PHBench

O que é PHBench?

PHBench is an open benchmark for predicting whether a Product Hunt launch will eventually raise a Series A round. The site frames the problem as ranking launches from a 24-hour Product Hunt signal window and evaluating models on verified funding outcomes within an 18-month window.

It combines a labeled historical dataset, a public leaderboard, and weekly prediction views. The benchmark page says models are trained and ranked on 67,292 launches from 2019 to 2025, with 528 verified Series A events, and that the public test set stays held out until final submission.

O que PHBench pode fazer?

Model leaderboard

Ranks models that predict Series A outcomes from Product Hunt launch signals, with a public leaderboard that compares methods such as ensembles, LightGBM, XGBoost, and language-model baselines.

Weekly predictions

Scores new Product Hunt launches and surfaces weekly prediction results, including lift rankings for the current week’s launches.

Historical launch dataset

Uses a dataset of 67,292 Product Hunt launches spanning 2019 to 2025 and 528 verified Series A events, giving the benchmark a documented evaluation base.

Signal analysis

Shows which signals are most associated with Series A outcomes, including launch-day rank, maker follower count, votes-per-comment ratio, and topic-related features.

Reproducible methodology

Documents its benchmark setup with manual label auditing, feature documentation, a held-out public test set, and hash-pinned reruns for submissions.

Project resources

Provides project links for the dataset, methodology, paper, GitHub, and newsletter so users can inspect the benchmark and follow updates.

Casos de uso

“Benchmark model performance”

Researchers or builders can use PHBench to compare different modeling approaches on the same Product Hunt-to-Series-A task and inspect how their method ranks on the public leaderboard.

“Review weekly launch signals”

Founders or operators who follow Product Hunt can review weekly predictions to see which launches the benchmark ranks as more likely to raise a Series A.

“Study launch signal patterns”

Data practitioners can examine the signal analysis section to understand which launch features appear most predictive in the benchmark and which ones are treated as noise.

“Use a reproducible reference”

Teams interested in reproducible evaluation can use the documented methodology, held-out test set, and citable paper to understand how the benchmark was built and submitted.

Perguntas frequentes

What does PHBench measure?

PHBench is an open benchmark for predicting whether a Product Hunt launch will eventually raise a Series A funding round within 18 months.

Is there a public pricing page?

The site shows an open benchmark, leaderboard, dataset, methodology, and prediction views. It does not show a public pricing plan on the pages provided; the pricing URL returns a 404.

How are predictions evaluated?

The benchmark is built on Product Hunt launch signals and ranks models on held-out test data. The home page says submissions rerun on a hash-pinned test set, and the public test set is held out until final submission.

What kind of output does PHBench provide?

The site presents weekly predictions for Product Hunt launches and a leaderboard of models. It also shows a “Get weekly predictions” call to action and a predictions page with scored launches.