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Getting Began with Basis Fashions in iOS 26


With iOS 26, Apple introduces the Basis Fashions framework, a privacy-first, on-device AI toolkit that brings the identical language fashions behind Apple Intelligence proper into your apps. This framework is out there throughout Apple platforms, together with iOS, macOS, iPadOS, and visionOS, and it offers builders with a streamlined Swift API for integrating superior AI options straight into your apps.

In contrast to cloud-based LLMs akin to ChatGPT or Claude, which run on highly effective servers and require web entry, Apple’s LLM is designed to run fully on-device. This architectural distinction provides it a singular benefit: all knowledge stays on the person’s gadget, guaranteeing privateness, decrease latency, and offline entry.

This framework opens the door to a complete vary of clever options you possibly can construct proper out of the field. You’ll be able to generate and summarize content material, classify info, and even construct in semantic search and customized studying experiences. Whether or not you wish to create a sensible in-app information, generate distinctive content material for every person, or add a conversational assistant, now you can do it with just some strains of Swift code.

On this tutorial, we’ll discover the Basis Fashions framework. You’ll be taught what it’s, the way it works, and the best way to use it to generate content material utilizing Apple’s on-device language fashions.

Able to get began? Let’s dive in.

The Demo App: Ask Me Something

foundation-models-demo-app.png

It’s at all times nice to be taught new frameworks or APIs by constructing a demo app — and that’s precisely what we’ll do on this tutorial. We’ll create a easy but highly effective app known as Ask Me Something to discover how Apple’s new Basis Fashions framework works in iOS 26.

The app lets customers sort in any questions and offers an AI-generated response, all processed on-device utilizing Apple’s built-in LLM.

By constructing this demo app, you may learn to combine the Basis Fashions framework right into a SwiftUI app. You will additionally perceive the best way to create prompts and seize each full and partial generated responses.

Utilizing the Default System Language Mannequin

Apple offers a built-in mannequin known as SystemLanguageModel, which supplies you entry to the on-device basis mannequin that powers Apple Intelligence. For general-purpose use, you possibly can entry the base model of this mannequin by way of the default property. It’s optimized for textual content technology duties and serves as an amazing start line for constructing options like content material technology or query answering in your app.

To make use of it in your app, you may first must import the FoundationModels framework:

import FoundationModels

With the framework now imported, you will get a deal with on the default system language mannequin. Right here’s the pattern code to do this:

struct ContentView: View {
    
    personal var mannequin = SystemLanguageModel.default
    
    var physique: some View {
        change mannequin.availability {
        case .obtainable:
            mainView
        case .unavailable(let motive):
            Textual content(unavailableMessage(motive))
        }
    }
    
    personal var mainView: some View {
        ScrollView {
            .
            .
            .
        }
    }

    personal func unavailableMessage(_ motive: SystemLanguageModel.Availability.UnavailableReason) -> String {
        change motive {
        case .deviceNotEligible:
            return "The gadget just isn't eligible for utilizing Apple Intelligence."
        case .appleIntelligenceNotEnabled:
            return "Apple Intelligence just isn't enabled on this gadget."
        case .modelNotReady:
            return "The mannequin is not prepared as a result of it is downloading or due to different system causes."
        @unknown default:
            return "The mannequin is unavailable for an unknown motive."
        }
    }
}

Since Basis Fashions solely work on units with Apple Intelligence enabled, it is vital to confirm {that a} mannequin is out there earlier than utilizing it. You’ll be able to test its readiness by inspecting the availability property.

Implementing the UI

Let’s proceed to construct the UI of the mainView. We first add two state variables to retailer the person query and the generated reply:

@State personal var reply: String = ""
@State personal var query: String = ""

For the UI implementation, replace the mainView like this:

personal var mainView: some View {
    ScrollView {
        ScrollView {
            VStack {
                Textual content("Ask Me Something")
                    .font(.system(.largeTitle, design: .rounded, weight: .daring))
                
                TextField("", textual content: $query, immediate: Textual content("Sort your query right here"), axis: .vertical)
                    .lineLimit(3...5)
                    .padding()
                    .background {
                        Shade(.systemGray6)
                    }
                    .font(.system(.title2, design: .rounded))
                
                Button {

                } label: {
                    Textual content("Get reply")
                        .body(maxWidth: .infinity)
                        .font(.headline)
                }
                .buttonStyle(.borderedProminent)
                .controlSize(.extraLarge)
                .padding(.prime)
                
                Rectangle()
                    .body(peak: 1)
                    .foregroundColor(Shade(.systemGray5))
                    .padding(.vertical)
                
                Textual content(LocalizedStringKey(reply))
                    .font(.system(.physique, design: .rounded))
            }
            .padding()
        }

    }
}

The implementation is fairly simple – I simply added a contact of fundamental styling to the textual content subject and button.

foundation-models-demoapp-ui.png

Producing Responses with the Language Mannequin

Now we’ve come to the core a part of app: sending the query to the mannequin and producing the response. To deal with this, we create a brand new perform known as generateAnswer():

personal func generateAnswer() async {
    let session = LanguageModelSession()
    do {
        let response = attempt await session.reply(to: query)
        reply = response.content material
    } catch {
        reply = "Did not reply the query: (error.localizedDescription)"
    }
}

As you possibly can see, it solely takes a number of strains of code to ship a query to the mannequin and obtain a generated response. First, we create a session utilizing the default system language mannequin. Then, we move the person’s query, which is called a immediate, to the mannequin utilizing the reply methodology.

The decision is asynchronous because it normally takes a number of second (and even longer) for the mannequin to generate the response. As soon as the response is prepared, we will entry the generated textual content via the content material property and assign it to reply for show.

To invoke this new perform, we additionally must replace the closure of the “Get Reply” button like this:

Button {
    Job {
        await generateAnswer()
    }
} label: {
    Textual content("Present me the reply")
        .body(maxWidth: .infinity)
        .font(.headline)
}

You’ll be able to take a look at the app straight within the preview pane, or run it within the simulator. Simply sort in a query, wait a number of seconds, and the app will generate a response for you.

foundation-models-ask-first-question.png

Reusing the Session

The code above creates a brand new session for every query, which works effectively when the questions are unrelated.

However what if you need customers to ask follow-up questions and hold the context? In that case, you possibly can merely reuse the identical session every time you name the mannequin.

For our demo app, we will transfer the session variable out of the generateAnswer() perform and switch it right into a state variable:

@State personal var session = LanguageModelSession()

After making the change, attempt testing the app by first asking: “What are the must-try meals when visiting Japan?” Then observe up with: “Counsel me some eating places.”

For the reason that session is retained, the mannequin understands the context and is aware of you are searching for restaurant suggestions in Japan.

foundation-models-suggest-restaurants.png

In case you don’t reuse the identical session, the mannequin gained’t acknowledge the context of your follow-up query. As an alternative, it’ll reply with one thing like this, asking for extra particulars:

“Certain! To give you one of the best solutions, might you please let me know your location or the kind of delicacies you are excited by?”

Disabling the Button Throughout Response Technology

For the reason that mannequin takes time to generate a response, it’s a good suggestion to disable the “Get Reply” button whereas ready for the reply. The session object features a property known as isResponding that permits you to test if the mannequin is at present working.

To disable the button throughout that point, merely use the .disabled modifier and move within the session’s standing like this:

Button {
    Job {
        await generateAnswer()
    }
} label: {
    .
    .
    .
}
.disabled(session.isResponding)

Working with Stream Responses

The present person expertise is not superb — for the reason that on-device mannequin takes time to generate a response, the app solely exhibits the end result after the complete response is prepared.

In case you’ve used ChatGPT or related LLMs, you’ve most likely observed that they begin displaying partial outcomes virtually instantly. This creates a smoother, extra responsive expertise.

The Basis Fashions framework additionally helps streaming output, which lets you show responses as they’re being generated, reasonably than ready for the whole reply. To implement this, use the streamResponse methodology reasonably than the reply methodology. Here is the up to date generateAnswer() perform that works with streaming responses:

personal func generateAnswer() async {
    
    do {
        reply = ""
        let stream = session.streamResponse(to: query)
        for attempt await streamData in stream {             
		        reply = streamData.asPartiallyGenerated()
        }
    } catch {
        reply = "Did not reply the query: (error.localizedDescription)"
    }
}

Similar to with the reply methodology, you move the person’s query to the mannequin when calling streamResponse. The important thing distinction is that as an alternative of ready for the total response, you possibly can loop via the streamed knowledge and replace the reply variable with every partial end result — displaying it on display because it’s generated.

Now while you take a look at the app once more and ask any questions, you may see responses seem incrementally as they’re generated, creating a way more responsive person expertise.

foundation-models-stream-response.gif

Abstract

On this tutorial, we coated the fundamentals of the Basis Fashions framework and confirmed the best way to use Apple’s on-device language mannequin for duties like query answering and content material technology.

That is only the start — the framework affords far more. In future tutorials, we’ll dive deeper into different new options akin to the brand new @Generable and @Information macros, and discover extra capabilities like content material tagging and power calling.

In case you’re seeking to construct smarter, AI-powered apps, now could be the proper time to discover the Basis Fashions framework and begin integrating on-device intelligence into your initiatives.

Cisco Catalyst 8300 Excels in NetSecOPEN NGFW SD-WAN Safety Checks


In cybersecurity — similar to in Formulation One racing — efficiency is barely significant underneath stress. A automotive isn’t judged whereas standing nonetheless; it’s judged flying at 200 mph, navigating real-world turns, climate, and competitors.

Equally, a safe connectivity platform should be validated not simply in very best circumstances, however underneath the true calls for of recent networks — with excessive site visitors masses, refined threats, and evolving assault methods.

That’s precisely why the current NetSecOPEN certification is so vital. The Cisco Catalyst 8300 Edge Platform was validated in a high-demand, real-world SD-WAN atmosphere — reflecting the identical site visitors circumstances, risk complexity, and operational pressures our clients navigate every single day.

The outcome? Distinctive safety efficacy and constant efficiency as a Subsequent-Era Firewall (NGFW) designed particularly for department environments, even underneath the complicated, high-pressure circumstances of recent enterprise networks.

NetSecOPEN testing brings transparency and relevance to safety efficiency metrics, so clients could make knowledgeable selections with confidence that lab-validated outcomes mirror real-world community efficiency and risk efficacy.

NetSecOPEN is an impartial nonprofit consortium that provides standardized community safety testing, guaranteeing clients can belief and replicate ends in their very own environments. In collaboration with labs like SE Labs and College of New Hampshire Interoperability Lab, it offers clear, real-world efficiency metrics for essential infrastructure 

At present’s organizations face a risk panorama that’s extra dynamic and complicated than ever earlier than. Cyberattacks have gotten quicker, stealthier, and extra evasive, placing immense stress on safety infrastructures.

On the identical time, the necessity for high-performing, dependable connectivity continues to rise — pushed by hybrid work, cloud-first methods, and the necessity to join customers and purposes throughout distributed environments.

The Cisco Catalyst 8300 Edge Platform is designed precisely for this world. It unifies networking and safety collectively in a single highly effective platform — combining best-in-class SD-WAN capabilities with built-in NGFW safety tailor-made for department environments.

This convergence simplifies operations, allows real-time risk response, and delivers constant safety with out compromising efficiency. With built-in superior routing capabilities, it helps organizations meet complicated connectivity calls for with ease.

For patrons trying to consolidate separate SD-WAN and NGFW home equipment, the 8300 offers a compelling possibility — lowering complexity whereas delivering high-performance safe connectivity on the community edge.

And as a part of Cisco’s Hybrid Mesh Firewall resolution, the Catalyst 8300 strengthens department safety whereas complementing different enforcement factors comparable to Cisco Safe Firewall and Firewall as a Service (FWaaS) from Cisco Safe Entry. Total, the Cisco Hybrid Mesh Firewall resolution delivers a complete safety platform that allows distributed safety coverage enforcement all through the community infrastructure, whereas offering centralized coverage administration by way of Safety Cloud Management.

The place conventional, legacy firewalls typically wrestle to maintain tempo — missing real-time visibility, scalability, and built-in intelligence — the Cisco Catalyst 8300 Edge Platform ensures that safety and connectivity work seamlessly collectively. It allows organizations to maneuver quicker, keep protected, and cut back complexity as they develop — whereas delivering the real-world efficiency and safety in the present day’s networks demand.

The Cisco Catalyst 8300 Edge Platform was rigorously examined as a high-performing Subsequent-Era Firewall (NGFW) purpose-built for department environments inside an SD-WAN deployment. The analysis simulated real-world enterprise site visitors and evasive threats — designed to mirror typical department and edge use instances.

The testing yielded distinctive outcomes throughout key efficiency indicators:

  • 99.21% malware detection fee
  • 100% detection fee for evasive threats
  • 98.88% block fee underneath heavy load circumstances
  • Sustained throughput of three.69 Gbps for HTTP site visitors throughout testing

See the complete NetSecOPEN certification report for particulars.


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MCP Safety at Wiz with Rami McCarthy


Wiz is a cloud safety platform that helps organizations determine and remediate dangers throughout their cloud environments. The corporate’s platform scans layers of the cloud stack, together with digital machines, containers, and serverless configurations, to detect vulnerabilities and misconfigurations in context.

The Mannequin Context Protocol, or MCP, is rising as a possible commonplace for connecting LLM functions to exterior information sources and instruments. It has quickly gained traction throughout the business with broad backing from firms reminiscent of OpenAI, Microsoft, and Google. Whereas the protocol affords nice alternatives, it additionally introduces sure safety dangers.

Rami McCarthy is a Principal Safety Researcher at Wiz. He joins the podcast with Gregor Vand to speak about safety analysis, AI and secrets and techniques leakage, MCP safety, provide chain assaults, profession recommendation, and extra.

Gregor Vand is a security-focused technologist, and is the founder and CTO of Mailpass. Beforehand, Gregor was a CTO throughout cybersecurity, cyber insurance coverage and common software program engineering firms. He has been based mostly in Asia Pacific for nearly a decade and will be discovered through his profile at vand.hk.

 

 

Please click on right here to see the transcript of this episode.

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How does Capital One stack? It begins with utilized analysis and leveraging information to construct AI fashions. Their engineering groups use the facility of the cloud and platform standardization and automation to embed AI options all through the enterprise. Actual-time information at scale permits these proprietary AI options to assist Capital One enhance the monetary lives of its clients. That’s know-how at Capital One.

Be taught extra about how Capital One’s fashionable tech stack, information ecosystem, and utility of AI/ML are central to the enterprise by visiting www.capitalone.com/tech.

Cisco Companies and Assist Demos at Cisco Stay: A Recap!


What an unimaginable time we had at Cisco Stay in San Diego not too long ago! For individuals who joined us, you understand Cisco Buyer Expertise (CX) introduced its A-game with a lineup of interactive demos designed that can assist you deal with your largest IT challenges and obtain your small business objectives. Whether or not you’re trying to construct AI-ready information facilities, create future-proof workplaces, or strengthen digital resilience, we had one thing for everybody.

In case you couldn’t attend, don’t fear—we’ve obtained you lined with a fast recap of demo highlights from the World of Options.

At its core, CX is right here that can assist you optimize your IT atmosphere, maximize your investments, and drive actual enterprise outcomes. From simplifying IT operations and retaining networks operating easily to accelerating transformation with automation and knowledgeable help, we’ve obtained the options it is advisable succeed.

Right here’s a take a look at a few of what we lined with thrilling demos showcased at Cisco Stay this 12 months:

AI-Prepared Knowledge Facilities

At Cisco Stay 2025, the AI-ready information middle demos highlighted how Cisco Buyer Expertise (CX) is remodeling information facilities for the AI period. Attendees noticed how expert-led implementation and modernization providers—that includes options like Cisco AI Pods—make it straightforward to replace current infrastructure or construct new, scalable environments. Automation and customized steering assist guarantee your information middle community performs optimally, whereas superior observability and AI-powered, proactive help hold operations resilient and prepared for the excessive calls for of AI workloads.

Future-Proofed Workplaces

Cisco’s demos confirmed how organizations can confidently modernize their workplaces to help distributed groups and AI-driven collaboration. With steering on deploying and optimizing applied sciences reminiscent of Cisco Areas, SD-WAN, Wi-Fi 7, Webex, and safe entry, companies can transfer shortly utilizing automation and Infrastructure as Code (IaC). Attendees additionally discovered how clever AI brokers and tailor-made function suggestions add much more worth, whereas AI-powered help ensures seamless, resilient office operations as work continues to evolve.

Digital Resilience

The digital resilience demos demonstrated how Cisco Companies assist organizations keep safe and operational it doesn’t matter what. AI-powered help helps forestall dangers, addresses points earlier than they disrupt enterprise, and accelerates response occasions when challenges come up. Skilled providers additional improve resilience by supporting fast deployment and optimizing safety, observability, and assurance—empowering companies to adapt to an ever-changing panorama.

Missed Cisco Stay? No Downside!

In case you couldn’t make it to the occasion, no worries! We’re at all times right here that can assist you discover how Cisco Buyer Expertise can help your IT atmosphere and enterprise objectives.

Curious to study extra? Attain out to your Cisco Account Government or contact us to begin the dialog.

We will’t wait that can assist you remodel what’s subsequent for your small business!

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Latest LF Decentralized Belief Lab HOPrS identifies if pictures have been altered


OpenOrigins has introduced that its Human-Oriented Proof System (HOPrS) has been accepted by the Linux Basis’s Decentralized Belief as a brand new Lab.

HOPrS is an open-source framework that can be utilized to determine if a picture has been altered. “If we’re doing issues like rotating photos, or cropping them, or altering the saturation, turning it into grayscale, or utilizing Photoshop’s generative tooling to circle round somebody’s face and swap somebody’s face out for a star … What are the instruments we are able to put out into the world and attempt to construct a greater framework? And one of many issues that’s come out of that is HOPrS,” stated Henrik Cox, product supervisor at OpenOrigins, throughout an LF Decentralized Belief meetup presentation final month

It makes use of methods like perceptual hashes and quadtree segmentation, mixed with blockchain know-how, to find out how photos have been modified. 

Perceptual hashing permits it to determine if something modified with the photograph itself or its metadata, by evaluating hashes of the picture with hashes from comparable information on a blockchain. It then produces a similarity or distinction rating. 

Nevertheless, perceptual hashing solely tells you if information are completely different, not what’s completely different or which file is the unique, Cox defined. That is the place quadtree segmentation is available in, breaking down the picture into 4 quadrants to determine the place within the picture the distinction is. 

In an instance, Cox drew a solar onto a photograph of an airplane, after which quadtree segmentation was used to determine that solely the highest proper quadrant the place the solar was drawn is completely different. Then, that quadrant is segmented down into even smaller items. “We create extra perceptual hashes as we get additional right down to determine the place it’s within the picture that issues are being modified,” he stated.

In response to OpenOrigins, HOPrS can be utilized to determine if content material is generated by AI, a functionality turning into more and more extra vital because it turns into harder to tell apart between AI-generated and human-generated content material. 

“The addition of HOPrS to the LF Decentralized Belief labs allows our group to entry and collaborate on essential instruments for verifying content material within the age of generative AI,” stated Daniela Barbosa, govt director of LF Decentralized Belief. “By contributing HOPrS to our labs, OpenOrigins is tapping into a worldwide community of builders, creating an accessible entry level for incubation, experimentation, and group constructing.”

Dr. Manny Ahmed, co-founder and CEO of OpenOrigins, added: “Contributing to open supply initiatives builds on our mission to create a worldwide belief infrastructure for user-owned, verifiable information provenance that’s free from centralized management.”