AI startup Reflection AI has developed an autonomous agent known as Asimov. It has been trained to understand how software is created by ingesting not only code, but the entirety of a business’ data to try to piece together why an application or system does what it does. Co-founder and Chief Executive Misha Laskin said that Asimov reads everything from emails to slack messages, project notes to documentation, in addition to the code, to learn everything about how and why the app was created. He explained that he believes this is the simplest and most natural way for AI agents to become masters at coding. Asimov is actually a collection of multiple smaller AI agents that are deployed inside customer’s cloud environments so that the data remains within their control. Asimov’s agents then cooperate with one another to try and understand the underlying code of whatever piece of software they’ve been assigned to, so they can answer any questions that human users might have about it. There are several smaller agents designed to retrieve the necessary data, and they work with a larger “reasoning” agent that collects all of their findings and tries to generate coherent answers to user’s questions.
Google’s AI agent discovers a critical SQLite vulnerability using threat intelligence and was able to actually predict that it was imminently going to be exploited for carrying out a cyberattack
Google is introducing a new approach to cybersecurity, focusing on artificial intelligence as the first line of digital defence. The company has introduced Big Sleep, an AI agent developed by Google DeepMind in collaboration with Project Zero, which has successfully stopped a security threat before it was launched. The tool is also being used to strengthen the security of open-source software, increasing defensive coverage. Google’s secure-by-design approach emphasizes human oversight, transparency, and privacy. In addition to Big Sleep, Google is infusing AI into its security infrastructure: Timesketch, the company’s open-source forensics platform, now integrates Sec-Gemini-powered agents to automate incident response. FACADE, an insider threat detection tool, processes billions of security events each day using contrastive learning, without relying on historical attack data. A new AI-assisted Capture the Flag challenge at DEF CON 33 will give participants hands-on experience working alongside AI agents in real-time cyber defence scenarios.
Analog Devices AI tool automates the end-to-end machine learning pipeline for edge AI, including model search and optimization using state-of-the-art algorithms and verifies model size against the device’s RAM to enable successful deployment
Analog Devices Inc. (ADI) has introduced AutoML for Embedded, an AI tool that automates the end-to-end machine learning pipeline for edge AI. The tool, co-developed with Antmicro, is now available as part of the Kenning framework, integrated into CodeFusion Studio. The Kenning framework is a hardware-agnostic and open-source platform for optimizing, benchmarking, and deploying AI models on edge devices. AutoML for Embedded allows developers without data science expertise to build high-quality and efficient models that deliver robust performance. The tool automates model search and optimization using state-of-the-art algorithms, leveraging SMAC to explore model architectures and training parameters efficiently. It also verifies model size against the device’s RAM to enable successful deployment. Candidate models can be optimized, evaluated, and benchmarked using Kenning’s standard flows, with detailed reports on size, speed, and accuracy to guide deployment decisions. Antmicro’s Michael Gielda, VP Business Development, said that AutoML in Kenning reduces the complexity of building optimized edge AI models, allowing customers to take full control of their products. AutoML for Embedded is a Visual Studio Code plugin built on the Kenning library that supports: ADI MAX78002 AI accelerator MCUs and MAX32690 devices — deploy models directly to industry-leading edge AI hardware. Simulation and RTOS workflows — leverage Renode-based simulation and Zephyr RTOS for rapid prototyping and testing. General-purpose, open-source tools — allowing flexible model optimisation without platform lock-in
DebitMyData’s platform combines reinforcement learning with blockchain-verified digital identity to offer real-time detection and mitigation of unauthorized AI-generated content, impersonation, and biometric spoofing at scale
DebitMyData, founded by digital sovereignty pioneer Preska Thomas, has launched its LLM Security API Suite, a next-generation platform that combines reinforcement learning with blockchain-verified digital identity. The suite offers the first plug-and-play APIs for Agentic Logos and Agentic Avatars, designed to secure AI at scale across commercial and regulatory settings. The interoperable identity infrastructure enables verification of authenticity and trust in AI outputs. The platform’s reinforcement learning core dynamically adapts to evolving AI manipulation techniques, delivering: Real-time detection and mitigation of unauthorized AI-generated content, impersonation, and biometric spoofing; Built-in global compliance with GDPR, HIPAA, AI Act, and digital sovereignty protocols, ensuring enterprise-ready, auditable privacy. Plug-and-Play Enterprise Security: Agentic Logos™: Secure your brand’s logos with a blockchain-verified fingerprint, enabling instant scanning and flagging of unauthorized usage across AI platforms—with zero technical barriers and GDPR-first privacy controls. Agentic Avatars™: Convert faces and voices into secure, self-authenticating digital signatures, verified via NFT credentials for safe identity gating in synthetic communications.
Delta Air Lines is testing new AI-based dynamic (“surveillance”) pricing system that tailors fares to individual customers based on the personal data collected; with plans to expand it to 20% by the end of the year
Delta Air Lines is testing a new AI pricing system that tailors fares to individual customers, a move that could reshape how airline tickets are sold and priced. The system, developed in partnership with Israeli startup Fetcherr, is already being used on 3% of Delta’s flights, with plans to expand it to 20% by the end of the year. Personalized pricing — or surveillance pricing as the Federal Trade Commission (FTC) calls it — is pricing tailored to the individual based on the personal data collected. That’s different from dynamic pricing, which is determined by market factors such as real-time supply and demand and pricing by competitors. While the price changes, everyone sees the same price at a given time. Airlines, ride-sharing and other companies already use dynamic pricing. In a nutshell, dynamic pricing changes based on when a consumer buys. Personalized pricing changes based on who the consumer is. Delta seeks to gain a “first-mover advantage,” President Glen Hauenstein added. “We do believe that we are ahead of our competitors in terms of implementing this and in changing our business processes and rules around it.” Ultimately, this is “a full reengineering of how we price — and how we will be pricing in the future,” Hauenstein said.
FedNow enters year three with increasing users, volumes, and competition, reporting a whopping 1,200% year-over-year increase in transaction volume, growing from 97,424 settled payments in the first quarter of 2024 to 1,310,017 in the quarter ending March 31, 2025
FedNow, which turned two years old on July 20, reported a whopping 1,200% year-over-year increase in transaction volume, growing from 97,424 settled payments in the first quarter of 2024 to 1,310,017 in the quarter ending March 31, 2025. At its one year anniversary last July, the Fed had enrolled more than 850 financial institutions into FedNow and had at least 1,000 more in the pipeline. But as of July 7, more than 1,400 financial institutions—including large and small banks and credit unions—were participating in the FedNow Service. Community banks and credit unions make up more than 95% of the platform’s total participants. That all sounds impressive until you look at data recently reported by competitors RTP and Zelle. The RTP network, which is operated by The Clearing House and owned by multiple large banks, makes FedNow’s transaction volume look tiny by comparison. RTP handles more than 1 million daily transactions and, in fact, set a new single-day record for payments volume at nearly 1.6 million transactions on Jan. 31. The RTP network currently reaches 70% of demand deposit accounts in the United States, meaning that millions of consumers and businesses are benefiting from instant payments through the 850 financial institutions connected to the network. Tim Scholten, founder and president of the credit union and community bank consultancy Visible Progress, told Tyfone that FedNow adoption is growing but slowly.
Clear Junction’s on-chain stablecoin transfer service allows fintechs and regulated FIs to send, receive and convert stablecoins starting with USDC (Circle) and USDT (Tether) across the Ethereum, Solana, and Tron blockchain networks
Clear Junction, a specialist in global payments and banking infrastructure for regulated financial institutions, has launched a new on-chain stablecoin transfer service as part of its fast-growing digital assets division. The service allows clients to send, receive and convert stablecoins – starting with USDC (Circle) and USDT (Tether) – across the Ethereum, Solana, and Tron blockchain networks. The new solution provides an essential bridge between traditional banking infrastructure and the rapidly evolving world of blockchain-based finance. It is the first product in a wider suite of digital asset services being developed by Clear Junction to meet increasing demand from fintechs, payment service providers (PSPs), and regulated financial institutions. Clear Junction’s new product directly addresses this gap. With the rise of digital asset adoption across both developed and emerging markets, the launch positions the company to serve a growing segment of businesses seeking secure access to blockchain-based payments – from crypto exchanges and fintechs to regulated e-money institutions (EMIs) and remittance providers. Key features include: Support for Ethereum, Solana, and Tron networks; Send, receive, and convert functionality; Available via platform interface; and Full compliance controls and transaction visibility.
Splitit taps Antom’s payment processing platform to enable merchants to offer card-linked installment payment option using consumers’ existing credit and without requiring credit checks, applications and any change to their credit card relationship
Splitit and Ant International’s Antom have partnered to enable merchants to offer their customers a card-linked installment payment option. The companies will launch this offering in the United States and then expand it globally. This payment option is powered by Splitit’s embedded white-label platform that enables card-linked installment payments using consumers’ existing credit, and Antom’s payment processing platform and merchant network. For consumers, the card-linked installment payment option will provide purchasing flexibility, with no credit checks, no applications, no hidden fees and no change to their credit card relationship. For merchants, it will provide a way to offer flexible payments at scale, with less complexity and higher approval rates. This option will help improve conversion rates and increase average order value. Splitit and Antom will also simplify international payment processing for merchants who engage in cross-border commerce. “This collaboration supports our commitment to helping merchants achieve sustainable growth across international markets,” Gary Liu, general manager of Antom at Ant International, said
ONDWallet multi-chain platform to incorporate robust KYC and AML controls, verifiable credentials, and permissioned layers for managing identity-bound wallets to enable compliant transactions in tokenized RWAs
The tokenization of real-world assets is expected to reach $16 trillion by 2030, with the market for Real-World Assets (RWAs) expected to reach $16 trillion. However, existing tokenization platforms are often designed for speculative crypto trading, lacking compliance and control features. ONDWallet aims to address this by incorporating robust KYC and AML controls, verifiable credentials, and permissioned layers for managing identity-bound wallets. The platform supports various assets, including tokenized real estate, debt instruments, commodities, NFTs, and standard cryptocurrencies. It is natively multi-chain, with integrations with Ethereum, Polygon, Avalanche, and protocols like Centrifuge, Maple, and Ondo Finance. ONDWallet’s dynamic oracle integration keeps off-chain data updated and accessible, ensuring data integrity and privacy. The $ONDW token supports the ecosystem, serving two core functions: paying for in-app services and staking for platform revenue. The project is currently in its Private Sale and Beta Rollout phase, with plans for a mainnet launch and public sale in Q3-Q4 2025.
Digital marketing platform for financial advisors Wealthtender can automatically structure FAQ content to be more easily surfaced in Google AI Overviews and as direct answers in AI tools by embedding FAQ schema on advisor websites and profiles
Wealthtender, a digital marketing platform for financial advisors and wealth management firms, announced the launch of AI-Optimized FAQs, extending its range of features that play a valuable role in Search Engine Optimization (SEO) and Answer Engine Optimization (AEO). By embedding FAQ schema, a specialized code recognized by search engines and answer engines, Wealthtender automatically structures FAQ content to be more easily surfaced in Google AI Overviews and as direct answers in AI tools. Brian Thorp, Wealthtender founder and CEO. “With traditional search engines evolving to include AI Overviews and the rapid adoption of AI-powered tools like ChatGPT and Gemini, FAQs published on advisor websites and Wealthtender profiles, especially when enhanced with FAQ schema, are more powerful than ever for building trust, visibility, credibility, and increasing the likelihood of an advisor landing on a prospect’s shortlist.” Upon activation of the AI-Optimized FAQs feature, advisors can publish up to 10 questions and answers on their Wealthtender profiles that showcase their expertise and areas of specialization, address common questions, and appear more prominently when prospective clients use Google, ChatGPT, Gemini, and other AI search tools to find and evaluate financial advisors.