Topic: labor

5 stories found

Sunday, September 20, 2026

open_source62

langgenius/dify — Build Agentic workflows, RAG pipelines, with rich AI model and tool support on one collaborative workspace. Deploy on cl

Agentic offers a collaborative workspace for building AI workflows and RAG pipelines with extensive model and tool support, enabling seamless deployment options from cloud to self-hosted environments, facilitating rapid transition from prototypes to production.

github.com

Saturday, September 19, 2026

trending59

Microsoft director: AI scraping 'the largest theft of labor in human history'

Microsoft's director warns that artificial intelligence is facilitating a massive, unprecedented theft of labor, arguing it could be the largest inhumanity to workers in history. This claim highlights concerns about AI replacing human jobs on an unprecedented scale.

tomshardware.com

Friday, September 18, 2026

trending59

Microsoft exec called AI scraping 'the largest theft of labor in human history'

Microsoft executive Brad Smith described artificial intelligence scraping as "the largest theft of labor in human history," highlighting concerns over job displacement and ethical implications of AI technologies. This statement underscores the growing debate on AI's impact on employment and workforce displacement.

techcrunch.com

Friday, September 11, 2026

trending59

The Waymo effect: how AI is quietly making research less collaborative

AI-driven tools, exemplified by Waymo, are increasingly automating research processes, leading to a shift where collaboration among researchers has become less frequent. This trend matters as it could alter the traditional academic environment, potentially impacting knowledge sharing and innovation dynamics.

researchagenda.news

Thursday, September 10, 2026

research40

X-CoSD: Communication-Efficient Cross-Vocabulary Collaborative Speculative Decoding

A new paper proposes X-CoSD, a communication-efficient method for collaborative speculative decoding that involves an on-device small language model generating candidates while a server large language model verifies them, aiming to improve distributed inference processes in large language models. This approach is crucial as it could enhance the efficiency and scalability of AI applications across devices.

arxiv.org

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