Apertus 1.5 Release Notes
A Blog post by Swiss AI Initiative on Hugging Face
More from the blog
IBM releases SOTA Granite Time Series PatchTST-FM-r2 model with commercial-friendly license
A Blog post by IBM Research on Hugging Face
GPT-Lab
· gpt-lab.eu
Aug 5, 2026
How NGINX Vulnerabilities Have Evolved
What 17 years of advisories reveal about recurring weaknesses, risky modules,patching speed, and static-analysis limits The short version: NGINX usually releases small fixes at or before public disclosure. Its biggest recurring problem is memory safety, while newer protocol code such as HTTP/2 and HTTP/3 has become a major area of risk. Why look at NGINX? NGINX runs as a web server, reverse proxy, and gateway across a large part of the Internet. A flaw in request parsing, protocol handling, or m…
Gemma Scope 2: helping the AI safety community deepen understanding of complex language model behavior
Open interpretability tools for language models are now available across the entire Gemma 3 family with the release of Gemma Scope 2.
Jupyter Agents: training LLMs to reason with notebooks
Related papers
ECCOLA - a Method for Implementing Ethically Aligned AI Systems
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
AI-powered Code Review with LLMs: Early Results
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
LLM-based agents for automating the enhancement of user story quality: An early report
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
System for systematic literature review using multiple AI agents: Concept and an empirical evaluation
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.