Google adds Gemini 3 agents to Workspace with cross-app actions

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Show HN: Fabro – open-source dark software factory Hi — I created Fabro to free myself from supervising a fleet of Claude Code tabs running in a REPL (read-eval-prompt-loop). Model launches reshape the race because they force rivals to answer on capability, distribution, and rollout speed.

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GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning arXiv:2604.02721v1 Announce Type: new Abstract: Competitive programming remains one of the last few human strongholds in coding against AI. Policy stories matter because compliance friction can slow adoption even when model quality keeps improving.

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Local Reinforcement Learning with Action-Conditioned Root Mean Squared Q-Functions arXiv:2510.06649v2 Announce Type: replace-cross Abstract: The Forward-Forward (FF) Algorithm is a recently proposed learning procedure for neural networks that employs two forward passes instead of the traditional forward and backward passes used in backpropagation. Policy stories matter because compliance friction can slow adoption even when model quality keeps improving.

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GrandCode: Achieving Grandmaster Level in Competitive Programming via Agentic Reinforcement Learning arXiv:2604.02721v1 Announce Type: new Abstract: Competitive programming remains one of the last few human strongholds in coding against AI. Policy stories matter because compliance friction can slow adoption even when model quality keeps improving.

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Local Reinforcement Learning with Action-Conditioned Root Mean Squared Q-Functions arXiv:2510.06649v2 Announce Type: replace-cross Abstract: The Forward-Forward (FF) Algorithm is a recently proposed learning procedure for neural networks that employs two forward passes instead of the traditional forward and backward passes used in backpropagation. Policy stories matter because compliance friction can slow adoption even when model quality keeps improving.

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Show HN: ACE – A dynamic benchmark measuring the cost to break AI agents We built Adversarial Cost to Exploit (ACE), a benchmark that measures the token expenditure an autonomous adversary must invest to breach an LLM agent. This matters because it changes how the market reads current momentum, execution quality, or adoption potential.

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