Glass-based substrates are slowly beginning to push out organic substrates commonly used in PCBs due to often superior material properties. One area where glass substrates have however struggled is with through-hole vias and providing the conductive copper path through them. A 2024 article by [Keith Best] gives a good overview of the topic, with recent news showing how much companies like Intel are pushing for glass substrates, specifically for the packaging of dies.

One major advantage with vias in glass substrates is that they can be much smaller, enabling smaller than 0.1 mm diameter holes with far finer pitch. The challenge here is to make perfect holes with a laser that are defect-free, as well as have the intended diameter.

After that this through-glass via (TGV) has to be coated or filled with copper, much like their organic equivalent. Said TGV can be fully filled with copper, or use plating and add dielectric filler. Detecting flaws in such a finished TGV is important.

In a 2025 review article of glass substrate technologies by [Pratik Nimbalkar] et al. published in Chips the state of the art at the time was covered. The need for ever higher-density integration options with ASICs is highlight here, especially now that many chips today consist of multiple interconnected dies inside a single package.

The complications of creating TGVs with femtosecond laser pulses in Borofloat 33 glass are highlighted by [Daniel Franz] et al. in a 2025 research article, with microcracks and backside ablation observed without proper precautions, something which previously was often resolved by an etching step following said laser drilling. The main issue here is the post-drilling residual stress from the thermal shock, which the authors demonstrate can be largely prevented with careful tweaking of the laser drilling parameters.

As pointed out in a 2024 review article by [Chen Yu] et al. glass substrates are useful for far more than just high-density chip packaging. Glass substrates are also chemically resistant, have a higher heat resistance, are largely transparent to RF and can be hermetically sealed against outside influences. This makes them great for various advanced sensors and communication devices.

Meanwhile, if you wanted to do some metal-depositing on glass at home, we covered this recently.

The PlayStation Portable, or the PSP, was discontinued over a decade ago, but its cultural presence never fully faded. Most recently, the fast-fashion brand Zara gave it a second life in the most unexpected form: the company has dropped a crossbody bag modelled after the PSP 1000. 

The Zara PSP Crossbody Bag is exactly as delightful and absurd as the name sounds. The shadow drop came without an announcements or media campaigns, but the retro gaming community has already taken notice of it. 

What does the bag actually look like?

It is actually a relatively small crossbody bag whose front face is a silicone recreation of the PSP 1000, in convincing detail. The bag comes with embossed buttons, logos (on the front and the back), an analogue nub, and a vinyl panel standing in for the iconic 4.3-inch widescreen display. 

The adjustable shoulder strap also carries a PSP branding, along with the classic triangle, circle, cross, and square shapes. Clearly, Zara doesn’t want the product to look like a cheap knockoff, and the result shows. 

The bag measures 4.3 x 7.9 x 2 inches, has a main zipper compartment, and is made from polyurethane thermoplastic on the front face with a silicone overlay and a polyester shell and lining.

How much does it cost, and where can you get it?

For now, the Zara PSP Crossbody Bag is available at $35.90 in the United States and £19.99 in the United Kingdom, available directly via the company’s official website and in stores. The bag is only available in one color, black.

I also see the trademark symbol on the website, implying that this is some sort of licensed deal between Zara and Sony, rather than an unofficial product, even though neither company has confirmed the arrangement. 

At $35.90, it could be among the most affordable pieces of PSP memorabilia you might ever own, but only if the PSP mattered to you. 

When you think of Memorial Day sales, you probably think of mattresses and other home goods. And while those items are definitely discounted, now is also a good time to purchase tech. Personally, I’m not buying anything right now unless it’s discounted—and fortunately many of our top picks are. Whether you’re shopping for a power bank, a new pair of headphones, or some other gadget, I’ve rounded up the best Memorial Day deals for your perusal. Most of these deals end at the end of the day.

Check out our buying guides for more recommendations, including the best headphones, the best laptops, and the best cheap phones. You might also want to check out our additional Memorial Day deals coverage.

Updated Monday, May 25: We’ve checked prices, removed expired deals, added 6 new deals, and ensured accuracy throughout.

WIRED Featured Deals:

Sony WH-1000XM5 for $248 ($152 off)

The Sony WH-1000XM5 have a very frustrating name, but they’re the predecessor to our favorite wireless headphones, and they’re still an excellent pick if you don’t want to shell out for the new WH-1000XM6. They go on sale frequently, but rarely drop this low in price, which comes within $5 of their all-time low. If you’re in the market for over-ear headphones, they’re hard to beat. They’re comfortable, portable, lightweight, and stylish, and they’ll make your music sound great no matter what you like to listen to.

Diotima received €500,000 under Enterprise Ireland’s Commercialisation Fund last year.

AI edtech start-up Diotima, founded by former secondary school teacher Siobhan Ryan, has spun out from Trinity College Dublin (TCD).

The platform aims to enable educators to use AI to create assessments and individualised feedback to improve learning outcomes and lighten burdens on teachers.

The spin-out will be led by edtech commercialisation specialist Jonathan Dempsey as CEO, with Ryan, also a biochemist and environmental scientist, becoming chief product officer and learning lead.

Development engineer Daniel Fernandez and AI engineer Dr Long Mai, who have both worked on the Diotima project, will also join the inaugural team.

Dr Eoin Lane, an AI regulatory compliance expert who was formerly the global head of AI and data science at the Bank of New York Mellon, is a governance consultant to the Diotima project.

“This all started when I was working as a teacher and I had a vision for how AI could enhance teaching and learning even before any of the models like ChatGPT launched,” said Ryan.

“I then worked with Tom Pollock and Learnovate to develop this vision into a real-world project.”

Diotima began its partnership with Learnovate in February 2025 and received €500,000 in funding under Enterprise Ireland’s Commercialisation Fund, which supports third-level researchers in translating their research into commercially viable solutions.

The idea was to develop an AI-enabled edtech platform to help teachers and other educators create assessments, as well as provide feedback to learners, all in compliance with European and Irish legislation.

Specifically, the platform meets requirements under the EU AI Act, which has strict regulations around the usage of AI in high-risk sectors such as education.

“We aim to position Diotima as a leader in responsible AI for education,” Ryan said. Diotima will continue to engage with prospective customers and stakeholders for a go-to-market strategy while also seeking new investment.

“Using responsible AI, Diotima promises to develop into a revolutionary platform for learners in secondary schools and professional education organisations, delivering formative feedback and better outcomes overall,” said Pollock, Learnovate’s impact, licensing and commercialisation manager.

Learnovate launched its ‘Responsible AI for Learning’ initiative earlier this year to enable AI implementers and practitioners involved in teaching and learning to share knowledge, interpret guidelines and comply with AI regulations.

The initiative is made up of professionals from all four education domains – schools, higher education, vocational education and training, and professional education – as well as representatives from the Department of Education, teaching unions and other sectors.

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

• AI-powered hackers now exploit software flaws faster than companies can patch systems
• Mobile phishing scams now outperform traditional email attacks across corporate environments worldwide
• Unauthorized AI tools are quietly leaking sensitive company information across global workplaces

For the first time in nearly two decades, exploiting software vulnerabilities has overtaken stolen passwords as the primary way hackers breach corporate networks.

Verizon’s 2026 Data Breach Investigations Report claims the exploitation of vulnerabilities now accounts for 31% of all confirmed data breaches.

Over the past two decades, technical debt meant outdated architecture, messy code, and poorly maintained documentation. That definition is no longer sufficient in the AI era, where failure modes are more subtle and often non-linear. AI systems are introducing new layers of technical debt that live across prompts, models, and data dependencies — making these layers less visible, harder to measure, and often more dangerous than traditional debt.

A crisis hiding in plain sight

The complexities of AI systems and their associated failures have been well documented. A 2025 MIT study found that 95% of AI projects fail to reach production or deliver value. A similar study by S&P Global Market Intelligence found that 42% of businesses scrapped multiple AI initiatives in 2025 — a sharp increase from 17% the previous year. Various reasons are cited for these failures, but most of them point to poorly designed and implemented systems that are complex to manage and have multiple hard-to-monitor failure points, leading to a rapid accumulation of AI debt. 

Traditional technical debt was localized to the codebase, and bugs were usually easily reproducible. Consequently, bugs could be easily identified during tests and fixed through rearchitecting the codebase. However, AI debt is much more distributed, manifesting across prompts, models, data pipelines, and all associated infrastructure. It is also more intermittent: Due to the probabilistic nature of AI, systems do not always respond the same way, leading to intermittent failures. This makes it much more challenging to identify risks during testing, and also creates a need for more continuous monitoring even post-deployment to prevent gradual drift and worsening performance.

The new forms of AI debt

AI debt typically manifests across four new forms, each of which comes with its own set of risks.

Prompt debt is the most visible of these. A modern version of ‘spaghetti code,’ this can include undocumented prompt tweaks, accumulated ‘quick-fix’ prompts that lead to inconsistencies, neglected version control of prompts, and ‘prompt stuffing’ (the cramming of extraneous data or context directly into AI prompts). All these combine to make prompts a form of untyped, untested code without any version control, leading to increased brittleness and vulnerabilities.

Model dependency debt is another increasingly common form of AI debt. Most enterprises now depend on a mixture of external models developed by leading foundation model providers; applications and agents are built on top of API calls to these models. Consequently, application logic now depends on models that are external to the core system, and that cannot be clearly controlled. As models update, performance varies and reproducibility is lost — prompts tuned for one model may fail or perform poorly when switched to another model, whether an update from the same provider or from another provider.

Most enterprise AI deployments today use retrieval-augmented generation (RAG), which pulls in additional context from enterprise data repositories. Retrieval debt is a consequence of these repositories having messy data, duplicated documents, and outdated information. This causes AI to return technically correct answers that are outdated and no longer relevant, causing downstream failures. Unlike hallucinations, these are harder to detect because they were correct, perhaps even until recently, and hence look correct to any tester. 

Evaluation debt reflects the lack of standardization in testing and monitoring for AI models and applications. While AI benchmarks exist, they tend to focus on narrow tests and reflect point-in-time results. Most enterprises lack consistent testing standards, ground truth datasets, and real-time monitoring of deployments; there is no equivalent yet of continuous integration /continuous delivery (CI/CD) for prompts. As a consequence, CIOs and CTOs do not have clear visibility into model performance and cannot track improvements or worsening of models. 

All of these are in addition to traditional forms of technical debt, which still manifest across the tools and systems that AI applications and agents interact with, read from, or write to. A rapid increase in the adoption of AI-generated code (often deployed without inadequate testing) is further aggravating inconsistencies within, and poor maintainability of traditional codebases. 

The new forms of AI debt combine with these earlier forms of technical debt to compound rapidly and create large-scale risks that can cause catastrophic failure of entire enterprise deployments. Solving for these risks is made even more challenging by the distributed nature of AI ownership – most systems span engineering, product, data, and business teams, leading to unclear accountability when an error is identified. 

As a result, these risks manifest in the form of escalating compute costs, inaccuracies in AI outputs, and increasing exceptions that need to be handled by humans — leading to projects often stalling and failing due to unclear return-on-investment stories and a lack of trust from users. 

How enterprises can prevent AI debt

AI debt will not be solved by ‘better’ models — failure rates remain high despite models already having high accuracy. The solution to AI debt requires better system design, integration, controls, and changes in organizational culture. 

First, prompts need to be treated as code. This involves careful version control, documentation, and rigorous testing both pre- and post-deployment for all possible prompt configurations. Best practices from the traditional world of coding — such as the use of smaller prompt blocks instead of large prompt-stuffed walls, or reducing the use of hard-coded parameters — can also help mitigate AI debt. 

Second, evaluation needs to be built into the entire AI infrastructure stack. Continuous evaluation pipelines need to be established and must reflect a wide variety of metrics measuring both technical and business-aligned metrics. In addition, AI observability systems should be integrated to monitor output quality, failure rates, model drift, and data drift.

Third, explainability should be included by default in all AI results to make up for limited reproducibility. Data lineage, models used, and the steps followed should be clearly traceable so as to allow auditability of results and correction in case of any systemic errors. 

This requires explicit AI debt reduction programs and associated budgets, similar to earlier waves of investment in security or in cloud modernization. These need to be driven at a CXO level by key leaders to prevent costly rework later.

Conclusion: A stitch in time

Enterprise AI deployments are not just static code; they are living systems that interact with the entire enterprise stack. As a result, the defining challenge in an agentic enterprise will not be building or deploying intelligent systems, it will be maintaining these systems to ensure continued reliability during real-world operation.

Enterprises that seek to proactively identify and mitigate AI debt from the design phase itself are the likeliest to build sustainable AI platforms that deliver significant long-term productivity boosts across the organization. 

Vikram is a principal at Cota Capital, where he invests in early-stage enterprise tech and deep tech companies.

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• Kansas City schools replace 30,000 Windows and Chromebooks with Apple devices
• Concerns raised over financial loss from retiring functional school computers
• District cites security, durability, and “student pride” as reasons for the Apple switch

The Kansas City Public Schools district has announced a sweeping transition which will remove tens of thousands of non-Apple devices from its classrooms.

According to information on the district’s website, administrators will replace more than 30,000 Windows PCs and Chromebooks with Apple hardware over the coming months.

Laptops have always come in all shapes and sizes, but recently I’ve seen ultrabooks become super popular. Maybe it’s just me who’s been drowning in work with my other colleagues, but wherever I go, I only see these thin and light laptops. A big part of this push can be attributed to efficiency gains, which mean we can fit more power into these chassis and get serious performance out of them. There’s one brand that’s always bet big on ultrabooks, and that’s Asus. Their ZenBook lineup is probably what comes to mind when anyone thinks about a serious laptop for professionals, at least in India.

I’ve tested several of these ZenBooks in the past, and they’ve always passed my tests with flying colors. However, 2026 has been proving to be a big challenge with rising memory prices driving costs up and value down. The latest iteration of the ZenBook 14 landed on my table a couple of weeks back. Asus has refreshed it with the latest Ryzen processor and a new OLED touch display option. To do the laptop justice, I switched my MacBook (it’s easy, my work is on Chrome, mostly) and made the ZenBook 14 UM3406GA my daily driver. Here’s how it stacks up in 2026.

Design & Hardware

If you remember the last ZenBook 14 (which we reviewed previously), then the new iteration would feel right at home. The laptop is available only in stealth black, and I’m a fan. It’s super elegant, and I’m a bit biased towards stealthy machines. The ZenBook 14 would fit perfectly for a corporate meeting or a quick stop at the nearby cafe to catch up on some work. Sadly, if you like experimenting with colors, there’s no other option. For some fun, the Vivobook lineup should come in handy.

The new Asus logo adds to the premium appeal, and everyone whom I gave the laptop to loved the design. There’s just one small problem, though: fingerprints. Asus has yet to fix this problem, as the black finish picks up sweat and grease from your palms and leaves them printed on the surface unless you use a cleaning cloth. It’s not a major problem, but something worth mentioning.

Minor complaints aside, what the Asus ZenBook 14 does best is portability. The 14-inch form factor is best for people who are constantly on the go but still need enough screen space to multitask. To put this to the test, I took the laptop on a recent work trip to Delhi, where the 1.2 kg weight didn’t put much stress on my shoulders. I took it out to work at the airport, and everything went fine without hiccups. The metal construction meant I wasn’t worried about putting too much pressure on the backpack. I also tested the build myself and observed no flex in either the keyboard deck or the display panel. The hinge holds the display firmly enough, though it’s a little stiffer than I’d like. It lifts the laptop ever so slightly, which can be annoying, especially coming from a MacBook.

As far as ports are concerned, Asus has your back. The left side houses a couple of Type-C ports, with one supporting USB 4.0 Gen 3 (40 Gbps data bandwidth, DisplayPort, and Power Delivery). Beyond that, there’s a full-size HDMI 2.1 port, an audio combo jack, and a USB 3.2 Gen 1 Type-A port on the left. I had no problems connecting accessories to the laptop.

Keyboard & Trackpad

A good keyboard is one of the cornerstones of an ultrabook, since most people spend hours typing documents (like me writing this review). Fortunately, the one on the ZenBook 14 is quite good. Coming from a MacBook, my keyboard standards aren’t that high, and I got used to this one quickly. The keyboard is generously spaced, so there’s no cramped action. Typing feels clicky enough, and there’s good feedback in the end. Backlit support is present, and, thankfully, Asus hasn’t gone with gray keycaps for contrast, which would make them visible at night.

The same praises can be carried to the touchpad as well. Sure, it’s not the haptic one I’m used to, but I’ve seen plenty of people who love physical touchpads. If that’s you, you’re in luck: the one on the ZenBook 14 is quite large, and I didn’t find any dead zones. Instead, Asus has bundled a few extras with the trackpad, including a light-up number pad that turns on when you press the button on the top right.

Display & Camera

Last year’s Asus ZenBook 14 came with arguably the best display ever, with a 3K OLED 120Hz panel. This year, though, Asus has decided to switch things up. The new one gets a 14-inch FHD+ OLED panel with a 60Hz refresh rate. If that sounds underwhelming, there is now support for touch. This adds a whole new dimension to using the laptop, and I’d much rather have this functionality over the tad bit of extra sharpness. Beyond that, watching content is an absolute breeze on the ZenBook 14. I was catching up on Better Call Saul (I know I’m late), and the 100% coverage of the DCI-P3 color space kept everything stunning with vibrant yet natural colors. The contrast was excellent, and I could make out the different details on the faces.

The ZenBook 14 is also VESA DisplayHDR True Black 500-certified, so HDR content should pop out a bit. In everyday use, I measured a peak of around 450 nits, which is plenty for working in a bright cafe or on a cloudy day outdoors.

As for the camera, the 1080p webcam performs as you might expect. The sensor is sharp enough not to make the videos look muddy, but it struggles in low-light environments, with noise creeping in. Aside from the basics, Asus has bundled several AI features, including 3D noise reduction and AI Noise Cancellation. Both of these work fine, and I actually enjoyed my time giving presentations on the laptop.

Performance & Gaming

Performance is another pillar of the ultrabook experience since nobody wants to deal with an underpowered processor that hangs up during an important call. With the Asus ZenBook 14, you get the latest AMD Ryzen AI 5 430 processor, running on the Zen 5 architecture, along with 16 GB of LPDDR5X-7500 RAM and 512 GB PCIe 4.0 NVMe M.2 SSD. Right off the bat, the everyday performance of the ZenBook 14 is excellent. I write reviews, so my work mainly happens in Chrome, with about 20 tabs open for research. The laptop handled all that blissfully well, without a single hiccup. I could have multiple apps running in the background, too, and it wouldn’t break a sweat.

Sadly, I’m no coder or video editor, but I did try my hand at both. I downloaded VSCode and edited a 4K Instagram reel, both of which went surprisingly well. Sure, you cannot expect the laptop to handle ten 4K streams, but for casual editing and reels, it’s perfectly fine. Large coding projects are compiled in seconds, so if you’re a college student, this could be worth a look.

But what about the actual numbers? I know benchmarks don’t often tell the whole story, but they do help paint a picture. Keeping up with that spirit, I turned on performance mode and ran Cinebench R23, where the ZenBook 14 scored 1,098 in the single-core and 7,032 in the multi-core tests. In 3D Mark’s Wild Life Extreme test, the laptop scored 2,655 points. Finally, in the Night Raid test, the number reached 20,792.

Given the very decent benchmark scores, I thought we should play a few games on the ZenBook 14. But before that, please note that this is not a gaming laptop, by any stretch of the word. Still, if you only play eSports titles like Counter-Strike and Valorant, the ZenBook 14 might surprise you. At medium-to-high settings, I got over 100 FPS in both games, and the experience was jitter-free even during high-intensity matches.

Battery Life & Speakers

AMD has made quite a few gains in efficiency with its newest chips, and they help a lot with battery life on the Asus ZenBook 14. On a typical workday, which involves using Chrome, editing spreadsheets, and watching tons of YouTube videos, I get roughly 12 hours of SoT. This is almost MacBook-level battery life, and something I quite frankly didn’t expect. Charging is handled by a 65W fast charger, which gets the laptop from 20% to 80% in under an hour.

Usually, I’m not a fan of downward-facing speakers, since they inherently limit output depending on the surface’s hardness. While that factor is in play here, the speaker quality is top-notch. Compared to my aging MacBook Air, the ZenBook 14 sounds more full, with an emphasis on the mids. The vocals sound super clear, and the treble is nice. There’s Dolby Atmos support for the people who watch a lot of movies without headphones.

Verdict

At ₹114,990, the new Asus ZenBook 14 has everything a professional needs, and in my two weeks of testing, I couldn’t find anything that made me go, “Oh, I wish they had done this better.” The design is sophisticated yet stands out, with premium materials. The OLED panel is bright and colorful, and the touch functionality adds a new way of using the laptop, especially for kids. Performance keeps up with anything you might need, even demanding tasks, and a little dabble in the gaming world. Honestly, the ZenBook 14 gets a solid recommendation from me.