Kevin Weil's Warp raises $215M for robot chemistry labs that make 996 look part-time

Nothing big in the last 24 hours. Here's what led.

Kevin Weil's Warp Sciences raises $215M to build robot chemistry labs

OpenAI's former product chief, who led its science work, came out of stealth with a Series A led by Lightspeed, at an $865M post-money valuation (Bloomberg, via @Techmeme). Warp will make and test molecules for biotech and pharma in US labs where AI designs the experiments and robots run them 24/7, and use that data to train AI for science (@TheRundownAI). It starts with expert chemists making molecules: "First be the hands, then build the nervous system, and then over time become the brain" (@lightspeedvp).

Mostly congratulations from backers, who liked the plan to bring this work back from China: "the mission of keeping core scientific capabilities here at home feels especially urgent" (@katie_haun). @AndrewCurran_ summed it up as "using AI and robots to make and test molecules for companies developing new drugs".

Heat: medium · 422K views, 23 posts · quiet on Reddit

1. Figure 04 is in "full design lock"

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Brett Adcock made Figure's all-hands public: the team has doubled and the roadmap has changed over the past year. Figure 04's parts have started shipping, its actuators are being brought up in-house, and its new hands track fingertips to the millimeter; he calls it "the iPhone 1 of humanoids" (@TheHumanoidHub).

The robot stayed behind a blur, and r/accelerate's top reply was "Looks a bit blurry. I hope Figure 5 is more focused." @speedify: the fingertip tracking "is the part that turns 'looks cool' into 'can actually pick things up without dropping them.'"

Heat: medium · 220K views, 22 posts · #12 on r/accelerate

2. NEURA unveils 4NE1 Gen 4

Shown at the opening of the TUM RoboGym in Munich, where companies teach robots real tasks, and at GTC Berlin. It's 20% lighter, with better hands and "Smart Limbs" with built-in reflexes, and runs NEURA's Neuron AI model (@clankrmedia).

The walk let people down: r/singularity's top reply was "Moves like it's the rotary phone of humanoid robots." Even a fan, @clankrmedia, said "The walking policy could use a bit more work".

Heat: low · 77K views, 20 posts · #13 on r/singularity

3. Maxinsights raises $30M and launches a glove for touch data

It films real work at natural speed in homes, restaurants and hotels, 2M+ hours so far, it says. New: MaxGlove records grip force and touch that a camera can't, and MaxLoop scores every clip the same day. The seed was led by Peak XV, and it targets 1M hours of force data by 2027 (@humanoidsdaily).

Six of our roster accounts engaged. @CyberRobooo says 2 million hours of chores in real homes "genuinely surprised me", while @heyshrutimishra asks "how much of it carries over to a robot hand that feels the world very differently".

Heat: medium · 205K views, 31 posts · quiet on Reddit

4. NVIDIA releases Isaac Lab 3.0

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The robot-learning framework gets a modular core where you pick the physics and rendering backends (Newton, the new OVRTX renderer, 3D Gaussian scenes), plus RL post-training and LEAPP, which packages a trained policy for real hardware.

Few takes yet outside NVIDIA's team. @RobotCaveCom points to a full Isaac Lab 3.0 walking workflow for Microduck. NVIDIA wrote it with Pollen Robotics and Hugging Face.

Heat: low · 41K views, 20 posts · quiet on Reddit

HumanoidPose reads a humanoid's joints from plain video

Jiaxi Jiang and team recover a humanoid robot's full-body joint states from RGB video alone, with one model for many robots, trained only on synthetic renders of human motion retargeted to 16 humanoids (@cs_jiaxi_jiang). Joint-angle error is 18 to 32% lower than the strongest baselines on three real robots. It matters because any video of a robot becomes motion data, without access to the robot's own sensors.

COWBOY: one sim-trained policy grasps with base, arm, hand and camera together

Jiahui Yang and team (CoRL 2026) train one generalist grasping policy in simulation that coordinates a mobile base, an arm, a dexterous hand and an active camera end-to-end, and deploy it zero-shot in the wild. It matters because the grasp adapts to the scene around the object, not just the object, with no real-world training.