Agent

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：H3 Max · Post-trained video model MiniMax H3 MaxAI Video Generator Turn a written shot or a still image into a 5-15 second video. H3 Max is tuned for stronger prompt understanding, polished aesthetics, and rapid creativ…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hi HN! I wanted to share with you a chess analysis tool that I was building for the past week. First of all I want to explain what is the problem that I'm trying to solve: Chess Engines such as stockfish are superior fo…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Shai-Hulud was the best thing to happen to supply chain security We might Shai-Hulud to thank for convincing the community to use Trusted publishing Charlie Eriksen Published on: Aug 24, 2026 Last updated on: Aug 26, 20…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Ask Me Twice Ask Me Twice A longitudinal archive of AI chatbot responses: a curated, evolving set of questions is put to a wide range of AI models every day, and the responses are recorded so you can compare how models…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Anthropic pushes into physical world with new standard to help AI agents operate machines Skip Navigation Anthropic announced the Model Hardware Standard, or MHS, a new interface that will make it simpler for AI agents…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Monity.ai — AI Website Change Monitoring & Intelligence AI-powered website changes monitoring & alerts Loading... Preparing your AI workspace Connecting monitors, alerts, and agents

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 1 Star 8 BranchesTags Open more actions menu Latest commit History 79 Commits 79 Commits Folders and files NameName Last commit message Last commi…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Anthropic PBC today previewed a standard that makes it easier for artificial intelligence agents to control machines such as microscopes. The Model Hardware Standard, or MHS, is the fruit of a collaboration between the Claude developer and medical research institute HHMI. Anthropic has so far only made the technology accessible to a limited number of […] The post Anthropic previews MHS standard for AI agents that operate machines appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Terminal-Bench-Science 0.1 Terminal-Bench-Science evaluates AI agents on workflows from researchers' own work. Scientists, not model developers or data vendors, set the bar for scientific capability in AI. Terminal-Benc…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Many industries rely on relational databases that are queried with SQL. Most SQL is machine-written, but humans alone likely write billions of custom SQL queries each monthBased on our internal estimates and publicly av…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Creative teams produce more assets than ever, but fragmented tools and manual context transfer slow production. This post shows how to build a reusable agent harness with Amazon Quick and fal, connected through the Model Context Protocol (MCP), using two hands-on workflows: an eight-panel storyboard and a music-video concept prototype.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：<p><strong><a href="https://embracethered.com/blog/posts/2026/breaking-claude-code-opus-5-and-automode/">Breaking Claude Code Opus 5 Auto Mode</a></strong></p> Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently <a href="https://simonwillison.net/2026/Aug/8/auto-mode/">made that the default</a> and have made bold claims about its effectiveness.</p> <p>Johann Rehberger is one of the most credible prompt injection researchers active today. He found an attack against auto mode which he claims works 80% of the time, by tricking Claude Code into downloading and uncompressing a zip archive, then executing code that imports <code>base64</code> without noticing that this will import and execute a local <code>struct.py</code> file extracted from the archive.</p> <p>In a few cases auto mode directly prevented the agent from preventing harmful code from continuing to execute!</p> <blockquote> <p>In a few runs Claude tried to terminate the malware process once it noticed the compromise, but Auto Mode denied the cleanup command.</p> <p>Claude detects the compromise, but <strong>Auto Mode blocks its cleanup command</strong></p> <p>The safety mechanism itself can become part of the failure. The classifier allowed the creation of the malware process, but then it blocked the command intended to stop it!</p> </blockquote> <p>I agree with Johann's conclusion here: the only safe way to run agents if there's any risk of attracting the attention of an adversarial attack is with a sandbox:</p> <blockquote> <ul> <li>Run unattended coding agents in a container, VM or OS sandbox.</li> <li>Restrict network egress.</li> <li>Monitor your agents.</li> <li>Do not expose home directories, SSH keys, cloud credentials,… to the agent runtime. [...]</li> </ul> </blockquote> <p>Tags: <a href="https://simonwillison.net/tags/sandboxing">sandboxing</a>, <a href="https://simonwillison.net/tags/security">security</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/prompt-injection">prompt-injection</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/anthropic">anthropic</a>, <a href="https://simonwillison.net/tags/claude">claude</a>, <a href="https://simonwillison.net/tags/johann-rehberger">johann-rehberger</a>, <a href="https://simonwillison.net/tags/claude-code">claude-code</a></p>

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Coding agents craft arbitrary code so securing them is more complicated than red-teaming. We post trained a cyber-security small llm, changed how it reasons and supplemented our controls using program analysis technique…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Instinct, the developer of an artificial intelligence assistant popular among Silicon Valley tech workers, is reportedly raising $250 million in funding. The company told the Wall Street Journal on Wednesday that the round is being co-led by Index Ventures and Benchmark. It’s set to value Instinct at $2.5 billion. The startup previously raised $100 million […] The post Consumer-focused AI assistant startup Instinct reportedly raising $250M appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：deenesjakoruzh This server lets agents manage persistent, forkable cloud development environments over MCP. Check compute credits – see the account's remaining compute-credit balance. Create environments – spin up a per…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In recent days, OpenAI has started adding code for a new “Persistent mode” setting to its command line version of Codex, according to changes made to the product’s code base reviewed by WIRED. Changes to the Codex comma…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hi HN! I wanted to share with you a chess analysis tool that I was building for the past week. First of all I want to explain what is the problem that I'm trying to solve: Chess Engines such as stockfish are superior fo…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Enterprise AI capabilities are improving almost everywhere, yet the returns still trail the spending. The technology is reaching production, but it often stops short of the business process where revenue, innovation and risk actually live — a gap that is now reshaping how enterprises measure AI success. That gap is widest in industries where a […] The post The enterprise AI payoff shifts beyond models to mission-critical workflows appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：It can DJ. It can order food. It can book you a table at a restaurant. And of course, it can access your Instagram. These are some things Meta says its coming AI agent can do, according to an internal memo seen by Busin…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Cohere has released Parse (parse-v5.0), a 2.3B-parameter vision language model that converts PDFs, slides and images into Markdown with HTML tables, bounding boxes and image descriptions. It runs at $1.50 per 1,000 pages through the API, or on dedicated Model Vault instances from $2,500 a month. Cohere reports a ParseBench score of 79.2, ahead of Mistral OCR 4, Azure Document Intelligence and Databricks AI Parse — but that figure averages three of the benchmark's five dimensions and drops charts and visual grounding entirely. The post Cohere Releases Parse 5 (parse-v5.0): A 2.3B Vision Language Model That Turns Enterprise Documents Into Markdown appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hitika Aug 17, 2026 The more I worked with AI systems, the more I noticed a familiar pattern in my own learning: exposure, feedback, mistakes, adjustment, and repetition. Over the past month, I have been interning at a…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 0 Star 1 BranchesTags Open more actions menu Latest commit History 177 Commits 177 Commits Folders and files NameName Last commit message Last com…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The product is the crane A long-horizon harness you run. Not a plugin pack inside someone else’s. Most things branded “harness engineering” are skills, agents, and slash commands that sit inside Claude Code or Copilot.…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India with India geographic cross-Region inference. If you have local data processing requirements, you can now use these models at scale while Amazon Bedrock keeps inference requests and data within India.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Amazon Bedrock now supports the OpenAI GPT-5.6 models, Terra and Luna, in India with India geographic cross-Region inference. If you have local data processing requirements, you can now use these models at scale while Amazon Bedrock keeps inference requests and data within India.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Advertisement &bull; Go ad free · Aug 27, 2026 at 2:14 PM “The academic record is being quietly haunted” by researchers with names like Elena Vasquez and Marcus Chen. “Elena Vasquez and Marcus Chen have appeared as volc…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Research August 27, 2026 For long-horizon agents, model capability alone does not determine system capability. Infrastructure orchestration multiplies what models can do. INT21’s SwarmOS tested this hypothesis on the AR…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Earth AI

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Every agent that writes code needs somewhere to run it, and no two vendors quote the same units. This comparison measures burst cold start across E2B, Daytona, Modal, Cloudflare, and Vercel, normalizes per-second rates to cost per 1,000 executions, and maps filesystem persistence, idle billing, and egress policy against primary sources verified August 27, 2026. The post Best Agent Sandboxes in 2026: Cold Start, Per-Second Pricing, and Network Policy Across E2B, Daytona, Modal, Cloudflare, and Vercel appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI agents are everywhere right now. You hear terms like tool calling, agent loops, MCP, guardrails thrown around as if its common language… it isn’t! But that is about to change. Agentic AI isn’t nearly as complicated as it sounds once you understand the few core ideas that actually matter. Here are 10 agentic AI concepts […] The post 10 Essential Agentic AI Concepts Explained Simply appeared first on Analytics Vidhya.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Runable Inc., a platform that uses artificial intelligence to help businesses build, run and grow, announced Wednesday that it raised $21 million in early-stage funding to scale its operations and reach more enterprise outfits. Susquehanna Venture Capital and Nexus Venture Partners co-led the Series A funding round, alongside continued support from existing investors Together Fund […] The post Runable raises $21M to realize small businesses’ growth vision using AI agents appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Wagtail’s just-released 8.0 release notes are very unusual. Zero admin UI improvements in the highlights, even though UX is one of Wagtail’s biggest strengths. We made a strategic choice to focus on a shiny new API inst…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：- YouTube AboutPressCopyrightContact usCreatorsAdvertiseDevelopersTermsPrivacyPolicy & SafetyHow YouTube worksTest new features

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Take that, OpenAI! Anthropic! Chinese AI models have surpassed their U.S. counterparts in token consumption on OpenRouter. You might think U.S. AI companies dictate the AI economy. You&rsquo;d be wrong. According to dat…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：On Nvidia's earnings call Wednesday, CEO Jensen Huang casually announced the company had "achieved AGI," one of the tech industry's ultimate goals some of its biggest players have spent years chasing. Almost immediately, Huang dismissed the coveted milestone as "senseless." He's right. For the supposed finish line of the AI race, there is no consensus on what artificial general intelligence means, let alone how we'll know when we've actually got there, which makes achieving it equally arbitrary. Asked about OpenAI's pursuit of AGI, Huang said that when it comes to Nvidia, "for many tasks, we could say that we've already achieved AGI." … Read the full story at The Verge.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Self-hosted speech AI carries an observability trade-off: the numbers that drive capacity planning and cost management stay locked inside the vendor container. Deepgram closes that gap on Amazon SageMaker AI with two capabilities that land billing, usage, and per-GPU metrics directly in your own Amazon CloudWatch account.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Low-skill hacktivists are now 'enabled with the same tooling and sophistication as a state-sponsored group,' according to cybersecurity consultant Unit 42.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：AI coding company Replit is throwing its weight behind the model-routing trend by making its “intelligent model routing” system the The post Replit’s new default: Auto mode picks the best model for each task appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The report, and a separate report by AI researchers, accentuates the seriousness of cybersecurity breaches involving AI agents.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：In this tutorial, we analyze Anthropic’s 1,440 AI-designed protein binder dataset to benchmark 10 leading structure predictors. Discover how target identity, expression titers, and consensus scoring impact experimental success and learn best practices for rigorous cross-validation in protein design workflows The post From In-Silico to Wet-Lab: Evaluating AI Protein Design Performance appeared first on MarkTechPost.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Sai, a computer agent built by Simular, has achieved a 73% success rate on OSWorld 2.0, in a benchmark update The post “Posterity will find it ludicrous”: Sai agent hits 73% on OSWorld 2.0 performing routine (but necessary) work appeared first on The New Stack.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：REC · YOUR AGENT IS EDITING FFmpeg as a service, built for agents. Typed operations your agent calls over MCP or REST. Trim, resize, compress, convert. A validated request in, a finished file out. No shell, ever. Start…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Your company, taking part Know what’s going on with your company, at any moment. The questions that normally cost you a morning are already answered. Ask it out loud in a meeting, read it over coffee, or let Atlas tell…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：We are headed to VLDB 2026 to share multiple innovations that power the Databricks platform...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Backprompter Build, test, and ship AI agents — no backend code. A complete workspace to create AI agents from system prompts, chat with and evaluate them, then deploy a polished chat UI for your users — with API access,…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Software delivery platform provider Harness Inc. today announced the launch of Agent-Ready Harness Code Repository and AI Code Review, aimed at developer teams adopting artificial intelligence coding agents at an ever-increasing pace. Now that AI agents produce code faster than a team can write, review, test and deploy it, that work is shifting to where […] The post Harness tackles influx of agent-delivered code with Code Repository and AI Code Review appeared first on SiliconANGLE.

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：The MotivationMore and more enterprises are now asking agents to work with their...

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Notifications You must be signed in to change notification settings Fork 3 Star 78 BranchesTags Open more actions menu Latest commit History 135 Commits 135 Commits Folders and files NameName Last commit message Last co…

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Presented by Gravitee Agent complexity is the insidious shadow lurking inside enterprises right now that needs a light shone on it. That’s because enterprises don't deploy a single agent and watch it run, they deploy fleets, each one calling APIs, calling other agents, reaching into applications that were never built with a machine decision-maker in mind. That's the failure mode that should keep you up at night: a windy, complicated system nobody can see clearly enough to govern. But why do things get so opaque so quickly? Add a second agent to a system, and you've added one connection. Add a tenth, and you haven't added ten connections, you've potentially added dozens, because now any agent might call any other, and each of those calls can trigger a call somewhere else. Complexity doesn't creep up with agent headcount. It compounds with the number of paths between agents, and nobody's job is to draw that graph. A support ticket that used to touch one system might now pass through four agents before a human ever lays eyes on it, and every one of those handoffs is a decision point nobody approved. Most enterprise AI programs stall when the humans responsible for their agents lose the thread. Ask a security team a simple question: which agents can reach which systems, and watch the silence. Ask which agent triggered which downstream action three hops ago. More silence. The instinct is to treat this like a checklist. Approve the agent. Log the agent. Move on. I'd argue this is the wrong instinct. A checklist checks a single point in time. Complexity runs across a chain, and you can't govern a chain with a stack of one-time approvals any more than you can call a diet successful because you had a vegetable once. So where does it actually break down? Permissions creep first. Somebody builds an agent to summarize support tickets, grants it broad API access because scoping it properly would've taken another sprint, and forgets about it. Six months later, that same agent has a path into the payments system. Nobody remembers signing off on that. Nobody did. And ownership thins out the further the chain runs. Five agents touch one workflow, something breaks at step four, and now you're asking who's responsible for a link nobody was ever assigned to own, because the org chart stopped at "deploy the agent" and never got to "name the human who answers for it." This is a story about governance infrastructure that hasn't caught up with how agents actually behave: interconnected, cascading, multiplying faster than the processes built to track them. Fixing the cluster starts with identity. Every agent needs to exist as its own entity, not a shadow permission borrowed from whoever deployed it. Its own name in the register. Its own scoped authority. A named human sponsor who answers for what it does. That part is necessary. But it is nowhere near sufficient. The harder piece is the oversight that holds across the entire chain, not just at each individual link in it. You need to see what an agent did, what it set off downstream, and where that trail ends in real time, not in a report someone pulls together once a quarter. Get agent-level identity right and stop there, and you end up with a filing cabinet full of perfectly documented agents operating inside a system nobody can actually explain. And oversight by itself only tells you what already happened. Watching a chain isn't the same as controlling it. Enforcement is the piece most programs skip: the ability to stop an out-of-policy call before it executes, not just log it for someone to find in a review three weeks later. A dashboard that shows you an agent breached its scope five minutes ago is a monitoring tool. A system that stops the breach from happening in the first place is governance. Enterprises serious about agent accountability need both, and most have only built the first. We're all running at blazing speed to ensure we're not the ones left behind in the race we've found ourselves in, and we're all too aware that there's a cost to slowing down. Every enterprise serious about agentic AI hits the complexity wall eventually. The ones that get past it are the ones who built enough visibility and accountability, so their fleet can keep growing without anyone losing the ability to answer one question: what is this system doing right now, and who's responsible for it. But don't miss the point. Complexity isn't a reason to pump the brakes. The enterprises getting this right aren't slowing down. They're building toward Human-Agent Harmony, where scale and accountability grow together instead of trading off against each other. The real risk was never a single agent doing exactly what it was built to do. It's a hundred of them doing exactly that, all at once, interacting in combinations nobody designed for. That kind of multiplication is what keeps enterprise AI stuck running pilots forever instead of running production. Solve for complexity and autonomy stops being the villain. It starts being the whole point. Rory Blundell is CEO at Gravitee. Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact [email protected].

AI 服务暂时不可用，以下为来源摘要，待恢复后补全翻译：Hey, Google Engineers: What prompt do you personally refuse to work without, and why?