Lovell House | © Julius Shulman Few residential projects in the history of architecture have so concisely synthesized the aspirations of the modernist movement as the Lovell House. Designed by Richard Neutra and constructed between 1927 and 1929, this hillside residence overlooking Los Angeles is more than a stylistic landmark. It is a rigorous exploration of space, health, and industrial modernity. Commissioned by Dr. Philip Lovell, a health advocate and proponent of naturopathy, the house was conceived not as a retreat from the world but as a manifesto supporting rational living. For Neutra, who had recently arrived in the United States from Europe, this project represented a professional breakthrough and a profoundly personal alignment of ideological principles. Lovell House Technical Information Architects1-6: Richard Neutra Location: 4616 Dundee Drive, Los Feliz, Los Angeles, California, USA Area: 446 m2 | 4,800 Sq. Ft. Project Year: 1927 – 1928 Photographs: © Julius Shulman Philip Lovell wanted a house that would express his ideas of health, hygiene, and fitness. I wanted a house that would express my ideas of modern architecture. In the end, we built both. – Richard Neutra 7 Lovell House Photographs © Michael Locke, Flickr User © Michael Locke, Flickr User © Barcelo Photography © Barcelo Photography © Barcelo Photography © Barcelo Photography © Julius Shulman © Julius Shulman © Julius Shulman © Julius Shulman © Julius Shulman © Julius Shulman Context and Commission The interwar period witnessed a radical reevaluation of domestic space, partly driven by technological advancements and shifting attitudes toward hygiene and well-being. Within this cultural milieu, Dr. Lovell, already known for commissioning the Lovell Beach House by Rudolf Schindler, sought to build a residence that would embody his beliefs in clean living, sun exposure, and fresh air. His choice of Neutra, an architect trained in Vienna and influenced by figures such as Adolf Loos and Erich Mendelsohn, signaled a deliberate move toward a more European, functionalist approach. The Lovell House was Neutra’s first major commission in the United States and his opportunity to engage with the burgeoning discourse around the International Style. Lovell’s trust in Neutra and the architect’s commitment to creating a scientifically ordered environment set the stage for one of the most iconic modern homes in American architecture. Design Strategies and Construction Innovation On the hills of Los Feliz, the Lovell House is among the earliest examples of residential steel-frame construction in the United States. Neutra’s use of this system, more commonly associated with commercial or industrial buildings then, allowed for a remarkably lightweight structure that could seemingly float above its steep site. The steel frame also permitted large spans and cantilevers, freeing the plan from traditional load-bearing constraints and enabling expansive glazed openings. The house is organized as a vertical sequence of spaces, with each floor serving a distinct function. The lower level includes areas for exercise and recreation, aligned with Lovell’s ideals, while the upper floors house living and sleeping quarters. Circulation is carefully orchestrated, with a suspended exterior stair emphasizing the connection between levels without interrupting the building’s formal clarity. Transparency and openness are guiding principles throughout the design. Ribbon windows, floor-to-ceiling glazing, and open terraces blur the boundary between inside and out, capitalizing on Southern California’s climate while reinforcing Neutra’s belief in architecture’s capacity to mediate between human beings and their environment. Materiality and Environmental Integration The Lovell House’s material language is deliberately spare. Industrial steel, concrete, glass, and stucco are deployed precisely, rejecting ornamentation in favor of surface, proportion, and rhythm. Neutra’s choice of materials was aesthetic and deeply aligned with the project’s hygienic ethos: smooth, cleanable surfaces, built-in furniture, and controlled daylight contribute to a sense of physical and psychological well-being. Despite its machine-like clarity, the house is far from alienated from its context. The steeply sloping terrain becomes an active component of the design. Outdoor terraces, bridges, and gardens mediate between the constructed and natural environments, encouraging movement, exposure to sunlight, and engagement with the landscape. Neutra’s sensitivity to site and orientation ensured that the building was not merely placed on the land but woven into its topography and climate. Legacy and Influence The Lovell House has long occupied a pivotal place in the architectural canon. Featured in the 1932 Museum of Modern Art exhibition that introduced the International Style to an American audience, it exemplified the movement’s nationalist ideals in built form. Its influence on subsequent generations of architects in the U.S. and abroad cannot be overstated. For Neutra, the project marked the beginning of a prolific career dedicated to what he termed “biorealism”: integrating architecture with its inhabitants’ physiological and psychological needs. The house’s emphasis on health, efficiency, and environmental responsiveness would become hallmarks of his later work. Lovell House Plans Sketches | © Richard Neutra Floor Plans | © Richard Neutra Elevations | © Richard Neutra Elevations | © Richard Neutra Isometric View | © Richard Neutra Lovell House Image Gallery About Richard Neutra Richard Neutra (1892–1970) was an Austrian-American architect whose work played a pivotal role in defining the architectural identity of mid-20th century California. Deeply influenced by European modernism and trained under figures like Adolf Loos and briefly Frank Lloyd Wright, Neutra brought a rigorous, human-centered approach to design that emphasized clarity, health, and environmental responsiveness. His architecture fused technological precision with psychological and physiological considerations, resulting in dwellings that were as much about wellness as they were about aesthetics. Best known for projects like the Lovell House and the Kaufmann Desert House, Neutra’s legacy is a model of modern architecture’s potential to harmonize life, nature, and innovation. Credits and Additional Notes Client: Dr. Philip Lovell Design Period: 1927 Construction Period: 1928–1929 Site Conditions: Steep hillside lot in Los Feliz with expansive views over Los Angeles Construction System: Steel-frame structure with gunite (sprayed concrete) and stucco cladding Architectural Style: International Style / Modernism Neutra: Complete Works by Barbara Mac Lamprecht, Peter Gossel, Dion Neutra, Julius Shulman

Roblox announced on Thursday that creators can now sell physical merchandise directly within their experiences and games on the platform with the launch of new Commerce APIs. The offering provides creators with a new way to generate revenue beyond the purchases of virtual goods or one-time fees to access specific experiences. The company first began testing the ability to buy physical merchandise within a few branded experiences in mid-2024. Last September, Roblox announced that creators would soon be able to sell physical items in experiences thanks to a partnership with Shopify. Now, Roblox is making that integration available to all eligible creators and brands after completing a closed beta test of its Commerce APIs. The ability to purchase physical products will be available in all-ages experiences for U.S. users ages 13 and up. Roblox plans to add more partners and expand the feature to additional markets in the future. For brands, the Commerce APIs offer a new way to reach customers. For example, beauty brand Fenty is going to launch a shoppable experience within Roblox to offer an exclusive Gloss Bomb shade called “Grape Splash.” Image Credits:Roblox Eligible creators and brands can also bundle physical purchases with digital items via the new Commerce APIs. That means that you could purchase a physical hoodie and also receive one for your digital avatar. Techcrunch event Join us at TechCrunch Sessions: AI Secure your spot for our leading AI industry event with speakers from OpenAI, Anthropic, and Cohere. For a limited time, tickets are just $292 for an entire day of expert talks, workshops, and potent networking. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you’ve built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | June 5 REGISTER NOW Roblox’s new Approved Merchandiser Program (AMP) lets creators and brands connect their real-world or online store purchases to avatar items and accessories on Roblox. The AMP program and a badge on physical products will serve as a mark of authenticity to show consumers that the items are official. Products with the badge will include a code for a unique digital item relevant to the IP that they can use on Roblox. “Our vision for the future of shopping is an experience that’s more exciting and social than the traditional online shopping experience,” the company said in a blog post. “On Roblox, you can explore virtual stores, try on clothes, and share the experience with others. This naturally builds interest and purchase intent. Now we’re taking it one step further, bridging the gap between virtual and real-world shopping by enabling Roblox users to purchase physical items.” Roblox notes that creator studio Twin Atlas used Commerce APIs in their games to sell physical products and ended up seeing six-figure commerce revenue during the first few weeks. Around 90% of their total orders are placed via their in-game commerce integration, Roblox says.

May 15, 2025The Hacker NewsCompliance / Penetration Testing Imagine this: Your organization completed its annual penetration test in January, earning high marks for security compliance. In February, your development team deployed a routine software update. By April, attackers had already exploited a vulnerability introduced in that February update, gaining access to customer data weeks before being finally detected. This situation isn't theoretical: it plays out repeatedly as organizations realize that point-in-time compliance testing can't protect against vulnerabilities introduced after the assessment. According to Verizons 2025 Data Breach Investigation Report, the exploitation of vulnerabilities rose 34% year-over-year. While compliance frameworks provide important security guidelines, companies need continuous security validation to identify and remediate new vulnerabilities before attackers can exploit them. Here's what you need to know about pen testing to meet compliance standards — and why you should adopt continuous penetration testing, if your penetration testing goals go beyond minimum standards. The current state of pen testingCompliance-driven pen testing If your organization is like many, you might conduct penetration tests primarily to satisfy regulatory frameworks like PCI DSS, HIPAA, SOC 2, or ISO 27001. But if your pen testing focuses on simply checking off compliance boxes — instead of developing comprehensive security postures — you're creating a dangerous disconnect between security theater and actual threat protection. Limitations Compliance-focused pen testing has several limitations that leave organizations vulnerable. Surface-level security: Compliance-focused penetration testing typically addresses only compliance-relevant vulnerabilities. If your organization focuses its pen testing exclusively on meeting compliance requirements, you're just scratching the surface — and missing the chance to identify vulnerabilities that fall outside the scope of regulatory frameworks. These undetected weaknesses can give attackers an attack vector into your systems, potentially leading to devastating data breaches and operational disruptions. Static nature: Cyber attackers and the digital landscape move fast. Compliance standards? Not so much. During the months (or years) it takes for regulatory frameworks to catch up with new threats – and the gaps between compliance-focused penetration tests – malicious actors are actively developing exploits for emerging vulnerabilities. By the time these weaknesses appear on compliance checklists, attackers may have already compromised countless systems. False sense of security: Organizations often mistake compliance for security, believing a passing audit score means they're sufficiently protected. But the reality is that compliance certifications represent minimum standards that sophisticated attackers can easily bypass. Companies with successful audits may lower their guard when they should be working on strengthening their defenses beyond basic requirements. The importance of continuous pen testing Embracing continuous security testing offers organizations numerous benefits. Beyond compliance: Proactive and continuous penetration testing can reveal vulnerabilities that scheduled compliance checks might miss. Skilled human testers can uncover complex security flaws in business logic, authentication systems, and data flows, while automated scans keep an eye on any changes that might happen over the development cycle. By implementing regular, comprehensive testing, your organization can stay ahead of attackers rather than merely satisfying auditors. You'll be doing much more than passing the next compliance review — you'll be developing a resilient security posture capable of withstanding more sophisticated threats. Continuous improvement: Security threats constantly change, forcing organizations to adopt ongoing testing instead of point-in-time assessments. And regular penetration tests can expose vulnerabilities before attackers can exploit them. For example, Pen Testing as a Service (PTaaS) helps organizations achieve continuous security validation without overwhelming internal teams. With PTaaS, your organization can detect new threats in time and quickly take steps to remediate them. Instead of reacting to breaches after they occur, PTaaS lets you stay a step ahead of attackers by using real-world testing to continuously strengthen your security. Key components of a pen testing strategy with security in mind To implement penetration testing that truly helps safeguard your systems, focus on these key strategic components: Regular or continuous testing To effectively address vulnerabilities in real time, your organization should regularly conduct penetration tests — including after significant system changes and before major deployments. Ultimately, your ideal pen testing frequency and depth will depend on your assets — their complexity, criticality to your business operations and external exposure. For example, if you have an online store that holds critical customer data and payment information — and is regularly updated with changes and plugins — you may want to employ continuous testing. On the other end of the spectrum, your marketing department's fall-campaign microsite may only need quarterly or annual assessments. Integration with other security measures Want to maximize your organization's security effectiveness? Combine penetration testing with External Attack Surface Management (EASM). By identifying your digital footprint and testing critical applications based on the latest threat data, your team can prioritize high-risk vulnerabilities while ensuring no internet-facing assets remain unmonitored, unprotected or untested. Customization and threat-led penetration tests Your organization faces unique security challenges based on your industry, technology stack, and business operations. By tailoring penetration testing, you can focus on your business's specific threat profile — testing the areas where breaches are most likely to occur based on the most active threat actors and those that would cause the most damage — rather than wasting time and resources on cookie-cutter assessments. Overcoming challenges Despite the clear benefits, many organizations struggle with common penetration testing implementation challenges related to resources and culture. Resource allocation Resource issues — including budget constraints and shortage of qualified security personnel — prevent many organizations from implementing adequate penetration testing programs. But PTaaS and combined discovery and testing services like Outpost24s CyberFlex service solve these challenges by providing access to certified testers through a predictable subscription model, eliminating budget spikes and the expense of maintaining specialized in-house expertise. Cultural shift To move beyond compliance-driven security, your organization's leadership must champion a cultural shift prioritizing continuous testing and proactive risk management. When security becomes embedded in your organizational culture, pen testing transforms from a periodic checklist item into an ongoing process of discovering and addressing vulnerabilities before attackers can exploit them. Taking action with integrated solutions For the greatest level of security, your organization must know every application in your environment and test each one thoroughly. And a combined solution like Outpost24's CyberFlex can help. Integrating EASM and PTaaS on a platform level, allows cybersecurity experts to identify all internet-facing applications, use detailed categorizations to prioritize risks, and test business-critical applications with flexible, human-led assessments. By shifting to proactive penetration testing, your organization can prevent attacks before they happen — and satisfy compliance requirements. Ready to go beyond compliance and elevate your application security? Request your CyberFlex live demo today. Found this article interesting? This article is a contributed piece from one of our valued partners. Follow us on Twitter  and LinkedIn to read more exclusive content we post. SHARE    

As I mentioned recently, SmartGym has become one of my favorite apps. Anyone who uses it as a workout companion can easily see why it was named Apple Watch App of the Year in 2023, chosen as a finalist for the Apple Design Awards, and recently featured by Apple as one of the best apps optimized for iOS 18. Now, the app just got even better, with its biggest update yet. More personalization Tracking your performance and streaks is one of the best ways to stay motivated. SmartGym now offers custom goals, complete with history, targets, achievements, and detailed infographics to help you visualize progress over time. And speaking of personalization, one of the app’s most unique features, the Smart Trainer, uses AI to generate personalized routines based on your goals and available equipment. In this update, the Smart Trainer UI has been completely redesigned. Users can now group routines together, customize them more easily, and tweak individual settings with less friction. Heart rate tracking You may remember that recently, Beats released the Powerbeats Pro 2 with built-in heart rate monitoring. Now, SmartGym supports the feature, and uses it to better calculate calorie consumption during the exercise. Even more helpful: it now displays live heart rate zones during workouts, an especially useful addition for HIIT sessions or cardio training. Better UX for mid-session use One of SmartGym’s greatest strengths is its UX for mid-training interactions. It does its best to get out of the way and quickly deliver the exact information, exercise editing tools, or stats you may need. Now, it allows you to quickly find an alternate exercise for any given workout, which is great for those busy days at the gym when that chest fly machine is really on demand. The app now also features a “listen” option that reads out loud the description of its vast database of nearly 700 exercises, which will surely be very useful for when you are not familiar with a new workout routine. To top it off, the app just crossed out one of my biggest wishes for it, which is to display a persistent rest timer right on the interface. Syncing and integration Much, if not all, of these new features will now work in sync or integrate between multiple devices. From displaying custom goals as an Apple Watch complication to syncing of exercise changes between the iPhone and the iPad, this SmartGym update really packs many actually useful features for both gym rats and casual home fitness fans alike. SmartGym is free to download on the App Store, and offers a wide variety of subscription options. But the developer Matt Abras was nice enough to send five promo codes for SmartGym’s Annual Plan. If this app is for you, act fast! 9XA7P9YREA3F 364MPJRE4LKJ E7H67W6LKHWR 4Y36HW43KR6J RW7JPLERM34M Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel

The data quality used in pretraining LLMs has become increasingly critical to their success. To build information-rich corpora, researchers have moved from heuristic filtering methods, such as rule-based noise removal and deduplication, to model-driven filtering, which leverages neural classifiers to identify high-quality samples. Despite its benefits, this approach still faces key issues: it lacks efficient validation mechanisms to assess data quality promptly and often relies on manually curated seed datasets that introduce subjectivity. While early datasets like C4 and Pile laid the groundwork for model development, recent efforts like RefinedWeb, Dolma, and DCLM have scaled significantly, incorporating up to trillions of tokens. Model-driven filtering has gained traction in these newer corpora for its ability to refine massive datasets and enhance LLM performance across downstream tasks. Nevertheless, the effectiveness of model-driven filtering is limited by the high costs and inefficiencies of current validation methods and the absence of clear standards for seed data selection. Recent datasets, such as FineWeb-edu and Ultra-FineWeb, have demonstrated improved model performance by using multiple classifiers to cross-verify data quality. These datasets outperform previous versions on benchmarks like MMLU, ARC, and C-Eval, indicating that refined filtering methods can enhance English and Chinese understanding. To further optimize this process, some studies propose using LLMs for multi-dimensional data evaluation via prompts or leveraging token-level perplexity scores. These innovations aim to lower computational overhead while improving data quality, ultimately enabling more effective training with fewer tokens.  Researchers from ModelBest Inc., Tsinghua University, and Soochow University developed an efficient data filtering pipeline to improve LLM training. They introduced a verification strategy that uses a nearly-trained LLM to evaluate new data by observing performance gains during final training steps, reducing computational costs. A lightweight fastText-based classifier further enhances filtering speed and accuracy. Applied to FineWeb and Chinese FineWeb datasets, this method produced the Ultra-FineWeb dataset, containing 1 trillion English and 120 billion Chinese tokens. LLMs trained on Ultra-FineWeb showed notable performance gains, confirming the pipeline’s effectiveness in improving data quality and training efficiency.  The study outlines an efficient, high-quality data filtering pipeline to reduce computational costs while maintaining data integrity. It begins by using a cost-effective verification strategy to select reliable seed samples from a candidate pool, which are then used to train a data classifier. Positive seeds are sourced from LLM annotations, curated datasets, textbooks, and synthesized content, while negatives come from diverse corpora. Classifier training avoids over-iteration, focusing instead on high-quality seed selection. A fastText-based classifier is used for scalable filtering, offering competitive performance at significantly lower inference costs compared to LLM-based methods, with preprocessing steps ensuring balanced, clean data input.  The models were trained using MegatronLM with the MiniCPM-1.2 B architecture on 100B tokens. Evaluations used Lighteval across English and Chinese benchmarks. The results show that models trained on Ultra-FineWeb consistently outperformed those trained on FineWeb and FineWeb-edu, individually and in mixed-language settings. Ultra-FineWeb-en achieved the highest English average score, while Ultra-FineWeb-zh improved performance on Chinese tasks. Ablation studies revealed that Ultra-FineWeb maintains balanced token lengths and benefits from efficient filtering strategies, highlighting its superior quality and effectiveness in improving model performance.  In conclusion, the study presents Ultra-FineWeb, a high-quality multilingual dataset comprising about 1 trillion English tokens and 120 billion Chinese tokens. Built upon FineWeb and Chinese FineWeb, it leverages a novel, efficient data filtering pipeline featuring a fastText-based lightweight classifier and a low-cost verification strategy. The pipeline enhances filtering accuracy, reduces reliance on manual seed data selection, and ensures robust performance with minimal computational overhead. Experimental results show that models trained on Ultra-FineWeb consistently outperform those trained on earlier datasets, demonstrating improved performance across benchmarks. The methodology ensures reproducibility and offers valuable insights for optimizing data quality in future LLM training.  Check out theTwitter and don’t forget to join our 90k+ ML SubReddit. Sana HassanSana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.Sana Hassanhttps://www.marktechpost.com/author/sana-hassan/Coding Agents See 75% Surge: SimilarWeb’s AI Usage Report Highlights the Sectors Winning and Losing in 2025’s Generative AI BoomSana Hassanhttps://www.marktechpost.com/author/sana-hassan/Rethinking Toxic Data in LLM Pretraining: A Co-Design Approach for Improved Steerability and DetoxificationSana Hassanhttps://www.marktechpost.com/author/sana-hassan/A Step-by-Step Guide on Building, Customizing, and Publishing an AI-Focused Blogging Website with Lovable.dev and Seamless GitHub IntegrationSana Hassanhttps://www.marktechpost.com/author/sana-hassan/NVIDIA AI Introduces Audio-SDS: A Unified Diffusion-Based Framework for Prompt-Guided Audio Synthesis and Source Separation without Specialized Datasets

Zoho has announced Zoho Payments as a unified payment platform for US businesses, aimed at helping CIOs streamline financial operations with enterprise-grade security and scalability. Fully integrated with Zoho’s ecosystem — including Zoho Books and CRM — Zoho Payments supports card and ACH transactions across 135+ currencies, according to a company statement. “Businesses need a payment solution that not only offers flexibility, but is also tightly connected to their financial operations,” Raju Vegesna, chief evangelist at Zoho, said in a press note. The platform taps into Zoho’s AI capabilities — from Analytics 6.0 to DDPM — to automate workflows and accelerate digital transformation in a cashless economy. The move positions Zoho against entrenched players such as Stripe and PayPal, offering a natively integrated alternative built for mid-size companies and large enterprises. Native integration Zoho Payments aims to eliminate third-party gateways by integrating with Zoho’s finance and sales apps and reducing system sprawl — a top CIO pain point. Built to mirror the synergy seen in Zoho Analytics 6.0 and DDPM, it is designed to focus squarely on end-to-end financial workflows. “Our transportation company used third-party portals before, but the extra logins and disconnected integrations disrupted our flow,” James Martin, Founder and CEO of Keystone Transport Services, said in a press release. “Zoho Payments, integrated with Zoho Books, unified our processes and cut onboarding time significantly. Now, one person can easily accomplish over 100 times the work as before,” Martin noted.  “Zoho Payments’ native integration simplifies IT environments and reduces costs,” said Divya Baranawal, principal analyst at QKS Group. “With automated reconciliation and payout tracking, it eases finance workloads while aligning IT with business goals.” Market positioning Zoho Payments provides enterprises with PCI DSS Level 1 compliance and real-time fraud detection, mirroring Zoho’s security focus in Analytics 6.0. The offering supports over 135 currencies, enabling global transactions while maintaining local compliance. Zoho Payments will support both one-time and recurring payments, catering to e-commerce and subscription-based business models. Industry data indicates a broader trend towards cashless transactions, with approximately 87% of transactions now cashless, according to Capital One Shopping data, and 81% of US consumers preferring card payments. This shift is driving businesses to adopt payment systems that accommodate these evolving preferences. In a crowded field, Zoho’s edge lies in native plug-and-play integration with Zoho Books and CRM, contrasting with Stripe’s API-heavy setup and PayPal’s SMB focus. Adyen offers strong global coverage but involves more complex integration. While Zoho’s pricing is comparable to Stripe’s, transparency remains limited. “Zoho Payments’ value proposition hinges on AI-driven automation via Zia Agents and Projects Plus, offering CIOs real-time insight and agility,” said Baranawal. Still, Stripe’s API flexibility remains a key advantage for organizations requiring highly customized solutions.

Over the past few years, Okta has stated its commitment to ending the threat of identity-enabled cyber crime and attacks. As part of its Secure Identity Commitment, Okta has been keen on “elevating our industry’ by accelerating its capabilities and embracing new technology, such as AI, and und the digital transformation of nonprofits and advance inclusive pathways into tech. Therefore, when an announcement was made of a standard around identity security in security-as-a-service (SaaS) applications, it was worth taking notice. Named the Interoperability Profile for Secure Identity in the Enterprise (IPSIE), the concept is of an open standard which provides a framework for SaaS companies to enhance the end-to-end security of their products across every touchpoint of their technology stack. Announcing it in October 2024, Okta CEO and co-founder Todd McKinnon said there is a “need [for] massive standardisation” and “move to a world where every app, every device, every workload all speak a common language”. McKinnon said that by adopting IPSIE, users will get complete visibility into their identity environment and the threat surface, and they can provide access to the right applications at the right time and take real-time actions in response to threats. Okta’s announcement stated that the point of IPSIE is to “foster a more open, consistent, flexible SaaS ecosystem by empowering organisations to adhere to a higher level of security, more seamlessly and efficiently integrating among tech stacks”. This open standard will provide the framework for any enterprise application to be discoverable and governable. By adopting IPSIE, users will be able to gain complete visibility across the identity threat surface, enable consistent security outcomes across SaaS applications, and build secure-by-default SaaS applications more seamlessly and efficiently. On that final point, Okta states that any app built to the IPSIE standard adheres to a higher level of security by ensuring that it can be governed, have entitlements managed, can support multi-factor authentication and posture management, as well as feature real-time Universal Logout. So far, 50 enterprise SaaS applications have joined the cause and integrated with IPSIE – including Google, Microsoft Office 365, Slack and Salesforce – to support modern identity best practices aimed at enhancing security and reducing operational burden. Harish Peri, senior vice-president of product marketing at Okta, tells Computer Weekly that IPSIE is a way to ensure that every app and API conforms to a standard whereby its identity can be secure: “We are leading the way with the OpenID foundation, and we’re part of the working group for the creation of IPSIE interoperably profiled for secure identity of the enterprise.” Far from working alone, Okta has enlisted members of the OpenID Foundation to create the IPSIE Working Group, which will develop profiles of existing specifications with a primary goal of achieving interoperability between independent implementations. Gail Hodges, executive director of the OpenID Foundation, says that while the development of the IPSIE was initially getting off the ground in this first year, she felt the concept was “great”, adding: “I’m really encouraged as the foundation is moving more and more towards lining up specifications; like a lot of our work internally, they’re intended to kind of sync up with each other so that you could layer specifications on top of each other. “I see the work of IPSIE and a group of subject matter experts looking to do exactly that – line up the specifications together. So there’s even more consistency in how those specifications are configured, so there will be even greater benefits of interoperability and security associated with deploying a more complex stack. I think it’s fantastic.” Shiv Ramji, president of customer identity cloud at Okta, says the ultimate ambition with IPSIE is to “make it easy for customers to choose the right default path, which is to be secure, and I think they’ll do that if the value is clear to them, and, over time, it will be”. The concept of IPSIE from Okta is to gain industry-wide adoption, but Ramji was keen to make the point that Okta is “one participant”, and if every participant adopts the standards, “we will deliver better security outcomes for the entire software as a service ecosystem”. One factor Ramji stressed is the support for Universal Logout. Okta describes this as a concept where you can terminate users’ sessions, and their tokens, for supported apps when your identity threat protection identifies a risk change. Specifically, a user session is the time during which a user is authenticated and authorised to access apps secured by Okta, while an app session refers to sessions that an app generates to allow users to access the app’s resources. Universal Logout can be configured to terminate a users’ sessions in generic Security Assertion Markup Language (SAML) and OpenID Connect (OIDC) apps. Stephen McDermid, EMEA chief security officer at Okta, says the concept of Universal Logout will help to mitigate and minimise risks, “so that you’re not waiting for your SOC or your SIEM solution to respond in real time”. He adds: “I think the fact that there’s talk about the risks that IPSIE is trying to address reassures me that we’re going in the right direction for us – and for other vendors as well. The more vendors we can get to agree to it, the better the solution becomes.” This is why SaaS companies are integrating Okta’s software development kit, Ramji says, with companies now adopting this, “we’re changing the type of integrations that we do with these SaaS applications because we can do signal sharing”. In terms of integrations, Ramji says there were more than 150 in April 2025, and users “are asking us what are the ways they can support the adoption of these standards”. Out of those 150 integrations, is this something that the customer can implement on their own, rather than waiting for Salesforce, for example, to do it, for them? Ramji says if a user is using Auth0 today, they can switch IPSIE and Universal Login on and go into their Okta dashboard to enable the Universal Logout cable. “They have to enable it to opt in, as it’s an opt-in mechanism,” he says. “It’s easy to turn it on. As we roll this out initially, a lot of this will be opt-in, and then over time we can look at ways to make that easier, or maybe look at other options, but for now, it’ll be opt-in.We don’t want behaviours in companies where their applications where users are being logged out without working it out, so this is a deliberate thing that they need to roll out.” Peri says Okta’s largest existing customers asked, “How soon can you get all of our apps IPSIE-fied?”, and levels of IPSIE are being defined, but he adds that this is not an Okta-driven initiative or about asserting dominance, but “about doing the right thing for the industry, as the more people that are in it, the better is for everybody”. So, how well will IPSIE be adopted? Computer Weekly contacted a number of other authentication suppliers to find out. Chris Anderson, duo product CTO at Cisco, confirms that the firm had joined the IPSIE Working Group, which aims to develop profiles of existing specifications and achieve interoperability between independent implementations, stating: “While it’s still early days, we believe that interoperability across standards is key to greater success in identity security.” Andras Cser, vice-president and principal analyst at Forrester, says that standards that anyone can implement, proposed by one supplier, generally “do not fare very well”, but with the backing of the working group and OpenID Foundation, could work out well. He points at the example set by the FIDO Alliance, which “started out as a bunch of vendors coming together”. However, Cser believes that if IPSIE could follow FIDO’s lead, then it has a chance to work. “The use case behind FIDO was a lot smaller than IPSIE, it was just authentication and second factor and biometrics, that was the design and try not to boil the ocean,” he says. “Single sign on, logout and token verification are largely resolved by SAML and OpenID, and there’s a scanner for those things. “There’s also a very concrete and distinct use case behind sharing risk signals – there’s a new login from a new IP address, from a new device and that makes a lot of sense.” He claims that single sign on, token revocation and logout have been resolved, while user lifecycle management, and while other areas are being addressed further down the line. He adds that IPSIE is trying to resolve things, “30% of which are not solvable in the security domain only, 60% are addressed by other standards, and 10% is the key part of what IPSIE is trying to do”. Less than a year since its announcement, the conversations around IPSIE suggest it will take a long time to gain full traction and industry adoption, but there is persistent positivity on the side of Okta, its main supplier driver: the criticism comes from it being too broad and “putting everything in the kitchen sink”. Time will tell, but all revolutions need to start somewhere. Read more about identity management and SaaS applications Identity and access management tools and features for 2025. How to build an effective IAM architecture. Seven cloud IAM challenges and how to address them.

In this tutorial, we demonstrate how to construct an automated Knowledge Graph (KG) pipeline using LangGraph and NetworkX. The pipeline simulates a sequence of intelligent agents that collaboratively perform tasks such as data gathering, entity extraction, relation identification, entity resolution, and graph validation. Starting from a user-provided topic, such as “Artificial Intelligence,” the system methodically extracts relevant entities and relationships, resolves duplicates, and integrates the information into a cohesive graphical structure. By visualizing the final knowledge graph, developers and data scientists gain clear insights into complex interrelations among concepts, making this approach highly beneficial for applications in semantic analysis, natural language processing, and knowledge management. !pip install langgraph langchain_core We install two essential Python libraries: LangGraph, which is used for creating and orchestrating agent-based computational workflows, and LangChain Core, which provides foundational classes and utilities for building language model-powered applications. These libraries enable seamless integration of agents into intelligent data pipelines. import re import networkx as nx import matplotlib.pyplot as plt from typing import TypedDict, List, Tuple, Dict, Any from langchain_core.messages import HumanMessage, AIMessage from langgraph.graph import StateGraph, END We import essential libraries to build an automated knowledge graph pipeline. It includes re for regular expression-based text processing, NetworkX and matplotlib for creating and visualizing graphs, TypedDict and typing annotations for structured data handling, and LangGraph along with langchain_core for orchestrating the interaction between AI agents within the workflow. class KGState(TypedDict): topic: str raw_text: str entities: List[str] relations: List[Tuple[str, str, str]] resolved_relations: List[Tuple[str, str, str]] graph: Any validation: Dict[str, Any] messages: List[Any] current_agent: str We define a structured data type, KGState, using Python’s TypedDict. It outlines the schema for managing state across different steps of the knowledge graph pipeline. It includes details like the chosen topic, gathered text, identified entities and relationships, resolved duplicates, the constructed graph object, validation results, interaction messages, and tracking the currently active agent. def data_gatherer(state: KGState) -> KGState: topic = state["topic"] print(f"📚 Data Gatherer: Searching for information about '{topic}'") collected_text = f"{topic} is an important concept. It relates to various entities like EntityA, EntityB, and EntityC. EntityA influences EntityB. EntityC is a type of EntityB." state["messages"].append(AIMessage(content=f"Collected raw text about {topic}")) state["raw_text"] = collected_text state["current_agent"] = "entity_extractor" return state This function, data_gatherer, acts as the first step in the pipeline. It simulates gathering raw text data about a provided topic (stored in state[“topic”]). It then stores this simulated data into state[“raw_text”], adds a message indicating the data collection completion, and updates the pipeline’s state by setting the next agent (entity_extractor) as active. def entity_extractor(state: KGState) -> KGState: print("🔍 Entity Extractor: Identifying entities in the text") text = state["raw_text"] entities = re.findall(r"Entity[A-Z]", text) entities = [state["topic"]] + entities state["entities"] = list(set(entities)) state["messages"].append(AIMessage(content=f"Extracted entities: {state['entities']}")) print(f" Found entities: {state['entities']}") state["current_agent"] = "relation_extractor" return state The entity_extractor function identifies entities from the collected raw text using a simple regular expression pattern that matches terms like “EntityA”, “EntityB”, etc. It also includes the main topic as an entity and ensures uniqueness by converting the list to a set. The extracted entities are stored in the state, an AI message logs the result, and the pipeline advances to the relation_extractor agent. def relation_extractor(state: KGState) -> KGState: print("🔗 Relation Extractor: Identifying relationships between entities") text = state["raw_text"] entities = state["entities"] relations = [] relation_patterns = [ (r"([A-Za-z]+) relates to ([A-Za-z]+)", "relates_to"), (r"([A-Za-z]+) influences ([A-Za-z]+)", "influences"), (r"([A-Za-z]+) is a type of ([A-Za-z]+)", "is_type_of") ] for e1 in entities: for e2 in entities: if e1 != e2: for pattern, rel_type in relation_patterns: if re.search(f"{e1}.*{rel_type}.*{e2}", text.replace("_", " "), re.IGNORECASE) or \ re.search(f"{e1}.*{e2}", text, re.IGNORECASE): relations.append((e1, rel_type, e2)) state["relations"] = relations state["messages"].append(AIMessage(content=f"Extracted relations: {relations}")) print(f" Found relations: {relations}") state["current_agent"] = "entity_resolver" return state The relation_extractor function detects semantic relationships between entities within the raw text. It uses predefined regex patterns to identify phrases like “influences” or “is a type of” between entity pairs. When a match is found, it adds the corresponding relation as a triple (subject, predicate, object) to the relations list. These extracted relations are stored in the state, a message is logged for agent communication, and control moves to the next agent: entity_resolver. def entity_resolver(state: KGState) -> KGState: print("🔄 Entity Resolver: Resolving duplicate entities") entity_map = {} for entity in state["entities"]: canonical_name = entity.lower().replace(" ", "_") entity_map[entity] = canonical_name resolved_relations = [] for s, p, o in state["relations"]: s_resolved = entity_map.get(s, s) o_resolved = entity_map.get(o, o) resolved_relations.append((s_resolved, p, o_resolved)) state["resolved_relations"] = resolved_relations state["messages"].append(AIMessage(content=f"Resolved relations: {resolved_relations}")) state["current_agent"] = "graph_integrator" return state The entity_resolver function standardizes entity names to avoid duplication and inconsistencies. It creates a mapping (entity_map) by converting each entity to lowercase and replacing spaces with underscores. Then, this mapping is applied to all subjects and objects in the extracted relations to produce resolved relations. These normalized triples are added to the state, a confirmation message is logged, and control is passed to the graph_integrator agent. def graph_integrator(state: KGState) -> KGState: print("📊 Graph Integrator: Building the knowledge graph") G = nx.DiGraph() for s, p, o in state["resolved_relations"]: if not G.has_node(s): G.add_node(s) if not G.has_node(o): G.add_node(o) G.add_edge(s, o, relation=p) state["graph"] = G state["messages"].append(AIMessage(content=f"Built graph with {len(G.nodes)} nodes and {len(G.edges)} edges")) state["current_agent"] = "graph_validator" return state The graph_integrator function constructs the actual knowledge graph using networkx.DiGraph() supports directed relationships. It iterates over the resolved triples (subject, predicate, object), ensures both nodes exist, and then adds a directed edge with the relation as metadata. The resulting graph is saved in the state, a summary message is appended, and the pipeline transitions to the graph_validator agent for final validation. def graph_validator(state: KGState) -> KGState: print("✅ Graph Validator: Validating knowledge graph") G = state["graph"] validation_report = { "num_nodes": len(G.nodes), "num_edges": len(G.edges), "is_connected": nx.is_weakly_connected(G) if G.nodes else False, "has_cycles": not nx.is_directed_acyclic_graph(G) if G.nodes else False } state["validation"] = validation_report state["messages"].append(AIMessage(content=f"Validation report: {validation_report}")) print(f" Validation report: {validation_report}") state["current_agent"] = END return state The graph_validator function performs a basic health check on the constructed knowledge graph. It compiles a validation report containing the number of nodes and edges, whether the graph is weakly connected (i.e., every node is reachable if direction is ignored), and whether the graph contains cycles. This report is added to the state and logged as an AI message. Once validation is complete, the pipeline is marked as finished by setting the current_agent to END. def router(state: KGState) -> str: return state["current_agent"] def visualize_graph(graph): plt.figure(figsize=(10, 6)) pos = nx.spring_layout(graph) nx.draw(graph, pos, with_labels=True, node_color='skyblue', node_size=1500, font_size=10) edge_labels = nx.get_edge_attributes(graph, 'relation') nx.draw_networkx_edge_labels(graph, pos, edge_labels=edge_labels) plt.title("Knowledge Graph") plt.tight_layout() plt.show() The router function directs the pipeline to the next agent based on the current_agent field in the state. Meanwhile, the visualize_graph function uses matplotlib and networkx to display the final knowledge graph, showing nodes, edges, and labeled relationships for intuitive visual understanding. def build_kg_graph(): workflow = StateGraph(KGState) workflow.add_node("data_gatherer", data_gatherer) workflow.add_node("entity_extractor", entity_extractor) workflow.add_node("relation_extractor", relation_extractor) workflow.add_node("entity_resolver", entity_resolver) workflow.add_node("graph_integrator", graph_integrator) workflow.add_node("graph_validator", graph_validator) workflow.add_conditional_edges("data_gatherer", router, {"entity_extractor": "entity_extractor"}) workflow.add_conditional_edges("entity_extractor", router, {"relation_extractor": "relation_extractor"}) workflow.add_conditional_edges("relation_extractor", router, {"entity_resolver": "entity_resolver"}) workflow.add_conditional_edges("entity_resolver", router, {"graph_integrator": "graph_integrator"}) workflow.add_conditional_edges("graph_integrator", router, {"graph_validator": "graph_validator"}) workflow.add_conditional_edges("graph_validator", router, {END: END}) workflow.set_entry_point("data_gatherer") return workflow.compile() The build_kg_graph function defines the complete knowledge graph workflow using LangGraph. It sequentially adds each agent as a node, from data collection to graph validation, and connects them through conditional transitions based on the current agent. The entry point is set to data_gatherer, and the graph is compiled into an executable workflow that guides the automated pipeline from start to finish. def run_knowledge_graph_pipeline(topic): print(f"🚀 Starting knowledge graph pipeline for: {topic}") initial_state = { "topic": topic, "raw_text": "", "entities": [], "relations": [], "resolved_relations": [], "graph": None, "validation": {}, "messages": [HumanMessage(content=f"Build a knowledge graph about {topic}")], "current_agent": "data_gatherer" } kg_app = build_kg_graph() final_state = kg_app.invoke(initial_state) print(f"✨ Knowledge graph construction complete for: {topic}") return final_state The run_knowledge_graph_pipeline function initializes the pipeline by setting up an empty state dictionary with the provided topic. It builds the workflow using build_kg_graph(), then runs it by invoking the compiled graph with the initial state. As each agent processes the data, the state evolves, and the final result contains the complete knowledge graph, validated and ready for use. if __name__ == "__main__": topic = "Artificial Intelligence" result = run_knowledge_graph_pipeline(topic) visualize_graph(result["graph"]) Finally, this block serves as the script’s entry point. When executed directly, it triggers the knowledge graph pipeline for the topic “Artificial Intelligence,” runs through all agent stages, and finally visualizes the resulting graph using the visualize_graph() function. It provides an end-to-end demonstration of automated knowledge graph generation. Output Generated from Knowledge Graph Execution In conclusion, we have learned how to seamlessly integrate multiple specialized agents into a cohesive knowledge graph pipeline through this structured approach, leveraging LangGraph and NetworkX. This workflow automates entity and relation extraction processes and visualizes intricate relationships, offering a clear and actionable representation of gathered information. By adjusting and enhancing individual agents, such as employing more sophisticated entity recognition methods or integrating real-time data sources, this foundational framework can be scaled and customized for advanced knowledge graph construction tasks across various domains. Check out the Colab Notebook. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 90k+ ML SubReddit. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/Google DeepMind Introduces AlphaEvolve: A Gemini-Powered Coding AI Agent for Algorithm Discovery and Scientific OptimizationAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Rime Introduces Arcana and Rimecaster (Open Source): Practical Voice AI Tools Built on Real-World SpeechAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Step-by-Step Guide to Build a Fast Semantic Search and RAG QA Engine on Web-Scraped Data Using Together AI Embeddings, FAISS Retrieval, and LangChainAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Agent-Based Debugging Gets a Cost-Effective Alternative: Salesforce AI Presents SWERank for Accurate and Scalable Software Issue Localization

The ninth edition of the Teaching with Digital Technology Awards, a student-elected program presented annually to faculty members at the Massachusetts Institute of Technology includes MIT Media Lab instructor Zachary Lieberman (Media Arts and Sciences).  He was joined this year by his colleagues Eran Egozy (Music and Theatre Arts), Christopher Noe (Management), Anthony Pennes (Electrical Engineering & Computer Science / Mechanical Engineering), Scott Sheffield (Mathematics), Shen Shen (Electrical Engineering & Computer Science), and Eric So (Management). Lieberman, the New York-based artist with past residencies at Eyebeam and the Barcelona Hangar Center, was honored for his deft integrations of digital technology into the classroom, joining other past recipients like Ben Fry and Alex Kilian. Outside of academia, clients such as the hit indie band OK GO are among his many arts and entertainment sector clients. Extruded blob digital artwork made using openFrameworks. Image courtesy Z...

Before organizations can effectively deploy generative AI tools, the data on which they must be trained has to be preprocessed to ensure it’s organized, standardized, and cleaned from outliers, duplication, and errors. Without that, attaining ROI from any genAI project is next to impossible. In fact, over the past couple of years, case studies have shown one-in-three AI projects fail to reach completion or produce an ROI because of unclean data. A Gartner survey this week indicated that one top-level position in particular is key to genAI deployment success: the Chief Data & Analytics Officer (CDAO) — so much so, that the position has now been given executive status at most companies. (Gartner added “analytics” to the CDO title a few years ago to indicate a changing role, which now involves combining data management with analytics strategy to generate insights and business value.) “The integration of AI into business strategies has further solidified the CDAO’s role as a top-tier position within companies,” said Sarah James, a Gartner senior director analyst. Beyond data and analytics, CDAOs are now central to genAI readiness — critical for driving better business outcomes, as shown in Gartner’s 2025 CDAO Agenda Survey for 2025.“This year is a critical one for CDAOs, as AI presents a new opportunity for them to establish their rank in AI leadership,” James said. The exact mission and function of the CDAO role, however, remain unclear. In Gartner’s CDAO Agenda Survey for 2024, 74% of respondents reported that executive leadership has confidence in their data and analytics (D&A) function, but the rationale for that confidence is not fully substantiated. Only 49% had established business-outcome-driven metrics that allow stakeholders to track D&A value. “Respondents reported having an average of 14 primary responsibilities — a year-over-year increase of 56% from 2023 — suggesting a tactical rather than strategic focus,” James said. “There is clearly work to do for CDAOs to establish themselves, their function and their value to the organization.” Meanwhile, Gartner predicts that by 2027, 75% of CDAOs who aren’t seen as essential to their organization’s genAI success will lose their C-level position. CDAOs are currently uniquely positioned to lead genAI efforts thanks to their data expertise and cross-organizational reach, according to James. “Their role is pivotal — no one else is better placed to connect the dots and drive AI success,” she said. According to the survey, 70% percent of CDAO’s have the primary responsibility for building AI strategy and an operating model for the organization. In light of that, 36% of CDAOs now report to the CEO, up from 21% in 2024. Tom Davenport, a distinguished professor of IT and management for Babson College, said he was surprised Gartner’s survey found 70% of CDAOs reportedlyhave primary responsibility for genAI efforts. His research shows it’s usually shared with CIOs, CTOs, and CDOs. If trues, he said, it would mark a major shift in the CDO/CDAO role, even as AI is a “hot topic” many execs want to lead. “That said, responsibility for AI will have its challenges for CDOs, who already face a number of them,” Davenport said via email. “An IBM survey of CEOs released last week found that only 25% have seen the expected ROI from their AI projects. It won’t be easy for CDOs to address that issue.” At the same time the CDAO or CDO role is gaining importance, only 8% of companies considered to be leaders in genAI deployments are scaling the tech at an enterprise level, which means embedding the technology into core business strategy, according to a report by professional services firm Accenture. Scaling AI starts with data, according to Accenture, and while 70% of the 2,000 companies it surveyed recognize its importance, few fully leverage their proprietary data. And only 15% are “AI reinvention-ready” and have built the essential capabilities needed to scale AI successfully, according to the firm. The role of CDAO role is expected to soon evolve in three distinct directions, according to Gartner: Expert D&A Leader: A senior data leader (often a vice president or head of data) with a cross-functional view, overseeing business intelligence, Mobile Device Management, reporting, and data platforms. The person typically reports to IT and supports data strategy across the business. Connector CDAO: Someone who bridges CxOs with D&A, embedding smart solutions into products and services. They drive the AI agenda and act as key leaders in an organization. Pioneer CDAx: A transformational exec who combines the CDO, CDAO, and CDAIO roles, leads cross-functional D&A and AI efforts, ensures ethical governance, and champions innovation. Some believe CDO and CAIO roles might have a built-in expiration date. An earlier Gartner survey of 400 software engineering leaders indicated that up to half of their software development teams employ various genAI tools to augment workflows, acting as force multipliers rather than replacements. Davenport agreed, and has written about the phenomenon in the past. After years of being focused primarily on data — not employee — management, surveys show CDOs often lack the skills to tackle the cultural challenges critical to a data-driven culture, and their roles remain less understood than other C-level positions, Davenport said. “Surveys of CDOs also suggest that their jobs are less well understood than other C-level executives,” he added. In one recent article, Davenport argued there are too many technology-focused C-level executives in many companies, including CIOs, CTOs, Chief Data and/or Analytics Officers, Chief Digital Officers, and CISOs. “The CIO role is much better understood and while it, too, once was characterized by short tenures, it’s much more familiar than the CDO role,” he said. “Many CDOs focus primarily on data governance and management activities for which it is difficult to demonstrate value. Those who also have analytics and AI responsibilities have a somewhat easier time of that.” Given the critical role now of genAI in shaping business strategies, aspiring CDAOs should focus on developing expertise in data, analytics and AI technologies, James said. The demand for CDAOs who can effectively lead genAI initiatives, navigate the technology’s complexities, and leverage it for competitive advantage is increasing, she added. “Individuals interested in this career path should aim to acquire skills in AI readiness, data governance, and strategic leadership, as these are essential for driving successful AI integration within organizations,” she said.