I built a tool that helps predict HN front page success

Name: I built a tool that helps predict HN front page success
Availability: InStock
Author: margotli

by margotli·May 3, 2026·26 points·23 comments

Visit Project View on HN

AI Analysis

●MidCrowd Pleaser

Optimizes for HN algorithms when the best strategy is just building something good.

Strengths

•Trained on a massive 1.6M post dataset for statistical relevance.
•Surface-level insights like 'GitHub links work 3x better' are genuinely useful.

Weaknesses

•Optimizing for a community that explicitly hates optimization feels ironic.
•HN virality is too stochastic for a model to be reliably predictive.

Post Description

Hey HN community,

I built a tool that helps optimize your post for hitting the first page of Show HN.

How it works: I used a Hugging Face dataset of all Hacker News posts from the past 3 years and trained a model that predicts how successful your post might be. There's still a lot of randomness on HN, so nothing is guaranteed, but the tool helps optimize your post for higher odds.

A couple of interesting findings:

- GitHub repo links work x3 better than regular domains - Open-source tools have a steady virality rate (13.9% - one of the highest) - "I built" outperforms "We built" - Using parentheses and mentioning technologies (Lua, Postgres, Rust, etc.) helps a ton.

You can try the tool at wannalaunch.com or read the blog posts for more insights from the analysis. The model is also available as open source if you want to retrain it or look under the hood.

Happy to hear the feedback!