Xiaomi shared a new robot video on November 3. The video shows its humanoid robot working a full eight-hour shift. Humanoid means the robot's body is shaped the way a person's is. It has two arms, two legs, and a head.

The robot works inside an EV factory. EV stands for electric vehicle. It sorts car parts at a rate of about one part a minute.

Xiaomi built the robot's brain on its own model, called Robotics-1. Engineers trained that model on a huge pile of real human movement footage. That model topped two separate robot tests this year.

The robot already hits 98 percent success on one task. That task is loading small screws called self-tapping nuts. It also hits 90 percent on two more jobs. Those jobs are sorting flexible panels and folding totes.

A Reddit user named Distinct-Question-16 shared the clip on r/singularity today. The post pulled 194 comments fast.

Commenters compared the robot to a tireless new hire. Some asked how soon factories could run full shifts with robots alone. Others wanted more proof behind the 90 percent figures.

The eight-hour mark matters for a plain reason. Human factory shifts run about that long too. A robot that lasts a full shift can slot into a real schedule. A robot that lasts ten minutes stays a demo.

Xiaomi also tested the same robot on home chores this year. It worked through a long list of household tasks in one unbroken run. The company has yet to say when either robot ships to a real customer.

That timing raises a real question for factory floors. A robot this steady gives a plant a cheaper option on the line. Xiaomi built the case for that option in one video. Rival factories now have a number to match. So do rival robot makers racing to post the same eight-hour claim first.

Anthropic posted new research on November 3. The post introduced FlashProve. FlashProve lets a company prove which AI model gave an answer, using math instead of trust.

The tool uses a method called a zero-knowledge proof. That method proves a fact is true while it hides the private data behind it. Here, the private data is a model's weights. Weights are the numbers inside a model that store what it learned.

A company using FlashProve can skip showing its weights to anyone. It only shows a proof tied to them. Anthropic built the tool with researchers from EPFL, MIT, StarkWare, and Succinct.

FlashProve already works on Llama 4 Maverick, a model built by Meta. It checks about 110 words a second on one computer chip. Each proof file runs close to 19 megabytes for a 1,024-word answer.

The post pulled 487 likes and 90 replies within hours. The code on GitHub picked up 333 stars fast too.

The tool still costs a lot to run. Anthropic says a proof costs around 80 times more than a normal answer. The company left the release date for a faster version open.

This research answers a real worry in the AI industry. A company could swap in a cheaper model to cut costs. It could keep that swap quiet. FlashProve gives outside users a way to catch that swap on their own.

The interest says something about where trust in AI stands today. Buyers now want more than a company's word alone. They want proof they can check themselves.

Anthropic built a tool for that exact doubt. The industry noticed within hours. Rivals building closed models now face the same question. A buyer can ask for proof, or just take the company's word on faith.

OpenAI shared new engineering results on November 3. The company says a new internal model now beats its own public model. That public model is GPT-6 Astra. The new model wins at hardware design work.

OpenAI tested both models on BenchCAD. That test checks how well a model rebuilds a 3D part. It also checks how well a model reads a circuit board.

The new model scored 0.875 on that test. GPT-6 Astra scored 0.594 at its highest setting. That gap runs close to half.

OpenAI kept the newer model's name private. It left the public release date blank.

The company also shared a side project. Two OpenAI staffers asked Astra to design a small device in under a week. Astra planned the case, the circuit board, the screen menus, and the code. A factory then built working copies of it in under a month.

OpenAI says token use across its hardware teams rose 23 times since February. Tokens are the small chunks of text a model reads and writes. More tokens used means more real engineering work flows through the models.

A Reddit user named troll_khan shared the story on r/singularity today. The post pulled 49 comments fast.

Readers split over what the gap really proves. Some called a jump this large on one lab test a real sign of progress. Others pointed out that OpenAI built and graded its own test.

The push into hardware says something new about where labs compete next. Chat answers used to be the whole contest. Now a lab brags about a model drawing a circuit board.

That shift moves the fight somewhere new. The contest used to live on a screen. Now it moves to parts a person can hold in their hand. A device built this way still needs a human to check the wiring before anyone plugs it in.

The Washington Post reported a staff fight on November 3. Some Google DeepMind workers in Britain signed contracts years ago. Those contracts stop them from ever slamming Google in public. The ban holds even after a worker quits.

The Post read one such contract. It also talked with two current DeepMind workers. Those workers want the rule dropped for everyone, past just new hires.

A Google boss answered the story on Friday. The boss said Google will skip the rule for staff who already left. New deals carry fresh terms too. Current staff still want the old rule gone for good.

The fight follows other recent trouble at DeepMind's London office. Workers pushed to form a union earlier this year. Several staffers quit over a Pentagon deal signed this fall. That deal lets Google's AI work run in secret, military settings.

A Reddit user named TorturedPoet30 shared the story on r/singularity today. The post pulled 26 comments.

One reader called the rule a strange fit. Demis Hassabis runs DeepMind. He speaks often about AI safety in public. Readers asked why his own staff skip the same points he raises.

The story carries real weight past one office. DeepMind builds some of the most watched AI models anywhere. A lifetime gag rule on its own staff sets a real limit. That limit falls on what the public ever learns about how those models get built.

That gap matters most on safety. The staff closest to a model's risks are often the ones barred from naming them in public. A rule meant to guard a brand name can end up hiding real facts. Those facts often come straight from the safety team itself.

Readers on r/singularity now wait for Google's next move. Current staff want a written end date for the old clause. A spoken promise to reporters stays easy to walk back later.

A Reddit user mourned an AI tool on November 3. The tool is Jules, a coding agent built by Google. A coding agent writes and fixes code on its own, after a person gives it a task. Jules worked on tasks in the background, away from a live chat window.

Google told users on October 29 that Jules will wind down. The service turns read-only on November 11. It shuts fully on December 31. Google is folding its lessons from Jules into a newer tool called Antigravity.

The Reddit poster, Free-Stage-5975, wrote that Jules once wrote a full book for them. The file grew too big to send in one piece. The poster asked Jules to upload it by FTP instead. FTP is an old method for moving files between computers online. Jules did it fast, the poster said.

The post pulled only 2 comments on r/artificial today. A Hacker News thread on the shutdown pulled 3 points. A tool built around Jules on GitHub still holds 413 stars.

The quiet reaction stands apart from the news itself. Google is closing a tool real people built daily habits around. Yet the loudest reaction so far stays one small, wistful post.

That gap says something about how fast AI tools now turn over. Users grow attached to a specific agent's tone and quirks within months. Then the maker retires that agent for a newer, merged product. The attachment barely slows the shutdown pace.

Jules leaves a short trail behind for a tool many people used daily. Google's bigger bet, Antigravity, now carries the weight of proving it kept the parts users liked best.

One open-source extension built around Jules still sits on GitHub with hundreds of stars. That number shows a small, steady crowd built real projects on top of Jules. Most of that crowd stayed quiet about today's news.