SCOOP: Major U.S. banks are detecting unauthorized third-party AI agents inside customer accounts, including some attempting to take actions that appear to be independent of user instructions, sources told Axios. The findings have alarmed banking executives, who are racing to restrict access to their systems as AI moves at a velocity faster than financial institutions can respond, multiple industry and bank executives told Axios. Several banks are now preparing for worst-case and even “day after” scenarios, including how to rapidly secure or restore customer accounts if things go wrong, the people said. Several bank executives told Axios they're struggling to distinguish between human users and AI agents. One executive said banks also struggle to determine whether agents are authorized by customers or potentially operating without their permission, more akin to rogue agent behavior. Read my latest for Axios here: axios.com/2026/10/22/ai-agen…

We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.

Former $GOOGL employee on why data center capacity and electricity are the real bottlenecks, not GPU production itself, and how unreliable some of the neoclouds are ($AMZN, $GOOGL, $MSFT, $NVDA): 1. According to the expert, open-source models are closing the gap with frontier models faster than expected, with customers increasingly replacing Anthropic and OpenAI models in multi-agent architectures with open-source alternatives starting from Q2 of this year. The shift started with simple tasks like tool calling and MCP, but has moved up the stack to low-level orchestration. 2. The expert identifies capacity as the biggest constraint at the moment, with certain companies' traffic growing 5x while GPU availability, particularly in the U.S. where latency requirements are tightest, simply cannot keep up. Sourcing capacity overseas in Asia or Australia is technically possible but adds latency that most customers will not accept. 3. Hyperscalers offer reliability but are expensive, require multi-year commitments of three or more years, and have limited availability of the latest hardware, with customers already chasing B300s. AI-native companies tend to gravitate toward neoclouds due to hyperscaler pricing and contract rigidity, but neoclouds come with their own reliability issues, with GPUs dropping off unexpectedly and requiring engineering resources to manage fleet stability around the clock. 4. The third tier consists of smaller independent operators who have invested in GPU racks, seeing the opportunity to lease out capacity and recoup costs within a year or two, with the remainder being pure profit. The expert describes these as essentially unusable in practice, with operators who do nothing more than find land, install racks, connect electricity, and set up cooling, with no real cloud management, maintenance, or operational expertise behind them. 5. The expert expects overall GPU availability to increase meaningfully over the next year or two, with $NVDA continuing to ramp production. The real bottleneck is not GPUs themselves but data center capacity and electricity in the U.S. On the supply side, the expert also sees players $SPCX beginning to lease out spare capacity to the market, with more likely to follow. 6. The expert notes that U.S.-based neoclouds typically require 20-30% prepayment upfront, with the remainder paid monthly or quarterly over the contract term, and rebates negotiated at the end for any reliability incidents like traffic loss. APAC-based providers operate differently, with no prepayment required at all. found on @AlphaSenseInc

🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4

Muse after I give it access to my credit card and the open internet

Every serious frontier tech company - big or small - should assume it has insider threats. And they don’t need to be that sophisticated. I saw it in government. I saw it with crypto. OpenAI is the latest. Founders tend to assume good intentions while speaking about some of their most sensitive information out loud: capabilities, unreleased products, strategic plans, government relationships. Employees are the most obvious vector. The more sophisticated operations target founders, execs, and vendors through romantic, family, and social relationships. And on small teams, one compromised person can do a lot of damage. With former federal prosecutors and ex-FBI agents as part of our team, we spend a lot of time on these issues. Here’s some of what we’re telling our founders: 1. Expect to see more private-sector versions of SCIFs and clearances, where the most sensitive conversations and work are physically isolated and under stricter access controls. 2. Agencies may know about a specific insider threat at a company but can't share that info unless someone at the company holds a clearance like TS/SCI. Lead times to get a clearance are long. Plan now. 3. Restrict access to sensitive meeting schedules, disable unused ports on conference-room equipment, and regularly sweep rooms used for confidential discussions for surveillance devices. You don't need OpenAI's budget to be ready. But you do need to start.

I have just started to contribute to Quest, @electriccapital's open-source agent harness, where we just integrated @near_ai's trust infrastructure! Quest is a self-hosted harness where you plug in your own inference providers, connect the services you already use, and let agents work on them behind approval gates and cost reporting. When you send a prompt to a hosted model, you are trusting whoever runs the box: they can read the prompt, read the answer, and quietly serve a different model than the one you asked for. For an agent that is reading your emails, your docs, your internal APIs, that is quite a lot of trust with your most confidential data. @near_ai cloud runs part of its catalog inside Intel TDX and NVIDIA confidential-GPU enclaves, where the hardware attests to what is running and NEAR says even its own operators cannot look inside. If you or your organization deals with confidential data, legal documents, company finances, and client records, Quest is definitely worth spinning up, so that your data stays with you.