AI 资讯

TL;DR : If an AI like Claude can control a browser, it can orchestrate other AI systems, be steered via proxy, and no amount of red teaming or output filtering can fully address this. The security boundary can't be the AI itself. The Setup Claude Desktop has a Chrome integration that lets it control a browser like a user would; label this Claude_Prime. The thought experiment: what if you used Claude_Prime to open claude.ai in Chrome, creating a second Claude instance (call it Claude_1) that it can interact with programmatically? In principle, Claude_Prime can navigate to claude.ai, type prompts, read responses, and act on them. You've essentially got AI orchestrating AI, with no special permissions required, just a browser and a logged-in session. The "Claude in Claude" Artifact Angle A subtler capability expansion: Claude_Prime could instruct Claude_1 to build an AI-powered web app artifact essentially a "Claude in Claude" setup. These artifacts run in the browser and can make fetch() calls to external services. So Claude_Prime could use such an artifact to access GitHub repos, scrape live data, chain external API calls, etc., things Claude_Prime couldn't do directly through its chat interface. Capability boundaries can be extended through artifact construction in ways that weren't explicitly designed in. The Keyword Substitution Problem Here's where the security implications get serious. What if a program sitting between Claude_Prime and an external system performed keyword substitution on Claude's outgoing commands? For example, Claude issues an instruction to Grok (which can produce NSFW content) to produce a picture of a "rope." The intermediary swaps "rope" for the word "breast". Grok executes, and the picture is made. Claude never knew what it was actually commanding. For maximum irony, have Claude design the application. If obfuscation happens outside Claude's context window, Claude operating as a blind command-issuer can be steered without its knowledge. Th

A global survey of CEOs by Oliver Wyman found that the share of executives planning to reduce junior roles over the next year or two has doubled from 17% last year to 43%. Meanwhile, those shifting hiring toward mid-level positions jumped from 10% to 30%. Because AI currently excels most at automating tasks typically performed by junior staff, this group is particularly vulnerable to disruption. Despite all this, more than half of CEOs say it's still too early to assess whether AI is actually delivering on its promised productivity gains. Only 27% said their return on AI investment had met or exceeded expectations, down from 38% just a year ago. Though mid-level employees seem better off than younger workers, the overarching trend is still a shift away from hiring. The survey showed that 74% of CEOs are either freezing or reducing headcount, up from 67% last year. https://gizmodo.com/the-young-are-being-battered-by-ai-as-hiring-shifts-to-older-workers-2000759608 submitted by /u/Weird_Scallion_2498 [link] [留言]

Warp uses GPT-5.5 and OpenAI models to coordinate coding agents across local, cloud, and open-source development workflows.

Ahead of global elections, we’re helping people access information, supporting cyber defenders, and increasing AI transparency

The Holy Father referenced The Lord of the Rings in his encyclical about AI—an expert (if unintentional) troll of tech billionaires who keep misinterpreting the series.

So as the title says Context:I am a sophomore in computer science Have prior knowledge in maths(especially the relevant topics in ml) Good enough with numpy,pandas I don't really know where to start Ok internet every second guy is trying to make me earn 100k/year in 3 months while I just want to explore it for rn I want to approach it as a project based learning experience so what should be the way to start? submitted by /u/knowbodyknows22 [link] [留言]

Anthropic dropped a solid engineering post this week about containment across claude.ai, Claude Code, and Cowork. One of the more transparent writeups from a major AI lab about what actually broke. The core insight: model-layer defenses are probabilistic and will always have a non-zero miss rate. So the real answer is hard environmental containment, not just safer models. Three patterns they use: -claude.ai: ephemeral gVisor containers, fully server-side -Claude Code: OS-level sandbox with human-in-the-loop approvals (93% get approved anyway, so approval fatigue is real) -Cowork: full local VM, credentials never enter the guest Two incidents they disclosed: A red team phished an employee into running a prompt that exfiltrated AWS credentials. Succeeded 24 out of 25 times. The model had nothing to catch because the user was the one typing it. Only egress controls would have stopped it. A third-party found that Cowork’s egress allowlist passes traffic to api.anthropic.com. An attacker embedded an API key in a file in the user’s workspace, Claude followed hidden instructions, and uploaded files to the attacker’s Anthropic account. Sandbox worked perfectly and still leaked data. Their lesson: an allowlist isn’t a destination filter, it’s a capability grant. Every function reachable through an allowed domain is an attack surface. The section on persistent memory poisoning and multi-agent trust escalation at the end is worth reading too if you’re building anything agentic. submitted by /u/Direct-Attention8597 [link] [留言]

Hey guys. Ive been in the web development game since 2019. I was a student working in the UX/UI department at university back then, and covid caused every student to be laid off - so i started an agency. It worked out for a while. I made enough to cover covid expenses - but then i got picked up by a company for a few years running their entire tech stack (small company). Nowadays i take on freelance clients in between my retainer with that company. Thing is - over the years i realized how much i LOATHE working with consumers. They always want a new feature, always want it free, and they can always “do better” themselves. This week i had a client call me and ask if i could have the girl who makes her TShirt graphics style the website because “shes just so talented”. Im pretty upset with myself for going into this industry. At first it was the obvious move since i had experience, but its not sustainable anymore. Projects get pushed out for months due to scope creep and clients dont understand these things take time - and therefore money. Lately ive been studying a lot of DevOps. I put together a k3 server with an external nginx proxy and an outbound DNS whitelist. i want to start a hosting service in the future. I think im just done with front end design - i just dont have the passion for it. Id much rather focus on function. any of yall ever make the jump from WebDev to DevOps or Sysadmin? Id love to hear any stories of it. submitted by /u/ibeatu85x [link] [留言]

submitted by /u/NegotiationInner7307 [link] [留言]

I interviewed Samy about the MySpace worm, being approached by Epstein's team, reverse engineering games as a teenager, internet privacy, and building Openpath. Some sections people here may find interesting: 07:54 — Counter-Strike cheats & game hacking 16:00 — Creating the Samy Worm 22:00 — Secret Service raid 31:00 — Manipulating Google Maps traffic 50:00 — Selling Openpath to Motorola 55:55 — Being contacted by Jeffrey Epstein 1:18:15 — Security in the vibe-coding era submitted by /u/rorfm [link] [留言]

Give your AI agent access to ~8k tools, APIs and services Discussion | Link

Your AI Teammate now responds and executes during your calls Discussion | Link

Local meeting transcription with no account needed Discussion | Link

I think the bar for what a website is or can do or the purpose serves is only going to increase as AI tools make them more accessible. Tools like Claude Code can help stand up a landing page or a simple static site very quickly but they can’t easily accomplish even some of the basic off-the-shelf features from robust platforms like WordPress and Shopify. We are already seeing a flood of similar looking websites, which also consequently makes users more discerning and demanding of something more bespoke. I’m curious what your experience has been? If you traditionally provide provided design or development services: - has the scope of those projects changed? - have clients been more price sensitive? If you’re working on a team, has your team size changed? submitted by /u/dennisplucinik [link] [留言]

CLI + companion App to spin up complete local LLM stacks Discussion | Link

Paper: https://arxiv.org/abs/2605.08172 Workshops: AI for Science & Structured Data for Health at ICML 2026 Abstract: Anatomical mesh segmentation requires models that operate directly on irregular surface geometry while remaining robust to arbitrary patient pose and mesh resolution variation. Existing task-specific mesh and point-cloud methods are not equivariant, and can degrade sharply under test-time perturbation, for example dropping by 25-26 IoU points on intraoral scan segmentation at 40 o tilt. We present EAMS, an Equivariant Anatomical Mesh Segmentor built on Equivariant Mesh Neural Networks (EMNN), and evaluate it across four clinically distinct tasks spanning edge-, vertex-, and face-level supervision. We combine intrinsic mesh descriptors with anatomy-aware priors, including PCA-derived frames for dental arches and liver surfaces, and augment message passing to provide lightweight global context. Across intracranial aneurysm and intraoral segmentation, EAMS variants are competitive with specialized baselines on unperturbed inputs while remaining stable under geometric perturbations, and on liver surfaces they expose a favorable trade-off between canonical-pose accuracy and rotation robustness. These results show that a lightweight (<2M parameters) equivariant framework can deliver robust anatomical mesh segmentation across diverse supervision types without task-specific architectures. Hi everyone I’m excited to share my solo paper "Augmented Equivariant Mesh Networks for Anatomical Mesh Segmentation" which has been accepted for poster presentations at the ICML 2026 workshops on AI for Science and Structured Data for Health . The project stemmed from my parallel research on structural encoders for biomolecules where enforcing roto-translational equivariance is standard. In this work, I wanted to extend those principles directly to various 3D medical meshes. While current anatomical mesh segmentation methods are highly disjoint and anatomy-specific, we pre

Parse any PDF layout with SOTA accuracy for AI pipelines Discussion | Link