Transformers.js v4.1 is out 🎉 Something that has literally never run in a browser before is now possible. One line of code. Can you guess what it is? 👇 (hint: think small 🤏) https://t.co/W4z5lQaoHB

Given the increasingly closed-source nature of the U.S. AI ecosystem, it is now more important than ever to push for the proliferation of open model and dataset releases. Datamule (@johngfriedman), @TeraflopAI, and @daftengine collaborated to release 43 Billion Tokens of SEC EDGAR data.

Cool Hermes cheat sheet found on Reddit! Definitely take a look 👀 https://t.co/3fM4XeKSHk

Cool Hermes cheat sheet found on Reddit! Definitely take a look 👀 https://t.co/3fM4XeKSHk

GlotOCR Bench OCR Models Still Struggle Beyond a Handful of Unicode Scripts paper: https://t.co/RVCcLMVomo https://t.co/0BV5zMJEbJ

https://t.co/iyO3WF4IUO

https://t.co/iyO3WF4IUO

Introducing Gemini 3.1 Flash TTS 🗣️, our latest text to speech model with scene direction, speaker level specificity, audio tags, more natural + expressive voices, and support for 70 different languages. Available via our new audio playground in AI Studio and in the Gemini API! https://t.co/5PpBdhQMNg

Today I solved my second open Erdos problem with GPT-5.4 Pro. It's quite a remarkable day, because within the last 24 hours, two other Erdos problems has been solved as well with AI. Still over 500+ to go. https://t.co/dkrhZSlyBR

Three months ago https://t.co/kWw1dAMOZg

Introducing Gemini 3.1 Flash TTS, our most expressive model yet, topping quality and cost on the Artificial Analysis leaderboard 🔊 Control style, pace, add multiple speakers, and delivery with audio tags, switch expression mid-sentence in 70+ languages. Tutorial below! https://t.co/Ozam4wdBER

@sigdel29 @SylonZero I already did this: https://t.co/kiuZ7QXLzb

We help founders and business owners build real and reliable software with @emergentlabs But once you ship, the real work begins: customers, ops, follow-ups, reminders, hiring. Endless small tasks Today we start taking that off your plate Introducing @buildwingman beta https://t.co/MoWFbULWpq

Been looking for something like this for some of my work. The challenge for me has always been having a reliable, always-on agent that has full context, my tools, just gets stuff done, and that I can interact with from anywhere. Just saw this in my timeline and was happy with how simple it was to set up personal agents. In just a couple of minutes, I created a few research analysts to do research in the background and report to me on WhatsApp. It has memory, skills, and the good stuff you would expect in an agent. Very exciting to see this new wave of personal agents to help get real work done.

Introducing Nucleus-Image: the first sparse Mixture-of-Experts diffusion model 17B parameters. Only 2B active. 10x more parameter-efficient than leading diffusion models. Toe-to-toe with GPT Image 1, Imagen 4, and Qwen-Image: from pure pre-training alone. No DPO. No RL. No preference tuning. Day 0 support in 🤗 Hugging Face diffusers. Fully open-source under Apache 2.0. Weights, training code, and dataset recipe - we're not holding anything back <3

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A cachorrinha de uma amiga morreu. Então ela usou um projetor e um umidificador para trazer ela de volta ao seu quarto. https://t.co/slZyBq2Qj1

Today, we released Lyra 2.0, a framework for generating persistent, explorable 3D worlds at scale, from NVIDIA Research. Generating large-scale, complex environments is difficult for AI models. Current models often “forget” what spaces look like and lose track of movement over time, causing objects to shift, blur, or appear inconsistent. This prevents them from creating the reliable 3D environments required for downstream simulations. Lyra 2.0 solves these issues by: ✅ Maintaining per-frame 3D geometry to retrieve past frames and establish spatial correspondences ✅ Using self-augmented training to correct its own temporal drifting. Lyra 2.0 turns an image into a 3D world you can walk through, look back, and drop a robot into for real-time rendering, simulation, and immersive applications. ➡️ Learn more: https://t.co/ROR7miJeCU 📄 Read the paper: https://t.co/1osU9EGjGD

Introducing Gemini on Mac. It’s the first time we’re bringing the @Geminiapp to desktop. The team built this initial release with @Antigravity, and it went from an idea to a native Swift app prototype in a few days. More features on the way! https://t.co/YRy0Pqq6zo

Cumulative inflation since Jan '21 is between 23.6% and 27.1%, depending on which index we're using: https://t.co/dKXzuV0SiJ

It’s never too late to pivot to a better business model https://t.co/YctUXcDJJC

Banger paper from NVIDIA. Agentic reasoning needs models that are not just capable, but efficient at long-context inference. The agent model layer is moving toward open, long-context, high-throughput architectures. This paper introduces Nemotron 3 Super, an open 120B parameter model with 12B active parameters, built as a hybrid Mamba-Attention Mixture-of-Experts architecture. The headline numbers are strong: up to 1M context length, comparable accuracy on common benchmarks, and up to 2.2x higher throughput than GPT-OSS-120B and 7.5x higher throughput than Qwen3.5-122B. The model combines several efficiency bets, including NVFP4 pretraining, LatentMoE for accuracy per FLOP and per parameter, and MTP layers for native speculative decoding. It is trained on 25 trillion tokens, then post-trained with supervised fine-tuning and RL. Paper: https://t.co/VcqUPjylzF Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c

Nemotron 3 Super Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning paper: https://t.co/hOd6ss4tLV https://t.co/C7SOCZsA5c

New course: Spec-Driven Development with Coding Agents, built in partnership with @jetbrains, and taught by @paulweveritt. Vibe coding is fast, but often produces code that doesn't match what you asked for. This short course teaches you spec-driven development: write a detailed spec defining what to build, and work with your coding agent to implement it. Many of the best developers already build this way. A spec lets you control large code changes with a few words, preserve context across agent sessions, and stay in control as your project grows in complexity. Skills you'll gain: - Write a detailed specification to define your mission, tech stack, and roadmap, giving your agent the context it needs from the start - Plan, implement, and validate features in iterative loops using a spec as your agent's guide - Apply the same repeatable workflow to both new and legacy codebases - Package your workflow into a portable agent skill that works across agents and IDEs Join and write specs that keep your coding agent on track! https://t.co/hI4GwuvhtN

Today we launched Gemini 3.1 Flash TTS, our most expressive and controllable text-to-speech model yet. This launch [excitement] includes audio tags! 🗣🏷 Audio tags [explanatory] are a seamless way to guide vocal style, pace, and delivery using natural language commands embedded directly in your text. Want a different tempo or tone? [amazement] Just tag the audio to steer the AI-speech output! The model supports 70+ languages (24 of which are high-quality evaluated languages, including: Japanese, Hindi, and Arabic). Watch the audio tags in action in the demo below ↓

Gemini 3.1 Flash TTS is rolling out in Google Vids and is available today in preview via the Gemini API and in @GoogleAIStudio. Whether you’re creating a pitch deck or recording a passion project, transform your scripts into studio-quality narration: https://t.co/MG2YIQwKb6

A new and troubling risk is emerging around AI. An attacker targeting Sam Altman reportedly had a broader list of AI executives, raising concerns that individuals in the industry could become targets. It signals a shift. As AI’s influence grows, so do the stakes, and the risks are no longer just digital. https://t.co/50J0jk5tFz @fortunemagazine @marcoquiroz10

"I used a billion tokens this week. I'm not even in the top 100 Codex users at OpenAI." We sat down with @jxnlco (creator of Instructor, now on @OpenAI's Developer Experience team) to talk about how zero-latency inference is changing the way engineers work. https://t.co/PDyHtYRXec

Rethinking On-Policy Distillation of Large Language Models Phenomenology, Mechanism, and Recipe paper: https://t.co/E9e8kOjKvn https://t.co/60yYxgkqAA

Nvidia released Lyra 2.0 on Hugging Face Explorable Generative 3D Worlds paper: https://t.co/HcxsBD2yEh model: https://t.co/bC32ADfvDS https://t.co/RwdR7DUEcY

Habitat-GS A High-Fidelity Navigation Simulator with Dynamic Gaussian Splatting paper: https://t.co/pMpYxXukh7 https://t.co/n7UyazeDge

This AI car drove through 506 cities, a Tokyo typhoon, and Arctic darkness without a single external map. @wayve_ai is giving Waymo and Tesla a run for their money, and most people haven't noticed yet. @l2k sits down with CEO @alexgkendall, who breaks down how they got here and where it's going next. In his words: "taking it from expensive retrofit vehicles to mass-market vehicles that you can buy or manufacture." The old self-driving playbook is dead. The question is who writes the new one. Is your next car going to drive itself before you even think to ask for it?

Robots are maturing. AI models are advancing. Deployments are rarely scaling. Today, deploying a robot still means stitching together systems that were never designed to work together. For every robot. Every time. The industry needs a unified intelligence infrastructure that any robot, any model and any workflow can plug into. We call this an intelligence grid. Infrastructure that makes intelligence composable, deployable, and continuously adapting. Every connected robot evolves as the platform advances. More on what we're building: https://t.co/AMpxxFmtuc