Category: Technology

Google Pixel 11 Pro XL vs Pixel 9 Pro XL: Is it actually an upgrade?

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Google Pixel 11 Pro XL vs Pixel 9 Pro XL: Design and display

Google Pixel 11 Pro XL vs Pixel 9 Pro XL

(Image credit: Apoorva Bhardwaj / Android Central)

Honestly, there isn’t much of a difference between both phones. Google retained similar dimensions over the last two years, and the result is that the Pixel 9 Pro XL is nearly the same size and weight as the Pixel 11 Pro XL.

This is easily noticeable while holding the phones next to each other. If anything, I like the in-hand feel of the Pixel 9 Pro XL. Google sent me the all-matte Obsidian model of the 11 Pro XL, and it is dull. I like the matte texture, but the phone itself doesn’t look exciting, and I would’ve preferred the vibrant Canyon model instead.

New ‘Slack Code’ turns AI coding into a team activity

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With the launch of “Slack Code” channels, Slack this week introduced a new way for developers and non-tech staff to work collaboratively with coding agents.

While Slack already integrates with several coding agents, developers have to interact with them independently to get work done. The aim of Slack Code is to make agents available to multiple coworkers in shared, project-based “code channels.” 

“We built code channels to be this multi-player AI experience and bring more development into Slack,” said Sateja Parulekar, vice president of product marketing at Slack. “It’s not just engineers and product managers who are working with the coding agents; it’s the marketers, the product designers who are now able to interact with a coding agent in a safe, governed environment.”

Slack code channels are intended for one-off coding sessions and projects — building a new application feature, updating a web page, or fixing a bug, for example. 

When the coding agent is tagged in a Slack channel or DM with a request, it automatically creates a new code channel, adds links to required documents, and invites relevant participants. 

The code channel functions just like a normal Slack channel, with participants able to send messages and files as usual, while also letting them interact with a coding agent and track outputs via the channel’s “session artifacts.” These artifacts include “code diffs” that highlight changes to code made by the agent, as well as Slack “canvas” documents and live HTML that displays prototypes and previews created by the agent. 

All participants can use natural language to direct the agent, making suggestions and signing-off on outputs, for instance. Any updates are then highlighted in the original Slack channel. And when a task or project is complete, the code channel is automatically archived.

Slack Code channels can also be customized — Slack admins can create guardrails to prevent non-technical workers from shipping code without engineering review, for example.

Code channels adhere to Slack’s existing security and permissions model, the company said, with agents able to access conversations and data based on controls set for a Slack workspace.

At launch, four agents integrate with Slack Code — Claude Code, Devin, GitHub Copilot, and Vercel — though Slack intends to expand the list of options over time. Slack Code is available at no extra cost on all Slack subscriptions.   

“Slack Code closes the gap between where work gets done and where code gets written with dedicated spaces built for output along project-based channels right in Slack,” said Wayne Kurtzman, research vice president at IDC. “This furthers the Salesforce goal of making work more seamless and multi-player within the Slack environment, regardless of the other applications the business is using. 

“While Slack Code may seem early to market for some companies, others are leveraging the first-mover advantage.”

While software development and coordination work “has happened in Slack forever,” according to Will McKeon-White, senior analyst at Forrester, the new features make it “easier to just stay in Slack only and [open] up who is involved with the actual code creation.”

For IT staffers who enable Slack Code, the immediate consideration involves  permissions, he said — “a persistent challenge for anything multiplayer.” McKeon-White cites various approaches: “…Some inherit user permissions based on task, some have persistent permissions with different ‘invoking’ permissions for users, some inherit the permissions of the room. Each has flaws.” 

The longer-term challenge, he said, is around multi-agent collaboration. 

“Multi-agent collab today in successful environments isn’t very dynamic,” said McKeon-White. “Having true dynamic agents cooperating can create extremely interesting emergent behavior,” he said, pointing to recent Anthropic research on the topic.

While Slack Code is aimed at software development-related tasks, Slack also envisages a similar cross-functional, collaborative approach to AI agent interactions applied to a wider variety of purposes: marketing campaigns, for instance, or legal document reviews. 

“That might not involve code per se, but it does involve a very detailed review of a document and tracking changes and a preview of what’s going to be sent out to the customer,” said Parulekar. “In the future, we see this extending to a lot of different teams and use cases for technical and non-technical users.”

Other features announced Thursday include “agent DMs” that allow for individual interactions with an agent and a new “agents tab” where users can view existing agent conversations.   

Parulekar said Slack Code feeds into a wider Slack strategy centered on a “multi-player experience” for human workers and AI agents. This includes the recent announcement of Claude Tag, which makes Anthropic’s agent available to teams.

“Whether it’s the user experience that we’re evolving — like the agent tab, or coding channels where you can plan, code, test, and ship inside of Slack — it’s all part of that bigger vision of making Slack a great home for agents and furthering that vision of multiplayer AI,” she said.

A New Finance Leader Checklist: 30-60-90-Day Action Plan

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Your first 90 days as a finance leader will likely be a whirlwind. Taking time to create a plan and map the steps to achieve it can help you stay on track and measure your progress during this busy time.

To get started, here’s a new finance leader checklist to structure your plan in more detail. The checklist will help you plot your steps to success over the coming months.

Beginning with information gathering and learning in month one, you’ll get tips on who to meet with, how often, and where to focus your conversations.

As you enter your second month, you’re probably establishing informed opinions on what you’ve been learning about the company, its people, and your role. You’ll start to identify opportunities for improvement that can maximize your impact and make a lasting difference for the business.

In your third month, you should be starting to hit your stride in your daily work and establish a certain level of trust with your colleagues. This is a great month to focus on finding solutions to challenges. It’s also time to finalize your ideas on the business and develop an initial plan of actions you want to take.

Inherent, founded by DeepMind alumni, says its AI ‘teammate’ just outperformed Anthropic and OpenAI at replicating research

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Inherent, a London AI lab founded by Google DeepMind alumni, says its AI agent just outperformed much larger models from Anthropic and OpenAI using a fraction of the size.

Of all the startups launched by Google DeepMind alumni, Inherent has gotten relatively little attention. But while better-funded rivals have yet to show the world anything concrete, the London-based team is starting to share what it’s been building.

Just weeks after emerging from stealth with a $50 million seed round, the British startup says its newly released AI agent, Faraday, has outperformed larger, better-known models at a specific task: independently reproducing the findings of published scientific papers without being told the answer in advance.

That may sound like a mere party trick given Inherent’s much loftier goal — building AI that can discover new scientific knowledge and not just verify old results. But paper replication is a standard training exercise for human scientists, too, cofounder and chief scientist Edward Hughes said. “Many PhD students actually start by doing this.”

Beating other AI systems at the task wasn’t the point, Hughes told TechCrunch; how they got there was. “What was most interesting to us about this was not so much the result of beating those frontier agents — which of course we liked — but was actually the way we went about building this.”

Here’s the part that should catch an investor’s eye: measured against Anthropic’s Claude Opus 4.8 and OpenAI’s GPT-5.5 — both much larger, frontier-scale systems — Faraday runs on a comparatively tiny model called Qwen 3.6 that has just 27 billion parameters. (Roughly speaking, “parameters” is a proxy for a model’s size and, typically, its training costs, as well.) Inherent’s bar for success was also higher than simply accuracy. Beyond replicating results, it wanted Faraday to demonstrate “research taste” — an instinct for what experiments are worth running and how to design them well.

Teaching something as intangible as taste is hard, which is where reinforcement learning comes in. It’s a training method that rewards an AI system for good outcomes rather than spelling out rules for it to follow. Rather than training its agents primarily on the study of how science itself is conducted, Inherent leans on this reward-based approach, betting it will generalize better to its longer-term goal of agents capable of contributing across many scientific fields.

“We’re always guided by that north star of building an AI scientist agent and imbuing our agents with taste,” Hughes said. That focus has also shaped what Inherent chooses not to build. Rather than developing its own coding tool, it had Faraday use OpenAI’s GPT-5.5 Codex instead, much the way human scientists lean on existing software rather than building everything themselves, according to the company.

Inherent is also trying to avoid building agents that simply tell users what they want to hear. Instead, Hughes said, the goal is modeled on his favorite kind of teammate — the kind who comes back and says: “I got curious about this, and I went off and I did these experiments. What do you think of these results?”

That collaborative instinct extends to how Inherent operates as a company. Its dozen employees all work in person out of an office in King’s Cross — the once-rundown London neighborhood that Google DeepMind’s presence helped turn into one of the world’s top AI hubs. “We believe that London is the place to be,” Hughes said.

Hughes is bullish on London’s density of AI talent, but he has also added his voice to calls to end “garden leave” — the practice, common in the U.K., of barring departing employees from joining or starting a rival company for months after they resign. It’s a restriction American researchers generally don’t face, giving U.S. startups a head start on hiring talent who’ve left a prior role. “This is a personal view rather than a company view, but I was affected by the garden leave problem,” he told TechCrunch.

Hughes eventually got around that constraint and started Inherent alongside two other DeepMind alumni and a fourth cofounder. The startup isn’t slowing down either. It plans to grow its headcount to “about 20 to 25” by the end of the year. Given its ambitions in world models as well, and with Demis Hassabis’s new role leaving some DeepMind staff unsettled, Inherent’s hiring push could make it an appealing landing spot for DeepMind employees weighing a move.

Pictured from left to right: Inherent co-founders Louis Kirsch, Kaloyan Aleksiev, Tantum Collins and Edward Hughes.

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Samsung Galaxy Z Fold 8 Ultra review: Let’s call it faux Ultra

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Our expert reviewers spend hours testing and comparing products and services so you can choose the best for you. Find out more about how we test.

I knew Samsung hadn’t changed the design with the Galaxy Z Fold 8 Ultra, but it was still surprising to see just how similar it is to last year’s model — I had to double-check the box to confirm I wasn’t sent the Z Fold 7 again.

That sums up the Galaxy Z Fold 8 Ultra in a nutshell; at the surface-level, it is indistinguishable from the Z Fold 7. It is just as thin and light, weighs the same, and has the same in-hand feel. I was holding out on Samsung moving to a design with rounded edges — similar to what Google does with the Pixel 11 Pro Fold — but that isn’t the case.

Strategies for CFOs to Navigate Tariffs and Global Disruptions

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In today’s landscape, the phrase “unprecedented times” has become a cliché, yet the reality remains: the World Uncertainty Index has been steadily rising since 1990. Recent global events—such as COVID-19, the Ukraine war, inflation, and tariffs—have left CFOs grappling with heightened uncertainty about the impact of external pressures on their organizations.

Many executives describe this era as “the end of predictability,” where established trends give way to sudden disruptions in an increasingly complex environment. Alarmingly, two in five executives still feel unprepared for future market interruptions.1

“In four weeks’ time anything might happen. Or even four hours. This dramatically increases demand for scenario-based analysis and decision-making.” – Dr. Carl-Christian von Weyhe, CFO Middle & Eastern Europe, SAP

To tackle these challenges, CFOs must adopt new methodologies tailored to their organizations. This guide explores how forward-thinking finance leaders are effectively managing uncertainty by:

  • Rethinking traditional forecasting methods.
  • Embracing AI to enhance insights.
  • Improving data quality for better decision-making.
  • Weighing tough choices regarding supply partners, expense management, and pricing strategies.

Explore this guide, The end of trends: How CFOs can lead through tariffs and other external disruptions, to discover actionable strategies and tools that can help you navigate uncertainty effectively. Download it today.

Ramp launches its own AI model router, called Router

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Corporate expense management platform Ramp is hot on the heels of Stripe in setting up toll houses for AI inference.

Ramp on Wednesday evening launched its own AI model routing service, dubbed Router, that lets users and companies use and switch between various large language models through an API. The company says it has been using the router it built for its own AI usage needs over the past three years.

The service is only available in the United States. It’s free to use for the remainder of 2026 (users will still have to pay for AI model inference costs), and it comes with a $26 credit launch offer. The company did not say how much the service will cost next year.

In its function, Router is pretty similar to how OpenRouter operates, though the latter offers many more AI model options than Ramp’s current offerings.

Router offers access to models from OpenAI, Anthropic, DeepSeek, Moonshot, Minimax, Nvidia, xAI, and Z.ai. It also provides several “strategies” to help its customers route AI requests to models based on their preferences. For example, one lets users set a preference for model providers’ flex usage tiers, while another lets Router choose which model to route queries to based on up to three user-specified benchmarks. Users can also choose to route only difficult problems to expensive models or test models easily without having to switch.

Users get a dashboard, too, that lets them see token spend, cost, latency, fallback attempts, and other details.

Notably, Router has an opt-out data retention policy: it will record model inputs, outputs and tool calls for one year by default, though the company says it will remove “personally identifiable information before using that content to improve the product.”

For Ramp, entering the model routing business offers a two-pronged opportunity: it gets to tap the booming AI inference market, and also offer its existing clients a model routing service that fits in neatly with its existing products, which includes AI token usage monitoring and token spend management.

And, if Router proves as attractive of a model testing arena as OpenRouter has, Ramp may also be able to build long-standing relationships with AI labs and inference providers worldwide. That could help Ramp, which raised $750 million at a $44 billion valuation in June, gain new customers and a new point of entry for selling its expense management products.

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Google Messages RCS is breaking for Galaxy users after using Smart Switch

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What you need to know

  • Samsung Galaxy users are reporting RCS issues in Google Messages after switching to a new phone with Smart Switch.
  • Some affected users are seeing error code 3100 when trying to enable RCS or send messages on their new Galaxy phones.
  • Google says corrupted cache files transferred through Smart Switch may be responsible for the RCS connection problems.

Samsung Galaxy users are reporting a surge in RCS issues with Google Messages after recently switching to a new device.

Samsung recently shut down its Messages app in the U.S., asking both new and existing users to switch to Google Messages instead. Google Messages is the only messaging app on Galaxy phones that supports RCS, making it the most sensible replacement. However, it seems that some Samsung users are running into problems getting RCS to work after setting up their new Galaxy devices.

As spotted by Android Authority, several users have taken to Reddit to report that RCS stops working after switching to a new Galaxy phone and transferring their data using Samsung Smart Switch. Some affected users are seeing error code 3100 when trying to enable RCS or send RCS messages from their new device.

Google Gemini in google messages

(Image credit: Jay Bonggolto / Android Central)

It looks like Google has already identified a potential cause. A Google representative said that the issue may be related to corrupted cache files that were transferred during the Smart Switch process.

2026 SaaS security report: Closing the unmanaged trust gap

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More than 27.6 billion SaaS security events reveal a growing challenge for IT teams.

SaaS environments now run on a web of trusted users, apps, shared files and automated workflows. As those connections multiply, so do the blind spots attackers can exploit.

Discover the SaaS security shifts that will be critical to improving visibility, reducing exposure and protecting trusted business connections in 2026.

In this report, you’ll learn:

  • Why guest accounts, OAuth integrations and external file sharing are expanding the SaaS attack surface
  • How attackers are using trusted accounts, infrastructure and workflows to hide inside normal business activity
  • Which defensive shifts can help IT teams improve identity visibility, reduce alert noise and respond faster

Warp’s new system is an out-of-the-box software factory for AI development

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Companies are still grappling with exactly how software development should work in the AI area, but one early answer is the so-called software factory. Essentially an agent loop that’s built around the traditional stages of software development, the software factory approach has become a popular way for companies to remake their engineering organizations for the AI era.

Now, a system from Warp could make that transition a lot easier. On Tuesday, the AI coding company introduced Warp Factories, a new system designed to make building and operating AI software factories as easy as possible.

Operating as an infrastructure layer, Warp Factories gives companies a simple environment for deploying agents and a roadmap for how to use them.

To be clear, many companies are already having success with the factory model without any help from Warp. Stripe has been particularly public about its technical progress, developing a “minions” system to automate development within its own codebase. Ramp has made similar progress, developing a background agent that can monitor its own code after it is deployed.

An analytics screen from Warp FactoriesImage Credits:Warp Factories

As Warp CEO Zach Lloyd sees it, the target market for Warp Factories will be smaller companies without the resources to develop a system from the ground up.

“[If you look at] things like running your agents in the cloud and steering those agents as they run, or bringing the work that they’re doing into your local environment, or setting up memory that goes across those agents, or setting up evals that go across those agents — it’s actually a huge infrastructure undertaking to do this right,” Lloyd told TechCrunch.

In Warp Factories, the architecture is already built out of the box, with many of the most difficult decisions already made. Warp’s system is based on the standard phases of software development (triage, specification, implementation, review, and verification) but the agentic approach means any of those steps can be automated.

Users can choose their own coding model and harnesses as necessary; the system works as well with Codex as Claude Code. It also integrates with ticketing systems like Linear and Jira, and messaging systems like Slack and Teams, in an effort to plug in seamlessly to existing workflows.

Beyond just shipping code, Warp Factories will also give managers the tools to track how well the factory is performing. With all the agents running in the same environment, it’s easy to compare performance metrics for different configurations, and to keep an eye on the overall token spend. Warp Factory also allows for self-improvement loops to optimize the overall system, automating management of the process itself.

Even so, Warp Factories is not built to completely replace software engineers — just give them an easier way to collaborate with the new agentic workforce. In Lloyd’s own experience, there are still a lot of tasks that require a human at the wheel.

“We automate like 30% of our tasks, 30 to 35% on a weekly basis,” Lloyd told TechCrunch, “and as models improve, as the context improves, as the harness improves, I think that that number is going to go up over time.”

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