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.
PHBench
ClaimPHBench is an open benchmark for predicting Series A funding from Product Hunt launch signals. It combines a labeled dataset, public leaderboard, and weekly predictions.
Overview
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.
Features
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.
Use Cases
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.
Pros and Cons
Pros
- Clear benchmark focus: predicting Series A from Product Hunt launch signals.
- Large labeled dataset with 67,292 launches and 528 verified Series A outcomes.
- Public leaderboard and model comparisons make the evaluation setup easy to inspect.
- Methodology is documented with manual label auditing and a held-out test set.
- Weekly predictions page gives a concrete user-facing output beyond the benchmark.
Cons
- The pricing page is not available on the provided site crawl, so public pricing terms cannot be confirmed.
- The source pages do not document integrations, APIs, or export formats.
- Capability details are strongest for benchmark and prediction views; broader product workflow documentation is limited in the provided sources.
FAQ
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.
Quick Facts
- Category
- Developer Tool
- Product type
- Open benchmark
- Primary use
- Predicting Series A from Product Hunt signals
- Dataset size
- 67,292 launches
- Verified outcomes
- 528 Series A events
- Source domain
- phbench.com