Google Gemini 4 agents just beat Minecraft with no human intervention, OpenAI is building a humanoid robot, and MapQuest will now show you where Flock cameras are in your neighborhood

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Google Gemini 4 agents just beat Minecraft with no human intervention.

OpenAI is building a humanoid robot. And MapQuest will now show you where flock cameras are in your neighborhood.

It's Monday, October 19th. And here's a quick look at some of the news that's happening in the sphere of tech this morning from Engadget.

Google's DeepMind AlphaGo AI is famous for winning at Go. But the company's latest Gemini AI just mastered a far more complex game. Four Gemini 4 Argon agents beat Minecraft's final Ender Dragon foe, with no human intervention by working individually while sharing tips, threats, and mistakes. The experiment shows how general, agentic AI models can now beat complex games, a feat that used to require custom AI software. To tackle Minecraft, the Gemini 4 team set four AI agents into a sandboxed, randomized Minecraft survival world with a goal to defeat the Ender Dragon. No roles, strategies, or game-specific tools were assigned. Each agent controlled a player using the open-source MindFlayer library, and coordinated with teammates through in-game chat and shared memory. During gameplay, agents quickly elected to split-up tasks. They warned each about threats, gave lessons and tips, provided coordinates and even recovered items from dead teammates. Each settled Ender roles. For example, one made tools, another gathered food and supplies, while a third lit a key portal, and then another built Enderman shelters and landed the final blow. The process took over 27 hours, with the majority of time spent on the Ender Dragon. The team said, The four agents wrote and ran 2,783 programs, exchanged 229 messages, and died 605 times along the way. A fifth agent called The Observer monitored the session as an independent witness, both to check for cheating and to publish live and summary reports. The agents used 1.35 billion input tokens, and the final cost was $810 at Gemini's list price, or $1,620 at the standard price. The team wrote, To our knowledge, this is the first end-to-end autonomous completion of Minecraft by AI agents, achieved using only the standard Mindflayer library, without pre-scouted seeds or other game-specific tools written by a human.

Winning required the agents to keep a coherent plan across thousands of dependent steps for more than a day.

While OpenAI is already busy with the development of AI models and new consumer hardware, CEO Sam Altman and his team apparently have time for one more project, a humanoid robot. On the Sources Podcast, Altman explained that the human form is logical, because the world is very much designed for people, he said. Doors, keyboards, and tools are all designed around a certain type of body, and a robot that can move around in the same spaces should probably have a similar form factor. As Altman put it, matching that form factor seems good. When asked if OpenAI will actually build one, his response was much more succinct. We will.

However, Altman is not so concerned with the nitty-gritty details of what that robot will look like. Those questions are less important than really figuring out the brain that makes the robot work, he said. Basically, giving a machine two arms and two legs doesn't get you very far if it doesn't know what to do with them. This is still some distance from becoming a product. Altman told Fortune, I think we'll have a cool demo in 2027 I don't think there will be robots walking the streets for a few years longer. OpenAI has not disclosed what that demo will showcase, what its humanoid will look like, or when anyone outside the company will get close to one. Calling it a robot brain almost makes the problem sound neat, but it isn't. A capable humanoid must be able to understand what its cameras and sensors are seeing, interpret an instruction, decide how to carry it out, coordinate its body and then correct itself when the real world introduces something inconvenient. When picking up a mug, for instance, it needs to identify the mug, find the handle, estimate the grip, and exert the correct amount of force to lift it. Otherwise, you're left with a coffee spill and a floor cleaning job. OpenAI is already recruiting people to address those issues. Its robotics roles span motion control, state estimation, actuator control, and whole robot behavior, as well as the machine learning infrastructure to train physical AI systems.