Other weeks: Technical Board (with Task Forces spun up as needed).

The same Zoom link will be used for all meetings, and agendas are kept in HackMD, and meeting notes are posted to GitHub for wider visibility (details here).

Follow Euro-BioImaging and our partners to hear more about upcoming developments, community opportunities, and how you can get involved in shaping the BioImage Model Zoo. 

And don’t forget to subscribe to the Euro-BioImaging Newsletter!

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In retrospect: AI4Life Challenges

In retrospect: AI4Life Challenges

AI4Life’s mission isn’t just to assist individual projects , it also seeks to benchmark and push the boundaries of AI methods for bioimage analysis. Over three years, the project has organised three public challenges, inviting the computational community to compete on core imaging tasks under shared datasets, metrics, and conditions.

Overview & evolution

First Challenge: unsupervised denoising

AI4Life launched the inaugural challenge focusing on unsupervised denoising of microscopy images. Rather than relying on paired “noisy/clean” image sets, participants were invited to apply algorithms that learn denoising from noisy data alone.

This setup reflects a practical constraint in microscopy: obtaining “clean” reference images is often difficult or impossible. The goal was to reduce noise while preserving delicate image features like edges, textures, and fine detail.

Four datasets were used, with two types of noise (structured and unstructured) represented. https://ai4life-mdc24.grand-challenge.org/ai4life-mdc24/

The 2024 AI4Life Denoising Challenge drew 104 registrants from 27 countries, resulting in 151 submissions across four leaderboards (each corresponding to a dataset/noise type).
Top results included combinations of algorithms like COSDD and N2V, which delivered strong performance across modules. 

Find out more: https://ai4life.eurobioimaging.eu/ai4life-denoising-challenge-2024-results/

Second & Third Challenges: transitioning to supervised denoising

Following the insights from 2024, the 2025 challenge shifts focus to supervised denoising, now combining noisy and clean images to train models. The change allows for more precise performance evaluation and potentially better denoising when ground truth is available. The 2025 edition moved to include supervised denoising (paired noisy/clean) and a specialised calcium-imaging track. 

The MDC25 and CIDC25 result pages (hosted on Grand-Challenge) hold the detailed leaderboards and entries; the CIDC25 platform remains accessible for benchmarking and late submissions:

These two challenges together drew 91 submissions from 13 countries across eight leaderboards, with more details at: 

https://ai4life.eurobioimaging.eu/ai4life-denoising-challenges-2025-results/

 

Why these challenges matter

  • Benchmarking: Standardised challenges let us compare methods fairly, across diverse datasets and noise types.
  • Broad community engagement: By opening up to anyone (not just project partners), AI4Life attracts fresh ideas and cross-pollination from adjacent fields.

From calls to conversations: reflections from our experts

Challenges are not just competitions; each involves substantial coordination, data curation, evaluation, and community outreach. To bring that human side forward, we recorded conversations with several experts who played key roles in designing, running, and evaluating these challenges.

Watch the full video and hear their stories: 

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In retrospect: AI4Life Open Calls

In retrospect: AI4Life Open Calls

Over the past 3 years, AI4Life has launched a series of three annual Open Calls to support life scientists facing unmet image analysis needs. 

These calls invited scientists working with biological or microscopy images to propose analysis problems where deep learning makes a difference. Proposals were selected based not just on novelty, but on broader impact, reusability, and feasibility. 

What each Open Call offered
  • Technical support and collaboration: Project experts provided guidance on the development of analysis workflows, data improvement and deep learning solutions.
  • Consultation phase (from Open Call 2 onward): Before full project selection, top applicants engaged in a consultation phase to assess data readiness, existing methods’ suitability, and possible quick wins. In some cases, the consultation itself was enough to resolve issues.
  • Public dissemination: At the end of each project, the developed workflows, some subset of data, and trained models were made publicly available (e.g. via the BioImage Model Zoo or open archives).
  • Potential elevation into public challenges: Projects selected via the Open Calls addressing common needs could be turned into public challenges, inviting more involvement from the computational community to improve or benchmark solutions.
Evolution through three editions

First Open Call (Spring 2023) 

https://ai4life.eurobioimaging.eu/open-calls/first-open-call/ 

  • 72 proposals submitted from various life science domains, from cell and developmental biology to marine and plant research.
  • 67 aimed to improve existing workflows, while fewer proposed new AI-based methods.
  • The lack of annotated (“ground truth”) data was a recurrent bottleneck, revealing a widespread need for annotation support and AI-readiness training.

Second Open Call (Autumn 2023)

https://ai4life.eurobioimaging.eu/open-calls/second-open-call/ 

  • Introduced a two-phase process, beginning with a 1-hour consultation phase to assess project feasibility before full project support.
  • Project consultations increased interaction between applicants and experts, leading to better alignment of expectations and higher-quality collaborations.

Third Open Call (Spring 2024)

https://ai4life.eurobioimaging.eu/open-calls/third-open-call/

  • Received 28 applications across Europe, showing continuing demand despite a more targeted scope.
  • As in OC2, top applications underwent consultation before selected cases advanced to full technical support.

Across all calls, an international review committee has guided project selection by evaluating scientific impact, feasibility, reusability, and alignment with AI4Life’s capabilities.

Use Cases: real stories of impact

All projects selected through the Open Calls are showcased at the Use Cases page, illustrating how AI4Life support can turn complex bioimage tasks into reproducible, shared pipelines.

https://ai4life.eurobioimaging.eu/open-calls/use-cases 

These use cases show the diversity of problems addressed (2D/3D, multi-modal, multi-label) and how the solutions are made open for reuse.

 

From calls to conversations: reflections from our experts

Behind every open call was a dedicated group of experts guiding applicants, shaping proposals, and co-developing solutions. Watch the video below to hear their stories:

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AI4Life Standards and Interoperability

AI4Life Standards and Interoperability

by Teresa Zulueta-Coarasa, Fynn Beuttenmueller, Anna Kreshuk, Beatriz Serrano-Solano

In AI4Life, we believe that interoperability and standardisation are the backbone of a healthy AI research ecosystem, allowing data and models to be reused and combined across different research groups, institutions, and platforms. Without standards, valuable datasets and AI models often remain underutilised, difficult to reuse, reproduce, and less impactful than they could be.

One of the main goals of AI4Life has been to create and promote standards for sharing AI models and AI-ready datasets for biological images. By doing this, we aim to ensure that data and models are truly FAIR (Findable, Accessible, Interoperable, and Reusable) so they can support scientific discovery for years to come.

Setting standards for biological image datasets

In January 2023, the BioImage Archive organised a workshop that brought together 45 experts from diverse backgrounds: data producers, annotators, curators, AI researchers, bioimage analysts, and software developers. Together, they defined recommendations for sharing annotated, AI-ready biological image datasets.

These recommendations are grouped under the acronym MIFA:

  • Metadata: clear information about datasets and annotations.
  • Incentives: giving proper recognition to dataset creators.
  • Formats: adopting a small set of interoperable formats, such as OME-Zarr.
  • Accessibility: making datasets openly available in repositories like the BioImage Archive.

The MIFA guidelines have been published in Nature Methods (https://www.nature.com/articles/s41592-025-02835-8). They are expected to help researchers more easily train and evaluate AI models across diverse biological imaging tasks and unlock the value of archived imaging data.

A standard for AI models

In addition to datasets, AI4Life also supports a model metadata standard. This standard describes how pre-trained models should be documented so that others can find, reuse, and integrate them into their work. It is openly available and registered in FAIRsharing, a trusted global resource for standards, repositories, and policies.

The model standard is implemented through the bioimageio.spec Python package, which provides a versioned metadata format for models. It works with the bioimageio.core library offers utilities and adapters to make models compatible with different tools and frameworks.

With this approach, models can be shared in a way that is:

  • Findable: authors and citations are clearly tracked.
  • Accessible: models and their documentation are available through the bioimage.io website.
  • Interoperable: the model metadata allows for programmatic execution through the bioimageio.core library makes models seamlessly usable through all our Community Partner tools or simply through Python or Java code.
  • Reusable: thanks to the metadata, model inference can be executed in a standardised way even without access to the model’s original code.

The BioImage Archive has developed the AI4Life Model Evaluation Platform to benchmark datasets and models more directly, building bridges between the BioImage Archive and the BioImage Model Zoo. 

While pre-trained models are already very useful, they are even more powerful when bundled together with their training datasets and training code. Model metadata supports linking to datasets and code by introducing the corresponding metadata field and a minimal description format for datasets and notebooks. 

The dataset description is currently available in bioimageio.spec serves as a starting point; plans are underway to extend this with deeper integration of the MIFA guidelines. In the future, this will make programmatic access to well-described datasets even easier, enabling researchers worldwide to train, compare, and improve AI models for bioimaging.

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AI4Life Denoising Challenges 2025: Results

AI4Life Denoising Challenges 2025: Results

by Vera Galinova

The AI4Life Denoising Challenges returned in 2025 with two new tasks: the Microscopy Supervised Denoising Challenge (MDC25) and the Calcium Imaging Denoising Challenge (CIDC25). Both aim to benchmark and improve methods that address noise in microscopy data, a common obstacle for biological and medical imaging.

Why denoising matters

Microscopy is a key tool in life sciences, but image quality is often limited by acquisition noise. This noise can mask fine structures or dynamic processes, making quantitative analysis more difficult. Deep learning–based denoising methods, which learn directly from data rather than relying only on predefined filters, are increasingly used to address this challenge.

The Challenges

Microscopy Supervised Denoising Challenge (MDC25)
 This challenge focused on supervised denoising, where models are trained with pairs of noisy and clean images. The setup allowed participants to directly assess how well their methods recover ground truth structures, and to explore strategies for making denoising more precise and consistent across diverse microscopy data. The results can be viewed at https://ai4life-mdc25.grand-challenge.org/results

Calcium Imaging Denoising Challenge (CIDC25)
This task addressed calcium imaging, a widely used technique to record neuronal and cellular activity. Because calcium signals are both spatially and temporally structured, effective denoising needs to preserve not only cell morphology but also the temporal dynamics of activity traces. The challenge provided synthetic datasets with known ground truth to allow controlled evaluation across different noise levels and image content. Participants were also encouraged to develop unsupervised approaches and new evaluation strategies that could be applied to real experimental data, where noise-free ground truth is not available. The challenge is still running!

Participation in 2025

MDC25

  • Leaderboard 1: 3 participants / 2 methods
  • Leaderboard 2: 6 / 3
  • Leaderboard 3: 13 / 4
  • Leaderboard 4: 7 / 4

CIDC25

  • Preliminary (Content Generalization): 5 / 3
  • Final (Content Generalization): 8 / 3
  • Preliminary (Noise Level Generalization): 5 / 3
  • Final (Noise Level Generalization): 8 / 3

What’s next?

This year’s challenges highlighted how supervised and unsupervised methods can be applied to different types of microscopy data and evaluation settings. While the AI4Life grant has now concluded, the Calcium Imaging Denoising Challenge platform remains accessible, and late submissions are welcome for benchmarking purposes.

👉 https://ai4life-cidc25.grand-challenge.org/

 

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AI4Life & Leica deepen collaboration: 22 New BioImage.IO models now available in AIvia 15

AI4Life & Leica deepen collaboration: 22 New BioImage.IO models now available in Aivia 15

Leica Microsystems has rolled out Aivia 15, the newest version of its AI-driven image analysis software. Among its enhancements are 22 additional pre-converted BioImage.IO models

The release of Aivia 15 with its 22 new BioImage.IO models represents an important step in AI4Life’s mission to make AI-driven tools more accessible to life science researchers. With an improved interface, faster performance, and direct integration of community-curated models, Aivia 15 makes it easier for users to apply AI in their everyday workflows.

As part of the ongoing AI4Life–Leica collaboration, Aivia 15 now offers drag-and-drop support for 22 additional BioImage.IO models, bringing the total to 26 pre-converted models ready to use in Aivia’s recipe console.

This builds on the first phase of the collaboration launched in July 2023, when AI4Life and Leica began working together to make BioImage Model Zoo models interoperable with Aivia by converting them into a format directly usable within the software. You can read more about the initial collaboration here
https://ai4life.eurobioimaging.eu/ai4life-leica-collaboration/

By integrating these new models, AI4Life connects community-driven developments with industry users, broadening the choice of available models and supporting more reproducible research.

 

More information:

https://www.aivia-software.com/post/pancreatic-phase-contrast-cell-segmentation-bioimage-io

https://www.leica-microsystems.com/company/news/news-details/latest-ai-software-simplifies-image-analysis-and-speeds-up-insights-for-scientists/

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AI4Life & GloBIAS webinar series: 4 webinars covering the whole AI4Life ecosystem

AI4Life & GloBIAS webinar series: 4 webinars covering the whole AI4Life ecosystem

From February to August 2025, AI4Life joined forces with GloBIAS to host a 4-part webinar series, exploring how AI is transforming bioimage analysis — from accessing pre-trained models to building reproducible infrastructures and running open calls for the life sciences community.

The series ran every second month, on the second Thursday at 4 pm CE(S)T, and brought together experts from the AI4Life consortium to share practical tools and lessons learned. All sessions were recorded and are freely available on the AI4Life YouTube channel.

We were thrilled to welcome more than 200 registered participants across the series, with an average of 30–40 attendees per session, tuning in from across the globe.

Webinar 1 — AI4Life: What can it do for you and your images?

Speakers: Anna Kreshuk (EMBL), Wei Ouyang (SciLifeLab)
Chair: Beatriz Serrano-Solano (Euro-BioImaging)

This kick-off session introduced AI4Life and the BioImage Model Zoo. Anna and Wei walked us through “killer features” the unified model metadata standard, and how community tools like ilastik, Icy, and BiaPy can all run these models.

Webinar 2 — Towards FAIR and high-quality AI-ready data for bioimage analysis

Speakers: Teresa Zulueta-Coarasa (EMBL-EBI), Estibaliz Gómez de Mariscal (Human Technopole)
Chair: Ana Stojiljkovic (University of Bern)

All about annotated image datasets FAIR and ready for AI. Teresa presented the BioImage Archive’s approach to curating and annotating datasets, while Estibaliz shared insights on ensuring data quality and robust evaluation in the deep learning era.

Webinar 3 — Building scalable and reproducible AI infrastructure for Bioimaging

Speakers: Wei Ouyang (SciLifeLab), Iván Hidalgo-Cenalmor (BSC)
Chair: Marco dalla Vecchia (CNRS)

This session featured two complementary platforms:
– BioEngine: deploy and run BioImage Model Zoo models locally or institution-wide, with minimal hassle.
– DL4MicEverywhere: containerised workflows for reproducible, portable deep learning across Google Colab, desktops, and HPC clusters.

Webinar 4 — Lessons learned from 3 years of open calls and challenges

Speakers: Vera Galinova (HT), Mehdi Seifi (HT), Caterina Fuster-Barceló (UC3M)
Chair: Florian Jug (HT)

A behind-the-scenes look at AI4Life’s open calls and challenges: what worked, what we learned, and how our experiences can help image analysis facilities worldwide.

Although the AI4Life project is about to conclude, the recordings remain a rich resource for anyone interested in AI for microscopy and bioimaging.

 

Watch more content on the AI4Life YouTube channel: https://www.youtube.com/@ai4life 

 

 

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The 3rd AI4Life Challenge now live too

The 3rd AI4Life now live too

We’re excited to announce the AI4Life Calcium Imaging Denoising Challenge 2025, focused on unsupervised denoising of calcium imaging data, a critical step for improving signal extraction in functional microscopy.

Hosted on the Grand Challenge platform, we invite researchers, developers, and imaging enthusiasts to design robust, generalizable algorithms that preserve both the spatial structure and temporal dynamics of neuronal activity in calcium imaging.

Participants will:

  •  Access synthetic calcium imaging datasets with controlled noise levels.
  • Develop and submit unsupervised or self-supervised denoising models.
  • Be evaluated on spatial and temporal reconstruction quality using hidden ground truth data.
  • Address realistic generalization tasks across unseen content and varying noise settings.

Whether you’re exploring new ideas or validating established models, this challenge is a unique opportunity to contribute to core problems in bioimage analysis—and help advance reproducible, FAIR, and translational solutions for neuroscience and cell biology.

The challenge will run until August 11th.

Get started now: https://ai4life-cidc25.grand-challenge.org/

Whether you’re a seasoned researcher or new to the field, this challenge is an opportunity to test and improve your models on real-world microscopy data, and support FAIR, open science while doing it.

Get started now!

👉 https://ai4life-mdc25.grand-challenge.org/

 

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Release of the New BioImage Model Zoo Website

Release of the New BioImage Model Zoo Website

The BioImage Model Zoo unveils a complete redesign—offering a more collaborative and interactive platform for the community, with rapid model testing feedback to enhance the contributor experience.

Built as part of the AI4Life project, the revamped platform emerged from the community feedback focused on serving the research community. Early user input revealed that contributor experience is key to enabling greater model ingestion and community engagement. We therefore rebuilt the website from the ground up, focusing on streamlined uploading, testing, validation, and review workflows powered by scalable infrastructure.

What’s new?

The new website comes with a bunch of new features:

  • Complete frontend rewrite with enhanced user experience and more powerful model search capabilities.
  • Intuitive model uploader with in-browser file editing support, streamlining the contribution process.
  • Instant model testing during the upload process, providing immediate feedback and enhanced quality control for contributors.
  • Collaborative review interface allowing reviewers to quickly edit, review, and approve models with real-time collaboration tools.
  • New Hypha-powered backend featuring advanced artifact management, user login, permission systems, real-time collaboration, and comprehensive APIs for programmatic access—enabling seamless model access and upload.
  • BioEngine server infrastructure powered by Ray, enabling robust model test runs and scalable compute resources. It also allows on-premise deployment with an intuitive wizard for easy setup.
  • Resilient storage infrastructure backed by S3 storage hosted at EMBL-EBI with automatic Zenodo backup for long-term preservation.

These upgrades transform the entire model sharing experience, whether you’re contributing cutting-edge models, discovering tools for your research, or collaborating with the community to advance reproducible AI in bioimaging. Looking ahead, this new foundation enables us to make AI models even more accessible through intuitive web applications and AI Agents, designed for federated deployment across institutions to bring powerful bioimaging AI directly to researchers worldwide.

Explore the new platform: https://bioimage.io

Behind the scenes: The rebuilding process was supercharged by the emergence of AI coding assistants—tools enabled rapid frontend development and backend integration, showcasing how “vibe coding” can accelerate scientific software development.