Search: model access | CurrentLens.com

Policy & Safety

AI Firms Limit Access to Models Amid Rising Dual-Use Risks

CurrentLens
Apr 28, 2026

Leading AI companies restrict access to advanced models like GPT-Rosalind due to safety concerns.

Latest
Trending

Agents & Automation

OpenAI Launches Three Academy Courses on Agents and Workflows

CurrentLens
Jun 13, 2026

OpenAI released three Academy courses focused on practical AI skills, building repeatable workflows, and applying agents in everyday work.

Models & Launches

Google Releases Gemini-SQL2; Gemini 3.1 Pro Scores 80.04% on BIRD

CurrentLens
Jun 13, 2026

Google Research announced Gemini-SQL2, a Gemini 3.1 Pro-powered text-to-SQL capability that posted 80.04% execution accuracy on the BIRD single-model leaderboard.

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.