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…

Our CEO @dillonrolnick sat down with @eastdakota, CEO of @Cloudflare, for a wide-ranging conversation about agents, the future of the internet, and the fight for digital sovereignty. Thanks to the @usv team for hosting!

Jensen closed his GTC Berlin 2026 keynote with this Hermes Agent demo as the finale. He introduced it by saying the way to build an agent is to ask an agent to build it for you, so he installed and set up a customized Hermes in one prompt. Then he used Hermes, running on @nvidia RTX Spark, to create a simulated training environment for the @huggingface Microduck. Hermes planned an obstacle course with Nemotron and had GPT-6 Astra build it using FreeCAD, Omniverse libraries, Blender, Newton, and Isaac Lab. Hermes trained thousands of simulated ducks on that course until the validated policy was ready to run on the real robot in the physical world. At Nous, we believe open source is a practical tool as much as a philosophical ideal. It gives users the ability to build powerful, complex workflows chaining numerous local+cloud models, devices, and tools themselves instead of waiting for a company to enable them.

📱 A 35B MoE model running on a phone at over 20 tokens/s. Meet Lookahead Expert Scheduling (LES), the inference technology behind MiniCPM-V 4.7’s efficient on-device performance. 🧠 The challenge: 35B total parameters, 3B activated per token, and limited device memory. Keeping experts in flash storage makes the model fit, but loading their weights can leave computation waiting. LES co-optimizes expert loading, compute scheduling, and weight access: ⚡ Expert prefetch and compute scheduling: Cross-layer routing prediction prefetches experts the next layer is likely to need. Experts selected by the actual router that are already loaded compute first, while the remaining required experts load in parallel. Prediction only guides prefetch and scheduling: every required expert is still loaded and used. No additional training is needed. 💾 Weight access optimization: Repack expert weights into contiguous storage to reduce scattered reads, and apply precise layer-wise mixed-precision quantization to reduce weight storage and transfer costs while preserving model capability. We also adapted DSpark speculative sampling to work alongside LES in the on-device inference pipeline. In our iPhone 18 Pro Max tests, the full system reaches 20.51 tokens/s, up from a 3.38 tokens/s on-demand loading baseline: 6.07× faster. 🔗 xitter.cf/OpenBMB/status/2112529… The original model architecture and expert routing stay intact. Smarter scheduling makes larger models practical on-device. 👉 MiniCPM-V GitHub: github.com/OpenBMB/MiniCPM-V 🤗 Model info: huggingface.co/collections/o… 💻 Technical Details: github.com/tc-mb/llama.cpp-o… #MiniCPM #OnDeviceAI #OpenSource

“A single cluster currently serves 155 trillion tokens/day, generating $38 in revenue per GPU-hour (at off-peak pricing).” Huge flex from DeepSeek. Not only did they open-source their inference engine, but they also open-sourced their revenue numbers. They didn't disclose which GPUs they're using here, but DeepInfer supports both NVIDIA Blackwell and Huawei Ascend. Even assuming they're renting B300s at $7 per GPU-hour, that's ~82% gross margin.

DeepSeek just open sourced its high-performance, large-scale inference framework DeepInfer in Apache-2.0 license. github.com/deepseek-ai/DeepI… Supported features: - Optimized kernels, model and sampler runtime - Parallelism: Attention DP / balanced CP, MoE EP + EPLB - DSpark and confidence-scheduled verification - SWA bounded replay - Asynchronous scheduling (zero-overhead batch scheduler) - Batch-invariant inference - Deterministic sampling - Structured output

MiniMax M3.1 Flash: Space Performance + Bunny Speed 🚀🐰 Yes, Space Bunny - which has topped global leaderboards with 200T+ tokens - was an earlier version of M3.1 Flash. 💰Redefining the performance economics of agentic AI. M3.1 Flash achieves frontier-level intelligence across key tasks such as Cowork (Automation-Bench: 62%; ALE: 30%), Coding (DeepSWE: 74%) and Visual Reasoning (BabyVision: 91%), at 1/3 the cost of M3 and 3x the speed. API pricing (flat rate): $0.15 / $0.60 per 1M input/output tokens, with cache reads at $0.015 / 1M tokens. Model weights will arrive on Oct 23. API: platform.minimax.io M Plan: minimax.io/m-plan MiniMax Code for desktop: agent.minimax.io/download