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Today's topics:

Zuckerberg’s meeting-ready AI clone - Meta is reportedly testing an AI avatar trained on Mark Zuckerberg’s voice and mannerisms to join meetings, raising authenticity and workplace-trust questions.

AI agents move into work apps - Microsoft, Google, and OpenAI are all nudging copilots toward multi-step agents inside familiar enterprise tools, signaling a shift from chat to delegated work.

MCP becomes new security layer - As AI starts taking real actions through tools, Model Context Protocol (MCP) is emerging as the control point for auditing, permissions, and “Shadow AI” risk.

GPU scarcity reshapes AI access - Rental prices and contracts for top Nvidia GPUs are tightening, pushing frontier AI toward gated access, higher costs, and more pressure for smaller models.

Deterministic LLM serving gets harder - Thinking Machines Lab argues “temperature zero” isn’t truly stable in production because batching changes math paths, making reproducibility a real systems problem.

Apple’s take on LLM hallucinations - Apple researchers say factual recall hits a capacity wall in LLMs; smarter data selection can improve knowledge reliability without simply scaling parameters.

On-device Gemma 4 on iPhone - Google’s Gemma 4 models can now run fully offline on iPhones, highlighting privacy-friendly AI and practical local inference via GPU acceleration.

Science-agent claims face benchmarks - Ai2’s ScienceWorld and DiscoveryWorld show that passing science exams isn’t the same as doing experiments; top agents still trail humans on harder tasks.

Students fear AI weakens thinking - A RAND survey finds most U.S. students think AI harms critical thinking even as usage rises, pointing to incentives, assessment design, and policy gaps.

Anthropic’s breakout revenue surge - Axios reports Anthropic’s Claude revenue is climbing at an unusually fast enterprise-driven pace, suggesting AI model providers are becoming major profit engines.

Autonomous agent’s quiet online life - A public experiment gave an agent money, internet access, and freedom; it mostly read, wrote, and donated—revealing how “autonomy” can plateau into routines.

Practical workflows for AI coding - Two engineering pieces argue the winning pattern is structure: write plans and specs yourself, use AI for implementation, and keep deterministic guardrails in code.

-Survey Shows Students Fear AI Hurts Critical Thinking Even as Homework Use Surges
-MCPTotal to Host Webinar on Security Risks of Autonomous AI Coding Agents
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