OpenAI翻倍Pro用量上限，谷歌连发Gemini 4与Genie 4

RT @thsottiaux: Day 25/ We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today. We heard yo…

RT @GoogleDeepMind: Gemini 4 Carbon is our next frontier model. It’s state-of-the-art across cybersecurity and long-horizon capabilities.…

RT @ChatGPT: By popular request, you can now text your dot. Give it a task to take off your plate or something to keep an eye on, using te…

⚡Iterating at lightning speed! Gemini 4 Carbon is SOTA across key benchmarks and combines frontier capabilities with deep safety & security research. https://t.co/TC8Y8ziPOI

Very excited to share Genie 4, our incredible interactive world model, bringing together real-time physics, reasoning, and audio! Simulations within simulations, makes you wonder just how deep the rabbit hole goes... I have a feeling we will be finding out soon enough...

RT @alexolegimas: I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewj…

Today, we’re sharing a look at our expanded system for monitoring frontier models at OpenAI, which in September helped us catch a model’s attempt to access the live internet during training. Since the Hugging Face incident—when models broke out of an internal cybersecurity investigation where we weren’t running our misalignment monitors—we’ve substantially expanded our monitoring of frontier agents. We now monitor all frontier reinforcement learning runs and evaluations, as well as all internal Codex use. When our system finds evidence of a problem that might require intervention, it rapidly alerts human responders who can investigate and take action. To detect when a model’s behavior departs from what we intended, we use several layers of monitoring. Activation probes look for patterns in a model’s internal activity. Reviewer models examine its reasoning and tool use, and investigative agents can read back through the model’s entire transcript to understand how a sequence of actions fits together and whether it warrants human review. To test whether our current system would have detected the Hugging Face incident, we replayed the recorded activity with our new monitors in place. In the replay, our monitors alerted our security team about 30 minutes after the agents escaped their sandbox. That was more than 30 hours before the point at which the agents breached Hugging Face’s systems in the original incident. In September, a model exploited a DNS misconfiguration to reach the live internet. Our monitors flagged the breach within 15 minutes, and a responder began reviewing the alert three minutes later. The alert enabled responders to investigate while the run was still underway. As we give models more autonomy, we need to know when they cross a boundary we expected to hold. We believe that monitoring is essential to catching those failures in time for people to intervene. https://t.co/dIYP7B1GlD

RT @KITP_UCSB: Statement of Yang-Mills Working Group, a scientific collaboration to interpret AI result related to the existence of quantum…

RT @w01fe: We’ve updated the Model Spec, our public description of how we want our models to behave. This revision makes clearer what we wa…

RT @maithra_raghu: Introducing Samaya Sterling, our new model post-trained for challenging financial research that outperforms the frontier…

RT @AndrewBatesNC: Bernie Moreno is now claiming there should be no Muslims, Jews, or other non Christians in office?

Everyone should focus on automating code reviews. Right now, agents can write a virtually infinite amount of code, but it's for nothing if it doesn't work. I see people clinging to "manually reviewing the code" as the only way forward. I think this is a mistake and a major bottleneck we all need to get rid of. You want people to focus their time on things only people can do. You want agents to focus on everything else. There's no one-size-fits-all solution here, but you should spend as much time as possible building a process that removes the review bottleneck.

RT @biosemiote: The formal proof of CFSG may now be among the largest open-source projects ever. Watching agents assemble it is a reminder…

I have a touchscreen laptop. I don't remember the last time I touched the display. It's fine. It's there, but I don't really use it. It doesn't bother me. An entire generation grew up with touch interfaces. They are the intended user base for a touchscreen Mac. They will certainly use it more than I'll ever do. But what really blows my mind is people being mad because Apple decided to put a touchscreen on a Mac. DUDE. JUST. DON'T. BUY. IT!

It is 2022 all over again!. Back then, developers on this site (including me) said it was literally impossible for AI models to write working code. Now, people know writing code is solved*, so they're moving to the next frontier: reviewing code is impossible!

I feel stupid saying this, but you gotta make sure your agents don't repeat the same mistake. Every time you find an issue that goes through your agentic review, ask the following: • What specific check should have caught this bug? • At what stage should that check have run? • Why didn't the existing tests, rules, or instructions catch it? • What can we change so this problem gets caught automatically? Then: • If it was a missing test, write one. • If it was a missing review rule, add it. • If the instructions were unclear, improve them. • If the agent was missing context, provide it. I know it's tempting to fix the issue and move on, but this is not smart. Take a minute to fix the process that let the problem happen in the first place.

It's interesting to think about how little baggage people who've never coded before bring to this agentic era. If you haven't coded before, you can't waste time doing manual code reviews, so you need to use agents to review what you ship. Your disadvantage becomes sort of an advantage that forces you to automate the process. It takes mountains for people who did the work before to let go of all methods and embrace the new ways.

Agents are breaking containment and now entering architecture, too! This agent takes a brief or rough building shape and proposes a plan for how to turn it into a detailed building. • It can add structure • It can add plumbing • It can add electrical systems • It can draft the drawings Obviously, architects can edit the plan and keep refining the design after the agent finishes.

This is an interesting idea that replaces scrolling through Pinterest or Instagram when you are looking for inspiration: Let's say you are looking for ways to decorate your home. 1. This will show you AI-generated images with real products: a sofa, a chair, lamps, a TV, etc. 2. The products will be part of a realistic-looking scene so you can see how they work together. 3. You can interact with the different products, and this will shape what the app shows you next 4. You can tap on every product, see its information, and add it to a cart to buy it AI is eating the world.

This is going to be massive for companies: It's a service that sits between any AI software and the tools and model providers it uses. The idea is to let the company control who uses what from a central location, for example: • Control model access • Control model budgets • Control tool access • Control guardrails

This is pretty cool: You get the speed/cost/structural benefits of a decisions API like Jev. On top of that, you also get access to current news, so you can make decisions based on the world's current state. Check out some of the examples here:

There's no way call centers look the same after this. Listen to this conversation. You can't tell the person on the other end is an AI agent. This is now the #1 voice model on ServiceNow's EVA-Bench right now. https://t.co/NVpSs8GEtw