OpenText’s €105m investment marks the single largest backing by a company with a Canadian HQ.

Canadian enterprise data management company OpenText is creating 400 highly-skilled jobs in Cork and Galway over the next three years through a planned investment of €105m.

The company helps organisations protect, govern and utilise their data for agentic AI, cybersecurity and sovereign cloud. Irish-based developers and researchers will design, deploy, secure and operate these AI and cloud capabilities for the European, Middle East and African (EMEA) markets.

This is the single largest investment into Ireland by a technology company headquartered in Canada and is supported by IDA Ireland.

The major investment is expected to advance the company’s service capabilities across the EMEA industries and public sectors. It is also expected to increase regional capacity for organisations operating in highly regulated and mission-critical environments which require greater control over data governance.

“This investment expands our EMEA R&D and operations capacity to deliver the trusted AI, cybersecurity, and cloud capabilities our clients already rely on globally, while giving European organisations greater regional support and flexibility across the cloud environments of their choice,” said Shannon Bell, the executive vice-president, chief digital officer and chief information officer of OpenText.

Speaking of the announcement, Michael Lohan, the CEO of IDA Ireland said: “This investment will strengthen Ireland’s leadership in AI and transformational technology and IDA Ireland looks forward to continuing to work closely in partnership with OpenText as it grows its business in Ireland and deepens its European presence.”

OpenText said that it intends to explore opportunities for university and research collaboration in Ireland as part of its long-term innovation and talent strategy. This includes partnerships with third-level institutions focused on AI, cybersecurity and secure digital operations.

Laying down its R&D plans, OpenText said that its research efforts will advance how AI agents are orchestrated and governed including multi-agent collaboration, system boundary enforcement and knowledge sharing across sovereign zones.

In data sovereignty, it is developing continuous compliance mechanisms that give organisations verifiable control over where data lives and how it is governed, while its cybersecurity research will focus on threat detection and response.

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OpenAI is under investigation by a coalition of state attorneys general, according to the Wall Street Journal. On Friday, June 12, the company received a subpoena seeking information and documents related to its activities and impact on users. The Journal said it viewed the subpoena sent by New York’s attorney general. 

Based on what the publication saw, the AGs are asking for documentation about the company’s advertising, user engagement and retention, as well as its handling of its users’ data and health information. They also want to know about the company’s activities related to minor and senior users, its deep learning models, its policies and its models’ sycophancy. 

“AI is a new and powerful technology, and we work every day to safely bring its benefits to people in a responsible way,” an OpenAI spokesperson said in a statement to the Journal. “We take the concerns raised by state attorneys general seriously and intend to engage constructively with their offices.”

It’s unclear what prompted the investigation, but tech companies developing AI products have been under scrutiny by state AGs for quite a while now. Last year, a group of 44 state AGs sent a letter to Meta, Google, Apple, Microsoft, OpenAI, Anthropic, Perplexity AI and XAI, asking them to protect children from being exposed to inappropriate and potentially harmful chatbot interactions. In April, Florida Attorney General James Ulthmeier opened a criminal investigation into OpenAI, because the suspect in the 2025 Florida State University mass shooting reportedly used ChatGPT.

More recently, another parent filed a wrongful death lawsuit against OpenAI, accusing the company of not implementing enough safeguards to protect users from taking their own life. The lawsuit claimed that the plaintiff’s daughter who died by suicide discussed her suicidal thoughts and plans with the chatbot in the months leading up to her death. However, the company didn’t alert the family or authorities. OpenAI was named as a defendant in the first ever wrongful death lawsuit linked to a chatbot, as well.

Just a few days ago, OpenAI filed paperwork with the Securities and Exchange Commission to go public. It hasn’t decided on timing and pricing yet.

OpenAI is forcing Mac users to update ChatGPT and other desktop apps, after a supply chain attack exposed signing certificates that Apple’s security systems use to verify trusted software.

The company disclosed the incident on May 13 and confirmed malware linked to the “Mini Shai-Hulud” attack infected two employee devices through the TanStack npm ecosystem. Investigators identified unauthorized access activity in a limited set of internal source code repositories connected to those employees.

OpenAI rotated its signing certificates and re-signed affected apps to prevent potential misuse of the exposed credentials. The company found no evidence that customer data, production systems, or intellectual property were compromised during the incident.

Apple’s macOS security protections will block apps signed with the older certificates after June 12, which makes the update mandatory for affected Mac users.

OpenAI confirmed the affected repositories included signing certificates used for applications across macOS, iOS, Windows, and Android. The company blocked future notarization attempts tied to the older credentials instead of revoking the certificates immediately and risking broken software installations for existing users.

Mac users must install updated versions before June 12. After that date, Apple’s security protections will stop trusting apps signed with the previous certificates.

Why macOS users need to update

Code-signing certificates help macOS verify that software comes from a legitimate developer. Apple’s Gatekeeper and notarization systems use those certificates to determine whether apps should be trusted, launched, or blocked.

Investigators found no evidence that exposed certificates were used to sign malicious software or distribute malware to users. OpenAI reviewed prior notarizations for signs of unauthorized activity and said it found no evidence of misuse.

Older versions of ChatGPT Desktop, Codex App, Codex CLI, and Atlas signed with the previous certificates may stop functioning or receiving updates after June 12. ChatGPT Desktop 1.2026.125, Codex App 26.506.31421, Codex CLI 0.130.0, and Atlas 1.2026.119.1 are the affected releases.

Supply chain attacks are becoming harder to contain

Modern apps rely on vast networks of open-source libraries, package managers, and automated development systems that can spread compromised code widely. A malicious dependency can traverse multiple organizations before developers detect the malware in the software chain.

Apple’s macOS security protections will block apps signed with the older certificates after June 12

The attack hit during an active rollout of new supply chain security protections across OpenAI’s development systems. Those protections included stricter package provenance checks, stronger CI/CD credential controls, and package-manager safeguards like minimumReleaseAge policies.

The two affected employee devices hadn’t yet received the updated protections when the malware reached the systems. OpenAI said the incident accelerated deployment of additional safeguards designed to reduce the impact of future supply chain attacks.

How Mac users can stay safe

OpenAI told users to install updated apps only through official websites or built-in update systems. The company also warned users to avoid installers distributed through ads, third-party download sites, email links, or unsolicited messages.

Mac users should verify they are running the latest versions of ChatGPT, Codex, and related OpenAI apps before June 12. Users who downloaded OpenAI software from unofficial sources should delete those apps and reinstall clean versions directly from OpenAI.

Most enterprise RAG pipelines start the same way: a text parser converts web pages and documents into plain text so they can be chunked and indexed for retrieval. That conversion step destroys retrieval signals — and according to new research, it’s responsible for the majority of wrong answers.

A research team from UC Berkeley, Princeton University, EPFL and Databricks published a paper this week introducing PixelRAG, a system that skips that conversion entirely. Instead of parsing pages into text, PixelRAG renders them as screenshots, indexes those images and feeds retrieved tiles directly to a vision-language model reader. Tested across 30 million screenshot tiles covering all of Wikipedia, it outperforms text-based RAG across six benchmarks, improving accuracy by up to 18.1% over text-based baselines.

Parsers are the wrong place to look for fixes, according to the research team.

“Improving parsers is an endless process because every website requires special handling,” Yichuan Wang, lead author and UC Berkeley doctorate student, told VentureBeat.  “Our goal was to explore whether recent advances in VLMs make it possible to bypass that entire problem and build a retrieval system that works across websites without site-specific engineering.”

HTML parsers destroy the retrieval signals that enterprise RAG depends on

The goal of the researchers was to develop a clean end-to-end architecture.

“Modern web RAG pipelines often involve rendering, parsing, cleaning, chunking, and many other handcrafted stages,” Wang said. “Every stage introduces potential cascade errors and abstractions that move us further away from the original webpage. We were interested in whether we could eliminate most of that complexity and operate directly on the rendered page.”

Wang also noted that parsing inevitably loses information. Images, visual hierarchy, typography, emphasis (e.g., bold text), tables, and layout are either discarded or converted into imperfect textual approximations. 

“No matter how good a parser becomes, some information is fundamentally lost during the conversion,” he said.

The research identifies three ways text-based RAG loses the answer before it reaches the reader. All three were measured on SimpleQA, a standard benchmark of 1,000 factual Wikipedia questions:

• Parser loss (36.6% of failures). HTML-to-text conversion destroys structured content so completely that no text chunk in the corpus contains the answer.

• Rank loss (55.2% of failures). The answer exists in the corpus but gets outranked by keyword-dense infoboxes that land at rank 1 for 75.9% of queries, pushing answer-bearing paragraphs to rank 20 or lower.

• Reader loss (8.2% of failures). The correct content reaches the reader but flattened structure causes misattribution.

How PixelRAG works 

Unlike a standard LLM that reads only text, a vision-language model takes images as input alongside text, meaning it can read a rendered web page the way a human does, with layout and structure intact. “For many structured information extraction tasks, we believe modern VLMs have an inherent advantage because they can reason jointly over both content and layout rather than relying on a flattened text representation,” Wang said.

PixelRAG is built around that principle, replacing the text parsing pipeline with a four-stage system that operates entirely on rendered screenshots.

• Rendering. Pages are rendered using Playwright, a browser automation library, at a fixed 875-pixel viewport and sliced into 1024-pixel-tall tiles. Wikipedia’s 7 million articles produce roughly 30 million tiles. Assets are cached locally and rendered entirely offline.

• Indexing. Each tile is encoded as a single 2048-dimensional vector using Qwen3-VL-Embedding-2B and stored in a FAISS approximate nearest-neighbor index. The full index runs to approximately 120 GB in fp16 and supports incremental updates without full re-indexing.

• Training. The retrieval model is fine-tuned on synthetic contrastive data generated from the datastore, using dynamic hard-negative mining to filter false negatives. LoRA, a lightweight fine-tuning method that updates a small fraction of model weights, is applied to both the language model backbone and the visual encoder. Training on approximately 40,000 pairs completes in under three hours on a single H100.

• Storage. Raw screenshot tiles for Wikipedia require 5.6 TB, but a render-on-demand approach eliminates persistent storage: embed all tiles, delete the screenshots and re-render pages on demand at query time. The vector index requires approximately 120 GB.

Six benchmarks, 10x agent token savings and one unsolved problem

Researchers tested PixelRAG across six benchmarks spanning factual Wikipedia QA, table-based queries, multimodal QA and live news retrieval. They said it outperformed text-based RAG on all six, including on tasks where questions are answerable from text alone. On SimpleQA it reaches 78.8% accuracy versus 71.6% for the strongest text parser, widening to 48.8% versus 42.5% on structured table queries. Teams need Qwen3-VL-4B class models or above to see the benefit. Smaller models trail text retrieval by more than 12.5 percentage points.

The agent cost advantage is the strongest near-term case for PixelRAG. In benchmark testing, an AI agent using PixelRAG as its search backend ran on 3.6 million prompt tokens versus 37.5 million for text retrieval, at 2 to 4 times lower cost than alternatives including Google, while achieving higher accuracy. Image compression can cut that token budget by a further third.

Visual chunking is the main unsolved problem. Text-based RAG systems have spent years refining how to split documents into meaningful retrieval units based on topic, section or semantic content. PixelRAG currently has no equivalent: it slices pages by fixed pixel height, meaning a table or paragraph can get cut in half mid-tile with no awareness of content boundaries. 

“The text retrieval community has spent years studying chunking strategies, while visual retrieval has received much less attention,” Wang said. “We think this is an important area for future research.”

What this means for enterprises

The retrieval quality problem PixelRAG addresses reflects a broader market shift already underway. VB Pulse Q1 2026 data from qualified enterprise respondents found intent to adopt hybrid retrieval tripling from 10.3% in January to 33.3% in March, the fastest-growing strategic position in the dataset. PixelRAG’s own authors point to hybrid deployment as the most practical near-term path — layering visual retrieval on top of existing text systems rather than replacing them.

For teams already running RAG pipelines, the path to those savings is more straightforward than a ground-up rebuild.

“A practical path is to use PixelRAG as an enhancement layer alongside existing text retrieval systems,” Wang said. “Hybrid retrieval that combines both text and visual search is straightforward and is likely how many production deployments would evolve.”

Maine has taken its public data breach reporting portal offline after fraudulent breach disclosures were published on the state’s website, prompting a review of procedures to prevent abuse in the future.

Yesterday, BleepingComputer reported that fake data breach disclosures had been submitted to Maine’s official breach notification portal impersonating Discord and the multiplayer social virtual reality platform VRChat.

At the time, VRChat told BleepingComputer the filing was fraudulent and had been submitted using the name of a fictitious employee.

In a statement published Friday, the Maine Attorney General’s Office acknowledged that data breach “hoaxes” were submitted through the state’s reporting system.

“The Office of the Maine Attorney General has been made aware of an apparent abuse of our data breach reporting system,” the statement reads.

“After conversations with VRChat, one of two affected companies, it has become clear that the reported data breaches were hoaxes submitted by an unknown entity unrelated to either company. These false reports have been removed from the database. We have no knowledge of any recent legitimate data breach reports from either VRChat or Discord.”

The Attorney General’s Office says it has now temporarily disabled public access to the breach notification database while it reviews reporting procedures to reduce similar abuse in the future.

Prior to the shutdown, submitted breach notices were automatically published to the public database.

“We don’t have any independent knowledge of the breaches, the submitting entity fills out the information and it goes directly onto the site. We will review the one you’ve flagged, thank you,” Maine Attorney General’s Office told BleepingComputer.

The notice states that companies can continue to submit breach notifications through the reporting service, but members of the public seeking copies of disclosures must now contact the Attorney General’s Office directly.

Maine’s data breach portal is commonly used by journalists, researchers, and threat intelligence firms to monitor newly disclosed security incidents and determine whether organizations are reporting cyberattacks or data breaches affecting consumers.

The incident demonstrates how automatically published breach disclosures can be abused to spread misinformation and damage a company’s reputation.

The fraudulent VRChat filing claimed the company suffered a data breach impacting over 2.4 million people and included a fabricated employee contact name in the disclosure.

After BleepingComputer contacted VRChat about the filing, the company confirmed the disclosure was fake and stated it had not submitted the notice to Maine authorities.

BleepingComputer also contacted Discord about the fraudulent notice submitted to the site but did not receive a response.

It is unclear how many additional fraudulent breach notices may have been submitted through the portal before the state suspended public access to the database.

Security teams log 54% of successful attacks and alert on just 14%. The rest move through your environment unseen.

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Just in time for America’s 250th anniversary, Disney Imagineers tapped Apple Vision Pro to help give one of their most iconic flight rides a patriotic makeover.

Disney has shared a brand-new behind-the-scenes video as part of the Disney Unscripted series on YouTube. This time, the company shows off what it takes to revamp one of its existing attractions.

The attraction in question is “Soarin’, at EPCOT, which has been rebranded to “Soarin’ Across America” for the 250th anniversary of the United States of America. Rather than focusing on wonders around the world, or in California for another version of the ride, Soarin’ Across America takes riders on an airborne adventure across the United States.

The reimagining requires a lot of work. From capturing all new aerial footage to crafting an all-new musical score, the project requires filmmakers, musicians, and Imagineers to work together.

In the video, we learn that Disney’s audio media designers donned the Apple Vision Pro to create a digital workspace during the music and sound effects mixing phase.

“So, usually for a Soarin’ attraction, we need to build scaffolding, but that was a ‘no-can-do’ for this project because we were on such an accelerated schedule,” Megan Duncan, one of Disney’s Senior Sound Editors, says in the video.

By using the Apple Vision Pro, a virtual digital workspace easily replaced that scaffolding and extra equipment. Most of the workflow only required an Apple Vision Pro, a custom desk attached to the flight simulator seats, and a small selection of audio mixing equipment.

SpaceX rented its Colossus 1 data centre to Anthropic not because it had surplus capacity, but because it could not make the facility work for its own AI models. Bloomberg reported on Friday that SpaceX encountered latency issues when trying to connect the Memphis site to two other data centre campuses located more than 10 miles away, compounded by aging network infrastructure.

The company had planned to train its most cutting-edge Grok models using a cluster of three facilities working together. Training large AI models requires ultra-fast connections between sites. If the links are older or lower bandwidth, they create delays that slow the entire cluster. SpaceX determined the facility would be more valuable generating revenue than sitting underutilised.

The hardware mismatch made things worse. Colossus 1 contains a mix of Nvidia chip generations, including Hopper and Blackwell systems alongside older accelerators. Colossus 2 and 3 were built more uniformly around Nvidia’s Blackwell chips. In a distributed training cluster, the workload is spread across machines that need to stay synchronised. Older chips create bottlenecks by forcing faster accelerators to wait. The cluster ends up performing closer to its slowest hardware, not its fastest.

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The result is that Anthropic is now paying $1.25 billion per month to use a facility that SpaceX’s own engineers could not fully utilise. Combined with the $920 million monthly Google deal, SpaceX is collecting approximately $2.17 billion per month in compute revenue from infrastructure it originally built for itself.

The revelation complicates the narrative SpaceX presented during its IPO roadshow. Musk’s company repeatedly stressed that Colossus 1 was built in just 122 days, exceeding industry averages. Speed of construction was a selling point. Bloomberg’s reporting suggests speed came at a cost: the facility was not built uniformly enough to serve as part of a larger training cluster.

SpaceX CFO Bret Johnsen said the company has not given up on internal AI services, including Grok. Musk has described the Anthropic arrangement as a 180-day lease with a 90-day mutual cancellation right, preserving the option to reclaim the capacity. “If compute gets super tight I said we might need it back at some point,” he said.

But Grok’s trajectory makes reclaiming the compute less urgent. Downloads fell from 20 million in January to 8.3 million in April. Paid conversion is a fifth of ChatGPT’s. Federal adoption has stalled. The product that was supposed to justify the data centre investment is underperforming, while the rental income from Anthropic and Google is now a $26 billion annualised revenue line. SpaceX built a data centre for AI training and accidentally became an AI landlord instead.

China has launched its first dedicated photonic computing laboratory in Shanghai, signalling that Beijing sees light-based chips as a strategic route around Washington’s tightening grip on conventional semiconductor exports. The Shanghai Key Laboratory of Integrated Photonic Computing Chips and Systems opened on 11 June at Shanghai Jiao Tong University, the state-backed Jiefang Daily reported.

The lab is a joint effort between the university and Shanghai-based Lightelligence, one of the country’s leading photonic computing startups. Lightelligence listed on the Hong Kong stock exchange in April, surging roughly 380% on its first day of trading, and claims to be the first company in the world to achieve large-scale deployment of hybrid optical-electronic computing, though that assertion has not been independently verified.

Why photons could matter for AI

Conventional AI chips push data through silicon circuits using electrons. Photonic chips swap electrons for photons, particles of light that travel faster and generate far less heat.

The theoretical payoff is significant. Photonic processors promise higher bandwidth, lower latency, and a fraction of the energy consumption, qualities that matter as training frontier AI models pushes data-centre power demands toward their limits.

Zou Weiwen, the lab’s director and a photonics professor at Shanghai Jiao Tong University, said optical computing was “an important pathway for achieving breakthroughs in computing power.” The facility will focus on photonic chip architectures, silicon-photonics integration, optical components, and the algorithms needed to make them commercially viable.

A hedge against export controls

The lab’s launch coincides with Beijing’s broader drive for technological self-reliance. Washington has restricted China’s access to advanced semiconductors since 2022 and has widened the rules repeatedly, forcing Chinese firms to hunt for alternatives.

That search has already shifted China’s AI chip strategy away from general-purpose GPUs and toward custom silicon. Photonics represents a more radical pivot, one that could let Chinese engineers sidestep lithography bottlenecks entirely by building on the country’s existing strengths in fibre optics and laser technology.

Chinese authorities have flagged photonics and photonic-electronic hybrid accelerator chips as strategic national priorities. Shanghai officials said they had mobilised coordinated funding across multiple science and technology programmes to back the effort.

Big ambitions, early days

Beijing is already pouring money into AI infrastructure through other channels. A reported $295 billion blueprint would build a nationwide network of data centres running largely on domestic chips by 2028.

Photonic computing, however, remains far from production-ready. Zou acknowledged that the field faces “fundamental scientific challenges,” citing the absence of a mature software and algorithm ecosystem capable of efficiently harnessing photonic hardware.

The gap between laboratory promise and commercial reality is wide. But with conventional chips increasingly hard to source and AI workloads growing exponentially, China is clearly willing to bet on the physics of light.

The 2026 World Cup is here, and if you’re still thinking about buying a new TV to watch the tournament in, we’d like it if you could take a beat and consider these five key features.

Big sports tournaments are usually when retailers bring out the big discounts, but before you snap up the cheapest deal you can find, we’ve laid out five features to give some thought to before you hit buy.

From size to HDR performance to motion processing, taking these five areas into consideration will help you in your search, and hopefully lead to you having the best AV experience to watch the tournament in.

Size

Bigger is, genuinely, better. Unless you’re not able to fit a bigger screen in your living room, we’d always recommend that you go for a bigger size than you currently have.

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The scale is the obvious benefit. Jumping from 55- to 65-inches reaps positives in terms of immersion. And of course, if you have multiple people around for a watch party, then having a bigger screen means you aren’t all cramming for space on the sofa and craning your necks to see what’s happening.

The last few years have seen a rise in the number of affordable, large-screen TVs. TCL’s 98-inch C7K is available for £1999, but for something considerably less expensive but still plenty big, Sharp’s 70GK4245K could be yours for less than £450.

Sound

George Lucas once said that sound is 50% of the experience. He was talking about films of course, but we’d say the same applies to anything, especially if we’re talking about sports.

Hearing the roar of the crow, feeling the intensity when something happens on (or off) the pitch, or the hush of the silence before a penalty is taken – sound matters and brings immersion to the experience of sports. So don’t buy a TV with tinny sound.

That’s easier said than done when even TVs that rack £3000 asking price have a sound that’s average. And a TV that has good sound might not have as good picture. As always, if you know (from reading our reviews, of course) that TV sound is on the weaker side, give it a boost with an external sound system.

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We’d also avoid most of the built-in audio modes on TV, such as sports. Very rarely do they provide the kind of all-encompassing, immersive experiences they suggest they can.

HDR

While not every sports tournament is produced and broadcast in 4K, the last few football tournaments have been in available in HDR. For the 2026 World Cup, you can view the tournament in 4K HLG HDR on the BBC iPlayer.

More expensive TVs offer a better HDR experience because they can hit higher levels of brightness and produce a better colour experience. If you want to watch the World Cup in the best way possible, we’d suggest having a look at 4K TVs priced within the £1000 to £2000 price range for a better HDR experience. We have you covered with out best 4K TV list.

Picture mode

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Leading on from the previous point is picture mode. Vivid (or Dynamic) is an option for some, but we find that too garish in terms of brightness and colours; and also brings in issues with the motion processing negatively affecting picture quality.

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Film (or Movie) may offer the best, most accurate colours; but this mode is often for watching in the dark or when the curtains are drawn (considering some of the match times, this might be more useful).

The picture mode we’d suggest you watch the World Cup in, is Standard mode. Standard mode gives blues and greens a boost – helpful for bringing that rich green tone of the grass – and while it adds some processing to the mix, it’s less heavy on the picture than it would be with Vivid.

It’s also brighter than Film modes and will have more of impact if you’re watching during the day, but a lot of the matches at the World Cup will be on evening/night-time in the UK.

Motion Processing

If you’re going to use Standard picture modes (or any mode other than Film/Cinema), your TV is going to automatically add some motion processing unless you dive into the settings and disable.

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If you prefer motion processing for your sports, there are some TVs that do it better than others. Sony, Panasonic, LG are towards the top of the list; Samsung not far behind without tweaking the settings a little bit, with the likes of TCL and Hisense behind and a little less consistent.

Motion processing performance can vary depending on the price. Some cheaper TVs do away with it completely (Roku models tend not to have it), but sometimes it’s better to have an affordable TV that doesn’t do it, than one that does it poorly.