Walmart is expanding its AI offerings by introducing a new shopping assistant. Customers can now access the Gen AI-powered “Sparky” on the Walmart app. “Sparky helps customers search to find items, synthesize reviews, and offers insights to prepare for any occasion — from looking up current sporting events and finding the right jersey to planning celebrations and picking out the perfect toy,” Walmart said. In addition to making recommendations, Sparky also provides instant and comprehensive answers to product-related questions, helping customers quickly understand specific features, compare items and make informed choices. Soon, Sparky will do even more — giving customers the power to customize their experience, from automatically reordering household essentials to booking services that simplify even the most complex shopping tasks. Walmart said the assistant will be multi-modal (able to understand text, images, audio and video), “seamlessly weaving into customers’ lives to unlock instant access to the products and services they need, whenever and however they shop.”
FINNY unveils intent search to help advisors pinpoint high-intent prospects faster based on real-time online behavior; advisors can select keywords related to their services
FINNY the AI-powered prospecting and marketing platform built specifically for financial advisors, has launched Intent Search, a feature that allows advisors to identify and engage with prospects actively seeking financial guidance. Powered by 1.8 billion proprietary intent signals that are updated daily, it enables advisors to surface high-intent prospects based on real-time online behavior. Advisors can select keywords related to their services. FINNY identifies prospects who have recently researched those topics, pinpointing what they’re interested in and when they were actively searching. FINNY has also released its Prospect Enrichment and AI Voicemails features. Prospect Enrichment enables advisors to upload external contacts and automatically matches them to FINNY’s database. Meanwhile, AI Voicemails allow advisors to deliver ringless, personalized voicemails at scale. They can select from multiple voice options to suit their preferences, and messages are able to circumvent spam filters. Each voicemail can be paired with a follow-up email to create efficient outreach that retains a human touch. Since its launch, the Prospect Enrichment feature has already led to the upload and enrichment of more than 8,000 prospects, signaling strong demand and immediate value.
Nasdaq’s private company dataset and API to deliver real-time pricing on private, pre-IPO companies, integrating primary round, secondary transactions, and accounting data
Nasdaq has partnered with Nasdaq Private Market (NPM) to provide greater price transparency and valuation visibility into private, pre-IPO companies, including unicorns and startups. The Tape D private company dataset, available through API integration via Nasdaq Data Link, addresses critical transparency challenges by helping investors evaluate private holdings with greater confidence, enabling banks to structure private transactions more effectively, supporting wealth advisors and shareholders in managing liquidity needs, and equipping private companies with valuable insights for capital raises and tender offers. The comprehensive data product delivers real-time private market pricing by integrating primary round data, secondary market transactions, and accounting data. The launch of this data partnership marks the latest step in Nasdaq’s commitment to enhancing transparency, access, and portfolio management capabilities across the public-to-private investment spectrum.
Apple debuts new user design experience Liquid Glass, which will bring greater focus to content, deliver a new level of quality to controls and keep users more attuned to what’s happening on screen “harmonizing” the user experience across all devices
Apple previewed a slick new software design and powerful software updates, including new features coming to its next-generation operating systems across devices that will receive a unified version 26. The new design features a new material called Liquid Glass, which creates a translucent effect similar to water that sits atop the display, refracting content below it and allowing colors to flow through. The company says this will bring greater focus to content, deliver a new level of quality to controls and keep users more attuned to what’s happening on screen. The new design extends across Apple’s entire device ecosystem, including iOS 26, iPadOS 26, macOS Ventura 26, watchOS 26 and tvOS 26. The company said the idea was to “harmonize” the user experience across all devices so they could expect every device to look and feel the same. It will affect buttons, switches, sliders, text and media in the user interface and shift dynamically according to user needs. Controls, toolbars and navigation within apps have been redesigned with rounded corners and they “float above” content so that they stay out of the way and avoid interrupting content. They also shift into thoughtful groupings, allowing users to find the controls they need. The Preview app, which originally comes from macOS, is coming to iPadOS 26. Preview is a dedicated app for creating a quick sketch, as well as viewing, editing and marking up PDFs and images with Apple Pencil or by touch.
Hirundo’s approach to AI hallucinations is about making fully trained AI models forget the bad things they learn, so they can’t use this mistaken knowledge
Hirundo AI Ltd., a startup that’s helping AI models “forget” bad data that causes them to hallucinate and generate bad responses, has raised $8 million in seed funding to popularize the idea of “machine unlearning.” Hirundo’s approach to AI hallucinations is about making fully trained AI models forget the bad things they learn, so they can’t use this mistaken knowledge to generate their responses later on, down the line. It does this by studying the behavior of AI models in order to locate the directions users can go in order to manipulate them. It identifies any bad traits, then investigates the root cause of those bad outputs, before steering the model away from them. It pinpoints where hallucinations originate from in the billions of parameters that make up their knowledge base. This retroactive approach to fixing undesirable behaviors and inaccuracies in AI models means it’s possible to improve their accuracy and reliability without needing to retrain them. That’s a big deal, because retraining models can take many weeks and cost thousands or even millions of dollars. “With Hirundo, models can be remediated instantly at their core, working toward fairer and more accurate outputs,” Chief Executive Ben Luria added. Besides helping models to forget bad, biased or skewed data, the startup says it can also make them “unlearn” confidential information, preventing AI models from revealing secrets that shouldn’t be shared. What’s more, it can do this for both open-source models such as Llama and Mistral, and soon it will also be able to do the same for gated models such as OpenAI’s GPT and Anthropic PBC’s Claude. The startup says it has successfully managed to remove up to 70% of biases from DeepSeek Ltd.’s open-source R1 model. It has also tested its software on Meta Platforms Inc.’s Llama, reducing hallucinations by 55% and successful prompt injection attacks by 85%.
Amperity vibe coding AI agent connects directly to the customer’s Databricks environment via native compute and LLM endpoints to quickly execute complex tasks such as identity resolution
Customer data cloud startup Amperity Inc. is joining the agentic AI party, launching Chuck Data, an AI agent that specializes in customer data engineering. Chuck Data is trained on massive volumes of customer data from more than 400 enterprise brands. This “critical knowledge” base allows it to execute tasks such as identity resolution and personally identifiable information tagging autonomously and instantly resolve customer identities, with minimal input from human developers. The agent is designed to help companies dig up customer insights much faster. Chuck Data makes it possible for data engineers to embrace “vibe coding,” so they can use natural language prompts to delegate these manual coding tasks to an autonomous AI assistant. The company said Chuck Data connects directly to the customer’s Databricks environment via native compute and large language model endpoints. Then it can quickly execute complex tasks such as identity resolution – which involves pulling data from multiple profiles into one – as well as compliance tagging and data profiling. One of Chuck Data’s core features is Amperity’s patented identity resolution algorithm, which is based on the proprietary Stitch technology that’s used within its flagship cloud data platform. The company said users can run Stitch on up to 1 million customer records for free, and for those with bigger records, they can sign up to Chuck Data’s research preview program to access free credits. It’s also offering paid plans that unlock unlimited access to Stitch, enabling companies to create millions of accurate, scalable customer profiles. huck Data provides yet more evidence of how CDPs are evolving from activation tools into embedded intelligence layers for the customer engagement data value chain.
Research shows latest large reasoning models (LRMs) experience “complete accuracy collapse”, often dropping to zero successful solutions beyond a certain point, when faced with highly complex tasks
The latest large reasoning models (LRMs) experience “complete accuracy collapse” when faced with highly complex tasks, according to a new paper co-authored by researchers from Apple. Researchers used controllable puzzles like the Tower of Hanoi, Checkers Jumping, River Crossing and Blocks World, allowing them precise control over the difficulty of the puzzles by adding more disks, checkers, people or blocks, while keeping the basic rules the same. This allowed them to see exactly when and how the AI’s reasoning broke down as problems got harder. As puzzle complexity increased, the performance of these frontier LRMs didn’t just get a little worse; it suffered a “complete accuracy collapse,” often dropping to zero successful solutions beyond a certain point.The researchers found that as the problems approached the point where the AI started failing, the LRMs began to reduce their reasoning effort, using fewer “thinking” steps or tokens, pointing to a fundamental limit in how they handle increasing difficulty. On simple problems, the LRMs sometimes found the correct answer early but kept exploring wrong solutions — a form of “overthinking” that wastes effort. On harder problems, correct solutions appeared later, if at all. Beyond the collapse point, no correct solutions were found in the thinking process. The study concluded that these findings point to fundamental limitations in how current LRMs tackle problems. While the “thinking” process helps delay failure, it doesn’t overcome these core barriers. The research raises questions about whether simply adding more “thinking” steps is enough to achieve truly general AI that can handle highly complex, novel problems.
Starling Bank’s Gemini-powered AI tool allows customers to use natural language in-app to directly interact with their spending data; transactions are listed by retailer and automatically sorted into over 50 customizable categories
Starling Bank launched an AI tool which helps customers better understand their spending habits. The first-of-its-kind feature, called ‘Spending Intelligence’, allows customers to ask questions about their money. This is the first time a bank has given customers the opportunity to use AI and natural language in-app to directly interact with their spending data. It’s the first phase of Starling’s wider plans to implement AI across customer touch points. “Customers can use AI to feed their natural curiosity about their finances so that they can make informed decisions about their budgeting, and better utilise Starling’s suite of money management tools, ” said Harriet Rees, CIO of Starling Bank. “By leveraging the power of Gemini and the secure, scalable infrastructure of Google Cloud, Starling is creating tangible value for its customers and empowering them with greater financial understanding. This is a brilliant example of how AI can be applied responsibly and effectively in the financial services sector,” said Graham Dury, Director, FSI, Google Cloud in the UK and Ireland. Transactions are listed by retailer and automatically sorted into over 50 customizable categories such as bills, transport, and groceries, making it easy to track spending patterns over time. For example, you can see how much you’ve spent at a specific retailer or on a category like dining out. A graph and detailed analytics show spending habits, breaking down individual payments within each category. This addresses a common limitation in banking apps where payments are visible but not easily analyzed.
Pagos launches first MCP server to allows LLMs and AI-powered tools to securely query real-time BIN-level card data directly from card networks
Pagos, an all-in-one provider of payments optimization solutions, today announces the launch of its first Model Context Protocol (MCP) Server, making it easier than ever for AI agents and applications to query the payments intelligence available only through Pagos. This MCP Server, designed for their direct-to-network BIN data solution, allows large language models (LLMs) and AI-powered tools to securely query real-time BIN-level card data directly from card networks. Developers and businesses can now plug this data directly into their AI workflows, making it easier than ever to: Get to know your customer base with card details like issuer, card brand, and country data; Design routing strategies around preferable approval rates and processing costs; Finetune your retry strategy; Create BIN-based fraud rules to identify and block issuing banks or specific BINs frequently associated with fraud or carding attacks; Reduce your interchange costs by identifying when a customer’s card qualifies for Level II or III data; Prototype AI tools without building a custom connector to Pagos.
Conferma’s partnership with JP Morgan Payments Network to enable businesses to issue virtual cards and make multi-currency payments via API that integrates directly with ERP, procurement, and expense management platforms
Conferma has expanded its strategic membership in the J.P. Morgan Payments Partner Network to include J.P. Morgan Payments’ virtual card solutions as part of its European offerings. This expansion offers European-based businesses the opportunity to issue and manage virtual cards effortlessly, unlocking multi-currency capabilities and providing greater flexibility to streamline payments across the region. J.P. Morgan Payments’ virtual card services offer businesses in Europe the opportunity to access virtual card services and benefit from trusted payment capabilities—simplifying procurement, boosting cash flow, and enhancing security. What this means for businesses: Expanded payment freedom: Corporates can issue virtual cards, eliminating international barriers and empowering businesses with greater global agility. Multi-currency efficiency: Virtual cards enable payment in local currencies, reducing FX costs and improving payment efficiency. Seamless integration: Conferma’s API technology integrates directly with ERP, procurement, and expense management platforms, making adoption effortless and quick. Stronger financial oversight: Businesses gain more visibility, control, and fraud protection, enhancing compliance and security. By combining J. P. Morgan Payments’ virtual card solutions through the J.P. Morgan Payments Partner Network with Conferma’s seamless technology, businesses can accelerate payments, enhance security, and cut operational costs.