What actual f*ck @DarioAmodei / @boris / @TheAmolAvasare Let me get this straight - I get this email telling me: 1. A cybersecurity "research" company gets into your internal systems while... *checks notes* "conducting a security testing exercise on its own initiative and without our authorisation" 2. You "cannot confirm whether any particular person’s information was viewed," then in the same email say "we have no reason to believe you are at heightened risk." 3. You also try to reassure by saying "the cybersecurity firm has certified in writing that it has deleted the accessed data and will not use it." Translation - they pinky promised they deleted everything they gained unauthorised access to. I've been hacking companies for 15+ years, so let me spell it out. Accessing systems without authorisation has a name -hacking. Calling it a "security testing exercise" doesn't change that. Honestly, the access itself doesn't bother me much. The irony does. I’ve spent over $50k on API credits in the last six months and contributing to CVP by accepting logging. On top of that, I've reached out to your teams to collaborate on security research (actual research - not the kind that has you send out privacy notices to your customers) and heard nothing. I'm getting deeply unserious security vibes and considering moving all my (and my clients) token spend to @OpenAI if I don't see material change here.

we have a winner for the most absurd AI startup ever this Peter Thiel backed company uses AI robots to stab fish in the brain so they taste better and last up to 3x longer it's called Shinkei and it modernizes a centuries-old Japanese technique dozens of Michelin-star restaurants are already lining up to buy the fish here's how it works: > the problem is when a fish gets caught, it usually just suffocates on the boat > that stress floods the meat with hormones and lactic acid, which hurts the flavor and texture > the old Japanese fix for this was spiking the fish through the brain right after the catch, which stops all that > but doing it by hand is impractical for most American fishing crews > so Shinkei built a fridge-sized robot that sits right on the boat's deck > the crew feeds in a fish and AI identifies the exact species, so it knows exactly where the brain and gills are > the robot then drives the spike through the brain and cuts near the gills in about 7 seconds > the fish goes into an ice slurry so the blood drains out > so the fish doesn't sit there stressing out, and that tasty flavor and texture is preserved example number 5,392,417 of how AI can be applied to literally anything

What we do all day, roughly: take a team's question ("what should the robot learn?") and turn it into clips ready to train on. Task design, our own recording hardware, processing, hand pose and language labels, quality checks. Same setup for all 50+ teams in our pipeline. Come grill us on any step at CoRL, Mon Nov 9:

Memory is what allows Claude to learn from experience. In a new Engineering Blog post, we share how “dreaming” helps Claude organize what it’s learned, correct its mistakes, and find what it needs more easily the next time.

Introducing Higgsfield Miracle. Our latest proprietary post-production technology vectorizes any video into layers for localized, pixel-level editing in real time, with world knowledge built in. Every frame holds the image depth needed for cinema-grade post-production. Now in private beta with select testers. Coming soon to Higgsfield MCP. Set up Higgsfield MCP in Claude to be the first to try it.

Today Physical Superintelligence PBC is announcing a proof of the Aaronson-Ambainis conjecture, one of the central open problems in quantum complexity, with major implications for quantum computing speedup. Thread 👇

I'm Sam, one of Anthropic's co-founders, and I lead much of our safety work. I'm going to have more to share on that work over the coming months. I thought I'd start by sharing a few thoughts on the latest report from @METR_Evals. The speed of progress in model capabilities is one of the most important questions in AI safety, and METR’s work here provides the public with an independent, external view into how this is happening inside Anthropic. I’ll talk more about some of the details below, but first some context for those new to this work. How quickly AI progress is accelerating has many important safety implications, since it determines how quickly the risks from advanced models increase. But, because most research happens within the frontier labs, it’s difficult for external observers and policy makers to get a strong sense of what that rate is. We kicked off the process with METR that led to this report earlier in the year, in anticipation of progress continuing to accelerate. For this report, we gave METR access to a range of sensitive internal data, including benchmark scores for unreleased models, estimates of how models are progressing, and interviews with our researchers. They had complete independence to form their own conclusions. Many of their conclusions match my own. AI is speeding up our research, but it’s hard to say exactly how much. While they conclude AI can't yet make the most important contributions to model training on its own, they think the pace of development could, by default, accelerate further. This work began well before and is separate from the embedded third-party evaluators that are part of our commitment to pacing the frontier, but it’s a good illustration of why we think evaluators are a good idea. The more access reviewers have, the clearer a picture they can form of the situation and the better-positioned they are to highlight situations like this one. We redacted a lot, and I accept that this makes it harder for others to check METR’s work. But we determined that sharing exactly what is driving the speedup isn’t the right thing to do, both because it’s commercially sensitive and because it risks handing out a roadmap for others to go faster. That would make the risks METR is worried about worse. That’s a tradeoff. I think we made the right call, though we’re continuing to assess the pros and cons here. In the coming weeks, we plan to share a report with our own analysis of the speed of AI progress within Anthropic, including where we stand against our Responsible Scaling Policy, plus new steps we’re taking to strengthen our safety and security practices as AI keeps improving quickly.

Introducing Personal Finance in Claude, in beta in the US. Connect the accounts you choose, and Claude answers your money questions in everyday language, from your own balances and transactions. Access is read-only, and Claude can't move, manage, or invest your money.

We submitted a ~100-page full report on our assessment to Anthropic. For the public version, Anthropic redacted a majority of the evidence we found most informative. As a consequence, the public must rely on our judgment more than we consider ideal.

We're excited to unveil VINCO, our frontier AI model trained to play and win 1v1 Catan. We've secretly deployed it on the global leaderboard, where it has reached both the #1# and #2# positions, consistently beating every human world champion.

Introducing Personal Finance in Claude, in beta in the US. Connect the accounts you choose, and Claude answers your money questions in everyday language, from your own balances and transactions. Access is read-only, and Claude can't move, manage, or invest your money.

Introducing GPT‑6 Cyber: a major advance in security research that pairs stronger capabilities with better model alignment. The model powered the Linux kernel research we announced yesterday, and security teams at companies like Mozilla, Palo Alto Networks, Calif, and Cisco—alongside our own at OpenAI—are already putting it to work. GPT-6 Cyber is available to select Daybreak Red customers and we are expanding access over time.

What if your devices, apps and agents knew what you wanted? You finish breakfast, and your Uber is booked. Running late, your agent sends a “Running late” text. You reach home, and your lights turn on. AI is proactive now. But it still needs your yes. It waits, for you to unlock your phone, open an app and tap approve. Every time, hundreds of times a day. AI needs its own interface. Aina is a ring with a screen that predicts and shows you the three things you’ll need next. One flick of your thumb, and it’s done. For everything. ✦ Cues: proactive suggestions, ready before you need them ✦ Voice: talk to your agents, take notes, dictate ✦ Remote: control your music, Mac and TV ✦ Calls: right from your hand Pre-orders open now

Some news: I’m joining OpenAI to lead product management for Core Products! I have a lot of respect for OpenAI for showing the world that scaling works, for bringing AI to over a billion people, and for believing great products are core to the mission. I’m excited to work with @thsottiaux, an incredible PM team, and everyone across OpenAI. Together, we’ll build agents that have your back and your team’s, and keep you in control, so you get more time for the people and work you love. Lots to learn, and lots to build. Please keep your feedback coming!

WSJ: By Wednesday morning, senior leaders at OpenAI were discussing whether the results meant the company would have to reset expectations regarding its data center capacity next year, according to a person familiar with the conversation.

lots of news breaking these days so I may as well sneak mine in if anyone cares and share that I recently joined @OpenAI 🫡 my time at Anthropic was a fever dream in the best way possible. got to help build the growth function, work alongside some really really talented people, and tackle some of the most challenging problems of my career. incredibly grateful for the experience and excited for what’s ahead. also in case you were wondering, Ant has better coffee/drinks and OAI has better snacks.

Some news: I’m joining OpenAI to lead product management for Core Products! I have a lot of respect for OpenAI for showing the world that scaling works, for bringing AI to over a billion people, and for believing great products are core to the mission. I’m excited to work with @thsottiaux, an incredible PM team, and everyone across OpenAI. Together, we’ll build agents that have your back and your team’s, and keep you in control, so you get more time for the people and work you love. Lots to learn, and lots to build. Please keep your feedback coming!

Modest intelligence is arriving faster than most people realize. Today, we're open-sourcing TwIL-8B. Just 8.8 billion parameters, yet approaching frontier-model scores on several difficult benchmarks: • 93% of frontier performance on coding • 92% on instruction following • 90% on contest mathematics All from a model small enough to run on your own hardware. But the benchmarks aren't the most important part of this announcement. For years, the AI industry has focused on building the most capable model, regardless of the cost required to run it. I believe that equation is changing. The next era of AI will be defined by the cost of completing work. Not simply the intelligence of the model, but how efficiently that intelligence can accomplish the task. If a model with 8 billion parameters can reliably complete the same work as one with hundreds of billions, why pay for the larger model? As smaller models become more capable, intelligence becomes something we can distribute, specialize, and run across the hardware we already own. That's the future we're building at @thewebAI TwIL-8B is available today. Open weights. And this is just the first of several announcements we're making today. HF Link: