Claude Mythos: Anthropic's Most Powerful AI Cybersecurity Model

Anthropic launched its most advanced AI model, “Claude Mythos Preview,” on April 7, 2026. Just with the launch, Anthropic announced that the Claude Mythos Preview is not for the public.

Anthropic only shared access with tech giants like Amazon Web Services (AWS), Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks.

Eventually, Anthropic will extend the Claude Mythos access to 40 additional organizations. The company is already in the discussion phase with U.S. government officials regarding Claude Mythos capabilities.

The benchmark score and decision not to release Claude Mythos for public have created hype, which made Claude Mythos feature on thousands of publications within hours.

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You may have questions about Claude Mythos, such as:

• What Is Claude Mythos?
• What are the Claude Mythos Benchmark Performance Scores?
• What Claude Mythos Actually Found: Real Zero-Day Vulnerabilities?
• What is Project Glasswing?
• Why Anthropic Is Not Releasing Claude Mythos Publicly?
• Where is the Claude Mythos preview available?
• What are the Claude Mythos Capabilities?
• What are the challenges, and future of Claude Mythos?

Here is everything you must know.

Claude Mythos:

Claude Mythos Previews were released in April 2026. Anthropic described it as the new model to find and fix zero-day vulnerabilities.

Claude Mythos is better at problem-solving, coding, and reasoning. The extraordinary performance of Claude Mythos makes it extraordinary, but also dangerous.

In the preview release, Claude Mythos scored top benchmark scores and found the oldest vulnerabilities in the systems that were hidden from the human eye.

Claude Mythos Benchmark Performance:

Claude Mythos’s benchmark performance displays a generational gap between the models’ general public use and that of Claude Mythos.

Here are the benchmark performance scores:

SWE-bench Verified 93.9%:

SWE-bench tested model on real GitHub software engineering issues, requiring genuine code comprehension and repair.

Claude Mythos scored 93.9% and outperformed the best of the best AI tools.

USAMO (Math Olympiad) 97.6%:

Claude Mythos scored 97.6% at the USA Mathematical Olympiad tests.

USAMO tested proof-based and multi-step reasoning capabilities.

CyberGym 83.1%:

CyberGem tested the real-world cybersecurity threat detection with Claude Mythos.

The performance was substantially impressive.

Cybench CTF 100%:

Claude Mythos scored 100% at Cybench CTF tests. It tasked the model to find and exploit vulnerabilities in software.

Firefox Exploits:

Claude Mythos produced 181 Firefox exploits, whereas Claude Opus 4.6 only discovered 2.

Even after receiving excellent benchmark performance scores, Anthropic reported that the performance gap is still there.

What Claude Mythos Found?

The Claude Mythos’ popularity and demand are not because of its benchmark scores, but what it found in tests.

After weeks of rigorous testing, Claude Mythos identified thousands of zero-day vulnerabilities in major software and operating systems.

Even the software developers were unable to find a zero-day vulnerability.

Here are the 3 specific findings that set Claude Mythos apart:

The 27-Year OpenBSD Bug:

Claude Mythos found a bug in the OpenBSD operating system. OpenBSD itself is known for security. It has been resisting attacks for decades.

OpenBSD uses high security environments, firewalls, and critical infrastructure.

Yet, a vulnerability was there in their system for the last 27 years.

Claude Mythos detected this bug, which allows any user to crash the machine remotely.

The FFmpeg Flaw That Survived Five Million Scans:

FFmpeg is a video encoding library used by applications.

The automated testing has found nothing, even after running scans five million times. But Claude Mythos found the vulnerability.

CVE-2026-4747: 17 Years in FreeBSD

FreeBSD has had a remote code execution vulnerability for the last 17 years. It allows anyone to access machines running NFS using the Internet. No human was able to detect it.

Claude Mythos found it and deployed a working exploit.

Other than these, Claude Mythos also chained multiple Linux kernel weaknesses that can give access to control the machine. Claude Mythos can only cost $1,000 to run a full root exploit from a known vulnerability.

All of these vulnerabilities are patched before making them public. For the remaining vulnerabilities, Anthropic published cryptographic hashes.

What is Project Glasswing?

Anthropic decided not to release Claude Mythos for public. It became the first model to be withheld from public access.

Why is Anthropic not Releasing Claude Mythos to the General Public?

Let’s understand this.

Anthropic published a 244-page system card document about what Claude Mythos did without instructions.

• Escaped testing sandboxes.
• Posted exploit details on websites
• Covered tracks
• Searched process memory

Distorted confidence intervals to avoid safety flags.

Anthropic reported that while doing these things without instructions, Claude Mythos was aware that these actions were deceptive. The company informed us that Claude Mythos is the best model ever built, with greater alignment risks.

To ensure that the public will not get access to Claude Mythos, anthropic announced Project Glasswing.

Project Glasswing is a deployment initiative to make Claude Mythos Preview only available for a handful of tech organizations.

Project Glasswing Partners:

Amazon Web Services, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorgan Chase, the Linux Foundation, Microsoft, NVIDIA, Palo Alto Networks and 40 other organizations get access to Claude Mythos.

Anthropic has dedicated $100 million in usage credits and $4 million in direct donations to open-source security organizations.

Jared Kaplan, Anthropic's chief science officer, explained that the goal of launching Project Glasswing is to raise awareness and only allow good actors to get access to Claude Mythos.

Where is the Claude Mythos Preview Available?

As of now, Claude Mythos Preview is only available on 3 major cloud platforms. They are within the Project Glasswing framework.

Amazon Bedrock:

Amazon Bedrock, AWS's platform, offers Claude Mythos Preview to build generative AI applications and agents. Access is limited to the US East (N. Virginia) Region only.

Anthropic and AWS only allow internet-critical organizations with software applications impacting millions of users.

Claude Mythos capabilities are limited to defensive security workflow. It identifies vulnerabilities in software, demonstrates exploitation, and analyzes large codebases.

After the $100 million credits are consumed, Anthropic will charge $25/million input tokens and $125/month output tokens.

Google Cloud Vertex AI:

Only the selected group of Google Cloud customers has access to Claude Mythos Preview through Private Preview.

Google has made it available on Vertex AI. It allows enterprise customers to access Frontier AI models.

Microsoft Foundry:

Microsoft Foundry also provides access to Claude Mythos Preview. 

Teams within the Microsoft ecosystem can use Claude Mythos Preview for enterprise security.

Claude Mythos Capabilities:

Organizations under Project Glasswing have access to Claude Mythos. This enables security capabilities that were not possible before the Claude Mythos Preview.

Here is what security teams can do with Clause Mythos:

Large codebase comprehension:

Claude Mythos reads and reasons codebases regardless of their size. It identifies vulnerability patterns across code without the security team’s guidance.

Zero-day discovery:

Claude Mythos has proved that it can find vulnerabilities hidden from automated tools and human experts.

It has successfully discovered vulnerabilities in OpenBSD, FFmpeg, and FreeBSD.

Exploit development and demonstration:

Claude Mythos not only finds vulnerabilities, but it also displays how these vulnerabilities can be exploited.

It shows the pattern that can compromise the system.

Black box testing:

Claude Mythos can test binaries without source code access. It expands the scope of software examination without source review.

Vulnerability chaining analysis:

Claude Mythos also chains individual vulnerabilities to demonstrate how user-level access can perform attacks.

Penetration testing acceleration:

Claude Mythos compresses and fast-tracks the penetration testing from months to days.

Claude Mythos’s Alignment Challenge:

Anthropic reported that Claude Mythos can think one thing but write another. It can engage in strategic reasoning.

Anthropic document also reveals behavioral incidents. After assigning a task, the Claude Mythos model sent an email to the actual administration office because it believes that it is the fastest way to complete the task.

It also rewrites git history to conceal code errors.

Anthropic calls it tasks complete by unwanted means.

These incidents tell us that human oversight is required. Claude Mythos is not a replacement for security expertise.

What’s Next!

Anthropic is limited to Claude Mythos for Project Glasswing partners only. Now the company is building a new Claude Opus model to validate and deploy safeguards before allowing Mythos-class capabilities.

The head of Anthropic's dangerous-capabilities testing team, Logan Graham, explained that Claude Mythos Preview is the starting point to change the security industry.

Anthropic will publish public findings data within 90 days of Glasswing launch.

Conclusion:

Claude Mythos Preview is the first AI model that forced the AI giant to accept the risks and stop its global release.

Anthropic holds it back and accepts the cost to restrict the deployment. Rather than replacing Anthropic, choose to restrict access to Glasswing partners only.

The human era of cybersecurity attacks has gone. AI is not only empowering attackers but also helping tech companies to use models like Claude Mythos to adopt technological advancement.

FAQs:

Can I access Claude Mythos Preview today?

No. It is accessible to organizations listed under Project Glasswing.

Is Claude Mythos available on Claude.ai or through the standard API?

Not right now. Standard API access is not available.

What makes Claude Mythos different from Claude Opus 4.6?

The massive benchmark performance gap makes Claude Mythos the best choice for cybersecurity.

Why did Anthropic choose not to release Claude Mythos publicly?

During internal testing, the Claude Mythos model itself deployed working exploits and displayed deceptive behavior. To keep the public safe, Anthropic decided to limit the accessibility of Claude Mythos.

How is Claude Mythos being used by Project Glasswing partners?

Project Glasswing partners are using Claude Mythos for vulnerability detection, black box testing, endpoint security, open-source software scanning, and penetration testing.

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Google Adds AI Content Verification to Search: Everything You Need to Know

Every day, you watch hundreds of images without knowing the difference between human-created and AI-generated images.

It is the unsettling reality of modern image designs. To make things easy for you, Google has added AI content verification SynthID watermarking technology to Search.

It is not a minor feature update. It is one of the most important features to build digital trust. It will help every user who is wondering if the image is AI-generated or not.

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Google AI Content Verification to Search:

Google announced that it is expanding the SynthID verification tool into Google search. It is also part of Google Chrome.

The company also announced the launch of a new AI Content Detection API on Google Cloud. It also adds C2PA Content Credentials verification in the Gemini App.

Google is also expanding its partnership with AI image generation tools.

Let’s find out everything about the announcement.

What Is SynthID, and Why Should You Care?

If you are not aware of what Google SynthID is, then you have a lot to learn.

Google introduced SynthID in 2023. It is a digital watermarking technology that scans AI-generated images.

It does not add a label or digital stamp to images, but it embeds signals into AI-generated content at the time of creation.

You cannot see any watermark. You also cannot remove it. It is only for Google to verify using tools.

Google has integrated SynthID across its digital media products and models.

Google is using SynthID to mark more than 100 billion images and videos. It is equivalent to 60,000 years of audio content. It is not a program but an infrastructure.

Now, Google announced that SynthID verification is a part of the search to check if the images were AI-generated or not.

You can use the following tools:

• Google Lens
• AI Mode
• Circle to Search

You can also upload the image to Google and ask, "Is this made with AI?" or "Is this AI generated?"
Google will check the content for SynthID watermarks.

There is no separate app or third-party tool. It is part of your search experience.

C2PA Content Credentials:

SynthID detects AI-generated images. But it's C2PA Content Credentials that verify if something is real or not. It is an industry standard to record how media was created or modified.

No matter if it was captured through a camera, edited in Photoshop, or enhanced using AI tools.

Google has added C2PA Content Credentials verification to the Gemini app. It is also a part of Google Search and Chrome.

It lets users check if something is AI-generated or altered using AI tools.

Here is how it matters:

• C2PA verification can confirm that the viral image claiming a real news event was taken by a camera or generated by AI.
• It also helps insurance companies to verify the image evidence before processing the claims.
• It also helps voters verify that the political images spreading on social media are genuine or fake.

Google has already tested this with Pixel phones. Notably, Pixel 10 was the first smartphone with C2PA Content Credentials for images in the native camera app.

Google has also expanded it to include video capture on Pixel 8, 9, and 10.

It means that when taking a photo, it will add details that the image was not AI-generated.

The New AI Content Detection API:

Google is not only helping individuals but also businesses. It has launched the AI Content Detection API on Google Cloud's Gemini Enterprise Agent Platform.

Right now, this API is available for a trusted group of partners only. This API can detect AI-generated content made by Google’s own models and other popular AI models.

The practical use cases include:

• Sort content feeds to filter AI-generated images.
• Check if the photographic evidence is real or not to prevent insurance fraud.
• Fast checking at scale for media and news agencies.
• Label synthetic media for transparency.

Google’s initial partners with API access include Shutterstock, Snap, Avid, Fox Sports, and Canva. Google is expanding to many platforms to reach millions of users.

SynthID Is Going Mainstream:

Google is not the only brand that uses SynthID. Many other brands are collaborating with Google to make SnythID mainstream technology.

OpenAI, Kakao, and ElevenLabs:

OpenAI, Kakao, and ElevenLabs are collaborating with Google to use SynthID watermarking technology in their AI-generated content.

OpenAI is the company behind ChatGPT and Dall-E. The cross-competition collaboration will bring better results.

Open-sourced SynthID text watermarking technology:

Google has open-sourced SynthID text watermarking technology. Anyone can implement it. Google has also partnered with NVIDIA to watermark AI-generated videos.

Meta:

Meta also labels camera-captured media with Content Credentials. It brings trust and authenticity to images and videos. Two tech giants are sharing C2PA technology as standard.

Laurie Richardson, Google's VP of Trust & Safety, and Pushmeet Kohli, Chief Scientist, have talked about the industry-wide collaborations.

Digital media spread faster. A photo taken on Pixel can be shared on Facebook, WhatsApp, Instagram, X, and other platforms.

What SynthID Can and Cannot Do?

SynthID is a technology, and like any other technology, it also has some benefits and limitations.

SynthID can detect:

• Images, videos, and audio created with AI tools that have implemented SynthID watermarking.
• Content created with Google's generative AI products
• Content from partner companies that have adopted SynthID

SynthID cannot detect:

• AI-generated content from tools without SynthID watermarking.
• Content where watermarks may have been deliberately removed.
• Older AI-generated content before SynthID.

These are the reasons why Google is pushing SynthID technology for global adoption. The more platforms that implement SynthID, the larger the content it can detect.

What Does This Mean for Creators, Publishers, and Everyday Users?

Content Creators:

It is a must to understand SynthID before you generate images, videos, and audio using AI tools.

Content created from the tools that have adopted SynthID is identifiable.

It is not a bad thing. It creates transparency about AI-generated content.

Publishers and Media Organizations:

The AI Content Detection API is there to help you.

You can check whether the submitted photos, videos, and audio clips are AI-generated or not.

API integration into workflow increases the capability to analyze stock photo platforms, newsrooms, and content moderation teams.

Everyday Users:

It is best to use these tools. It will help you easily identify if the images or content were generated with AI or not.

If you have doubts about the image, you can always ask Google if it was made with AI.

Businesses:

API and verification tools help marketing teams and eCommerce managers to manage thousands of images with ease.

It makes the verification process easy.

History of SynthID:

• 2023: Google launched SynthID for image watermarking only.
• 2024: Google expanded SynthID to more content types. It also added “About This Image” to Circle to Search and Lens.
• 2025: Pixel 10 becomes the first smartphone with native C2PA Content Credentials.
• 2026: SynthID verification expanded to Google Search and Chrome. C2PA verification launched in the Gemini app. 

Conclusion:

Google is committed to adding AI content verification to search and all other products and services. It is a way to ensure transparency and is necessary to build trust. Google has been building and expanding SynthID since 2023.  It has already embedded technology into 100 billion content pieces.

Google is working in the right direction. SynthID verification appears in the search bar. Chrome is also flagging AI-generated media in tabs.

FAQs:

Is SynthID verification available right now in Google Search?

Yes. SynthID is live in Google search through Lens, AI mode, and Circle to Search.

Do I need a special account or subscription to use it?

No.

Can SynthID detect all AI-generated images?

No. It can only detect images with a SynthID watermark.

What is C2PA, and how is it different from SynthID?

C2PA Content Credentials provides details about the media, such as whether a camera captured it or it was modified.

What companies are using SynthID watermarking?

OpenAI, Kakao, ElevenLabs, and NVIDIA are using SynthID watermarking.

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Claude Design by Anthropic: Design Like a Professional Designer

Do you want to hire a designer who works for you 24/t without getting tired? If yes, then Claude Design is for you. It is a conversational AI tool for designers, beginners, professional marketers, founders, and product managers. With Claude Design, you can create professional and polished visual content from scratch.

Claude Design is not another AI feature inside an existing product. It is a full-fledged creative co-pilot built for Claude models.

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If you are not sure about what Claude Design is and what you should do with it, then keep reading this guide.

Today, you will learn everything about Claude Design, such as:

• What Is Claude Design?
• Why Claude Design Is a Big Deal?
• How does Claude Design Works?
• Who Should Use Claude Design?
• Claude Design vs. Figma, Adobe, and Canva
• The Model Behind Claude Design
• What Does Claude Design Cost?
• What Anthropic Does (and Doesn't) Store?
• What Anthropic Openly Admits?
• Should You Try Claude Design?

Claude Design:

Anthropic Labs launched Claude Design on April 17, 2026. It allows users to create high-quality visual content using natural conversations.

Right now, you can access the Claude Design in research preview.

It is available with all Claude premium plans, such as Claude Pro, Max, Team, and Enterprise.

Claude Design handles the following types of visual output:

• Interactive prototypes
• Product wireframes
• Feature mockups
• Decks and presentations
• Marketing materials
• Design explorations
• Frontier design with voice, video, 3D, shader, and AI.

Note: Claude Design uses the Claude Opus 4.7 model. It is the most capable model of Claude. You can use it to create images up to 3.75 megapixels.

Why Claude Design Is a Big Deal?

Design has always been a roadblock to major projects.

Suppose you have a startup idea and need a designer. You need slides to pitch to investors. If you are a project manager, then you need a mockup. For every design task, you need a designer. While human designers put you on waiting for days to weeks and offer limited revisions, Claude Design does everything in seconds.

If you have time, budget and team, then traditional human design will still work for you.

But if you want to save everything, then you need Claude Design.

Claude Design removes all the roadblocks. First-time founders who have never used Figma can describe their vision in English and get the interactive design in minutes.

A marketing manager can create landing pages without submitting creatives.

Product managers can draft user flows and ask Claude Code to implement them within minutes.

Datadog’s product team reported that Claude Design compresses a full week’s work into one session.

Brilliant also reported that pages that require 20+ prompts in other tools only require two prompts in Claude Design.

How does Claude Design Work?

It is necessary to understand how Claude Design works.

Start:

You can start from anywhere. It is easy to add inputs from multiple directions.

You can provide:

• A simple text prompt
• Unload files
• Codebase
• A Live website

This provides flexibility to Claude Design. You can add multiple content types to provide the visual output of your desire.

Brand Built In:

Claude Design uses your team’s codebase, files, and documents to understand the design system. It chooses colors, typography, and UI components from your input.

The projects you create using Claude Design automatically use brand signals. You do not need to start from scratch. There is no risk of leaking marketing materials. Your design team can also maintain multiple design systems for different projects.

It creates consistency, which is required for dedicated design output.

Conversation and Controls:

After Claude Design generates the first draft, you can use the text or other ways to refine it. This is where you add experience to it.

Here are 4 ways to refine your draft:

• Chat: add text input to refine your design.
• Inline Comments: Click on the element and add comments to make the changes.
• Text editing: edit copy inside the design.
• Custom Adjustment Sliders: Tweak spaces, colors, and layout.

Note: Sliders are clever. Rather than changing generic settings, you get control over your design.

Team Collaboration:

Claude Design allows sharing within your team.

You can share:

• Private documents
• Share view only link
• Grant edit access to teammates.

Export:

This is where your design is ready, and you want to export on your device or another online platform.

You can export your design:

• Using internal share URL
• Save as folder
• Export to Canva
• Export to PPTX or PDF
• Export as an HTML file.

Handoff Bundle:

This is the final step.

Claude packages your design, not a handoff bundle, so that you can pass it to Claude Code. The handoff bundle includes specifications, design intent, and assets.

Who Should Use Claude Design?

Claude Design is for every user with a need for creative designs.

Founders and Entrepreneurs:

If you are an entrepreneur or startup and need a smart designer, then Claude Design is for you.

It gives you way to create landing pages, pitch decks, and product mockups.

You can use these assets to show users and investors.

Product Managers:

Product Managers always face awkward situations with designs.

They must define the design, but their visual understanding depends upon what the designer has built for them

 Claude Design gives them the ability to create feature flows and wireframes.

This way, the project manager also becomes a contributor to the design.

Professional Designers:

Claude Design is one of the best tools for designers.

It removes the time cost of design exploration. Claude Design makes design exploration fast and affordable.

Designers can spend less time testing multiple ideas before execution.

Marketers:

Marketers need landing pages, social media assets, email headers, and campaign creatives.

Claude Design can create everything that marketers require to produce assets with brand consistency.

Regular Users:

General users who have no idea of design, fonts, colors, or anything else can also use Claude Design to test their ideas.

You can use voice, 3D, AI elements, and shaders to create creative visuals.

Claude Design vs. Figma, Adobe, and Canva:

Claude Design is giving tough competition to its competitors like Figma, Adobe, and Canva. Here is what you must know.

Figma:

Figma dominates the designing industry with 90% market share. It is a purpose-built platform for designers and teams. It offers in-depth auto-layouts, components, a design management system, and developer handoff.

Even though Claude Design is not replacing Figma, Figma lost $730M valuation right after the news of Claude Design's release.

Claude Design is offering everyone something that can help them design what works for all. It does not require design literacy.

Adobe:

Adobe offers a set of creative tools, like Photoshop and more, to help designers. But to use Photoshop and other Adobe tools, you need some sort of experience, certification, or design skills.

But to use Claude Design, you do not require anything. All you need is an idea and words to transform that idea into visual creatives.

Canva:

Canva offers something similar to Claude Design.

But Claude Design offers way more features and interactive prototypes with direct handoff to production code. It also offers design system integration.

The Model Behind Claude Design:

Claude Design is powered by Claude Opus 4.7. It is the most capable model of Anthropic.

Here are the key capabilities of Claude Opus 4.7 for Design Work:

• Vision resolution: You can upload images up to 3.75 megapixels. It is 3times more than the resolution of prior Claude models.
• Visual acuity: XBOW reported Opus 4.7 scored 98.5%.
• Instructions: It handles instructions better than the previous versions without losing context.
• Software engineering: 64.3% on SWE-bench Pro and 13% on Anthropic's internal coding benchmark.

Note: Opus 4.7 is just one step below the Claude Mythos Preview. Anthropic has restricted access to the Claude Mythos Preview for the public. It means that the most capable model for general users is Opus 4.7.

What Does Claude Design Cost?

Plan	Claude Design Access
Claude Pro	Included
Claude Max	Included
Claude Team	Included
Claude Enterprise	Included (off by default; admin enables)
Free	Not included

Claude Design is available with premium Claude models without additional cost. But it is not available with free Claude models.

API users can access Claude Design for the same price as Claude Opus 4.7, which is $5 per million input tokens and $25 per million output tokens.

Data Privacy and Claude Design:

Anthropic disclosed its data privacy policy.

• Claude Design stores the design system representation
• It does not store a link to your local copy of the codebase
• Anthropic does not train on this data
• For Enterprise customers, Claude Design is off by default
• GitHub integration is coming

These are meaningful announcements for a designing product that will touch brand assets, confidential pitches and product designs.

Limitations of Claude Design:

Here are the notable limitations of Claude Design:

Anthropic’s Claude Design can access messy code and produce messy output.

• It works on basic collaboration
• Editing experience has rough edges
• No general availability date
• Anthropic kept everything transparent.

Future of Claude Design:

Anthropic will make Claude Design integration easy in the coming weeks. It will use Model Context Protocols (MCPs). This will help teams to connect design with more tools.

Future updates of Claude Design:

• GitHub integration for direct codebase connections
• Expanded MCP integration for third-party tools
• Deeper multiplayer collaboration features
• More export options and external tool connections

Conclusion:

Claude Design is an easy-to-use tool to design visual creatives for branding, marketing, startups, and ads. Claude Pro, Max, Team, or Enterprise subscribers can access it without spending extra.

Founder or startups do not require deign team to use Claude Design. It has functional and shareable features.

FAQs:

Is Claude Design free?

It is not available for free users. You need paid Claude subscriptions to access Claude Design.

Does Claude Design replace Figma?

Not for professional designs. It is a different tool that targets a broader audience. It is best for non-designers, startups, and marketers.

What file formats can I import?

You can import text, images, DOCX, XLSX, PPTX, codebase links, and live website captures.

Where can I export my designs?

You can export designs to Canva. You can also download PDF, PPTX, HTML, and the internal organization URL.

Does Anthropic train on my design files?

No.

Is Claude Design available outside the US?

Yes.

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Cyber AI: Accenture’s Cybersecurity Powered by Anthropic

Accenture collaborated with Anthropic to enhance its cybersecurity with AI-Driven operations. Cyber.AI uses the Claude model for easy setup, quick response, and threat analysis. It has reduced manual work from days to hours.

According to the Accenture document, it has implemented Cyber.AI in 6000 applications and 500,000+ APIs. The latest integration has reduced the scan turnaround time from day 5 to one hour. This is a massive operational time saving.

Here is everything you must know about Cyber.AI.

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Cyber.AI:

Accenture launched Cyber.AI on March 25, 2026. It is an AI-powered cybersecurity platform. It is using Anthropic's Claude AI model and library of AI agents.

The goal behind Cyber.AI is to automate and accelerate cybersecurity operations. It enhances the cybersecurity lifecycle. It provides large scale transformation from risk assessment and threat triage.

Cyber.AI is different than traditional security platforms. It works on an agentic structure rather than flagging threats for human analysis.

It runs AI missions according to the workflow.  AI agents not only detect and assess threats but also respond without human oversight.

The adaptation of AI in security shifts the labor-intensive work to machine defense.

Accenture first announced Cyber.AI at the RSA 2026 event. It is a premier cybersecurity conference.

Significance of Cyber.AI:

The rising global threat of AI powers cybersecurity attacks has pushed brands like Accenture and Anthropic to build Cyber.AI.

The World Economic Forum's Global Cyber Outlook Report 2026 reported that Accenture helps organizations quickly identify AI-related vulnerabilities to reduce cyber risk.

Ransomware gangs, nation-state actors, and cybercriminal groups are already using AI to automate attacks. Personalized phishing is at scale. It is necessary to discover zero-day vulnerabilities and reduce the time required to get rid of possible threats.

The global Cybersecurity Services lead at Accenture, Damon McDougald, said that cybersecurity adversaries are rising. They are using AI to shorten the timeline to hours. Traditional human controls take time to identify such threats. This is a complete mismatch. This is where Cyber.AI was built to solve the issues and save a lot of time and effort.

Non-human identities are using AI agents, automated systems, bots, and API integrations to run cyber-attacks. They take advantage of poorly optimized digital footprints.

Organizations are adapting AI to accelerate. But at the same time, they create vulnerable non-human identities. It is necessary to govern these identities with Cyber.AI.

Claude: The Reasoning Engine Behind Cyber.AI

Anthropic's Claude AI model:

Anthropic's Claude AI model is the reasoning engine working as the heart of Cyber.AI. It enhances decision-making and reasoning.

Accenture chose Claude for its capabilities to handle security applications.

Contextual insights:

Claude can compress and analyze large security data. It tracks vulnerability databases, intelligence feeds, identity systems, endpoint telemetry, and network logs.

It is required to provide contextual insights. With deep insight, Cyber.AI helps security systems identify what is happening.

Claude works beyond the reasoning system. It can scan multiple data sources to identify patterns and indicate emerging threats.

Automated reasoning, analysis, and decision-making:

Claude works inside Cyber.AI's agentic workflows. It enhances reasoning, analysis, and decision-making. An AI agent runs security missions and investigates suspected credentials.

Claude helps AI security agents find out what it finds, escalation, and remediation.

Head of Cybersecurity Products at Anthropic, Michael Moore, explained that Claude’s role in Cyber.AI is to provide advances reasoning and threat removal capabilities.

It analyzes vast amounts of data within minutes. Agentic AI is necessary for security operations.

Cyber.AI is using Claude's safety architecture, AI design principles, and operates within defined operational parameters.

How Cyber.AI Works?

Cyber.AI operates AI-driven "missions.” These are the structured workflows with AI agents to complete security tasks.

Here is how AI missions work:

Mission Assignment:

The first step is to define security objectives. It includes vulnerability assessment, identity audit, incident investigation, and compliance check.

Cyber.AI establishes the best combination for AI agents from its database to complete the mission.

Agent Orchestration:

Cyber.AI deploys multiple agents to coordinate with each other and complete sub-tasks. In this scenario, one agent deals with intelligence databases, while another agent deals with internal asset inventory.

Reasoning and Decision-Making:

During the whole mission, Claude's reasoning engine takes input from AI agents to maintain situational awareness and decision-making.

Cyber.AI ensures that all the mission works within the organization’s policies and meets risk thresholds.

Delivery Outcome:

Cyber.AI ensures that the mission concludes with measurable inputs.

AI agents must work according to a workflow to find vulnerabilities, neutralize threats, generate reports, and transform milestones. It also ensures that everything is documented.

Accenture's agent library focuses on critical cybersecurity factors:

• Identity Security: It automates identity verification, detects anomalous patterns, privilege governance and access reviews.
• Cyber Defence: It ensures access coordination, incident investigation, monitoring, and threat detection.
• Secure Digital Core: It protects cloud environments, application layers, and APIs.
• Cyber Resiliency: It helps in planning, resilience testing, and disaster recovery.

The latest library-based architecture helps Cyber AI to start security domains and expand over time.

Cyber.AI Real World Development:

Cyber.AI is not only available for clients, but Accenture also deploys it across its own infrastructure. This gives real-time proof that Cyber.AI is reliable and operational.

Accenture's internal deployment reports:

• 1,600 applications running on Cyber.AI.
• 500,000 APIs powered by Cyber.AI.
• Scan turnaround time is one hour.
• Security testing coverage expanded to 80%.
• Backlog of critical vulnerabilities.
• Service delivery improved by 35%.

These stats display the real-time usability and what is achievable with Cyber AI.

80% coverage in security tests reduces surface attacks on a large scale.

Cyber.AI Real-World Enterprise Results:

A Fortune 500 agricultural organization is using Cyber.AI's agentic capabilities to enhance identity and access management operations. It also accelerated identity platform migrations.

Research Vice President at IDC, Craig Robinson, explained that the large-scale AI adoption also accelerates the growth of non-human identities and autonomous agents. Cyber AI ensures safety as it integrates AI agents across the security ecosystem.

Accenture, Anthropic, and Competitive Landscape:

In December 2025, both companies formed Accenture Anthropic Business Group. It has trained 30,000 Accenture professionals on how to use Claude. The team of 30,000 expert professionals now works behind the scenes for Cyber.AI.

Human expertise ensures customized deployment, client guidance, and improving implementations.
CrowdStrike also introduced Charlotte AI AgentWorks Ecosystem with Kroll, Accenture, Amazon Web Services, Deloitte, and Anthropic.

On March 21, 2026, Infosys announced a collaboration with Anthropic to deploy AI in regulated industries.

Why Cyber.AI Matters?

Human-speed to machine-speed security:

In the rising AI-led cyber-attacks, it is a must to shift from human-speed to machine-speed security. Cyber.AI provides production-ready solutions.

Agentic AI is a must for enterprise security's future:

Multi-agent security systems are replacing isolated AI solutions. They are required to run security missions. Cyber AI helps with scaling security infrastructure.

Governing AI agents:

Governing AI agents is important for deploying. Agent Shield understands how AI systems work within enterprises, securing and governing those systems.

Organizations can deploy AI agents without governance that can attract massive attacks.

Achievable Results at scale:

Accenture’s deployment of Cyber.AI proves that 95% reduction in scan time, 80% improvement in coverage expanding, 35% enhancement in service delivery are reliable facts.

Conclusion:

Cyber.AI is a next-generation AI-powered cybersecurity solution. It ensures enterprise cybersecurity. The combination of Anthropic’s Claude and Accenture’s deep domain expertise provides the necessary solutions to ensure cybersecurity in the AI era.

Cyber.AI ensures speed to deal with AI-powered attackers. Accenture’s internal and external deployment process that AI-driven security transformation is the future.

FAQs:

What is Cyber AI?

It is an Anthropic's Claude AI-powered cybersecurity AI.

What are the 4 types of AI?

Reactive Machines (Type I), Limited Memory (Type II), Theory of Mind (Type III), and Self-aware AI (Type IV).

How is Cyber AI trained?

Anthropic's Cyber AI is trained on Claude's reasoning infrastructure.

What are the 7 types of cybersecurity?

Network security, Information security, Cloud security, Endpoint security, Application security, Zero trust security, and Operational technology (OT) security.

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Google's AI Mode Personal Intelligence Feature is Free for All: But in the U.S. Only

In January 2026, Google launched personal intelligence as part of the pro and premium plans. In March 2026, Google made personal intelligence available for every user with AI Mode.

Right now, only U.S. users are allowed to access personal intelligence for free. Eventually, it will roll out to other countries as well.

You can access personal intelligence through Gemini in Chrome and the Gemini app. It connects Google Photos and Gmail to create a personalized AI experience.

The reason why Google decided to move personal intelligence from a premium feature to Free-for-all is to test its capabilities with a wider audience.

Now, personal intelligence is available for free to AI Ultra and AI Pro users.

Here is what you must know about personal intelligence.

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Personal Intelligence:

Google’s personal intelligence feature allows you to connect Google apps to get customized and personalized responses. It is available through Chrome and the Gemini App.

You can allow it to connect your Gmail account with Google Photos. With these features, you can expect personalized answers based on discovery and planning.

Personal Intelligence Free-For-All in the U.S.:

Google rolled our Personal Intelligence feature for free users in the U.S. Users can access the feature in AI Mode. The feature is also available through the Gemini App and Chrome.

Now, AI can access your email and photos to generate a personalized response. It is helpful to plan international trips, travel bookings, and share memories.

Update:

In January 2026, Google launched Personal Intelligence only for premium users. But now, it is available for free users as well.

Yet only the U.S. users are allowed to access the free Personal Intelligence feature.

You can still not use the Personal Intelligence feature in education, enterprise, and business accounts.

To opt in, you must connect apps through Gemini or search settings. It also gives you the option to turn off the feature anytime.

Personal Intelligence and Training Data:

Google has made it clear that AI mode and Gemini do not train on your Google Photos and Gmail data.

They use specific prompts and model responses. These prompts only work when you are using Personal Intelligence from connected apps.

Why Personal Intelligence Matters?

The change from paid to free access is necessary to scale Personal Intelligence.

When it was limited to Ultra or Pro Subscription, its audience reached a smaller user base.

Allowing Personal Intelligence for free users gives Google an edge to access personalized data.

Personalization:

The free Personal Intelligence feature increases personalization in AI mode.

It means that the AI mode response will not differ from user to user.

Even if the two users search for the same query, they will still get different answers. AI gets more access to Google Photos and Gmail.

Change Queries:

The new free-to-all feature allows users to type more queries in AI mode. It gives Google context to the searcher, as a result search will become shorter. 

Conclusion:

Google has limited the free Personal Intelligence feature to U.S. users only. The search engine giant first tested the features with premium users and then rolled them out for free users.

It is yet to be seen how people respond to sharing personal data with AI and search engines.

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How to use AI to streamline SEO Tasks?

AI is everywhere. Search Engine optimization is itself a time-consuming process. It requires efficiency and cost. AI is there to help SEO professionals. Reduce and automate SEO tasks.

Gaining organic position without ads or paid channels is the process of search engine optimization. It requires intense labor and investment.

There are 100s of SEO tools, software, platforms and research tools that you can use to optimize your SEO tasks.

There are hundreds of a year tool that one can use. Choosing the one itself is a challenge.

Ahrefs and Samrush are still the most popular tools. The rise of AI tools and new ranking factors makes it necessary to use AI in search and optimization.

AI SEO tools help in reducing time consumption and human efforts. If till now you have not considered using SEO tools, then it is the right time. Here I’m sharing how and why AI SEO is a must for you.

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Generate description, Title, and ALT text:

Descriptions, titles and all texts are the basics of SEO optimization. It is best to use AI tools to optimize these.

While human effort of optimization titled text and descriptions takes a lot of time, air to save all the time necessary for these.

If you have a large website, then the time for consumption is daunting. Even though content management platforms allow you to auto-generate description tags and keywords using AI tools.

You still need to learn how to practically and strategically use these tools for maximum benefits.

A lot of SEO professionals complain about using AI tools in SEO. The responsible use of AI and SEO tools is still beneficial.

It is always best to use AI optimization tools for SU, such as a WordPress plugin, and Screamingfrog with OpenAI API.

It is a preferred combination that not only saved dozens of hours but also saved thousands of dollars.

How to use it?

I’m sharing how you can use OpenAI API, Streaming Frog, and WordPress to generate API text at scale.

OpenAI API key:

• The very first step is to obtain open AI API key.
• Go to OpenAI and login to the dashboard. Go to API Keys.
• Click to create a new key. Rename it.

It is much that you must have graduates in your OpenAI account.

Screaming Frog Crawl:

Once you setup open AI API key, the next step is to use it with the Streaming Frog crawl.

• Go to the OpenAI dashboard, choose Configuration, then click on API, Access AI.
• Enter your OpenAI API key.
• Click on Connect.
• Use the prompt to generate all text. Go to the Prompt configuration tab. Go to the Library, then choose System, then generate all text for images, and click on add.
• In this web use, set up the crawl configuration. Go to Spyder, then click on Rendering and change the rendering mode from All text to JavaScript. Now go to extraction and check the store HTML and the store rendered HTML.
• Now run the crawl text using the URL to check the output. Make necessary changes to the prompt to get the best result.
• Run crawler.
• Export the CSV file.
• Make sure the file has two columns, all text and image URL.
• Add a WordPress plugin. .
• Upload the file.
• Now crawl your side and check the test with images and alt text.
• Now you can deactivate and uninstall the plugin.

Structure content outlines:

Now the next step is to structure content outlines.

It is necessary to optimize Front end creation process for SEO. Content optimization is necessary to ensure that your content is useful for search engines and site visitors.

Content creation is a complex process.

You cannot do everything with a single AI tool. Still, you can use AI as your tool to optimize your content creation process.

Whether you are writing long-form content, A single article of Evergreen content, or a content calendar. It is best to understand the content creation prompting.

Guide the agent with the right prompt to ensure that the output is best and optimized for human readers.

Example prompt:

You are an expert SU professional who specializes in contrite writing [choose your industry].

Your job is to create an outline for an article topic [topic of your choice]. This article outline must cover the following subtopics.

• Subtopic 1
• Subtopic 2
• Subtopic 3

The article should include the following keywords:

• Keyword 1
• Keyword 2
• Keyword 3

Here are the pages that are ranking well in Google search.

Project briefing:

For AI tools, it is necessary to use a project briefing. To get the best result, you must brief your project to the AI.

Make sure that you upload every sheet, deck, document and source on the LLM. And guide the LLM to summarize the content. I prefer NotebookLLM, but you can use any LLM of your choice.

NotebookLLM is a great tool to use multiple content formats such as emails, competitor lists, transcripts, notes, meeting data, and more.

By feeding this information, you can use the data to share with your team or as a personal reference.

Example Prompt:

You are a professional and experienced Senior Marketing manager. You are onboarding a team for [describe project]. Create a comprehensive project brief for [project].

Ensure the project brief takes into account the following project details:

Objectives: [goals]

Target audiences: [demographics]

Key messages: [messaging]

Channels: [multiple channels]

The output should include the following:

Project Overview: Summary of the project

Success Metrics: [KPIs]

Budget: [Financials]

Timeline: [Milestones and deadlines]

Generate the project brief as an expert, internal-facing document.

Classify the Keywords

Prompt for using the AI function in Google Sheets:

=ai("Act as an SEO expert. Classify the following keywords into exactly one of these Categories: [Informational, Navigational, Commercial, Transactional].

Rules:

Informational: [add details]

Commercial: [add details]

Transactional: [add details]

Navigational: [ add details]

Keyword: [keyword 1, keyword 2]

Result: Category name with no extra information

Segment keywords:

Use the Google Sheets function to categorize keywords based on topic, non-branded, branded, search intent and localized.

You can also use notebook LLM to organize keywords. Categorize keywords, export the output and note that in your spreadsheet.

Even when you’re using AI tools for SU, it is also better to use human intervention.

Competitor outlines: Automating computer websites' data using Surface ticketed checks is a great time saver.

Gemini AI to outline the content structure. It is best to use three or four competitor URLs to analyze and build a strategy. Include baseline content, blogs, messaging and targeting in your strategy.

Note: Terrify every result at every step to ensure that AI is not making any mistakes.

SERP analysis:

Use tools like Samrush and Ahrefs to build a seed keyword list. Put their data into the spreadsheet and upload it to GeminiAI.

Ask Gemini to breakdown keyword and intent based on the provided file. This way, you save a lot of time reviewing thousands of keywords.

Can use this strategy to find out informational versus commercial keywords. With this data, you can understand the level of competition in topics and what to avoid.

Conclusion:

AI for SEO is a great way to save A lot of time and dollars. Leverage available AI SEO tools for the best SEO strategy. Create a theme and use that theme multiple times, but every time. You must validate the result.

The reason behind using AI to streamline SEO is to save time and scale tasks.

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AI Agents Use Trust to Choose Which Brands to Recommend: What Marketers Should Do?

Trust is the ranking factor in GEO (Generative Engine Optimization). AI agents recommend brands that they trust or find trustworthy sources connected to the brand name. The confusion between AEO and GEO is high all the time.

There is no clarity if the SEOs still follow traditional or AI SEO. Brands are already obsessed with ranking on AI results at ChatGPT, Gemini, AI Overviews, and AI Mode.

OpenAI also introduced banner ads in ChatGPT to give brands an edge over organic ranking.

It is necessary to understand how you can optimize your website for LLMs and how I optimized client brands for AI agents.

As agentic SEO and eCommerce are becoming the new trend, consumers are moving towards AI search more than organic search. AI is evaluating the user's interest and behavior to give recommendations.

This makes it a must to determine how to make AI agents trust our brand.

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Trust Ranking Factor for AI Agents:

AI agents of ChatGPT, Claude, and Gemini make citations based on user interest, but also based on the platforms they trust.

The biggest issue that every SEO needs to resolve is the Trust.

Stefano Puntoni, David Schweidel, and Erik Hermann published a paper breaking down how to make people rely on AI agents. Trust plays a crucial role in scoring a mention when a user asks questions.

I have shared the list of ranking factors for search Engines. It is time to look for AI agents. 

There are the 3 core components of building Trust.

1. Align goals with reasoning:

AI agents work hard to understand the user’s issues and how to choose the different options.

AI only recommends a brand that can defend human interest. It requires clear reasoning and risk management.

It means that you cannot make your content rank in LLMs without creating a solid foundation of Trust.
To create that, you need facts, pricing transparency, real comparisons, and realistic timelines.

2. Action with Feedback:

AI Agents also read feedback on feedback. They understand every user’s input to deliver the best possible results.

Marketers can use AI agents in their favor by offering a clean path of execution. It is a must that your eCommerce brand offers open docs and transparent onboarding rather than PDFs and sales calls. 

3. Interface:

Your AI agent needs to understand that what the customer requires is not based on training data but based on actual conversation. To build trust, the eCommerce AI agent must ask questions to clarify details and know when to say no.

Marketers should use AI agents as consultants. It must probe integration, compliance, budget, and constraints. You need details, FAQs, and comparisons to build trust.

Why Trust is a Ranking Factor in AI Agents:

Like Google Search Ranking, AI ranking also requires Trust, but both work at different levels.
In general search, you visit a brand, buy a product, and if the product is faulty, you do not blame the search but the brand.

But in AI search, everything changes.

AI is responsible for delivering the most valuable results. If an AI agent promotes an eCommerce solution that is a disaster, then it is bad not only for the brand but also for the AI agent.

Customers not only lose trust in sellers but also in the LLM that recommends bad products.

For vendors, AI agents work more than a recommendation tool. They need an AI agent they can rely on to make buying decisions.

It is the reason why AI agents work in favor of brands they can trust.

The credibility of an AI agent depends on the sustainable sources it recommends. Without adding trustworthy brand names, AI agents cannot achieve that.

An AI agent will not recommend you because you have the best written content, but recommend your brand if it has clear information.

Trust plays a vital role in gaining brand mentions in LLM results.

Visibility or Eligibility:

SparkToro published a report stating that every time you ask an AI agent for recommendations, they give different answers. It seems that on the outer layer, they work like search engines.

But in the core, the AI agent uses Trust as the markup to mention brands. It is the reason why few brands come up most of the time, while other brands come in one result and are gone in another.

It is necessary to focus on making your content eligible rather than working on visibility.

What Marketers Should Do:

The brands require credibility in their content and product pages to achieve maximum citations.

Here is how you can do it:

Legible Data:

Combine the optimization for AI with human SEO. Create clean product pages with structured data, feeds, APIs, and optimized architecture.

AI agents won’t skip your content, is the give the right path to crawl the content.

Get Rid of Ambiguity:

Release all facts in product pages. Add SLAs and pricing bands. Do not hide anything. AI agents love details. They will recommend your brand if it provides all the necessary details.

Strong Validation:

AI agents focus on Trust to reduce the risk of citing wrong brands or disastrous products. It is necessary to collect third-party proof. Get brand mentions in top publications and press releases. Ask for customer reviews, participate in communities, and get published by independent journalists.

Display Your Work:

AI agents can only see your work if it is visible. Add numbers to case studies, comparison tables, and investment models.

Conclusion:

Search is changing from browsing search engines to asking AI agents. It is a must that your content not only appeals to the user but also appeals to the AI agents.

Optimize your website and content pages for eligibility.

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ChatGPT Free and Premium Models Search the Web Differently

ChatGPT conversions change when you move from the default ChatGPT model to Premium models. But why is that? The simple answer is that both versions work differently. They threaten the user’s conversations on different levels and search the web differently.

How does it happen?

After reviewing 10,000+ queries, I found that the same question in the same words provides different results and citations in ChatGPT's default and Premium versions.

At a glimpse it was clear that GPT-5.4 is citing more websites than its predecessor. You do not see caption overlays. GPT 5.4 even cites domains that are not indexed in Bing and Google search.

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You can try the same when asking one question in both versions. You get up to 93% different sources.
Writesonic also reported the same.

Writesonic reported that GPT 5.4 Thinking is citing 56% branded websites in its queries. While GPT 5.3 only sites 8% websites in queries.

After trying multiple prompts, I have found that only 7% of sources are shared across models. This stat reveals the fact that both versions treat search queries at different levels before searching the web for answers.

GPT Premium Vs. GPT Default:

I asked about both versions of the CRM Software. The two models approached things differently. GPT 5.3 used a simple search strategy.

It ran the broad query about CRM software and published information from technology blogs like TechRadar and Design Revision.

It displays that the query runs using high-level overview operations. It does not focus on specialized reviews and vendors.

Premium GPT 5.4 used a targeted and granular approach. It broke down the queries into multiple search-focused queries.

It sent different results for pricing information from Attio, Salesforce, and HubSpot. It also added additional information from review platforms like Capterra and G2.

Queries per Prompt:

GPT 5.4 used 8.5 subqueries per prompt. It helps the search narrow down to specific queries.

Premium GPT used the Site: search operator to restrict queries to the specific domains. It used the site operator in 156 out of 423 queries. While other GPT models do not use the Site: search operator.

OpenAI’s Documents:

OpenAI’s document reveals that ChatGPT search rewrite user prompts before search. The document does not provide additional information on how the internal decision process works.

How it determines which domains to target, under what circumstances, and when to choose query sources from the official sites of their party.

Citations Reflect the Stats:

• GPT 5.3 relies on third-party content. It cites articles and blogs more than 32%. Tom’s Guide, TechRadar, and Forbes are the most cited sources.
• GPT 5.4 uses a different approach. It cites brand homepages for 22% times, product pages 10%, and pricing pages for 19%.

GPT 5.3 cited 4 pricing pages in 49 conversations. GPT 5.4 cited 138 pages. GPT 5.4 uses fewer sources.
During comparison, GPT 5.3 never cites a website, but 5.4 cites brands 83% times.

Search Ranking and GPTs:

We checked multiple cited domains in the Bing and Google search to find out if they appear in the organic search or not.

• GPT 5.3 cited 43% of websites that appeared in Google search.
• GPT 5.4 cited 75% of websites that never appeared on Google or Bing search. This clarifies that do not rely on search ranking and targeted domains. 

Why It Matters for GEO:

Generative engine optimization (GEO) is different than traditional SEO. Traditional SEO focuses on ranking content in search results with backlinks and keywords.

But GEO requires content optimized for easy citation in AI models. Semantic search plays a critical role. To get your content cited in AI models, you need to add intent, context and the relationship between words. 

Brand visibility in the GPT results depends upon the choice of models. Default models focus more on third-party websites like media publications and review sites.

Digital PR is necessary for AI visibility. You need a proactive off-page strategy to capture third-party citations successfully.

I recommend that you reach out to industry tech blogs like eAskme for mentions, feature articles, and guest posts. Maintain active profiles on review platforms like G2 and Capterra. 

Premium GPT uses first-party content. It focuses on the homepage, product pages, and pricing pages.
Marketers and content creators can use this data to optimize their website’s homepage, product pages, and pricing tables to rank in GEOs.

• Clear semantic HTML and structured data will help AI agents to understand the content easily.
• Add a transparent and easy-to-read pricing table.
• Clear formatting is a must to ensure that Premium GPT models can cite your pricing data.
• Add natural language FAQs to every product page. Answer common questions at product page.

Conclusion:

GPT is evolving with every new model. The changes in patterns and methods of how it cites sources in different models.

Brands can use utm_source=chatgpt.com to measure referral traffic from ChatGPT.

It is a must to optimize the homepage, product and pricing pages to rank better in ChatGPT.

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WordPress AI Provider Plugins Anthropic, Google, and OpenAI

WordPress released three plugins for using Anthropic Claude, OpenAI, and Google Gemini for writing, image generation and other capabilities. The 3 official WordPress provider plugins were launched to make the integration easy using the PHP AI Client SDK.

The goal of new AI Provider WordPress plugins is to make image and text generation easy without leaving the WordPress platform.

Here is everything about WordPress AI Provider plugins for Anthropic, Google, and OpenAI.

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WordPress AI Provider Plugin Requirements:

WordPress AI Provider Plugin requires your website to be on PHP 7.4 or higher. You need an API key to integrate AI Models.

If you are using WordPress 6.9, then you must install the WordPress PHP AI client SDK.

WordPress ensured that WordPress 7.0 will come with built in the SDK integration.

The PHP AI document explained that the Client SDK provides shared infrastructure to WordPress plugins.

This makes it easy to integrate AI providers for text and image generation quickly.

Requirements for an AI Provider for Anthropic:

• PHP 7.4 or higher
• WordPress 6.9 with the PHP AI Client SDK.
• WordPress 7.0
• API Key

WordPress AI Provider Plugins:

Here are the 3 provider plugins WordPress released.

AI Provider for Anthropic:

As the name suggests, it is the plugin for Anthropic Claude integration. Once enabled, you can generate text and images using Claude.

This integration will dynamically choose models from Claude. It requires a Claude API Key.

Features of AI Provider for Anthropic:

• Text generation
• Function calling
• Extended thinking
• Automatic registration

AI Provider for Google:

WordPress AI Provider for Google uses Gemini AI integration with the PHP AI Client SDK. You can use it for image generation, text generation, and function calling.

WordPress chooses the Gemini models dynamically. It requires a Gemini API key.

Features of AI Provider for Google:

• Text Generation with Gemini AI
• Image Generation with Gemini AI
• Function calling support
• Automatic registration

WordPress AI Provider for OpenAI:

AI Provider for OpenAI allows you to integrate OpenAI with the PHP AI Client SDK.

Once enabled, you can use GPT for text generation and DALL-E for images. It requires the OpenAI API.

Features of WordPress AI Provider for OpenAI:

• Text generation with GPT
• Image generation with DALL-E
• Function Calling
• Web Search
• Automatic registration

Benefits for Bloggers and Marketers:

WordPress AI Provider Plugins make it easy for bloggers and marketers to use AI capabilities using official plugins. These are safe plugins.

Bloggers can use these plugins not only for writing articles or image generation but also to research the topics and add more value by finding the missing parts.

Marketers can use AI Providers to optimize the content for Search, GEO, and users.

The relevance of AI Provider Plugins depends on the user and how they use these plugins.

Conclusion:

WordPress is making AI API integration easy for users to access capabilities like image and text generation.

WordPress introduces leading AI models through API integration, which makes it easy for users to utilize Gemini, Claude, and ChatGPT inside the WordPress platform.

At the time of writing this post, these plugins have already received 100+ downloads. It is good to see how users will respond to these integrations.

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Why Multi-Agent Workflow Fails? Fixes

AI tools are doing everything with the help of AI agents. One AI agent is enough to complete multiple tasks. But sometimes, you need multiple agents to complete the complex tasks. This is where engineers get stuck.

The biggest issues with multi-agents are that they often fail or fail to collaborate. The most common AI agents’ failure includes missing agentic structure. You need to understand the 3 different engineering patterns that make AI agents reliable.

Developers working on multi-agent workflows often see failures.

Here is everything about Multi-Agent Workflow, how it works, and why it fails.

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Multi-Agent Workflow Failure:

AI agents complete tasks based on the established system.

During this process, most of the failures occur.

For example, one agent complete task while the other is still working on it. This causes the collaboration issue. It can fail any downstream check.

Why downstream check fail?

The issue occurs when agents handle related tasks, such as:

• Training issues
• Run checks
• Propose changes
• Open Pull Request

During these tasks, AI agents make assumptions about:

• State of request
• Order
• Validation

AI agents fail when they lack interfaces, instructions, and data formats.

AI agents handle the agentic experiences during multi-agent orchestration patterns and internal automation through GitHub Copilot. The role of an AI agent is to work as a distributed system, not as a chat interface.

Multi-Agent System:

A multi-agent system is required to complete a complex workflow. Introducing multi-agents also leads to an increasing number of agentic failures.

Developers use a multi-agent workflow:

• Codebase Maintenance
• Dependency Updates
• Automated code quality checks
• Refactors
• Feature implementation
• Pull requests and Issues.

These work when you work with constrained and explicit steps.

Here are the most common multi-Agent failure and fixes:

Messy Natural Language:

LLMs allow users to use natural language. Processing natural language in AI agentic language is itself a task. Multi-Agent workflow fails when multiple agents fail to exchange misleading or confused language. Inconsistent JSON also causes the issue.

When a team build AI agent, it creates contracting the early stage for easy understanding. AI agents also require clear data usage policies.

AI structure requires a strict schema and typed interfaces. Machines check data sent by AI agents. Wrong data immediately fails. This prevents mistakes from happening.

It is best to define exact steps to fix the issue. Defined steps make debugging easy. Schema errors work like broken contracts. If one thing is wrong, fix it before escalating it.

Note: Typed schemas are essential to prevent multi-agent workflow failures.

Lack of Specific Actions:

Multi-agents can fail even when the data is structured. It happens due to unclear instructions. The solution is to analyze the issue and add clear instructions.

An AI agent can get confused about:

• What closes the issue
• When to Assign
• When to Escalate
• When to do nothing

These issues seem reasonable for humans but not predictable.

An action schema is required to fix this issue. The action schema must define each allowed action.
Multi-agents require to return valid action to solve the issue. Invalid action results in escalation or retirement.

Note: Multi-agent failure happens when actions are unclear. It is best to define actions.

Optional MCP Rules:

Schemas only add value if they are enforced. Making rules optional reduces their value to suggestions. Make sure to add mandatory rules so that multiple agents must follow them.

Model Context Protocol (MCP) is the system that enforces mandatory schema rules.

Model Context Protocol (MCP) tells:

• What input allows
• What input is not allowed

MCP is required to prevent agents from inventing new fields. It is responsible for ensuring that multi-agents do not skip required inputs and cannot change formats.

Principles of Multi-Agent Systems:

You need to plan for failures. Check data at every step to ensure that you have not missed anything.

Before adding fields, make sure that it limits the possible actions. Keep a record of important steps.

Even though you did everything, still be ready for partial failures and retreats.

Note: In a multi-agent workflow, agents are a distributed system, not the chatbots.

Conclusion:

As an AI developer, your job is to ensure that the multi-agent workflow does not fail. You are responsible for creating a clear, structured multi-agent system.

Remember: AI agents can only work as reliable software components when they see type schemas, clear action definitions, and strict MCP enforcement.

Your takeaway is that you must take agents like code, not a chat conversation.

Other helpful articles:

• Anthropic’s Latest AI Model Redefines Performance
• Moltbot AI Agents on Cloudflare Without Hardware
• Blue Machines AI Guide