Search: tooling | CurrentLens.com

AI Defense & Warfare

NSA Tests Anthropic's Mythos Preview for Vulnerability Assessment

CurrentLens
May 3, 2026

The NSA's new initiative leverages Anthropic's AI tooling to enhance cybersecurity measures against vulnerabilities.

Science & Healthcare

EVE Releases Open-Source 24B Earth-Intelligence LLM and Benchmarks

CurrentLens
Apr 16, 2026

EVE publishes EVE-Instruct, a 24B Mistral-based model and a suite of Earth-science datasets, benchmarks, and tooling for domain-specific LLM deployment.

Agents & Automation

OpenAI Expands Agents SDK to Help Enterprises Build Safer Agents

CurrentLens
Apr 16, 2026

OpenAI updated its Agents SDK to broaden enterprise agent-building capabilities as interest in agentic systems grows.

Latest
Trending

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.