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ios – After disabling permission to entry geolocation, native vital notification doesn’t arrive

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ios – After disabling permission to entry geolocation, native vital notification doesn’t arrive


My software has permission to show vital notifications (distant and native). The applying has logic configured to show vital notifications at a sure interval if the appliance is within the background

                    Log.i(">noti #(i) subsequent (i * self.preEpisodeInterval)s")
                    self.scheduleLocalNoti(
                        title: LanguageHelper.getTranslationByKey(LanguageKey.ALARM) ?? "",
                        physique: information.preEpisodeWarnMessage ?? "",
                        isCritical: true,
                        timeInterval: TimeInterval(i * self.preEpisodeInterval),
                        soundName: self.preAlarmModel?.information?.preEpisodeTone ?? "melodie.wav",
                        userInfo: [MotionTrackingManager.LOCAL_NOTI_PREALARM_USERINFO: ""]
                    )

    personal func scheduleLocalNoti(title: String,
                                   physique: String,
                                   isCritical: Bool = false,
                                   timeInterval: TimeInterval = 0,
                                   soundName: String? = nil,
                                   userInfo: [AnyHashable: Any]? = nil) {
        let content material = UNMutableNotificationContent()
        content material.title = title
        content material.physique = physique
        if isCritical {
            if soundName != nil {
                let notiSound = UNNotificationSoundName(rawValue: soundName!)
                content material.sound = UNNotificationSound.criticalSoundNamed(notiSound, withAudioVolume: 1.0)
            } else {
                content material.sound = UNNotificationSound.defaultCriticalSound(withAudioVolume: 1.0)
            }
        } else {
            if soundName != nil {
                let notiSound = UNNotificationSoundName(rawValue: soundName!)
                content material.sound = UNNotificationSound(named: notiSound)
            } else {
                content material.sound = UNNotificationSound.default
            }
        }
        if userInfo != nil {
            content material.userInfo = userInfo!
        }

        // Create the set off
        let set off = UNTimeIntervalNotificationTrigger(timeInterval: timeInterval + 1, repeats: false)

        let uuidString = UUID().uuidString

        Log.i("Schedule native noti for: (timeInterval + 1) with uuid: (uuidString)")
        // Create the request
        let request = UNNotificationRequest(
            identifier: uuidString,
            content material: content material,
            set off: set off
        )

        // Schedule the request with the system.
        let notificationCenter = UNUserNotificationCenter.present()
        notificationCenter.add(request) { error in
            if error != nil {
                // Deal with any errors.
                Log.e("errr: (String(describing: error?.localizedDescription))")
            }
        }
    }

The applying additionally has logic that requires geolocation monitoring with the Background Modes Location replace mark in Signing & Capabilities. If permission to trace geolocation is denied, the native notification isn’t displayed (with distant ones, it nonetheless works accurately).

What’s additionally fascinating: this ONLY occurs with builds in testFlight/appStore. The discharge construct on the emulator instantly from XCode works accurately and continues to show the notification even when entry to geolocation is denied

How can geolocation have an effect on the show of native vital notifications?

5 widespread assumptions in load testing—and why it’s best to rethink them

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5 widespread assumptions in load testing—and why it’s best to rethink them


Through the years, I’ve had numerous conversations with efficiency engineers, DevOps groups, and CTOs, and I maintain listening to the identical assumptions about load testing. A few of them sound logical on the floor, however in actuality, they typically lead groups down the incorrect path. Listed here are 5 of the most important misconceptions I’ve come throughout—and what it’s best to think about as a substitute.

1️⃣ “We must be testing on manufacturing”

A number of weeks in the past, I had a name with one of many greatest banks on the planet. They had been desperate to run load assessments straight on their manufacturing atmosphere, utilizing real-time information. Their reasoning? It will give them essentially the most correct image of how their methods carry out beneath actual circumstances.

I get it—testing in manufacturing appears like the final word means to make sure reliability. However once I dug deeper, I requested them: “What occurs if as we speak’s take a look at outcomes look nice, however tomorrow a sudden visitors spike causes a crash?” Who takes duty if a poorly configured take a look at impacts actual prospects? Are you ready for the operational dangers, compliance issues, and potential downtime?

Sure, manufacturing testing has its place, nevertheless it’s not a magic bullet. It’s complicated, and with out the proper safeguards, it might do extra hurt than good. A better strategy is to create a staging atmosphere that mirrors manufacturing as intently as doable, making certain significant insights with out pointless danger.

2️⃣ “Load testing is all in regards to the device—extra options imply higher outcomes.”

This is likely one of the greatest misconceptions I hear. Groups assume that in the event that they decide essentially the most feature-packed device, they’ll mechanically discover each efficiency situation. However load testing isn’t simply in regards to the device—it’s about understanding how your customers behave and designing assessments that replicate real-world eventualities.

I’ve seen firms put money into highly effective load testing instruments however fail to combine them correctly into their CI/CD pipeline. Others concentrate on operating huge take a look at hundreds with out first figuring out their software’s weak spots. Right here’s what issues extra than simply options:

  • Do you perceive your customers’ conduct patterns?
  • Have you ever recognized efficiency gaps earlier than operating the take a look at?
  • Are you making load testing a steady a part of your growth course of?

Essentially the most profitable groups don’t simply run assessments; they construct efficiency testing into their workflows and use insights to optimize their functions. Having the correct device is necessary, however the way you design your assessments and interpret outcomes issues much more.

3️⃣ “Time-to-market isn’t that necessary—testing takes time, so what?”

That is one that usually will get missed—till it’s too late. Some groups deal with load testing as a ultimate checkbox earlier than launch, assuming that if it takes longer, it’s no huge deal. However right here’s the actuality:

  • Each further day spent on load testing delays product launches, giving rivals an edge.
  • Improvement groups get caught ready for outcomes as a substitute of transport new options.
  • Clients count on quick, seamless experiences, and sluggish efficiency fixes can damage satisfaction.

I’ve seen firms take weeks to run full-scale load assessments, solely to comprehend that they’re lacking crucial deadlines. In as we speak’s market, velocity issues.

The answer isn’t skipping load testing—it’s making it environment friendly. As a substitute of treating it as a bottleneck, combine efficiency assessments into your pipeline. Use automated efficiency testing in CI/CD, run incremental load assessments as a substitute of 1 huge take a look at, and prioritize testing early in growth.

Load testing shouldn’t sluggish you down—it ought to provide help to transfer sooner with confidence.

4️⃣ “Extra customers? Simply make the machine greater.”

A variety of firms attempt to repair efficiency points by upgrading their infrastructure—extra CPU, extra reminiscence, greater machines. However right here’s the issue: scaling up doesn’t repair inefficient code.

I had a dialogue with a tech lead not too long ago who was fighting efficiency points. His first intuition? “Let’s improve the server capability.” However once we dug into the information, we discovered that:

  • A single database question was liable for 80% of the slowdown.
  • Customers weren’t simply “hitting the system” — they had been interacting in unpredictable methods.
  • The app was operating inefficient loops that brought on pointless processing.

Throwing {hardware} on the drawback would have masked the difficulty quickly, nevertheless it wouldn’t have solved it. As a substitute of specializing in infrastructure upgrades, ask your self: The place are the actual bottlenecks? Is it sluggish database queries, unoptimized APIs, or poor caching methods? Is horizontal scaling a greater choice? Distributing the load throughout a number of cases is usually more practical than simply including greater machines.

How are customers truly interacting with the system? Sudden behaviors can trigger slowdowns that gained’t be solved by including extra assets.

Scaling up buys you time, nevertheless it gained’t repair inefficiencies in your codebase.

5️⃣ “Open supply vs. business instruments—free is best, proper?”

It is a debate I hear on a regular basis. Many groups, particularly in startups, need to follow open-source instruments. They are saying, “We’d relatively put money into DevOps and use free testing instruments as a substitute of paying for a business resolution.” And I completely get that—open supply is nice for studying and experimentation.

However I’ve additionally seen firms hit a wall after they attempt to scale. They begin with an open-supply resolution, and the whole lot works high-quality—till they should:

  • Run complicated take a look at eventualities that require correlation and parameterization.
  • Handle large-scale distributed assessments throughout cloud environments.
  • Get devoted assist after they run into crucial points.

That doesn’t imply open-source instruments aren’t priceless—they completely are. They work nicely for groups with sturdy in-house experience and for tasks the place flexibility is vital. Nonetheless, groups that want to maneuver quick, deal with enterprise-scale testing, or cut back upkeep overhead may profit from evaluating several types of options that match their wants.

In the end, it’s not about free vs. paid—it’s about selecting the best device to your testing technique.

Last Ideas

Load testing is stuffed with myths, and it’s straightforward to fall into these widespread traps. But when there’s one takeaway, it’s this:

✔️ Don’t take a look at only for the sake of testing—take a look at with objective.

✔️ Perceive your customers earlier than you run the take a look at.

✔️ Make load testing a part of your course of, not a roadblock.

Have you ever encountered an assumption in load testing that turned out to be fully incorrect? Let’s focus on!

Unframe Emerges from Stealth with $50M to Remodel Enterprise AI Deployment

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Unframe Emerges from Stealth with M to Remodel Enterprise AI Deployment


Unframe, a next-generation enterprise AI platform, has formally emerged from stealth with a $50 million funding spherical led by Bessemer Enterprise Companions, alongside TLV Companions, Craft Ventures, Third Level Ventures, SentinelOne Ventures, and Cerca Companions. The funding marks a big milestone for Unframe because it accelerates international growth, fuels R&D, and scales supply of its turnkey AI options which can be already gaining traction with main enterprises world wide.

Unframe is redefining how giant organizations implement synthetic intelligence by eliminating the standard friction—lengthy growth cycles, safety bottlenecks, and the necessity for in-house AI experience. Its platform permits enterprises to go from idea to completely deployed, customized AI resolution in hours, not months. In contrast to generic AI instruments or narrowly targeted level options, Unframe is constructed to adapt to any enterprise use case throughout any system or division.

Immediate AI Options With out the Overhead

On the coronary heart of Unframe’s platform is what it calls the Blueprint Method, a strategy that gives the mandatory context to giant language fashions (LLMs) to ship hyper-relevant, domain-specific outcomes. This implies enterprises don’t want to coach or fine-tune fashions, which is historically some of the resource-intensive components of deploying AI. Unframe can also be LLM-agnostic, permitting clients to make use of or swap between private and non-private fashions with out being locked right into a single ecosystem.

From parsing chaotic information throughout legacy methods to analyzing structured and unstructured information with precision, Unframe’s AI brokers perform like area consultants that may combine with any system, automate complicated workflows, and provide actionable insights—all utilizing pure language inputs.

A New Mannequin for Enterprise AI: Safe, Quick, and Versatile

Enterprise adoption of AI has typically been hindered by issues over safety, compliance, and implementation overhead. Unframe is popping this problem right into a aggressive benefit. The platform permits for full integration with SaaS instruments, APIs, databases, and proprietary file methods—whereas making certain that buyer information by no means leaves their safe perimeter except explicitly allowed. Versatile deployment choices embody on-premise and public cloud, offering organizations with most management over information dealing with and regulatory compliance.

Unframe additionally introduces a radical shift in how AI providers are priced and delivered. The corporate operates on an outcome-based pricing mannequin: clients solely pay as soon as an answer is deployed and delivering worth. This removes the danger sometimes related to enterprise software program procurement and accelerates time-to-value.

Speedy Traction and Actual-World Impression

Regardless of solely just lately rising from stealth, Unframe is already serving dozens of Fortune 500 purchasers and has achieved hundreds of thousands in annual recurring income (ARR). Its early success underscores a rising enterprise want for extra agile and efficient AI options.

“Unframe’s unmatched accuracy, velocity, and high quality in processing structured and unstructured information have remodeled our operations,” stated the CIO of a worldwide funding financial institution.

“At Cushman & Wakefield, our AI+ technique is concentrated on bringing fast-paced and revolutionary options to unlock crucial insights, and we’re aggressively pushing ahead with Unframe,” added Salumeh Companieh, Chief Digital & Data Officer.

“Because of Unframe, we introduced our AI use case to life in simply two weeks—one thing our staff had been scuffling with for 9 months,” stated a VP of Innovation at a Fortune 100 insurance coverage firm.

A Seasoned Group with a Confirmed Observe File

Unframe is led by CEO and co-founder Shay Levi, who beforehand co-founded Noname Safety, scaling it to $40M ARR and a $500M acquisition by Akamai. He’s joined by COO Larissa Schneider and VP of R&D Adi Azarya, each veterans in scaling enterprise software program by means of IPOs, M&A, and worldwide growth. The management staff’s deep experience in cybersecurity, product growth, and operational scaling is foundational to Unframe’s fast progress and enterprise-grade providing.

“Unframe is flipping enterprise AI on its head by rapidly offering personalized software program based mostly on an enterprise’s precise wants,” stated Amit Karp, Associate at Bessemer Enterprise Companions. “I’ve the utmost confidence in Shay and the staff to redefine how software program is constructed and delivered to the enterprise world.”

Why It Issues

In an surroundings the place AI adoption is crucial however typically complicated, Unframe provides a daring various. By eradicating technical hurdles, compliance friction, and vendor lock-in, Unframe empowers enterprises to maneuver quicker, act smarter, and unlock the complete potential of their information—securely and on their very own phrases.

With a unified platform that helps limitless integrations, real-time collaboration, and autonomous activity execution, Unframe is positioning itself because the central nervous system for enterprise AI.

This funding spherical not solely affirms market confidence in Unframe’s mission however indicators a broader shift in enterprise expectations: from gradual, one-size-fits-all options to agile, safe, and fully customizable AI that delivers worth from day one.

Tariff struggle throws constructing of information facilities into disarray

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Tariff struggle throws constructing of information facilities into disarray



Forrester’s backside line? “Due to the long run planning and all the potential coverage modifications, I wouldn’t change my knowledge middle plans that a lot,” Nguyen mentioned.

Confusion reigns

Every single day it appears, the tariff scenario turns into muddier. For instance, in response to a reality sheet launched Wednesday, the White Home has briefly exempted semiconductors from tariffs, however not the aluminum used to construct the servers and racks that home them.

Moreover, Scott Bickley, advisory fellow on the Information-Tech Analysis Group, mentioned you will need to notice how the assorted nations match with the assorted parts.

“Nearly each main value middle for the buildout of an information middle might be severely impacted by the brand new tariffs. Servers and {hardware}, together with semiconductors, reminiscence, community parts, cabling, building supplies are going to see costs rise in a single day as soon as the tariffs go into impact,” Bickley mentioned. “Contemplate that China, which has a 54% full tariff, is a serious supply of uncooked supplies and uncommon earth parts important for manufacturing DC parts whereas Taiwan, at a 32% tariff fee, is the sole-source supplier nation for many superior chipsets utilized in AI, cell telephones, and any fashionable software footprint requiring excessive efficiency in a small footprint. South Korea (25% tariff) is a key supplier of reminiscence chips, whereas Japan (24%), Germany (20% EU fee), and the Netherlands (20% EU fee) are suppliers of sub-components like server racks, cooling methods, and semiconductor gear.”

However, he continued: “Now issue within the offshore/nearshore contract producers like Mexico and Vietnam (46%) for electronics manufacturing (meeting and distribution) and Malaysia (10%) for semiconductor packaging, and it’s clear to see that the whole know-how provide chain main into the information middle might be taxed at a number of touchpoints.”

Put all of that collectively and Information-Tech anticipates loads of enterprise knowledge middle ache.

Massive-name consumers group funds bid to seize burning-trash emissions

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Massive-name consumers group funds bid to seize burning-trash emissions


Frontier, a bunch of corporations committing $1 billion to again early-stage carbon removing approaches, can pay $31.6 million to fund the set up of emissions seize expertise at Norway’s largest waste incineration plant. 

The consumers group based by corporations together with Stripe and Google has dedicated $550 million towards novel carbon removing initiatives.

The Norwegian facility on the middle of the deal disclosed April 1 yearly burns 350,000 metric tons of things similar to food-soiled paper, cardboard and non-recyclable plastics. This materials is taken into account residual waste that may’t be in any other case recycled or reused beneath the European Union’s waste administration directives.

The power generated via the incineration supplies electrical energy and warmth to the town of Oslo. The Frontier contract covers a retrofit that can seize an estimated 350,000 metric tons of carbon dioxide emissions yearly, after which transport it by ship to Europe’s Northern Lights sequestration web site the place will probably be saved. 

The transaction covers credit for eradicating 100,000 metric tons between 2029 and 2030; these credit particularly cowl the biogenic emissions associated to the natural waste.  

Among the many consumers collaborating on this explicit deal are a number of Frontier founders together with Autodesk, H&M, JPMorgan Chase and Salesforce. Funding from the town of Oslo and Norway’s Longship initiative may also assist finance the retrofit.

Diagram of carbon capture process.
Hafslund Celsio’s methodology of eradicating CO2 by retrofitting a waste-to-energy facility with a seize unit.
Supply: Frontier

Widespread supply of power in Europe

“Frontier consumers aren’t solely enabling this challenge to get off the bottom but additionally validating a mannequin that may very well be replicated via Europe, with the potential to take away tens of hundreds of thousands of CO2 from the ambiance,” stated Jannicke Gerner Bjerkås, director of carbon seize and storage and carbon markets for Hafslund Celsio, which owns the incineration plant. The corporate is Norway’s largest district heating supplier. 

At the least 500 related services help district heating in cities throughout Europe. Waste rules on the continent have impressed their creation, and retrofitting them might seize an estimated 400 million metric tons yearly by 2050, in keeping with Frontier estimates. 

Microsoft disclosed a 10-year deal in Could 2024 masking carbon removing at a district heading system in Stockholm, Sweden. That facility burns wooden waste, and the strategy is called bioenergy with carbon seize and storage (BECCS).

Quite a lot of corporations in Europe are trying into carbon seize at waste-to-energy services, particularly because the EU might begin regulating these emissions extra carefully as a part of its buying and selling system, stated Robert Höglund, CEO of consulting agency Marginal Carbon and co-founder of researcher CDR.fyi. 

“Waste to power [carbon capture and storage] is considered one of few viable approaches for plastic waste that may’t be recycled,” Höglund stated. “The natural fraction of the waste is what determines how a lot [carbon dioxide removal] is generated, that fraction might go down with higher sorting, that’s additionally an element which will have an effect on the economics of a challenge as CDR credit could also be in a different way priced than fossil CO2.”

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