Hsuan Su @jacksukk

The models are improving the models. Locus, our automated AI research system, is SOTA on PostTrainBench and post-trains Qwen3 base models that surpass the human post-trained Qwen3 model. Today, LLMs post-trained end-to-end by Locus are in production to millions. 🧵👇 PostTrainBench evaluates agents' ability to post-train models on various domains given 10 H100 hours. We extend PostTrainBench via PostTrainBench+, which has a greatly expanded compute budget that provides clearer signal on automated post-training capabilities. We find that thousands of H100 hours help distinguish methods' performance post-training Qwen3 1.7B-Base models, and that Locus scales best. In this setting, modes trained by Locus collectively surpass the perforamce of the offical human post-trained Qwen3 1.7B model. In a test of generalization, we ran Locus on all live Kaggle competitions with prize money and public leaderboards. After 16 days, Locus achieved the 4th highest average rank among all participants.

This Thursday in SF. Come by to say hi, will be sharing some of our latest! luma.com/lk9se5mi

AUTONOMOUS @autonomousevent

a month ago

Today, we're announcing not one, but two of the @moonlake team on stage at AUTONOMOUS. @sharonal_lee and @sunfanyun, Co-Founders of Moonlake AI, will be on our Frontier Stage to discuss Where Robots Learn Before the Real World. 🗓️San Francisco, July 16th 📍 The Midway 🎟️

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@chrmanning is sharing our approach to embodied general intelligence at AI Engineers! Room 2006

3 years ago I was a researcher at @AIatMeta frustrated with LLM evals. Today we are announcing our $50M Series B to develop agent simulation research and infrastructure 🚀🚀🚀 Also releasing a preview of our Digital World Model 👀

Anand Kannappan @anandnk24

2 months ago

Today, we’re excited to announce our $50M Series B, led by @GreenfieldVC (formerly TPG Capital), with participation from @Lightspeed and @notablecap. 🚀 At @PatronusAI, we develop simulations and evals to train and improve AI. The first phase of AI was built on static

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🚀

Sharon Lee @sharonal_lee

2 months ago

Taking main stage at AWE 2026 to demystify world models alongside @bilawalsidhu. 9:55–10:45 AM, Grand Ballroom. Details in comments #awe2026

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Taking main stage at AWE 2026 to demystify world models alongside @bilawalsidhu. 9:55–10:45 AM, Grand Ballroom. Details in comments #awe2026

Thank you to everyone who came out to Moonlake’s Happy Hour at CVPR! We had 750+ sign-ups overall, so we couldn’t accommodate everyone this time - but our team will be around for the next few hours if you’d still like to chat. Grateful for the energy and support around Moonlake!

Join us tomorrow at CVPR for great food and even better company! We have an amazing group of researchers, founders, and industry leaders attending. We’re currently over capacity, but if you’d like to join, DM me a short blurb about your research and we’ll do our best to fit you in. luma.com/fxf4l3hv

Our team at Apple MLR is presenting 4 papers at #CVPR2026! If you're attending, stop by and chat with the authors about our research. 🧵 📌 Velox — Malik et al. apple.github.io/ml-velox/ Jun 6, 11:45 AM MDT 📌 AMusE — Chowdhury et al. arxiv.org/abs/2512.16250 Jun 6, 4:45 PM MDT

Heading to CVPR June 4–7. Shoot me a DM if you’d like to chat or have recommendations for events, talks, or meetups! I’ll also be speaking about Moonlake and our journey so far: luma.com/nc556ega Come and say hi if you’re around!

Warehouse example.

We generate both the engine code and the reskinned, photorealistic outputs. DMs are open if either sounds relevant to you! :)

Fan-Yun Sun @sunfanyun

3 months ago

Real-time neural rendering model @moonlake Works on from any physics/game engine to close the sim-to-real gap. If this is relevant to your team, I'd love to chat (my DM is open)!

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Here’s a sneak peak into a fully generated articulated, sim-ready engine output based on only input image. We work with point cloud and video inputs as well. Any annotations needed i.e., segmentation comes for free as we have full visibility and control all of the engine code base. Medical room example. Reference image (left), 3D agent output (right)

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Real-time neural rendering model @moonlake Works on from any physics/game engine to close the sim-to-real gap. If this is relevant to your team, I'd love to chat (my DM is open)!

Heading to the Robotics Summit & Expo in Boston right now (May 27–28). If you're here and want to chat about digital twins/ simulations DM me!

We’re opening a few more spots for enterprise design partners to work closely with us on Moonlake’s 3D agent + world generation stack. If your team works on environment art, world building, simulation, or large-scale content pipelines, we’d love to collaborate closely and shape the product around real production workflows. Current partners are using it for: • asset generation • scene/world generation • technical art workflows • rapid iteration + prototyping We’re keeping this small and hands-on for now. Feel free to DM me if this is relevant to your team!

Moonlake @moonlake

4 months ago

Introducing Moonlake's 3D Agent. Our agent acts like a technical artist that can build and reconstruct articulated assets and large-scale editable scenes with hundreds of objects from a single image and can improve its generations continuously. Learn more in the thread below.

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If you believe digital AGI will be solved before physical AGI, work on simulation. We will have: A. self-improving physics engine. Newton can already be agentically adapted to different downstream applications (and is built in the first place with this intention). B. on-demand generation of digital twins (e.g., @moonlake’s 3D agent and more generically, world models). All of the hardest problems in physical AI will be reduced to a single question: “does the labor value unlocked by the policy exceed the simulation compute cost needed to bridge the deployment gap?”

tian/天 @xtbot

3 months ago

@Ken_Goldberg I asked Codex to set up ROS middleware, configure a CSI camera, benchmark Gemma 4 models on my Jetson Orin Nano, adapt an OpenClaw-style runtime for VLM + reasoning (“Robotclaw” as I call it), and even build an iOS app to stream LiDAR, camera, GPS, and IMU data from an old iPhone

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Our agent is an autonomous system built for complex, multi-step workflows, not just one-off tool use or copilot. It behaves like a technical artist over time, building assets, placing objects step by step, and maintaining consistency as the scene evolves. It works through iterative refinement, improving its reward over long-horizon tasks spanning hundreds of steps. It is a long-running agent, not just a copilot, and can learn to one-shot generalize to your workflows.

Moonlake @moonlake

4 months ago

Introducing Moonlake's 3D Agent. Our agent acts like a technical artist that can build and reconstruct articulated assets and large-scale editable scenes with hundreds of objects from a single image and can improve its generations continuously. Learn more in the thread below.

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Introducing Moonlake's 3D Agent. Our agent acts like a technical artist that can build and reconstruct articulated assets and large-scale editable scenes with hundreds of objects from a single image and can improve its generations continuously. Learn more in the thread below.