Cohere spent the past year deploying North, its enterprise AI workspace, with actual customers doing actual work. Agentic question answering over company file systems. Data analysis across spreadsheets. Multi-session memory that has to hold up in production. Command A+ is what came out of that, a model shaped by a year of watching enterprise workflows break and figuring out why. The result is a 218B mixture-of-experts model with 25B active parameters at inference time, available today on Hugging Face under Apache 2.0. It replaces five separate models in the Command A family, each of which handled one thing. This one handles all of them, and on most of the tasks those specialist models were built for, it wins.

Dramabox just landed on Hugging Face and the demo space is live. Resemble AI built it on top of Lightricks' LTX-2.3, and the thing that makes it different from every other TTS model is simpler than you'd expect, you don't give it text to read. You write it a scene.

Anthropic just got hit with a designation the US government usually reserves for Chinese tech companies like Huawei. Not for a data breach. Just for refusing to let the military use its AI for mass domestic surveillance and autonomous weapons without human oversight. That's the short version. The longer version is messier, more interesting, and honestly a little hard to believe is happening in 2026. On Friday, President Trump ordered every federal agency to immediately stop using Claude, according to The Guardian. Defence Secretary Pete Hegseth followed up by labelling Anthropic a "supply chain risk to national security", a tag that bars any military contractor from doing business with the company. The same label America uses on Huawei. Applied, for the first time ever, to an American company, India Today reports. Anthropic's response was short and direct. "No amount of intimidation or punishment from the Department of War will change our position on mass domestic surveillance or fully autonomous weapons," the company said in a statement Friday night. So that's where we are.

OpenAI dropped their latest model yesterday and buried inside the benchmarks is a number that deserves more attention than it's getting. On GDPval, a test that puts AI agents through real professional tasks across 44 actual occupations, GPT-5.4 matched or outperformed human professionals 83% of the time. The previous version sat at 71%. That's not a small jump. And this isn't GPT writing emails or summarizing documents anymore. This version can move a mouse, click buttons, fill out forms, and work across applications the way a person sitting at a desk would. It scored 75% on OSWorld, a benchmark that tests exactly that. The average office worker scores 72.4%. The model is already better at operating a computer than most people who use one for a living & 83% is just the beginning of what this release actually means.

Think of it as an AI agent that lives on your machine. Not a chatbot that gives you code suggestions. An actual agent that can create files, edit code, run commands, debug errors, and work through multi-step tasks on its own.

Google's spending billions training AI models. OpenAI's hiring armies of engineers. And somehow, a startup in Bengaluru just outperformed both of them. Sarvam AI's new Vision model scored 84.3% on olmOCR-Bench—a brutal test that makes AI models read messy scanned documents, handwritten notes, and complex tables. Google Gemini 3 Pro got 80.2%. ChatGPT? A distant 69.8%. If you're thinking "okay, cool benchmark, but who cares?"—fair. Here's why this matters: billions of documents across India are locked away in regional languages. Government records in Gujarati. Medical files in Tamil. Historical archives in Bengali. The big AI models can read these languages, but they mess up constantly—wrong characters, mangled words, useless output. Sarvam doesn't. It's specifically trained on Indian scripts, and the results show. For the first time, Indian companies have an AI tool that can reliably digitize documents in 22 languages without sending everything to Google or OpenAI's servers.

For the past year, every serious AI conversation has circled back to the same dependency: Nvidia. If you wanted frontier performance, you needed their chips, If you wanted scale, you needed more of them. Then GLM-5 dropped & suddenly, benchmark charts that usually move inch by inch started shifting. There’s also a growing buzz online claiming GLM-5 may have been trained independently of Nvidia hardware, some even speculate about alternative stacks like Huawei’s. Nothing official confirms that. But the fact that people are even asking that question tells you how disruptive this release feels. Because the real reason people are talking isn’t just the size. It’s what GLM-5 is capable of. It is designed for longer, more demanding tasks where the model has to think in steps, plan ahead, and stay consistent instead of just giving a clever one-shot answer. It can handle multi-step workflows. It doesn’t lose track halfway through long contexts. And on Vending Bench 2, it ran a simulated business for an entire year and ended with a $4,432 balance. I’ve seen plenty of open models get close to the big closed systems before. But rarely do they feel balanced across everything. GLM-5 is one of the first open models in a while that doesn’t feel “almost there.” It feels like it’s actually in the same arena. And that’s why it’s suddenly everywhere.