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NEWS: Anthropic's new model, Claude Mythos, is so powerful that it is not releasing it to the public. Instead, it is starting a 40-company coalition, Project Glasswing, to allow cybersecurity defenders a head start in locking down critical software. nytimes.com/2026/04/07/techn… [Translated from EN to English]
→ View original post on X — @scobleizer, 2026-04-07 18:01 UTC
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I stole this framework, and it has genuinely been one of the most helpful hiring and onboarding guides my team uses. Being AI-first means nothing without solving real business problems. It's not enough to just use AI. I know plenty of folks using AI dozens of times a day, 7 days a week, who are still working at a Level 2. Even worse, they have no idea they're stuck as a surface user. You have to use AI for faster experimentation. To shorten the iteration cycle. To improve the actual outcome. Without those three, you're just wasting tokens. Most people I interview are at a 3 (solution-oriented, but not action-oriented). I want everyone to start at a 4 (action-oriented with a sense of technical, user, and business tradeoffs). And when they earn trust, we move up to a level 5 (full ownership of the problem, solution, and continued management of the work). Using AI doesn't replace your critical thinking. It means the work you can pull off now wasn't on the table a year ago, and your job is getting bigger. Save this for your next new hire. Source: this was a framework first introduced to me by Alex (@businessbarista) who was introduced to it by Steph (@stephsmithio). I added the AI parts.
→ View original post on X — @alliekmiller, 2026-04-07 18:01 UTC
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Imagine watching a concert not from a fixed camera angle, but from any angle. The catch? Volumetric video is incredibly hard to store and stream. Our work, PackUV, tackles exactly this problem. Learn more and see PackUV at work at Brown CS Blog: blog.cs.brown.edu/2026/04/07/packuv-video-native-representations-for-streaming-4d-scenes [Translated from EN to English]
→ View original post on X — @scobleizer, 2026-04-07 17:58 UTC
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Today, Niantic Spatial introduces Scaniverse — our flagship product and the gateway to our spatial intelligence platform and Large Geospatial Model.
— Niantic Spatial 🌎 (@NianticSpatial) 7 avril 2026
Here’s what’s new:
🔹Scaniverse (iOS mobile + web): Capture once, generate multiple outputs—VPS maps, meshes, and Gaussian splats… pic.twitter.com/ChYvXSsGbO
Today, Niantic Spatial introduces Scaniverse — our flagship product and the gateway to our spatial intelligence platform and Large Geospatial Model. Here’s what’s new: 🔹Scaniverse (iOS mobile + web): Capture once, generate multiple outputs—VPS maps, meshes, and Gaussian splats (Android coming soon) 🔹Collaborative mapping: Multi-user scans fused into a single, continuously improving model 🔹 On-device validation: Preview VPS coverage and test localization in real time 🔹VPS 2.0: Global-scale positioning—centimeter-accurate where mapped, reliable everywhere else (even where GPS fails) 🔹NSDK 4.0 (coming soon): A unified SDK across Unity, Swift, Android, and ROS 2 Read more: hubs.ly/Q049Tpkb0 #NianticSpatial #Scaniverse #VPS #GeospatialAI #AI
→ View original post on X — @scobleizer, 2026-04-07 17:57 UTC
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Very cool open-source traces from @TheZachMueller @LambdaAPI: huggingface.co/datasets/lamb… 150M tokens for @NousResearch's Hermes harness with Kimi-K2.5 & GLM 5.1 that was just released! clem 🤗 (@ClementDelangue) We keep saying we want open-source frontier agents. Fine. Then let’s build the dataset. @badlogicgames, creator of Pi, just shared some of his agent traces used to build Pi on @huggingface. I’m now sharing some of mine too, exporting them from @hermes, @opencode, and Claude via @tracesdotcom, and I’ll keep going. Why this matters: one of the biggest bottlenecks for open-source agent models is the data. And all of us are generating that data every day through our conversations with agents. If enough builders share even a slice of their traces publicly, we can create the largest crowdsourced open dataset for agents. Time to put your tokens where your mouth is and give a chance for open source to win! — https://nitter.net/ClementDelangue/status/2041189872556269697#m
→ View original post on X — @clementdelangue, 2026-04-07 17:57 UTC
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754B parameters, 1.51TB on Hugging Face Z.ai (@Zai_org) Introducing GLM-5.1: The Next Level of Open Source – Top-Tier Performance: #1 in open source and #3 globally across SWE-Bench Pro, Terminal-Bench, and NL2Repo. – Built for Long-Horizon Tasks: Runs autonomously for 8 hours, refining strategies through thousands of iterations. Blog: z.ai/blog/glm-5.1 Weights: huggingface.co/zai-org/GLM-5… API: docs.z.ai/guides/llm/glm-5.1 Coding Plan: z.ai/subscribe Coming to chat.z.ai in the next few days. — https://nitter.net/Zai_org/status/2041550153354519022#m
→ View original post on X — @clementdelangue, 2026-04-07 17:56 UTC
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How to create a medical condition that does not exist?
Post preprints of a fake disease (Bixonimania) and let AI take it from there
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Good policy works (and the solutions are actually known, believe it or not)
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Releasing one of our *largest* robotics project yet in the open
— Thomas Wolf (@Thom_Wolf) 7 avril 2026
We collected and annotated hours of clothes folding with open-arms and collaborators.
We then explored how to train the best clothes folding robotic model for bimanual setups.
And now we're releasing it all fully… https://t.co/ZaQeTJJHqe
Releasing one of our *largest* robotics project yet in the open We collected and annotated hours of clothes folding with open-arms and collaborators. We then explored how to train the best clothes folding robotic model for bimanual setups. And now we're releasing it all fully in the open: data, code, models, software, explorations, learnings, you name it Enjoy, play with it, use these learnings and share yours! PS: the hub is increasingly *the* place where robotics data is being shared and used, come take a look if you haven't yet. Robotics data has been our fastest growing dataset category by far over the past few months. LeRobot (@LeRobotHF) Releasing the Unfolding Robotics blog! Time to unfold robotics: we trained a robot to fold clothes using 8 bimanual setups, 100+ hours of demonstrations, and 5k+ GPU hours. Flashy robot demos are everywhere. But you rarely see the real story: the data, the failures, the engineering. We’re sharing everything: code, data, and details in the blog → huggingface.co/spaces/lerobo… — https://nitter.net/LeRobotHF/status/2041542790610297259#m
