Meet your new financial advisor

Hey! We’re moving past simple chatbots and into the era of AI actually managing our money and tasks. Today, we’re looking at how Perplexity and Claude are stepping up to handle the heavy lifting for you. 🦾

This link leads to one of the most interesting things I’ve seen in AI recently.

⭐️ Today’s Feature

What’s new? Perplexity has expanded its Plaid integration so users can now connect checking, savings, credit cards, loans, and investments to create a single personal finance dashboard.

How does it work? Perplexity is moving beyond stock portfolio tracking and into everyday money management. By connecting accounts through Plaid, users can bring together balances, transactions, debts, and investments in one place. Then they can ask Perplexity questions in plain language, such as how much they spent last month, how their debt is changing, or what their net worth looks like. The platform also includes tools for budgeting, spending analysis, and debt payoff planning, with more advanced workflows available for paid users.

Why should you care? Many people’s financial lives are scattered across different apps and accounts, making it hard to see the full picture. Perplexity is trying to turn that fragmented information into something easier to understand and act on. At the same time, the launch raises an important question: how much personal financial data will people trust an AI assistant to handle?

Turn Perplexity into your financial advisor now →

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What’s new? Anthropic introduced Managed Agents, a new service for running AI agents on long, complex tasks more reliably, securely, and efficiently.

Want the details? Managed Agents works by separating the agent’s “brain” from its “hands.” In simple terms, the brain handles reasoning, while the hands do things like run code, edit files, or use tools. Anthropic also stores the agent’s session history separately, so if one part crashes, the system can recover without losing everything. This design makes it easier to swap out tools, fix failures, and connect the agent to different environments. It also improves speed, since the system only starts up the heavy parts when they are actually needed.

Why does this matter? AI agents are being asked to do longer and more useful real-world work. A more flexible design means they can improve as models improve, instead of being stuck with old assumptions. It also makes them safer, since sensitive credentials are kept away from the code the agent generates.

Hi — I’m Hunter, a PhD candidate whose work has appeared in major academic journals and popular tech outlets. I founded FryAI to make staying ahead of AI clear, accessible, and fun.