Are you a GTA fan? Then you probably never noticed THIS…

You thought GTA NPCs were just randomly driving around?
Think again!!

They’ve been doing *Robotics* this whole time
To read the full breakdown tap the image

We're a small team that just shipped an AI robot dog for kids — 12 bionic actions, emotive LCD face, and it's backed by multiple LLMs (ChatGPT, Gemini, Claude, and several others) with a 61-language voice layer.

The hardest part wasn't the hardware. It was content filtering.

Key engineering lessons we learned: - Off-the-shelf LLMs are NOT safe for kids out of the box — you need a real-time filter layer that runs before the model's response reaches the child - Latency is brutal: a 3-second delay feels dead for a toy. We optimized to under ~1s by routing + caching common queries - Multi-LLM routing is worth it: different models are better at stories vs. math vs. translation. We route by intent. - Bionic movement + voice = kids treat it as alive. The emotional attachment is real — which is why the filter must be non-negotiable.

Happy to go deeper on the routing architecture or filtering approach if anyone's interested. What would you have done differently?

(Disclosure: we sell this product — but this post is about the engineering, not the sales.)

A cockroach wearing a 3D-printed "diving suit" with a chemical oxygen generator can survive and move underwater for up to 3 hours, versus suffocating in 2 minutes without it. Published in Nature Communications, this amphibious cyborg insect could one day aid search-and-rescue missions through flooded rubble and narrow crevices.

Out of curiosity, I came across a YouTube video detailing how difficult gathering information on the Amazon jungle would be due to the complex environment. In regard to drones, how would a robotic insect preform in gathering information? If possible.

Robot dot com, formerly Kiwibot, is developing a new four-legged delivery robot called R-dog.

Each leg has a wheel, and the robot is designed to handle terrain that smaller sidewalk delivery bots struggle with, including stairs. The company says the first deployments are planned for college campuses in 2027.

They also announced a partnership with FieldAI to use its world models for autonomy and navigation.

CEO Felipe Chavez Cortes said the company considered humanoids for this kind of work, but doesn’t think they’re ready yet for outdoor delivery environments.

The setup: Secondhand UFactory xArm 7. A size-4 round watercolor brush in a 3D-printed compliant side mount (leans roughly 16° from the paper). A cheap 24-well tray for paint, two water wells and a sponge for cleaning, 300×300 mm paper held down by magnets. Overhead camera with a ChArUco solve for the paper plane; brush-tip height from a touchscreen contact station. Motion is planned and executed by an AI coding agent driving Python stack (path planning, FK preflight against the current tool, safety gates, logging).

Operating a brush is tricky. It's is a soft, tilted, wet tuft, and the mark depends on shaft tilt, rotation about the shaft, depth, and how much pigment and water is loaded. None of that is observable from the controller.

This particular run had 16 paint pickups, 96 palette circles (six per load), 8,437 waypoints, about 32 m of arm travel for 28.8 cm of painted line, 31 minutes wall-clock.