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Hugging Face disclosed an intrusion that, according to them, was driven end to end by an autonomous AI agent system. Along similar lines, Sysdig recently published a blog on JADEPUFFER, which it assesses to be an agent-driven ransomware capable of adapting in real time. The report does not identify the victim or fully explain how Sysdig obtained visibility into the operation. The Hugging Face disclosure highlights three important things: autonomous AI intrusion, defensive asymmetry, and missing

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s have accelerated. All the typical trade-offs you might expect have not occurred. While the code is the visible part, what's happening under the surface is much more interesting. Agents now investigate production incidents, review pull requests, answer questions, analyze business data, triage support tickets, research sales accounts, and improve the systems that power Replit Agent itself. It feels like a single master intelligence threaded through every employee, even though it is not. It is an

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Models mentioned in this article 1 Dharma-AI/Dharma-OCR-LITE Image-Text-to-Text • 4B • Updated Apr 17 • 486 • 22 Spaces mentioned in this article 1 Running on Zero Agents 6 DharmaOCR Lite Demo 📄 6 Extract text and tables from document images More from this author 20 August 17, 2026 GPU Management: Why Idle GPUs Are the New Grounded Aircraft 97 July 30, 2026 Community Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to uplo

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1. Cost Is More Than Model Pricing We expected GPT-4.1 to be cheaper than Claude Sonnet 4.6. It wasn’t. Across 417 tasks on the AppWorld Test Challenge using the same CodeAct agent, Sonnet cost $79 total ($0.19/task) while GPT-4.1 cost $155 ($0.37/task) — nearly double. On paper, this makes no sense. GPT-4.1’s token pricing is lower on both input and output, and Sonnet takes roughly three times as many reasoning steps to finish the same tasks. By sticker price alone, GPT-4.1 should win easily. T

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ls “weren’t possible,” he noted, “and we felt it was worth disproving.” Learning by being broken The fourth strand carries the freshest idea in the talk. Almost everyone who pairs agents with world models uses the agent to get smarter inside the model. Odyssey’s PROWL turns that around: it uses a reinforcement-learning agent to improve the model itself. The reasoning is “garbage in, garbage out” - “your agent will never really be better than the quality of your learned environment model” - and y

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ave to check them, “but you can trust them a lot more than you could before.” In the labs, people have already stopped supervising every step - they run in bypass mode and let the agent work. When bigger models get cheaper Does the future belong to one large model doing everything, or to companies decomposing tasks across smaller, cheaper ones? Ted leans hard toward the former, and the argument is economic, not sentimental. He cited Noam Brown at OpenAI, who plots test-time-compute curves with c

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the spark comes. He wanted “system two style thinking,” and to get it he reached back into DeepMind’s own history. AlphaGo’s 2016 breakthrough came from self-play and search, with agents playing each other, taking feedback from the environment, and reinforcing what won. AlphaZero then showed the same recipe could scale from zero knowledge to superhuman play in months, limited mainly by compute. The AI co-scientist generalizes that idea. Instead of agents playing a game, they generate scientific

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线上执行有经济价值项目的能力。评估任务包括 3D 与 CAD、建筑、平面设计、视频与动画、音频、数据分析、Web 应用等。自动化程度上升：在 7 月的更新中，作者公布了对三个近期前沿模型的评估结果——GPT-5.5、Opus 4.8 和 Fable 5，分别取得 6.3%、8.3% 和 16.1%。“前沿在不到八个月内增长了三倍多，这是具有经济能力的 AI agent 进步速度的具体信号，”他们写道。任务类型：部分评估任务包括： 戒指设计：“重新制作客户现有的订婚戒指，将其祖母绿切割主石换成马眼形切割，交付更新后的 3D 模型以及逼真的玫瑰金和黄金渲染图。” 平面图与渲染图：“根据扫描的地籍图、现场照片和测量数据，制作一张干净的带尺寸标注的平面图、家具布局方案，以及重新设计的浴室的逼真渲染图。” 由高效人机协作驱动的本体工程。“专家专注于提炼行业知识，而算法从中学习以扩展本体构建并推动持续演进”。 “先语义搜索，再判别”：“在语义搜索阶段，动态演进的本体被外化为一个独立的本体知识库，从而无需重新训练模型即可持续更新本体，”他们写道。“在判别阶段，模型只判断该项目是否与检索到的本体条目匹

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With Roberta Raileanu, Senior Staff Research Scientist and Open-Endedness Team Lead at Google DeepMind, at RAAIS 2026. The most capable AI research agents can already nudge the state of the art. Give one an open problem, like optimizing a GPU kernel or fine-tuning a language model, and it will propose a hypothesis, run the experiment, read the result, and try again. What it still does not reliably do is make a conceptual leap. Over long horizons, these systems plateau exactly where human researc

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seem like they’re getting better. Forget about math. We did have a couple of examples of this kind of thing, but if you just look at why they’re getting better at being autonomous agents… They’re in an environment where they’re autoregressively producing the step that says “Let’s step back and do a search over the whole codebase,” and then “Let’s step back and assess my mistake,” is the thing that works. I assume what happened in the case of progress in science or maybe in math is you have front

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e 2.0. Previous iterations of the series have been released under a non-commercial license, so this change is more than welcome! Command A+ combines multi-modal, multi-lingual and agentic capabilities as a 218B-A25B MoE, making it usable with a single B200 (when using 4-bit). GLM-5.2 by zai-org : The biggest story in this Artifacts is GLM-5.2, which we have covered in a separate blog as well. The model continues to impress and is genuinely usable for everyday work, not a huge regression compared

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search. And also depends on the job - some jobs are mechanical and predictable enough that they were automated long before the modern era of AI, for example bank tellers or travel agents. And there are other jobs which require dealing on a daily basis with problems that are quite distant from the data distribution. Even software engineering (the jobs AIs are supposed to take first) is one such. I would be willing to bet that there’s overall more demand for human software engineers in 2028 than t

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, the landing pages we generated with Kimi K2.7 Code were roughly 16 times less expensive than those generated with a proprietary model like Claude Fable 5. With generative coding agents you rarely generate just one version of a landing page. More often, you generate many variations so you can explore different design directions, copy, and page elements. You then iterate on the ones that show promise, editing and refining through repeated cycles of experimentation and adjustment. With all the ba

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Description: AISI describe their cyber ranges as: “simulated network environments with multiple hosts, services, and vulnerabilities arranged into sequential attack chains. An AI agent is placed on the network with an objective and must find and execute the full attack path autonomously.” There are two cyber ranges: “The Last Ones” and “Cooling Tower”. They are scored based on how many “steps” an agent manages on average (pass@1) compared to a maximum that corresponds with total success (e.g.,

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ly retrieving over 2,200 specific flights, the rail schedules for trains from the TGV to the Shinkansen, and road speeds per country from multiple academic papers. And while those agents were running, it started coding. Then it launched yet more agents and tests to verify its code, all the while taking notes about its progress. The result was a fully functioning map of impressive sophistication that looked a lot like the 1881 original, but that doesn’t mean it was perfect. I noticed that a lot o

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ly exploited because of time or logistic constraints. LLMs offer a new way to do QA on top of the existing testing methodologies. The idea is to create a markdown file where an AI agent is asked to work as a QA engineer, performing a number of manual testings on the new release. For instance, in the case of DwarfStar (an inference engine for open weights LLMs) I use the following approach. In the markdown file, the agent is asked to check what are the new commits on top of the already released v

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Productivity Gemini Spark Wants to Run Your Entire Day Google I/O 2026 dropped Tuesday, and the headline is this: Gemini is no longer a chatbot you visit. It’s a Spark now: an AI agent living inside Gmail, Google Docs, Calendar, and Drive that actually does work on your behalf. The new Daily Brief feature sifts through your inbox, calendar, and tasks every morning, prioritizes what actually matters, and suggests next steps. Docs Live lets you verbally brain-dump ideas while walking or commuting

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ait for the new releases, honestly (btw, thank you DeepSeek). So, after those chaotic first days, I hope the project will focus on: quality benchmarks, potentially adding a coding agent that is also part of the project, a hardware setup here in my home that can run the CI test in order to ensure long term quality, more ports, and finally but as a very important point: distributed inference (both serial and parallel). For now, thank you for all the support: it was really appreciated :) AI is too

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Three months ago, we pushed hard on agent-generated code at Augment. PR volume went up. Confidence in what was shipping went down. We took two days off the roadmap to ask the team how they were feeling. The word cloud came back with frustration and uncertainty right alongside curiosity and excitement. One group named the shift directly: from "proud builder" to "proud coordinator." It wasn't just us Then we started talking to other engineering leaders and realized we weren't the only ones. The sa

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AI + ML Professor Fry's AI experiment shows light and dark sides of agentic tech British mathematician Professor Hannah Fry has shared a cautionary experiment involving an AI agent, a set of tasks, and a bank card number Fry's team gave it "to show us what it could do." The prof gave the agent, which was built with OpenClaw, some real-world chores to highlight both its capabilities and the risks of granting that level of autonomy. "In the spirit of experimentation," said Fry, "we decided to give

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