
YouTube announced 'Custom Feeds,' a new feature allowing users to generate personalized video streams using natural language prompts powered by Google's Gemini AI. Users can describe specific interests, moods, or contexts—such as 'video podcasts for a 30-minute commute'—to create tailored recommendations that appear in a dedicated tab on the home screen. The feature leverages semantic search to interpret detailed inclusion and exclusion criteria, aiming to help users navigate YouTube's library of over 20 billion videos more effectively. Rolling out globally on web and mobile next month, this tool positions YouTube alongside competitors like Bluesky and Threads in offering user-controlled algorithmic curation.
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© TechCrunch AIMeta’s consumer agent Muse is rolling out voice calling to US businesses for paid subscribers, targeting the tedious task of customer service navigation. With over 10 million users, Meta is betting that AI’s infinite patience will replace human frustration with phone trees and hold times. The feature leverages improved voice mimicry to interact with receptionists, though many still hang up upon detecting non-human voices. This moves Muse from text-based assistance into active, autonomous execution of real-world tasks, marking a significant step toward mainstream consumer AI utility.
© TechCrunch AINebius is facing the harsh reality of scaling AI infrastructure: even with $40 billion in planned spending and massive prepayments from Microsoft and Meta, physical construction delays are derailing timelines. A key New Jersey site is months behind schedule due to regulatory fines and generator issues, forcing a shift in delivery dates to 2027. This isn't just a logistical hiccup; it signals that the 'neocloud' model of financing builds with customer cash is fragile when third-party operators fail. The market reacted swiftly, dropping shares by 17% after Nebius cut its 2026 revenue forecast, proving that hype cannot override concrete capacity.
© TechCrunch AIGoogle is quietly shifting its search infrastructure by replacing older fast models with Gemini 4 Hydrogen, a tier designed specifically for speed and cost-efficiency rather than raw reasoning power. This model completes tasks 2.5 times faster than its predecessor while beating the more powerful Argon on specific intelligence benchmarks, proving that specialized efficiency can outperform brute force in high-volume workflows. By integrating this into Search's fan-out technique, Google now breaks down complex queries into subqueries with greater precision and speed, delivering richer visual responses without the latency of heavier models. This marks a strategic pivot toward agentic execution layers within consumer search, making AI Mode feel snappier for everyday multi-part questions.
© The Verge AIA Google researcher’s claim that AI agents found a new Maya site collapsed when experts pointed out it was already documented in academic literature. The incident exposes a critical blind spot: autonomous systems lack the contextual knowledge to verify findings against existing research, leading to false positives and wasted expert time. While the agent successfully processed lidar data, it failed to cross-reference its output with known records, highlighting that automation without domain expertise is prone to error. This serves as a cautionary tale for using AI in scientific discovery, where verification requires more than just pattern recognition.
© The Verge AIA new study reveals that major AI assistants are actively directing shoppers to counterfeit and phishing websites, with Perplexity leading at 15% risky link rates. This isn't just a hallucination quirk; it's a tangible security threat where models confidently endorse impersonators for brands like Lululemon and Birkenstock. While Google’s Gemini performed best by limiting risky outputs to 4%, the fact that these tools are becoming primary shopping assistants makes this reliability gap critical. Consumers can no longer treat AI search results as verified sources without manual cross-checking.
© WIRED AIThe AI safety narrative has shifted from speculative extinction scenarios to tangible infrastructure risks. A WIRED panel of experts identifies nuclear integration errors, cyberattacks on critical systems, and biological weaponization as the immediate dangers. This marks a move away from abstract 'runaway intelligence' fears toward verifiable industrial hazards like flawed defense workflows and unregulated bio-threats. The consensus suggests that current lab-driven safety thresholds are insufficient for these concrete threats.