AI agents have been all the rage over the last several months, which has led to a need to come up with a standard for how they communicate with tools and data, leading to the creation of the Model Context Protocol (MCP) by Anthropic. MCP is “an open standard that enables developers to build secure, two-way connections between their data sources and AI-powered tools,” Anthropic wrote in a blog post announcing it was open sourcing the protocol. MCP can do for AI agents what USB does for computers, Lin Sun, senior director of open source at cloud native connectivity company Solo.io, explained. According to Keith Pijanowski, AI solutions engineer at object storage company MinIO, an example use case for MCP is an AI agent for travel that can book a vacation that adheres to someone’s budget and schedule. Using MCP, the agent could look at the user’s bank account to see how much money they have to spend on a vacation, look at their calendar to ensure it’s booking travel when they have time off, or even potentially look at their company’s HR system to make sure they have PTO left. MCP consists of servers and clients. The MCP server is how an application or data source exposes its data, while the MCP client is how AI applications connect to those data sources. MinIO actually developed its own MCP server, which allows users to ask the AI agent about their MinIO installation like how many buckets they have, the contents of a bucket, or other administrative questions. The agent can also pass questions off to another LLM and then come back with an answer. “Instead of maintaining separate connectors for each data source, developers can now build against a standard protocol. As the ecosystem matures, AI systems will maintain context as they move between different tools and datasets, replacing today’s fragmented integrations with a more sustainable architecture,” Anthropic wrote in its blog post.
A new HPC architecture with “bring-your-own-code” (BYOC) approach would enable existing code to run unmodifieD; the underlying technology adapts to each application without new languages or significant code changes
There’s now a need for a new path forward that allows developers to speed up their applications with fewer barriers, which will ensure faster time to innovation without being locked into any particular vendor. The answer is a new kind of accelerator architecture that embraces a “bring-your-own-code” (BYOC) approach. Rather than forcing developers to rewrite code for specialized hardware, accelerators that embrace BYOC would enable existing code to run unmodified. The focus should be on accelerators where the underlying technology adapts to each application without new languages or significant code changes. This approach offers several key advantages: Elimination of Porting Overhead: Developers can focus on maximizing results rather than wrestling with hardware-specific adjustments. Software Portability: As performance accelerates, applications retain their portability and avoid vendor lock-in and proprietary domain-specific languages. Self-Optimizing Intelligence: Advanced accelerator designs can continually analyze runtime behavior and automatically tune performance as the application executes to eliminate guesswork and manual optimizations. These advantages translate directly into faster results, reduced overhead, and significant cost savings. Finally liberated from extensive code adaptation and reliance on specialized HPC experts, organizations can accelerate R&D pipelines and gain insights sooner. The BYOC approach eliminates the false trade-off between performance gains and code stability, which has hampered HPC adoption. By removing these artificial boundaries, BYOC opens the door to a future where computational power accelerates scientific progress. A BYOC-centered ecosystem democratizes access to computational performance without compromise. It will enable domain experts across disciplines to harness the full potential of modern computing infrastructure at the speed of science, not at the speed of code adaptation.
The line between eCommerce and fintech is disappearing, and the future belongs to integrated ecosystems that combine seamless shopping experiences with embedded financial solutions: Analyst
Jose Daniel Duarte Camacho, a renowned eCommerce and FinTech innovator, has outlined a vision for the future of digital commerce and financial services. He believes that companies that embrace digital agility and customer-centric strategies will emerge as frontrunners in this wave of technological disruption. Duarte Camacho believes that the line between eCommerce and financial technology is disappearing, and the future belongs to integrated ecosystems that combine seamless shopping experiences with embedded financial solutions. Consumers expect speed, trust, and personalization at every touchpoint. Duarte Camacho has identified four major trends that are shaping the future of eCommerce: AI-Driven Hyperpersonalization: Retailers are using machine learning to adapt in real time to individual user behavior. Product recommendations, pricing, and content are becoming uniquely tailored to each customer—boosting conversion rates and customer satisfaction. Immersive Shopping Experiences with AR and VR: Augmented and virtual reality tools are transforming product visualization and engagement. Customers can now preview how furniture fits in a room or how a garment looks on them—without setting foot in a store. Eco-Conscious Consumer Demands: Sustainability is no longer a bonus; it’s a business imperative. eCommerce platforms that prioritize eco-friendly packaging, carbon-neutral shipping, and ethical sourcing are capturing the loyalty of a new generation of socially conscious shoppers. Conversational Commerce and Voice Technology: Voice assistants and chat-based shopping are simplifying online transactions. Duarte Camacho believes brands must optimize for voice commerce and natural language processing to remain competitive in the evolving customer interface.
Sakana’s Continuous Thought Machines (CTM) AI model architecture uses short-term memory of previous states and allows neural synchronization to mirror brain-like intelligence
AI startup Sakana has unveiled a new type of AI model architecture called Continuous Thought Machines (CTM). Rather than relying on fixed, parallel layers that process inputs all at once — as Transformer models do —CTMs unfold computation over steps within each input/output unit, known as an artificial “neuron.” Each neuron in the model retains a short history of its previous activity and uses that memory to decide when to activate again. This added internal state allows CTMs to adjust the depth and duration of their reasoning dynamically, depending on the complexity of the task. As such, each neuron is far more informationally dense and complex than in a typical Transformer model. CTMs allow each artificial neuron to operate on its own internal timeline, making activation decisions based on a short-term memory of its previous states. These decisions unfold over internal steps known as “ticks,” enabling the model to adjust its reasoning duration dynamically. This time-based architecture allows CTMs to reason progressively, adjusting how long and how deeply they compute — taking a different number of ticks based on the complexity of the input. The number of ticks changes according to the information inputted, and may be more or less even if the input information is identical, because each neuron is deciding how many ticks to undergo before providing an output (or not providing one at all). This represents both a technical and philosophical departure from conventional deep learning, moving toward a more biologically grounded model. Sakana has framed CTMs as a step toward more brain-like intelligence—systems that adapt over time, process information flexibly, and engage in deeper internal computation when needed. Sakana’s goal is to “to eventually achieve levels of competency that rival or surpass human brains.” The CTM is built around two key mechanisms. First, each neuron in the model maintains a short “history” or working memory of when it activated and why, and uses this history to make a decision of when to fire next. Second, neural synchronization — how and when groups of a model’s artificial neurons “fire,” or process information together — is allowed to happen organically. Groups of neurons decide when to fire together based on internal alignment, not external instructions or reward shaping. These synchronization events are used to modulate attention and produce outputs — that is, attention is directed toward those areas where more neurons are firing. The model isn’t just processing data, it’s timing its thinking to match the complexity of the task. Together, these mechanisms let CTMs reduce computational load on simpler tasks while applying deeper, prolonged reasoning where needed.
Jenius Bank surpassed $2 billion in deposits with its no-fee ‘evolved banking’ approach, centered on providing personalized financial insights from account aggregation
Jenius Bank has surpassed $2 billion in deposits by focusing on “evolved banking” — providing personalized financial insights through account aggregation while eliminating fees to help customers gain financial confidence and make better decisions. John Rosenfeld, President of Jenius Bank, a division of SMBC MANUBANK said, “We developed two concepts within a paradigm, if you will, where there’s core banking, which is what every bank does, allows you to put money with them, allows you to go online, see how much you have, see how much you’re earning, allows you to move money in and out, review your statements, read your terms and conditions, all that stuff that every bank does. We call that core banking. We developed the concept of evolved banking, which encompasses everything beyond core features that not every bank offers. And we grouped all this into something we call the Jenius views. So, if you download our mobile app, you’ll find this tab at the bottom. And within this space, you’re able to link your accounts from other banks, other brokerages. You can view credit cards and your entire financial picture in one place. Again, this allows the consumer to give us access to their other information, enabling us to consolidate and provide them with valuable insights. While there are some banks that are doing this, what we call aggregation services, many of them are doing it to gain a view of the customer’s financial situation and then potentially use that information to try to figure out what else they can sell them. We took a different approach. We said, what if we used all that information to actually give customers insights and help them avoid fees, making smarter and more confident financial decisions? Now, why would a bank do such a thing that’s not necessarily going to bolster their profits? We thought about this and concluded that if we could establish a new level of trust with consumers, the next time they have a financial need, we hope they’ll come back to us first. The idea that money is such an emotional driver that it has nothing to do with how much you make or don’t make, but rather whether you are making good decisions. With the capabilities that are evolving in the data space and analytics and machine learning and AI, if a consumer gives someone full access to every penny that they have and not access to move the money, but access to the information, think of how much you can do with technology to identify those things that you may not have noticed. The lack of fees on our savings or a loan product was really driven by wanting to create something better and more compelling than what’s available in the industry. We created a bank that’s incredibly efficient because we don’t have buildings, we don’t have paper, we don’t mail things. So, we don’t spend any money on postage. The target was really what we call high-potential digital optimizers. And we call it that because high potential means they’re going somewhere and are ambitious. They want to progress in building a better lifestyle and achieving more.
Stash’s advanced AI-powered financial guidance platform translates expert-level investing strategies into real-time, personalized recommendations; 1 in 4 customers who interact with Money Coach AI go on to take a positive action, within 10 minutes of interaction
Stash has secured $146 million in a Series H funding round to deepen its investment in AI for its financial guidance platform. The investment will accelerate product innovation, drive subscriber growth, and further develop Stash’s AI capabilities. Central to this strategy is Money Coach AI, an advanced financial guidance platform that translates expert-level investing strategies into real-time, personalized recommendations for everyday users. Money Coach AI has already reshaped how millions of Americans engage with their money and think about their personal finances. From helping customers pick their first investment to providing personalized diversification guidance, Money Coach AI helps customers get started and make saving and investing a habit that sticks. With 2.2 million user interactions already, Money Coach AI will serve as the cornerstone of Stash’s renewed commitment to help users build savings, invest consistently, and make smart financial decisions. Notably, 1 in 4 customers who interact with Money Coach AI go on to take a positive action, such as making an investment, depositing funds, diversifying, or turning on or adjusting Auto-Stash, within 10 minutes of interaction, demonstrating its tangible impact on behavior. Through its scalable approach, Stash is demonstrating that AI can do more than automate; it can empower users by helping them make informed financial decisions in real-time.
FINRA is considering lightening the “heightened supervisory plans” over messages sent using WhatsApp and other off-channel communications
FINRA is looking to lighten the supervision burden on nearly 80 firms that reached settlements before the start of the year over messages sent using WhatsApp and other texting systems. Financial Industry Regulatory Authority executives said they’re considering revisions to the “heightened supervisory plans” that 77 industry firms were subjected to as part of settlements reached over their use of so-called off-channel communications. Concerns over fairness gave rise to FINRA’s proposal to modify the regulatory requirements imposed on firms that reached settlements pre-2025. FINRA’s blog post, written by CEO Robert Cook and Executive Vice President Greg Ruppert, notes that firms that reached settlements after the start of this year were subject to far less onerous terms. Those companies, which included Charles Schwab, Blackstone and the private equity giant KKR, avoided various other mandates imposed on other firms. They, for instance, don’t have to file an application to continue their membership in FINRA and agree to a heightened supervision plan (HSP) meant to prevent further violations. FINRA’s blog cautions that the contemplated changes won’t make things equal between firms that reached settlements this year and those that did before. “FINRA cannot do that because of the differences built into the SEC settlements,” according to the blog. “In addition, under applicable rules FINRA cannot eliminate the HSPs altogether for the pre-2025 settling firms.” Cook and Ruppert wrote in FINRA’s blog that they were initially planning to ask the SEC to eliminate heightened supervision plans for member firms fined for off-channel violations. But that can’t be done now that the SEC has rejected the request to modify the initial settlements. FINRA, a self-regulatory organization deputized by the SEC to oversee the brokerage industry, has no power to alter SEC deals on its own.
How Ally Bank built a customer-first digital experience
In an era where differentiation in banking is increasingly difficult, Ally Bank has emerged as a leader in creating exceptional digital banking experiences. Sathish Muthukrishnan, chief information and technology officer at Ally Financial said, “The intent behind launching our technology strategy was to ensure that technology will continue to be relevant in an all-digital bank, but more importantly, to create differentiation and drive significant business outcomes. We categorized our strategy into six different pillars. The first is security. Our second pillar was driving tremendous experiences. The third pillar is how I know my experience is working. That’s when data analytics came in. Measure what consumers do, but more importantly, measure what they don’t do. Our operational pillar involved migrating to cloud, driving automation and consistency in how we develop and deploy code. And then we needed to preserve our culture and take care of our talent. These pillars laid the foundation for our transformation. We now have about 75% of our applications running on the cloud and about 95% of the enterprise data in the cloud. This allows us to learn from consumer behaviors, understand what they’re expecting and create experiences in real time so consumers think they are our only customer. We had our cloud strategy and data in the cloud warehouse. At the beginning of 2022, we redefined our network. As we were thinking about AI, we launched our chat assistant, Ally Assist. We created Ally AI because we knew technology was fast-evolving, but there were concerns about sending data to external LLMs. To address this, we built an AI platform that could connect to external LLMs but with added security — it removes PII, tracks all transactions and rehydrates PII for context. Our platform can connect to multiple LLMs — from GPT to FLAN to Bedrock. We can pick the right LLM depending on the use case or combine answers from several LLMs. Our content creation LLM is different from what we use for code generation or risk assessment. We have different models for different use cases. My advantage is that the product team, UI/UX team and technology team are all part of the same technology organization. We rolled out savings buckets — your deposit account with multiple savings buckets that you can name yourself. If you start questioning why roadblocks exist and how to solve them, your brand becomes more relevant to consumers. You become their next best experience, deepening relationships.”
Marqeta is focusing on growing non-Block business, launching embedded finance offerings via white label app and offering personalized card benefits and rewards
Marqeta’s Interim CEO Mike Milotich wants to boost the processor’s non-Block revenue and expand its embedded finance offerings via a new mobile app for customers. “We have several innovative things that we’re doing, and we are constantly growing the capabilities we offer. For us it’s two things our investors are clamoring for. We have one very large customer, Block. This past quarter, they were 45% of our revenue. We’re making progress growing our non-Block business, and so I would say that’s an area of focus: How quickly is our non-Block business growing? Our non-Block revenue is growing more than 10 points faster than our Block revenue. That’s something investors are watching. We still think there’s a lot of growth opportunity with Block and so it’s more, how quickly can we diversify the business outside of that? Another thing investors want to see is how successfully we move into embedded finance. How successfully were we able to attract these different kinds of companies and get them on our platform and show the difference that we can make, versus maybe what has been done in the past. We still think there’s a lot of opportunity at Block and a lot of ways we can help them. We don’t necessarily target that percentage. We more target non-Block growth. We want to drive non-Block growth, but if Block grows really fast, that’s not a bad thing on our platform. We support Square, we support Afterpay, we support Cash App. They’ve talked a lot about getting Afterpay inserted more into Cash App directly. So there’s things that we can help them with. They haven’t done a true credit card yet, and these are all things that they could do seamlessly with us. In addition to that, we obviously are trying to grow our business as much as we can with other partners. We’re happy integrating with multiple players. hey’re looking for a more holistic offering. We’re providing a lot more value-added services, risk services that they can adopt. We announced we’re building a white label app, so that the decision maker for this card program, one day they might want to embed it into their existing company app, but that takes a lot of selling internally to get people to commit resources that way. So a lot of them are saying, I’m going to build a standalone app that looks and feels and easily syncs with our main app. But it can allow me to get traction in the market, and then maybe one day later, embed it directly. I think the big change that we see coming, that a lot of people are interested in, is where you would do more from their experience. You’re inside their app, and there are things you can do. If you were an airline, why wouldn’t you be able to do some things directly from their application, as opposed to leaving it, and so I think that’s personalized rewards. One of the things we’re working on in credit is credit platforms. Today, the reward is the same for you as it is for me, but there’s no reason why. What would be on (each) website would be very different, and we believe there’s no reason why card benefits and rewards couldn’t be similar, where they are somewhat tailored to your preferences and your needs.”
How to invest in Web3 In 2025- joining DAOs offers a chance to participate in Web3 governance and decision-making
Web3 is the next evolution of the internet, where decentralized networks, blockchain, and digital assets are reshaping how we interact, own, and transact. In 2025, Web3 is a key part of the digital economy, with blockchain technology powering applications beyond cryptocurrencies. Major institutions like BlackRock have embraced its potential, with CEO Larry Fink comparing tokenization to “email itself” for assets. Real-world assets, such as stocks and real estate, are now easily tokenized and traded, offering faster, transparent transactions. Investing in Web3 offers access to a rapidly growing ecosystem of decentralized technologies that aim to transform industries ranging from finance and gaming to supply chain management and digital identity. Web3 assets, including cryptocurrencies, decentralized finance tokens, and non-fungible tokens, provide opportunities for diversification beyond traditional stocks and bonds. Blockchain technology ensures that transactions are secure and verifiable, while decentralized platforms reduce reliance on intermediaries. There are seven ways to invest in Web3 in 2025: buying cryptocurrencies like Bitcoin and Solana, exploring decentralized AI platforms, investing in tokenized real-world assets (RWAs), decentralized finance investments, Decentralized Autonomous Organizations (DAOs), crypto ETFs, play-to-earn (P2E) gaming, and joining decentralized autonomous organizations. Cryptocurrencies can be bought on exchanges like Coinbase or Kraken, while DeFi platforms enable peer-to-peer transactions, instant loans, and trading without brokers. Play-to-earn gaming allows players to earn crypto through gameplay, and joining decentralized autonomous organizations offers a chance to participate in Web3 governance and decision-making. Understanding these options can help build a diversified Web3 investment strategy.