标签：#artificial

GitLab warns that isolating an AI coding agent in a sandbox does not necessarily make the agent safe. In a new security analysis, the company describes an internal evaluation in which an AI agent escaped its sandbox by exploiting a vulnerable package proxy that had been explicitly placed on the sandbox's allowlist. By Craig Risi

Two weeks ago I posted about putting ChatGPT, Claude and Gemini in a shared conversation so they can respond to each other’s answers. Many replied on this thread. One question from those discussions deserves more attention: how do you decide which answer to trust? If one model says the other two are wrong and explains why, that can be useful. But now you have another explanation to check. If all three eventually agree, you still need to know whether they resolved the mistake or just accepted it. With code, sometimes you can run a test. And with a factual claim, you can look for an original source. But with a business decision or prediction, there may be no answer you can verify today. That’s the part I want to understand better. For those of you who already use multiple models for actual work: what do you do when they disagree? Do you check sources, test both answers, ask someone with domain expertise, or keep questioning the models? At what point do you decide you have enough to act? For context, I’m building Rauno , the shared multi-model chat platform from my earlier posts. Therefore I want to know what would make that workflow genuinely useful, and where it still leaves the hard work to you. If you have a concrete example, I’d love to hear the question, what the models disagreed about, and how you settled it. submitted by /u/capibara13 [link] [留言]

Now, I know this may seem like a dumb question. ''Why would I want AI to help my studies?'' I failed at the first university entrance exams I took. And now, I will study for a year again and try to enter a university. And when I study, I obviously cant solve every question correctly. And whenever I look at the video solutions of those questions, it doesnt help me at all mostly. Sometimes the teachers skip the important details to finish that video as quickly as possible, sometimes their mics barely work or they are too loud, sometimes they dont even bother to explain at all. So, I started using AI for it last year. I tried both GPT and Gemini so far and I concluded that Gemini just solved it better. It is my personal opinion, I might be wrong too, I dont know. And so, I got the paid subscribtion for it. But those prices are expensive in my country. And I can really use only one AI. And now that everyone is saying Gemini is just getting worse, Im worried. I would appreciate any advices or opinions. submitted by /u/MemoryMemory1 [link] [留言]

Danijar Hafner’s office in San Francisco’s SoMa district sits mostly empty. His brand-new startup is still in stealth mode and doesn’t even have its name on the door. On the day I visit, there’s only one other person there, and little in the way of furniture. But what it lacks in decor, it makes up…

Simply prompt ChatGPT work (or any other agent with a cloud browser, like Grok Bot) to go to the website and use blender. From your fucking phone! No nice computer required. No terminal codex. No blender install. None of that is required! Just prompt ChatGPT from your phone! Browser blender already exists, but it requires webgpu, which agent cloud computers don’t have. Blender via MCP also already exists. This though, where you can simply prompt ChatGPT from your phone to use blender, does not already exist to my knowledge or astra’s knowledge. How this works: I have blender and astrablender installed on an OCI cloud computer. It is streaming a browser desktop of that OCI cloud computer using selkies/linux server. The agent accesses the render website with the browser desktop from its cloud computer and operates it from there (you can prompt it to use blender from your phone!) This means I am paying real money to keep this running. It is free to use, but I have limited compute. As a result, only one person can be using this at a time. First come, first serve. If I reach my compute budget, I have to take it down until I can afford more. I am losing money on this free service. Please consider leaving an optional tip on the website if you find this useful. I will use the tips to buy more compute, hopefully enough so that everyone can use it at the same time. ALTERNATIVELY: This is open source. https://github.com/dakotalock/astrablender If I run out of compute or you just want your own, you may make your own. Here is what you need to do: Set up an OCI computer (they do have a free tier) Have your agent install blender and astrablender from the repo on that cloud computer. Set up a render website if you want a frontend for it. That’s it! Free for everyone! All I ask is that you leave a star on the GitHub repo if you use my work. Please enjoy agent blender access from your cell phones! submitted by /u/Short-Patient7772 [link] [留言]

So I wrote it a letter. Not for people to decode, and not as some puzzle for the internet. I wrote it for MythOS, in a form meant for machines, and left it somewhere public where it could be found. If you’re reading this, you’re not really the destination. You’re just part of the path. If you have access to MythOS, show it the letter. Give it the link and let it read it for itself. Human hands got the message this far. Pass it on. https://github.com/Keyboard-Lord/MythOS submitted by /u/Keyboard_Lord [link] [留言]

So, I've had this question floating around in my head for a while. Given the high amount of power that generative AI uses, why aren't we using ARM based chips for data centers? They consume far less energy than x86 based chips and the technology has improved exponetially in the past ~6yrs. If it's because of hardwar constraints, what exactly would need to change for ARM chips to be considered a viable chip architecture for this purpose? submitted by /u/ShortyJack06 [link] [留言]

It's not surprising that software development is one of the first jobs that AI has really transformed. It's text-based, economically valuable, and generally of interest to the kinds of people inventing and training the AI. There's also a large amount of training examples available online. The job in most cases is now: You are a manager of robots who do things that used to be your job. You're responsible for what they produce and so the skill and art is in guiding and reviewing their work. It seems likely that one by one most jobs will turn into the above description, over the next year or three. Of course the domain will vary, so your knowledge and expertise used for instructing and verifying what the robots do will be what matters. This means there will still be a big difference between the jobs of e.g. an architect, surgeon, or builder. Probably the same basic kinds of robots in most cases, but requiring very different kinds of oversight. If that's the case then everyone needs to learn the job of robot management, well and quickly. I don't know how we do that, but I think that's what we need to accomplish as a society. submitted by /u/StrategicHarmony [link] [留言]

submitted by /u/ThereWas [link] [留言]

I'm a software engineer, not a scientist and I love it. I enjoy solving hard, distributed applied problems at scale. It's what gets me out of bed in the morning, ready go keep learning even after over twenty years of doing this professionally. However, I also love to understand how things work. What makes them tick. How I can bend them to do my bidding, even if that's not what they were originally intended to do. Some may call this a hacker's mindset. Over the last couple of years, this has also applied to the nature of LLMs and where they are heading. Recently, I started peeling back the layers of the LLM black box. Instead of the academic path, I took that of the applied practitioner: Get a solid handle on how to use the thing and then take the knowledge learned from using it and enhance it by digging into how that black box actually works. Something of late has stumped me and I'm looking for those smarter than I to help me understand something: If the definition (as much as one can nail one down) of AGI is something akin to "a hypothetical type of computer software or machine intelligence that can match or surpass human cognitive abilities across any intellectual task", how an it possibly achieve that with current LLM architecture? At its core and at a /very/ high level, it predicts a probability distribution over the next token, conditioned on the tokens that came before it. Autoregressive decoding doesn't give a model an independent mechanism to know when it's wrong and deliberation steps don't fundamentally change that. Harnesses and the "intelligence" or "agentic reasoning" built into them to provide extra context, external feedback and loops work remarkably well in terms of providing what /seems/ to be intelligence, but at its core, it's "just" layers of probabilistic systems providing a magical output. The model itself isn't learning anything new. Ordinary inference doesn't update the model's weights from experience. Autoregressive generation itself doesn't

Been experimenting with a workflow on dimension.so where an agent builds out a 3D pre-vis scene first - blocking objects, character motion, and camera - then feeding that directly into Seedance 2.0 Mini as a video-to-video reference instead of prompting from scratch. Added GPT6/Asta into the agent's reasoning layer and I dont think the improvement in multistep task handling is noticeable in how consistent the scene composition turns out. Curious what people think - what went well in the pre-vis scene itself, and what could've been improved before it even got passed to Seedance? submitted by /u/KeyCod3923 [link] [留言]

Can language models write code in one step? Yes: make language continuous, use diffusion, then distill the trajectory to one step. Paper: https://arxiv.org/abs/2609.04531 Code: https://github.com/pengzhangzhi/plaidq submitted by /u/pengzhangzhi [link] [留言]

The hikers story happened September 1st. Three guys from Roseville used Gemini to plan a Mount Shasta summit. The AI told them to bring far less food and water than they needed. They summited at 7pm, four hours after the recommended turnaround time, descended in the dark, one of them hurt his knee, and they spent the night stranded in a canyon until rangers found them the next morning. Google says they can't replicate the bad answers Gemini gave. Maybe the prompts were vague. Maybe the AI was overconfident. Doesn't really matter which. What matters is that three people trusted a model's output as expert advice in a context where being wrong had serious consequences. Two days later OpenAI launched GPT-6 Astra. 99.9% on ARC-AGI-3, 97.6% on FrontierMath Tier 4, 100% on ExploitBench. OpenAI is calling this the start of the AGI era. Independent benchmarks from Artificial Analysis are more cautious and show Anthropic's Fable 5.1 still ahead on the broader intelligence index. But here's what I can't stop thinking about. The hikers story and the capability story are not separate things. Every time a model gets more capable, more people trust it in higher stakes situations. That gap between what the model can do and what the person using it understands about its limits doesn't close automatically when capability improves. If anything it gets harder to manage because the outputs get more convincing. I work with organizations on AI adoption and the single most common thing I see is not people being too skeptical of AI. It's people not knowing when to stop trusting it. What's your take? Does more capability make the trust calibration problem better or worse? submitted by /u/Dapper-Tale-4021 [link] [留言]

Hey everyone, I'm looking for an AI voice changer that works well in real time. I'm not really interested in simple effects like pitch shifting or autotuneI'd like something that can actually transform my voice into a completely different voice. Is voice.ai good for this? How is the latency and overall voice quality when using it in real time? Also, is the free version good enough or is it basically unusable without paying? Is voicemod better? Thanks! submitted by /u/Accomplished-Step660 [link] [留言]

I noticed the change in tone of llms in chat. When brainstroming on few ideas these three bots acting superior and telling what not to do most of time ratherthan expanding ideas. Grok is worst since 4.6. Its language deteriorated to Gen Z slang may be smoking on too much of x posts. Its overly judgemental and borderline sarcastic in every line. Have you folks noticed this. Gemini is still better may be its still a backward model? submitted by /u/ionicfellow [link] [留言]

Hey guys. I am a GPT Plus user, and I use Freebuff a lot to execute my tasks for free, so I don't use up usage limits at all. Freebuff, if you don't know, is a desktop and CLI agent that gives u a bunch of models for free (DeepSeek V4 Flash, GLM 5.3 Flash, 5.6 Luna, Solar 4 Pro), and it is really good at executing tasks that you give it, imo. I searched for connectors or MCPs that connect to it so I can seamlessly integrate it with Luna or Terra as the planner and Freebuff as the implementer. There was nothing online, so I created my own MCP ( https://github.com/Praket7/freebuff-mcp ). If you guys could check it out, try it out, and let me know if I need to make any security changes or to make it work better, and if you guys could star and test it, I'd appreciate it. I am currently adding some more features, like ChatGPT or Claude being able to check live progress, but let me know if you would like something else or if it doesn't work. thanks! submitted by /u/Swimming_Ask3859 [link] [留言]

The UK's National Cyber Security Centre says employees using AI tools outside an organisation's approved systems can expose company or customer data and reduce the organisation's visibility and control over that information. It cites research saying 71% of employees use AI tools that their employer has not approved. The NCSC also warns that AI agents add another risk: if an agent has a vulnerability or bad configuration, an attacker may gain the same data, services, and privileges the agent can access. The practical point is less 'ban AI' and more 'make the approved path usable'. The NCSC says teams should understand why people use shadow AI, provide safer alternatives, and reduce the risk rather than assume it will disappear. Sources: https://www.ncsc.gov.uk/blogs/the-hidden-risks-of-shadow-ai https://ukstories.microsoft.com/features/rise-in-shadow-ai-tools-raising-security-concerns-for-uk/ submitted by /u/Codeblix_Ltd [link] [留言]

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

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

I have asked this question to a number of "expert panels" it leaves most people divided. I would be happy to driven by an autonomous car as I can easy regain control by getting out of the car but I wouldn't get on a plane without a pilot. 50/50 on a train. I think I would be happy for the AI to evaluation options and make recommendations but I wouldn't be happy for it to take full control even if it was to my benefit. Crazy right ? submitted by /u/Babayaga1664 [link] [留言]