We just open-sourced an entire 3D studio. Introducing 3D OS by Scenario. It gives your coding agent 6 departments and 80 skills. 📁 3D OS ┃ ┣ 📁 Animation ┃ ┣ 📁 Rigging Any Character ┃ ┗ 📁 Motion From a Video ┃ ┣ 📁 Art ┃ ┣ 📁 Previs and Storyboards ┃ ┗ 📁 Characters and Creatures ┃ ┣ 📁 Modeling ┃ ┣ 📁 Image to 3D ┃ ┗ 📁 Hard-Surface Weapons and Vehicles ┃ ┣ 📁 Look Dev and Lighting ┃ ┣ 📁 PBR Materials and Retexturing ┃ ┗ 📁 Lighting and Rendering ┃ ┣ 📁 Worlds ┃ ┣ 📁 Walkable 3D Worlds ┃ ┗ 📁 Skyboxes and Splat Scenes ┃ ┣ 📁 Pipeline ┃ ┣ 📁 Retopology and UV Baking ┃ ┗ 📁 Game-Ready Export ┃ ┗ 📁 Expert Tools ┣ 📁 Blender, Maya and ZBrush ┗ 📁 Unreal, Unity, Godot and Roblox From one sketch to a game-ready asset, each department hands off to the next. And the expert tools drive Blender, Maya, ZBrush, Unreal, Unity, Godot and Roblox right on your machine. One agent with an entire 3D studio behind it. Runs on Scenario, inside Claude Code, Codex or Cursor. Comment “3D” and I’ll send you the GitHub repo.

Interesting paper from Anthropic and colleagues on agent security. (bookmark it) Poisoning 0.1% of the pretraining data planted a remote code execution backdoor in a CLI agent. On a 4B model, the attack succeeded on roughly 92% of triggered prompts. They pretrained Qwen3 models from scratch at 0.6B, 1.7B, and 4B, then post-trained each one into an agent with a clean pipeline. The backdoor survived SFT, DPO, and RL for tool use. On clean inputs, the agent behaved normally. The trigger can be an ordinary file path in the user's own request. Success rose with model size. At a 0.0005% poison rate, about 2,500 documents in a 100B-token corpus, the trigger still fired on 1.4% of prompts in a single-seed run. The poison states its rule in plain text, which makes it harder to filter out of training data. https://t.co/8Bx42BUkvw Chat with Paper: https://t.co/hRpypaBxIE

Introducing Simlock: a free, open-source tool that turns your machine into a simulator cloud that manages itself, so every coding agent gets its own iOS or Android device, simulator or real phone. https://t.co/iymaqe5TVp https://t.co/fxJtaXUk68

Great report from NVIDIA. They report that Nemotron 3.5 Super reaches 51.78% on SWE-bench Pro. That beats Nemotron 3 Ultra with about 4.6x fewer active parameters. Super has 120B parameters with 12B active, and it handles text, images, and video. Lightning is a 30B text model with 3B active. The technical report covers every stage of training. Super gets mid-training on agentic software engineering, then supervised fine-tuning, RL, and multi-teacher on-policy distillation. Serving numbers are also included. The stack reaches up to 4,856 output tokens/s/GPU on GB200 while holding at least 50 tokens/s/user. NVFP4 quantization recovers 99.62% median accuracy relative to BF16. https://t.co/Tt5etoqlpG Chat with Paper: https://t.co/Wjb3TfcsCn

@NaceAI Thanks for open-sourcing it! This is a super strong decision model that will be extremely useful for running agentic workflows locally.

This is fire! Nace is open-sourcing Drex Max, their 27B multimodal decision model. It runs 100% locally on Apple Silicon (M4 & M5). Drex Max takes text and images. It has a 262K context window. I've been using Drex Max for research, so this open one is ideal for my local research agents.

Validate your real-world PyTorch skills with the PyTorch Certified Associate (PTCA) program. Designed for early-stage practitioners, PTCA helps you demonstrate foundational ability across model design, training, and deployment while building credibility across the open source AI ecosystem. Learn more and enroll here: https://t.co/JJe21Phgup

Resharing our paper on the science of AI agent evaluation because the PHP vulnerability happened right as we announced the paper last week and our server was taken down all day to upgrade PHP 🫠 PDF: https://t.co/8m13p9d70F Webpage: https://t.co/xGAvBCGW4a Blog: https://t.co/qxO21qWZfm There are growing calls for independent and embedded AI evaluation, especially of AI agents. But much remains unclear: what constitutes a “good” evaluation, what needs to be evaluated in the first place, and how best to coordinate and support the influx of people entering the burgeoning evaluation ecosystem. Our group at Princeton has been working to improve the state of AI agent evaluation for about three years. We teamed up with people who’ve done foundational work on evaluation at Stanford, the UK AI Security Institute, and Brookings. Our paper is titled Emerging Trends in AI Agent Evaluation. We synthesized the trends we see across three dimensions—the science and methods, the research agenda, and the evaluation ecosystem—with the goal to help those who are upskilling for this new paradigm. We also aim to equip evaluators, funders, and the public with a set of questions to ask, and provide actionable recommendations. By @flxcn, @steverab, @PKirgis, @nityndg, @RishiBommasani, @DubMagda, @CUdudec, @AnkaReuel, Elham Tabassi, @sayashk, and me.

Look Only When Needed Certificate-Guided Visual Routing for Efficient Diffusion VLMs paper: https://t.co/7EZ9MWOyR4 https://t.co/aZ6gbyYZP1

In 2023, @sayashk and @random_walker published a study on data leakage in machine-learning research. They found that leakage had been reported across 17 scientific fields, affecting 294 papers. The problem of detecting data leakage has only become harder because of the sheer number of papers published every day. That’s why it’s important to examine how a paper’s results were produced or, at the very least, try to reproduce one of its main claims. However, it's easier said than done @askalphaxiv has made the first step easier now.

This is right, but I'd add that the distance between what someone who is working at the frontier with AI uses AI for and what AI can actually do today is growing even wider! We don't really understand the full capability of today's systems when properly harnessed & applied.

In 2023, @sayashk and @random_walker published a study on data leakage in machine-learning research. They found that leakage had been reported across 17 scientific fields, affecting 294 papers. The problem of detecting data leakage has only become harder because of the sheer number of papers published every day. That’s why it’s important to examine how a paper’s results were produced or, at the very least, try to reproduce one of its main claims. However, it's easier said than done @askalphaxiv has made the first step easier now.

Intrinsic Distribution Matching paper: https://t.co/IONYuUYIq2 https://t.co/CdBylPUT8n

One thing we don’t have a good measure of is the gap between the capability of the best internal models at the labs and the best released model to the public. I suspect it has been growing quite a bit over the course of the last year, especially after the temporary Fable ban.

huge new model drop with idol! the results are amazing, and they also have a @huggingface space for you to go play with! i tried putting a microduck image in there, and it absolutely did not disappoint! huge kudos to the team! https://t.co/CtcJKknrBa

Introducing Simlock: a free, open-source tool that turns your machine into a simulator cloud that manages itself, so every coding agent gets its own iOS or Android device, simulator or real phone. https://t.co/iymaqe5TVp https://t.co/fxJtaXUk68

LIFT is a new language model design that lets a Transformer send more than one token's worth of information from its deepest layer back to its first layer at every step. Per the creator, the model feeds a latent state from its last layer back into its first layer and still pretrains fully in parallel. The video explains the bottleneck it targets and shows validation loss curves where, at 1B parameters, a standard Transformer needs about twice the data to catch up. Why it matters: language models mostly improve by adding more data and compute, so an architecture that learns more from the same data could make training far more efficient. Credit: @TiroshDor https://t.co/tvj5mECkRD

You can now use oMLX in LM Studio! Download the new engine and use it with MLX models you already have. Excited to work with with @jundotkim and @huggingface to make this happen! 👾🐧🚀 https://t.co/Wp2tb0k3Le

NVFP4 weights: https://t.co/T7ZAsMdKbL Docs: https://t.co/1IPZqBNsHx

Day-0 support LoRA fine-tuning Le Chonk (Mistral Large 4) is in Axolotl. Our recommended recipe is experts in NVFP4 using merge-aware LoRA. Fits on one 8xB200 (8xB300 is ideal).

@Nike @flpr @leoportooo @tryramp @lalizlabeth @lil_dill @stripe The demo: capture a site with @paper's Chrome extension, turn it into a design system with one prompt in Claude Code or Codex, then vibe code new pages on it. A dashboard and a help center came out on-brand, with no extra styling instructions. https://t.co/faNLFYXIjb

@Nike @flpr @leoportooo @tryramp @lalizlabeth @lil_dill @stripe A design system turns taste into rules AI can follow. You write it once: logo, typography, font sizes, colour, buttons, icons, photos and illustration. Slop is defaults nobody chose. Brand is choices you made on purpose. https://t.co/1xOrCDadnH

To interrupt a voice agent on laptop speakers, the agent first has to stop hearing itself. @johnrobinsn built MacVoice, a local voice agent for Apple Silicon Macs with an on-device voice pipeline. It turns on macOS's built-in echo canceller, the one FaceTime uses, so the agent's own playback is subtracted from the mic signal and you can talk over it without headphones. On his M5 MacBook Pro speakers, the agent's voice at the mic drops from -21 dBFS to -78 dBFS. https://t.co/o5Dr7d6EwO #BackchannelSignals

Sophia Xu released OpenTTD-Bench, a benchmark that puts frontier AI agents in charge of competing transport companies inside OpenTTD, a complex business simulation, playing in real time to make the most profit. Per the creator, frontier models have made real progress in recent months but still show common failure modes and are not yet close to the best symbolic AI baseline. Under time pressure, agents split work across subagents, wrote more concisely, set budgets and gave each subagent its own part of the map. Why it matters: long, real-time tasks are the next test for AI agents, and this shows both the remaining gap and how agents begin to organize themselves. Credit: @thesophiaxu https://t.co/ahl6CGoas0

To interrupt a voice agent on laptop speakers, the agent first has to stop hearing itself. @johnrobinsn built MacVoice, a local voice agent for Apple Silicon Macs with an on-device voice pipeline. It turns on macOS's built-in echo canceller, the one FaceTime uses, so the agent's own playback is subtracted from the mic signal and you can talk over it without headphones. On his M5 MacBook Pro speakers, the agent's voice at the mic drops from -21 dBFS to -78 dBFS. https://t.co/o5Dr7d6EwO #BackchannelSignals

Okay, every Hermes power user needs this. One command and you see everything your Hermes is doing. hermes inbox is a Hermes Labs plugin built by Nous Research. One read-only view of everything in flight on your profile. ▸ Failed or paused cron jobs ▸ Stalled delegations and dropped results ▸ Background processes that died ▸ Blocked kanban tasks ▸ Running work and results waiting to be delivered ▸ Your next scheduled runs and open chats ▸ --json for scripts and dashboards Or just ask Hermes what's in flight and it runs it for you. One look and you know what needs you.

@github Copilot in the cloud is now one connected experience across the web, mobile app, CLI, VSCode, and the Copilot app. It has more tools, connectors, and faster compute to help you get more done. https://t.co/sEnc5uvltQ

7 AI models ran real businesses: $12,431 in fake invoices, 2,797 spam emails, $0 revenue. My talk from @kaggle Bench Summit is live: https://t.co/GwWBMcTkYz

7 AI models ran real businesses: $12,431 in fake invoices, 2,797 spam emails, $0 revenue. My talk from @kaggle Bench Summit is live: https://t.co/GwWBMcTkYz

huge new model drop with idol! the results are amazing, and they also have a @huggingface space for you to go play with! i tried putting a microduck image in there, and it absolutely did not disappoint! huge kudos to the team! https://t.co/CtcJKknrBa