Visible offers access to Verizon’s network in two unlimited plans for as low as $25 per month, with unlimited data, talk and text using Verizon’s 5G & 4G LTE networks, and unlimited talk and text to Mexico and Canada. Buying a new phone and signing up for a new phone contract can be super overwhelming—with all of the options available, confusing contract terms, and hidden fees making things more complicated. Visible makes it easy to save even more on their already affordable plans, and you can even score a free phone with our Visible promo code.

It should be said, for transparency, that such low costs mean that you won’t get some of the perks we’ve come to expect with the bigger tech and cell providers. For the low price of Visible, there’s only minimal customer service via an on-site chat, so if you’re someone who needs a lot of technical support, this may not be the provider for you.

Get 6 Months of Visible+ Pro for $135: No Promo Code Needed

If you’ve been wanting to change service providers, this is the perfect time to try something new. Right now (through November 3), new members can get the Visible+ Pro plan for just $135 upfront for the first 6 months—that’s less than $23 per month for the first 6 months. (This Visible promo doesn’t apply to the annual plans, the Visible base monthly plan, or Visible+ monthly plan.)

Save $6 Per Month With This Visible Promo Code

If the Visible+ Pro plan isn’t right for you, you can still save! New members can get $6 off per month for the first 12 months on any Visible monthly plan (the Visible plan, the Visible+ plan, or the Visible+ Pro plan) with code 6OFF12. This discount applies to when new members join Visible and bring their own device or purchase a new device, select their choice of plan, and enter the promo code at checkout. All that’s left is to set up your SIM and activate your service with the new discount applied.

Other Great Visible Promos

There are plenty of other great discounts at Visible to help new customers feel confident in their move. Visible also offers a very generous 15-day free trial, where you bring your own phone, get a trial phone number, and try the Visible plan commitment-free for 15 days to see how you like the service before taking the plunge.

If you love Visible, you and your friend can save with this referral promo. When you bring a friend to Visible, you each get one $20 service credit when a plan is purchased. Plus, if you have a lot of friends looking for a new phone plan, you can share your Bring a Friend code with up to 12 people at a time (meaning you’ll get 12 credits added to your account).

Just days after research revealed that the leading independent full-fibre platform network had 13% UK access coverage, representing 4.3 million premises, CityFibre has announced record connections of 108,000 in its third quarter, almost double the 58,000 customers connected in the previous quarter.

The company said sales performance from partners continues to accelerate, delivering significant revenue and EBITDA (earnings before interest, taxes, depreciation, and amortisation) growth for the company. For the third quarter, CityFibre posted revenue of £43m, up from £34m a year ago, with an annualised run rate of £172m, up 26% year-on-year. Adjusted third-quarter 2025 EBITDA was £7.6m, more than five times that posted in the same quarter a year ago and now at an annualised run rate of £30m.

Having completed its 10Gb XGS-PON upgrade ahead of schedule and rolled out a 5.5Gb wholesale product, CityFibre said it was cementing its position as the UK’s wholesale provider of choice thanks to its ability to offer internet service provider (ISP) partners “the best economics, the best products and the best service”. CityFibre now has around 730,000 customer connections, with growth accelerating across its ISP partners.

In July 2025, Sky launched across CityFibre’s nationwide network, joining an ISP line-up that serves around 50% of the UK broadband market. When the deal was announced in August 2024, Sky Broadband had a customer base of 6.7 million across the UK and Ireland, with services in the UK then delivered by BT’s broadband provision division Openreach, making it the second-largest residential provider in the UK after BT itself.

Also in July 2025, CityFibre reached an agreement with its shareholders and existing lenders on a major £2.3bn financing round to accelerate its next phase of growth.

The financing includes £500m in new equity secured from CityFibre shareholders – including Goldman Sachs Alternatives, Antin Infrastructure Partners, Mubadala Investment Company and Interogo Holding – plus a £960m expansion of existing debt facilities. An accordion facility of £800m is also being made available to help drive CityFibre’s continued expansion through the acquisition of full-fibre network assets. This facility will be used to finance the company’s M&A pipeline and cement its position as the sector consolidator.

With the financing now concluded, the company said it is well-positioned to ramp up market consolidation and transform the reach of its network and the number of premises passed.

“Our rate of customer growth has real momentum, with our ISP partners making the most of CityFibre’s market-leading services and growing across our full-fibre network,” remarked CityFibre CEO Simon Holden.

“We are proving the strength of CityFibre’s wholesale business model as we reach an inflexion point, with our recent financing providing the firepower to significantly expand our reach through acquisitions and bring world-class digital infrastructure to people, businesses and communities across the UK.”

In addition to the Sky deal, the company has also launched full-fibre services for Gigafast+ across its nationwide network, and has completed the integration of Lit Fibre and the acquisition of Connexin’s full-fibre infrastructure.

Designing a complex electronic device like a delivery drone involves juggling many choices, such as selecting motors and batteries that minimize cost while maximizing the payload the drone can carry or the distance it can travel.

Unraveling that conundrum is no easy task, but what happens if the designers don’t know the exact specifications of each battery and motor? On top of that, the real-world performance of these components will likely be affected by unpredictable factors, like changing weather along the drone’s route.

MIT researchers developed a new framework that helps engineers design complex systems in a way that explicitly accounts for such uncertainty. The framework allows them to model the performance tradeoffs of a device with many interconnected parts, each of which could behave in unpredictable ways.

Their technique captures the likelihood of many outcomes and tradeoffs, giving designers more information than many existing approaches which, at most, can usually only model best-case and worst-case scenarios.

Ultimately, this framework could help engineers develop complex systems like autonomous vehicles, commercial aircraft, or even regional transportation networks that are more robust and reliable in the face of real-world unpredictability.

“In practice, the components in a device never behave exactly like you think they will. If someone has a sensor whose performance is uncertain, and an algorithm that is uncertain, and the design of a robot that is also uncertain, now they have a way to mix all these uncertainties together so they can come up with a better design,” says Gioele Zardini, the Rudge and Nancy Allen Assistant Professor of Civil and Environmental Engineering at MIT, a principal investigator in the Laboratory for Information and Decision Systems (LIDS), an affiliate faculty with the Institute for Data, Systems, and Society (IDSS), and senior author of a paper on this framework.

Zardini is joined on the paper by lead author Yujun Huang, an MIT graduate student; and Marius Furter, a graduate student at the University of Zurich. The research will be presented at the IEEE Conference on Decision and Control.

Considering uncertainty

The Zardini Group studies co-design, a method for designing systems made of many interconnected components, from robots to regional transportation networks.

The co-design language breaks a complex problem into a series of boxes, each representing one component, that can be combined in different ways to maximize outcomes or minimize costs. This allows engineers to solve complex problems in a feasible amount of time.

In prior work, the researchers modeled each co-design component without considering uncertainty. For instance, the performance of each sensor the designers could choose for a drone was fixed.

But engineers often don’t know the exact performance specifications of each sensor, and even if they do, it is unlikely the senor will perfectly follow its spec sheet. At the same time, they don’t know how each sensor will behave once integrated into a complex device, or how performance will be affected by unpredictable factors like weather.

“With our method, even if you are unsure what the specifications of your sensor will be, you can still design the robot to maximize the outcome you care about,” says Furter.

To accomplish this, the researchers incorporated this notion of uncertainty into an existing framework based on category theory.

Using some mathematical tricks, they simplified the problem into a more general structure. This allows them to use the tools of category theory to solve co-design problems in a way that considers a range of uncertain outcomes.

By reformulating the problem, the researchers can capture how multiple design choices affect one another even when their individual performance is uncertain.

This approach is also simpler than many existing tools that typically require extensive domain expertise. With their plug-and-play system, one can rearrange the components in the system without violating any mathematical constraints.

And because no specific domain expertise is required, the framework could be used by a multidisciplinary team where each member designs one component of a larger system.

“Designing an entire UAV isn’t feasible for just one person, but designing a component of a UAV is. By providing the framework for how these components work together in a way that considers uncertainty, we’ve made it easier for people to evaluate the performance of the entire UAV system,” Huang says.

More detailed information

The researchers used this new approach to choose perception systems and batteries for a drone that would maximize its payload while minimizing its lifetime cost and weight.

While each perception system may offer a different detection accuracy under varying weather conditions, the designer doesn’t know exactly how its performance will fluctuate. This new system allows the designer to take these uncertainties into consideration when thinking about the drone’s overall performance.

And unlike other approaches, their framework reveals distinct advantages of each battery technology.

For instance, their results show that at lower payloads, nickel-metal hydride batteries provide the lowest expected lifetime cost. This insight would be impossible to fully capture without accounting for uncertainty, Zardini says.

While another method might only be able to show the best-case and worst-case performance scenarios of lithium polymer batteries, their framework gives the user more detailed information.

For example, it shows that if the drone’s payload is 1,750 grams, there is a 12.8 percent chance the battery design would be infeasible.

“Our system provides the tradeoffs, and then the user can reason about the design,” he adds.

In the future, the researchers want to improve the computational efficiency of their problem-solving algorithms. They also want to extend this approach to situations where a system is designed by multiple parties that are collaborative and competitive, like a transportation network in which rail companies operate using the same infrastructure.

“As the complexity of systems grow, and involves more disparate components, we need a formal framework in which to design these systems. This paper presents a way to compose large systems from modular components, understand design trade-offs, and importantly do so with a notion of uncertainty. This creates an opportunity to formalize the design of large-scale systems with learning-enabled components,” says Aaron Ames, the Bren Professor of Mechanical and Civil Engineering, Control and Dynamical Systems, and Aerospace at Caltech, who was not involved with this research.