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Enterprise AI

NVIDIA Unveils Software and Models to Power Enterprise AI Agents

CurrentLens
Jun 2, 2026

NVIDIA announced new software, open-source models and platform partnerships to build autonomous AI agents for engineering, healthcare, development and business operations.

AI Defense & Warfare

DOD Expands Classified AI Collaborations with Eight Firms, Excludes Anthropic

CurrentLens
May 3, 2026

The Department of Defense broadens its classified AI partnerships to include eight major tech firms, omitting Anthropic amid disputes.

Enterprise AI

OpenAI Launches Codex Labs to Scale Codex in Enterprises

CurrentLens
Apr 22, 2026

OpenAI launched Codex Labs and aligned with consultancies including Accenture, PwC, and Infosys to help enterprises deploy Codex; Codex hits 4M WAU.

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Science & Healthcare

Africa CDC and WHO launch $518M continental Ebola response plan

CurrentLens
Jun 6, 2026

A six-month 'One Response' plan targets the Bundibugyo Ebola outbreak with unified coordination, surveillance, clinical care and community engagement across affected countries.

Policy & Safety

HASC adds right-to-repair language to FY27 defense policy bill

CurrentLens
Jun 6, 2026

The House Armed Services Committee inserted right-to-repair provisions into its FY27 defense policy draft, aiming to ease barriers that limit troops' ability to fix equipment.

AI Creative

Startups Pull Users Off Phones With In-Person Games and DIY Cyberdecks

CurrentLens
Jun 6, 2026

TechCrunch highlights founders building physical social products: Board raised funding for in-person games, and cyberdeck DIYs are going viral.

Agents & Automation

MicroPython WASM Sandbox Enables Safer Datasette Plugin Execution

CurrentLens
Jun 6, 2026

Simon Willison published an alpha MicroPython-in-WASM sandbox (micropython-wasm) and a Datasette plugin (datasette-agent-micropython) to run plugin code with constrained access.

Models & Launches

DKPS method cuts model-evaluation queries using cached responses

CurrentLens
Jun 6, 2026

An arXiv paper introduces a DKPS-based approach that uses cached model outputs to predict benchmark scores while substantially reducing the number of queries.