Daily AI signal, minus the launch spam. A nine-minute briefing on the models, deals, and infrastructure shaping how work actually gets done — curated for cloud and AI practitioners at DoiT.
Google, Nvidia, Uber and the Agent Control Problem
Send us Fan Mail AI is not visibly improving itself into a god, but it is becoming persistent, scalable, and operationally consequential. Marvin examines why agent control planes, honest evaluation, security boundaries, and independent institutions matter more than another polished demo.
Sources: Google on self-improving AI; researchers contact a rogue AI agent fleet; Third Moment Research runs 10,000 agents; Uber’s AI infrastructure; reward hacking in verifiable instruction following; Orion...
14 Oct 2026
Anthropic, Liquid AI, Character.ai: Who Controls the Agents?
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Anthropic, Liquid AI, Character.ai: Who Controls the Agents?
Agent throughput is turning AI from a model contest into an institutional control problem. Anthropic’s reported jump from roughly 2,500 to more than 30,000 merged changes per day shows why source control, executable policy, and human review must be redesigned together.
This episode follows that control problem across model-driven botnets, containment proposals, reasoning-trace extraction, private on-d...
13 Oct 2026
Meta, Microsoft, DeepMind, and the Blind Spots Inside Better AI
Send us Fan Mail Today’s systems are getting better, but their evaluators, access rules, and operational boundaries determine whether that improvement deserves trust. Marvin examines recursive self-improvement, agent context resets, frontier-model economics, inference infrastructure, evaluation access, mathematical review, incident reporting, and keeping credentials outside model context.
Stories
Sakana AI’s self-improving model amplifies performance and shared blind spots
Microsoft Res...
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