Launch-Day Diffusion: Tracking Hacker News Impact on GitHub Stars for AI Tools

TL;DR

This paper introduces a reproducible system to quantify and predict the impact of Hacker News exposure on GitHub star growth for AI tools, revealing key factors influencing viral success.

Contribution

It presents a fully automated pipeline for tracking and modeling launch effects from Hacker News to GitHub, including predictive models and insights on timing and tagging.

Findings

Repositories gain an average of 121 stars within 24 hours of HN exposure

Optimal posting hours significantly increase star counts

The 'Show HN' tag does not statistically affect star growth after controls

Abstract

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.