CyberKnow

“Companies that had 10 people running fulfillment are now doing it with 3 and a few AI tools. These jobs aren’t coming back.” - a small business operations expert in the Midwest. Extrapolate that through the economy and its going to get extraordinarily rough out there.

A French naval officer unknowingly revealed the location of the aircraft carrier Charles de Gaulle by sharing a public workout on the fitness app Strava. His run, recorded at sea, made it possible to pinpoint the carrier’s position in the Mediterranean near Cyprus. Although the carrier’s deployment was public, sharing its exact real-time location is a serious security risk. Source: Le Monde

I'm going to call this right now. We are going to have a large population with absolutely no critical thinking skills if they blindly trust AI for everything. We have all already seen it. They don't validate outputs. They don't really understand anything. They just ask questions, it looks good, and they go with it. There are going to be huge issues in every company as this continues over the years. The amount of technical debt and knowledge gaps are going to be insane. So much opportunity if you actually know what you're doing.

Oracle co-founder Larry Ellison delivers a sales pitch for his company's new AI-monitored body cameras, which live stream footage back to headquarters and cannot be turned off. "Citizens will be on their best behaviour, because we're constantly recording and reporting everything that's going on." "These are the kind of next generation systems we can build using AI."

BREAKING: Nvidia CEO Jensen Huang predicts $1,000,000,000,000.00 in revenue from AI chips through 2027.

🤯BREAKING: Alibaba just proved that AI Coding isn't taking your job, it's just writing the legacy code that will keep you employed fixing it for the next decade. 🤣 Passing a coding test once is easy. Maintaining that code for 8 months without it exploding? Apparently, it’s nearly impossible for AI. Alibaba tested 18 AI agents on 100 real codebases over 233-day cycles. They didn't just look for "quick fixes"—they looked for long-term survival. The results were a bloodbath: 75% of models broke previously working code during maintenance. Only Claude Opus 4.5/4.6 maintained a >50% zero-regression rate. Every other model accumulated technical debt that compounded until the codebase collapsed. We’ve been using "snapshot" benchmarks like HumanEval that only ask "Does it work right now?" The new SWE-CI benchmark asks: "Does it still work after 8 months of evolution?" Most AI agents are "Quick-Fix Artists." They write brittle code that passes tests today but becomes a maintenance nightmare tomorrow. They aren't building software; they're building a house of cards. The narrative just got honest: Most models can write code. Almost none can maintain it.