AI system arrests kid for ... eating chips

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BALTIMORE STUDENT HANDCUFFED BY AI … FOR EATING CHIPS 🤔

What happened? An AI security system at a Baltimore County high school mistakenly flagged a student’s bag of Doritos as a firearm, prompting police to respond with guns drawn.

Want the story? The system, installed last year to detect weapons on school cameras, uses AI to scan footage and alert authorities if it identifies what appears to be a gun. However, the software misinterpreted the shape of a 17-year-old student’s snack bag as a weapon. Police rushed to the scene, ordered the student to the ground, and handcuffed him before realizing the “gun” was a bag of chips. School officials have since acknowledged how distressing the event was for students and families.

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AMAZON’S NEW WAREHOUSE TOYS 🤖

What’s new? Amazon has unveiled two new technologies, Blue Jay and Project Eluna, which will serve to make warehouse operations safer, smarter, and more efficient for its front-line employees.

What are the technologies?

Why is this important? These innovations show how AI and robotics can make warehouse jobs safer and more sustainable—not by replacing people, but by supporting them. For customers, that means faster, smoother deliveries.

MISTRAL LAUNCHES AI STUDIO 👨‍💻

What’s new? Mistral has launched AI Studio, a new production platform designed to help enterprises move beyond prototypes and run AI systems reliably, safely, and at scale.

How does this work? AI Studio combines three key components—Observability, Agent Runtime, and AI Registry—to close the gap between experimentation and production. Observability lets teams track model behavior, measure performance, and build feedback loops from real-world data. The Agent Runtime ensures that AI agents run consistently across complex tasks with transparency and durability. Meanwhile, the AI Registry stores and manages every asset—models, datasets, and workflows—under strict governance and version control. Together, they create a single operational loop where every AI output can be traced, evaluated, and improved.

Why does this matter? Most enterprise AI efforts stall at the prototype stage. Mistral AI Studio provides the missing infrastructure—turning isolated experiments into governed, reproducible, and measurable systems. This will help organizations make AI dependable enough for real-world use.

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