Announcing Gemini 4 Carbon, our strongest model with state-of-the-art cybersecurity and long horizon capabilities. Carbon represents a step up across most benchmarks, delivering strong frontier performance in autonomous coding and cyber defense. We’re first making Carbon available to a subset of trusted cyber defenders in our Fairwind program. We’ll expand access as soon as possible.

So one of our ambassadors @divagr1925 has been working on something cool He found a way to use Qwen's Gated DeltaNet state as memory, and built a 32-bit RISC-V CPU directly into the model's weights. So instead of relying on predicting the answer to a calculation, the model can execute instructions internally. It can also run C and MicroPython programs. He calls it Qwenputer, love the name btw. The results so far are pretty impressive. On one arithmetic benchmark, accuracy jumped from 48.6% to 100%. Pretty cool to see developers experimenting with Qwen's architecture. Great work, my friend!

Use your preferred coding agent directly in @AndroidStudio. You can now choose from over 40 coding agents inside the IDE, giving your agents project context and access to Android Studio tools 🧵👇

The coding interview is dead, at least for my team at Anthropic. I've seen enough to know that it no longer provides signal. I removed it from loops this month. I have a spicy, optimistic take - I think there's an opportunity for junior engineers: felixrieseberg.com/hunger-be…

Just spoke to the Head of AI at a huge company. The #1 thing they're struggling with is... what do I do with the AI laggards at our company? Like most companies, they’ve found that a Super Pareto Principle is true within their org. 10% of their employees are responsible for 90% of the uplift in productivity/token usage from software engineering to non-technical knowledge work. They’ve spent a lot of time & resources on AI enablement from LLM foundations to function-specific trainings and skill building. And yet, the power law of productivity increases hasn’t budged. So now they’re left with a few sobering thoughts and hard questions: 1) 90% of people are overpaid and 10% are underpaid 2) We’d be better off allocating tokens proportionally to the power law, but how do we do that without killing culture? 3) Significant layoffs are likely warranted and wouldn’t impact overall output, but cultural backlash is a big concern? 4) How do we hire more power law people as a non technology company? 5) Is all of our efforts on training and enablement ACTUALLY worth it? 6) How do we not experience a brain drain where the top 10% all leave because they want to be surrounded by other AI-native 10x’ers?

Ben Horowitz to Databricks founders: "I'm not going to write you a check for $200,000. I'll write you a check for $10 million." When Ben first met them, they were six PhD students and their professor. "What they had was this thing called Spark, and the competitor was something called Hadoop. Hadoop had very well-funded companies already running toward it... so the clock was ticking." For professors and academics, Ben says, "it's a pretty big win if you start a company and make $50 million. You're a hero on campus... So I'm always a little nervous about a company that comes out of academia thinking too small." "I'm not going to write you a check for $200,000. I'll write you a check for $10 million," he told them and then explained why: "You need to build a company. You need to really go for it if you're going to do this. Otherwise you guys should stay in school." In August, Databricks surpassed a $7 billion run rate, growing at 80% YoY and was valued at $190 billion. You can read Ben's full profile on Databricks below. Video: @lennysan (2025)

🚨BREAKING: Anthropic just showed what Claude did inside 6 drug companies >3 months of regulatory writing done in 4 hours >~35,000 antibody designs in a month >human review time cut 89% >a cancer therapy part made 70% shorter, worked BETTER in lab tests also bought a one person company that took a muscular dystrophy drug from idea to mice in under 18 months big pharma is cooked

If you have NO experience with AI agents, you should start with Grok Bot. Best thing: You can set up an entire AI agent team in just 5 steps. 👉 youtube.com/watch?v=oiioSjlU…

Today, in collaboration with Anthropic, we’re excited to share rapid initial progress towards an antibody treatment for food allergy, which affects over 200 million people worldwide, discovered with the help of Claude. At Divergence Labs, we believe that AI can unlock the next generation of antibody therapeutics with more effective mechanisms than before. Such mechanisms are urgently needed in food allergy, which involve overcoming one of the tightest affinity protein pairs in immunology, IgE and its receptor FcεRI, on mast cells. Treatment with Xolair—the only approved antibody for food allergy—sequesters circulating IgE, but mast cells remain sensitized for weeks to months due to the extraordinarily slow dissociation of IgE from FcεRI. We envision a treatment that actively pulls IgE off of FcεRI, without waiting for natural dissociation. Like picking a lock, this requires the right combination of geometry and force. Our research team, working with Claude, ideated on mechanisms to accomplish this disruption, and converged on an antibody format with two arms with different functions and epitopes. Within one month, we designed, synthesized, and experimentally confirmed the biophysical function of the two arms, which we are now assembling into full therapeutic formats.

Every company can rent the same frontier models, so the edge comes from what a company's agents learn about its own business. Databricks' answer is a system of record built for agents. Before an agent can be trusted with real work, that record has to tell it: 1. What's true: the company's own data 2. What it means: how the business defines things (grounding agents in it raised accuracy from about 50% to 84.5% in Databricks' testing) 3. What it's allowed to do: an identity, a budget, and guardrails 4. Whether it worked: grading against the company's own definition of good Feed every correction back into the record, and the company keeps what it learns even when the model changes. "If you replace the AI model at your company and the new one inherits your definitions, permissions, evaluated cases, customer history, and hard-won exceptions, that is a foundation for a sustainable competitive advantage." From the latest Profiles of the Future by @bhorowitz and @mikemcg0 on Databricks & the Future of the Firm: a16z.news/p/databricks-and-t…

Day 23/ Much requested iCloud Main & Calendar plugins. Just kidding, today is a reset. Still much requested though!

I publish AI news every single day. The hardest part isn't writing. It's keeping up with facts that change overnight. "Has X launched yet?" "Is that feature still free?" "Did they actually raise funding?" A model answering from memory can sound confident and still be completely wrong. Parallel just launched the Decisions API to tackle this. → Ask a yes/no or multiple-choice question → It checks the live web → Get a probability + sources All for only $1 per 1,000 calls. Where I'd actually use this: ↳ Fact-check AI news before publishing ↳ Vet potential sponsors ↳ Stop AI agents from acting on outdated facts Basically RAG, but for decisions. Retrieval-augmented decisions. 😄

Today Physical Superintelligence PBC is announcing solutions to five long-standing problems in quantum information theory. psi.inc/research/oqp-batch-2… Thread 👇