GenAI

My experiences of effectively using AI for personal workflows, with a focus on Claude Code.

Sphinx-Needs combines Docs-as-Code with a powerful life cycle management for objects like requirements in automotive and industry specific documentations

CX-Titan is fully compatible with CX-Saturn. Business Application Providers, however, now need to certify their solutions against the CX-Saturn standards.

A comprehensive SDK for Eclipse Tractus-X components

Semantic Web Technologies (SWTs) provide an approach to the structuring and understanding of data. SWTs utilize ontologies, reasoners, and query languages to structure existing knowledge, validate kn...

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity...

ABSTRACT

Reference Implementations of CX-0084 (Federated Queries In Data Spaces) - eclipse-tractusx/knowledge-agents

As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that manipulate objects, navigate software, coordinate with others, or design experiments require predictive environment models, yet the term world model carries different meanings across research communities. We introduce a "levels x laws" taxonomy organized along two axes. The first defines three capability levels: L1 Predictor, which learns one-step local transition operators; L2 Simulator, which composes them into multi-step, action-conditioned rollouts that respect domain laws; and L3 Evolver, which autonomously revises its own model when predictions fail against new evidence. The second identifies four governing-law regimes: physical, digital, social, and scientific. These regimes determine what constraints a world model must satisfy and where it is most likely to fail. Using this framework, we synthesize over 400 works and summarize more than 100 representative systems spanning model-based reinforcement learning, video generation, web and GUI agents, multi-agent social simulation, and AI-driven scientific discovery. We analyze methods, failure modes, and evaluation practices across level-regime pairs, propose decision-centric evaluation principles and a minimal reproducible evaluation package, and outline architectural guidance, open problems, and governance challenges. The resulting roadmap connects previously isolated communities and charts a path from passive next-step prediction toward world models that can simulate, and ultimately reshape, the environments in which agents operate.

(Engineering Responsible AI , #1) Why the Ontology is your best defense against hallucinations

This paper presents boldsea, Boldachev's semantic-event approach -- an architecture for modeling complex dynamic systems using executable ontologies -- semantic models that act as dynamic...

Ontology interoperability is one of the complicated issues that restricts the use of ontologies in knowledge graphs (KGs). Different ontologies with conflicting and overlapping concepts make it...

Recent years have seen a surge in development of formal ontologies within the domain of built environment. Yet, experts and novices alike face difficulties in locating relevant ontologies, hampering adoption. This

spoiler: most tools will not even survive 90 days. this guy is literally giving away the exact 2026 playbook for AI Agents. he covers what to learn, what to build, and what to skip 👀 ↓ read this today