Social news platforms have become key launch outlets for open-source projects, especially Hacker News (HN), though quantifying their immediate impact remains challenging. This paper presents a reproducible demonstration system that tracks how HN exposure translates into GitHub star growth for AI and LLM tools. Built entirely on public APIs, our pipeline analyzes 138 repository launches from 2024-2025 and reveals substantial launch effects: repositories gain an average of 121 stars within 24 hours, 189 stars within 48 hours, and 289 stars within a week of HN exposure. Through machine learning models (Elastic Net) and non-linear approaches (Gradient Boosting), we identify key predictors of viral growth. Posting timing appears as key factor--launching at optimal hours can mean hundreds of additional stars--while the "Show HN" tag shows no statistical advantage after controlling for other factors. The demonstration completes in under five minutes on standard hardware, automatically collecting data, training models, and generating visualizations through single-file scripts. This makes our findings immediately reproducible and the framework easily be extended to other platforms, providing both researchers and developers with actionable insights into launch dynamics.

翻译：社交新闻平台已成为开源项目的重要发布渠道，尤其是Hacker News（HN），但量化其即时影响仍具挑战性。本文提出一个可复现的演示系统，用于追踪HN曝光如何转化为AI和LLM工具的GitHub星标增长。该系统完全基于公共API构建，分析了2024-2025年间138个仓库的发布数据，揭示了显著的发布效应：仓库在HN曝光后24小时内平均获得121颗星，48小时内获得189颗星，一周内获得289颗星。通过机器学习模型（弹性网络）和非线性方法（梯度提升），我们识别了病毒式增长的关键预测因子。发布时机是关键因素——在最优时段发布可能带来数百颗额外星标——而控制其他变量后，“Show HN”标签未显示出统计优势。该演示在标准硬件上五分钟内完成，通过单文件脚本自动收集数据、训练模型并生成可视化结果。这使得我们的发现可立即复现，且该框架易于扩展至其他平台，为研究者和开发者提供关于发布动态的可操作见解。