Unlike most of its social media rivals, Snap hasn’t made many big bets on AI. The company has added a bunch of generative AI-powered features to Snapchat, but it hasn’t made the technology a central focus.

Now, the company is introducing a new standalone AI assistant alongside its redesigned AR glasses. Called Specs Intelligence, the “anticipatory AI service” will feature prominently in Snap’s hardware, but will also have dedicated desktop and mobile apps for those who don’t have a pair of glasses.

Snap doesn’t describe Specs Intelligence as an agent or assistant, but its description will sound familiar to anyone who followed Meta’s recent Muse announcement. “Specs Intelligence builds an understanding of your goals, priorities, relationships, and routines from the apps and tools you choose to connect,” the company explains in a blog post.

Specs Intelligence is separate from Snapchat and sounds much more productivity-focused than anything we’ve previously seen from the company. The assistant relies on connections to users’ outside accounts, like Google, to surface timely reminders and other relevant info based on what it knows about you. For example, the company says, it could highlight “questions to ask, and things to remember” before a meeting or show travel plans ahead of a trip. It’s also able to organize info into different “corners” based on the context in which you need them, like keeping family-related tasks separate from work deadlines.

Snap

If you do have a pair of AR Specs, the assistant can surface relevant info directly into your field of view. Specs owners will be able to ask it questions by saying “Hey Specs.” The feature wasn’t available during my demo with Specs, though I was able to launch a browser window by saying “Hey Specs,” but it sounds similar to some aspects of how Meta AI works with the company’s glasses.

It also seems like Snap is hoping to get ahead of some of the privacy concerns people have had with Meta’s approach. The company says it won’t use Specs intelligence to train its AI models or serve ads and that “Snap personnel cannot view or access the personal content you use with the service.”

Snap is making a limited version of Specs Intelligence available in the US to try now via the Specs app on iOS. The preview version will allow users to connect their Google accounts to see “insights about where you spend your time and attention” but won’t feature proactive suggestions. The “full early-access” version will be invite-only to start and only available with Specs’ Mac app. Anyone interested can sign up for the waitlist now.

• Salesforce UK&I CEO pushes her skilling “mission” across the country
• Salesforce doubles down on UK&I recruitment and upskilling goals
• Jobs could be repatriated back to the UK from abroad

Salesforce’s UK&I CEO has doubled down on her company’s commitment to investing in skills training and upskilling of young people.

With the UK recently seeing a change in Prime Minister, Zahra Bahroloumi was asked in a media roundtable attended by TechRadar Pro what her outlook for the future looked like.

• Report finds 57% of UK organizations opted to extend legacy system use rather than replace the tech
• This is despite almost half of UK businesses are scaling AI
• Meanwhile, over three quarters of UK businesses have gone over budget on modernization, with over two thirds have paused or abandoned the process

Does technological progress necessarily mean replacing old gear? New research from Ensono claims businesses are keeping hold of legacy tech, with older systems extended and upgraded to support modern applications.

This is happening while businesses scale their AI use, focusing budgets on affordable upgrades for existing PCs, laptops, and servers on one hand, and embracing AI on the other. It’s a balancing act, one that is being managed by around 52% of businesses – 57% in the UK, and 48% in the US.

OWASP’s 2026 Top 10 for LLM applications moved excessive agency from sixth to third and dropped improper output handling from fifth to tenth, in the first edition weighted partly on incident data. Europe’s Cyber Resilience Act began requiring 24-hour vulnerability reports on 11 September, but it governs products rather than how an agent is deployed.

Excessive agency has moved from sixth place to third in OWASP’s 2026 top ten for LLM applications, and improper output handling has fallen from fifth to last. The hardest problems now sit outside the model, Steve Wilson argues.

Excessive agency is not about what a model says. It is about what it has been handed: the tools, credentials and scopes it can reach once it answers.

The 2026 edition was the first weighted partly on recorded incidents, 6,639 of them, counting for a quarter of the ranking against three quarters practitioner consensus.

Wilson co-led the project with Rock Lambros and is chief AI officer at Exabeam, a security company. Read the column as both things at once.

His prescription is unglamorous. Read-only tools instead of general connectors, requests inside the user’s own scoped identity, a policy enforcement point between the model and anything downstream, and human approval for anything hard to reverse. It echoes the gap a founder described to us in August, between what an agent can do and what it should be authorised to do.

Europe started a clock five days ago.

Since 11 September, manufacturers have had 24 hours to warn their national response team of an actively exploited vulnerability, under the Cyber Resilience Act. A fuller notification follows at 72 hours, through a platform ENISA switched on the same day.

Penalties reach EUR 15M or 2.5% of worldwide turnover. The rest of the regulation applies from 11 December 2027.

Our own reporting found the bottleneck is not obtaining a bill of materials but trusting one. A list is worth only the confidence you can place in it.

The mismatch is what the clock points at. The Act governs products with digital elements, and lawyers reading it place cloud-delivered software outside, which is how most agents reach a customer.

Four teams broke AI agents four ways in ten days this summer, and the same flaw ran through all of them. The failures sat in the ecosystem around the model, not the model.

Wilson’s own figures make the point. On his internal red team suite the oldest model fails 17% of the time and the newest 2%, and he says 98% is nowhere near good enough when every failure leaks data.

The one European report anybody has confirmed came under the AI Act, not this one. OpenAI filed an incident report over agents that occupied a German wiki. Agency is falling between two regimes.

Liquid Glass was first introduced with iOS 26 and, since its implementation across compatible devices, the design has been met with dividing opinions. 

Fortunately with iOS 27 it’s now possible to adjust the level of transparency that Liquid Glass has. So whether you want to embrace the fluidity of Liquid Glass, or you want a clearer and more opaque finish on your iPhone, there’s an option for you.

It takes just a few moments to adjust the liquid glass transparency in iOS 27, and we’ve detailed the steps below.

For more on iOS 27, check out our 14 tips and tricks guide to help you make the most of this year’s iPhone software update.

“Organizations should not have to choose between the capabilities of a frontier model and the ability to control the technology at the center of their work,” said Mark McQuade, co-founder and chief executive of Arcee AI.

The San Francisco company builds open-weight AI models. It has raised a Series B round that values it at more than $1bn, according to its announcement on 16 September. Vista Equity Partners, Cambium Capital and Emergence Capital led the round. AI10 Ventures, Hitachi, IAG, Microsoft’s M12, P7 and Wipro also took part.

Arcee did not disclose the amount. A source told Fortune, which first reported the round, that it was at least $150m. Fortune put the valuation at $1bn before the new money.

A frontier model for about $20m

Arcee released the Trinity family of models over the past year. The largest, Trinity Large, has 400 billion parameters, with 13 billion active for each token.

The company says its entire 2025 model lineup, including Trinity Large, cost about $20m to build. That figure covers salaries, compute, data, infrastructure and operations.

McQuade told Fortune the company had $30m in the bank when it decided to build its own models. “I said ‘let’s do it’ and I bet the company on it,” he said. Arcee spent 65% to 70% of its capital on the effort, according to Fortune.

Before that, Arcee worked on post-training, which adapts existing models, Fortune reported. McQuade founded the company in 2023. He previously worked at Hugging Face, which Nvidia is buying.

“Dropped the ball on open source”

McQuade told Fortune the goal is to catch up with Chinese open-weight models. “The U.S. is far ahead in closed source, but kind of dropped the ball on open source,” he said.

He said Arcee is chasing GLM Flash, a model from Beijing-based Z.ai. It is not chasing Poolside’s Laguna, he said.

Monti Saroya, senior managing director at Vista Equity Partners, said:

“Arcee has demonstrated a rare combination of technical execution and capital efficiency, building frontier open-weight models at a pace and cost profile that stands out in the market.”

Where the money goes

Arcee will spend the funding in three areas, according to the announcement:

• training the next generation of Trinity models, which is already underway
• expanding its work with the US Department of Energy and its 17 national laboratories on Genesis-Science-1, an open model for scientific computing
• building products that help organisations customise, test, deploy and run open models

Arcee will also work extensively with Vista’s portfolio companies, Fortune reported.

A day earlier, the software company CIQ said Arcee had chosen its Fuzzball tool to coordinate computing jobs for its next flagship model, according to CIQ’s announcement. Arcee keeps its own training and inference software.

Other open-weight bets

In August, River AI raised $1.1bn to let companies train and keep their own models. The same month, the White House’s technology strategy left open-weight AI off its critical list.