Imported robots join kitchen cabinets on Trump's national security threat list

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

Trump declares imported robots and industrial machinery a national security threat

A Section 232 proclamation says the US "produces less robotics and industrial equipment than it consumes" and relies on imports for most types. It sets no tariffs yet: Commerce and the Trade Representative must negotiate deals with trading partners, with an update due within 180 days, and tariffs, quotas or fees stay possible (@whwire). It covers industrial robots, machine tools, presses and laser cutters (@ECONOMYI11).

@aphysicist: "the bigger point: rebuilding american industry means building the machines that make everything else." @dirtman, who makes industrial robots in the US: "you don't have a sovereign industrial base if all the machines and robots inside it are imported." @MeganSmithTech: "tariffs on imported components could also raise costs for US robot builders".

Heat: low · 146K views, 24 posts in 11h · quiet on Reddit

1. Unitree's first results since listing: revenue up 70%, profit down 17%

Q3 revenue rose 69.93% to 665 million yuan, while net profit fell 17.37% to 113 million yuan as Unitree more than doubled its R&D spending (@ChinaBeigeBook).

@choblin29: a market value "now exceeding $40 BILLION, more than 100 times its annualized Q3 revenue", with profit "nearly HALF what analysts expected". @AshCrypto lists Unitree "Down nearly 50%" in China's tech stock rout.

Heat: low · 82K views, 18 posts · quiet on Reddit

2. Monumental's robots lay every brick of a building in Almere, with 15 houses next

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The Dutch startup's robots built curved walls with different bricks, bonds and indented text, doing "100% of every brick" on a real site, its co-founder says. Another 15 houses follow in the coming weeks.

@bercankilic: "Insane ngl". No other takes yet.

Heat: medium · 142K views, 1 post · quiet on Reddit

3. HaloBraid's robot braids hair up to five times faster than stylists

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Yinka Ogunbiyi's startup does the repetitive braiding while the stylist keeps the creative work, to save time and physical strain. It has $7M in seed funding and, it says, more than 7,000 salons on its waiting list (@Good_news_naija).

@namora_jes: "I love that the robot helps the braider without taking her job."

Heat: medium · 600K views, 4 posts · quiet on Reddit

4. GPT-6 Astra paints a picture with a real robot arm, from one chat

Asked to "make something dreamy", Astra designed three fish, planned 34 brush strokes, checked the arm's route in a digital twin, then painted it in about 30 minutes and cut the video to music it wrote. It plans and runs the studio's tools, and a checked executor drives the arm.

r/singularity's second reply: "Jesus. This is nuts."

Heat: low · quiet on X · #5 on r/singularity

5. Silica (YC F26): force-sensing actuators for cheap robot arms

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Silica builds force sensors into the actuator's gearbox, so a joint can feel something as light as a paperclip, for 10–15% more than a standard actuator; torque sensors usually cost several times the actuator. It says customers asked for more than 60,000 units in its first month of pre-orders, and samples ship in February (YC Launches).

Two robotics accounts on our roster engaged: @21stRobotics, "ROBOTS ARE FINALLY LEARNING HOW TO FEEL", and @boxcardavid, "As a bald actuator guy I'm very pro bald actuator guys".

Heat: low · 68K views, 4 posts · 2 votes on YC Launches

Robocurve's DataCenterBench: frontier models can flip a breaker, but none can plug in a cable

Nine frontier models drove robot arms through ten steps from data-center maintenance procedures, three tries each. GPT-6 Astra scored highest at 40%, and Gemini 4 Argon nearly matched it at a tenth of the cost, though about 10x slower (@robocurve). No model plugged in a charger, joined an Ethernet connector or pressed a cable into a clip. It matters because OpenAI wants models that build data centers, and connecting cables, the core of that work, is where every model failed.

DeFM: a foundation model for robots' depth cameras

ETH Zurich's legged robotics lab, with Berkeley, NVIDIA and Imperial, trained one encoder on 60M depth images that robot policies use frozen. In simulated dexterous grasping it reaches 81% of the teacher's performance, against 69% for the best other frozen encoder (@_bbelousov). It matters because robots rely on depth for sim-to-real, and depth had no general pretrained encoder like DINO for colour images.