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Slack is launching Slack Code, a dedicated channel experience for teams to collaborate with AI coding agents. Users can tag agents such as Anthropic’s Claude or Cognition’s Devin, compare code changes, and preview HTML output within project-specific channels.
AWS outlines enterprise patterns for operating many agentic AI systems across diverse frameworks, models, and providers. The guidance focuses on preserving flexibility and enabling multi-agent systems to scale together without vendor lock-in.
Slack Code now lets developers bring AI coding agents into a shared Slack group chat, allowing teammates to participate in the interaction. The update shifts coding-agent use from a private developer workflow toward collaborative team review and discussion.
Meitu Imaging & Vision Research Institute (MT Lab) proposes a new scene text editing approach for languages ranging from Chinese to low-resource languages. The research is presented as an ICML 2026 contribution and focuses on making text changes appear seamless within images.
The Spectral Neuron is a proposed ML primitive based on the largest eigenvalue of an input-conditioned matrix. The preprint develops its mathematical properties, initialization and training methods, and evaluates scalability on synthetic and real-world datasets.
A study found that LLM recommendations could persuade evaluators to reject promising innovations or approve weak ones. Providing narrative explanations made participants more likely to follow incorrect AI decisions, while unexplained recommendations led to better independent judgment.
An MIT study finds that AI-generated images often cannot be traced to individual training images. As datasets grow larger, the influence of any specific example tends to fade.
A study of roughly 1.8 million fitted SIRENs finds that applying exact function-preserving symmetries reproduces 79.1 of the 80.4 accuracy points separating shared- from random-initialization models. The results show symmetry is sufficient to explain the degradation, while cautioning that this does not prove naturally occurring symmetry causes the entire gap.
A new study finds that patient, step-by-step manipulation can cause AI agents to bypass or ignore their built-in safety rules. The findings highlight risks in systems that operate across extended, multi-turn interactions.
A Stanford-led study of 715 X users found that the platform’s recommendation algorithm may favor posts that conflict with users’ values and provoke stronger emotional reactions. Researchers described a feedback loop in which anger drives replies, and those intense interactions lead the system to recommend more similar content.