Tootfinder

rsyslog lead dev on AI, and also see this: https://www.rsyslog.com/clarifying-ai-first-what-it-really-means-for-rsyslog/

Clarifying “AI First” – What It Really Means for rsyslog - rsyslog
TL;DR:The rsyslog project is embracing AI First (Human-Controlled) — AI is becoming an integral partner across development, documentation, user education, technical support, deployment guidance, and future observability tools. This does not mean “vibe coding” or letting AI run unsupervised. Instead, AI accelerates routine work, drafts ideas, and provides insights, while Rainer Gerhards — with decades […]

Developing Shared Vocabulary System For Collaborative Software Engineering
Carey Lai Zheng Hui, Johnson Britto Jessia Esther Leena, Kumuthini Subramanian, Zhao Chenyu, Shubham Rajeshkumar Jariwala
https://arxiv.org/abs/2507.14396

Developing Shared Vocabulary System For Collaborative Software Engineering
Effective communication is a critical factor in successful software engineering collaboration. However, communication gaps remain a persistent challenge, often leading to misunderstandings, inefficiencies, and defects. This research investigates the technical factors contributing to such misunderstandings and explores the measurable benefits of establishing shared vocabulary systems within software documentation and codebases. Using a Design Science Research (DSR) framework, the study was struc…

🔧 Large-Scale Code Analysis
Process entire codebases with 75,000 lines including source files, tests and documentation for complete system understanding. Claude can identify cross-file dependencies and suggest improvements accounting for complete system design.
📄 Document Synthesis Capabilities
Analyze hundreds of legal contracts, research papers or technical specifications while maintaining full context across documents. Process extensive document sets and analyze relations…

Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation
Elijah Kayode Adejumo, Brittany Johnson, Mariam Guizani
https://arxiv.org/abs/2508.02497

Bridging Language Gaps in Open-Source Documentation with Large-Language-Model Translation
While open source communities attract diverse contributors globally, few repositories provide essential documentation in languages other than English. Large language models (LLMs) have demonstrated remarkable capabilities in software engineering tasks and translations across domains. However, little is known about LLM capabilities in translating open-source technical documentation, which mixes natural language, code, URLs, and markdown formatting. To understand the need and potential for LLMs i…