Predict the next Series A from a ProductHunt launch
PHBench: the first public benchmark predicting Series A funding from Product Hunt launch signals. We analyzed 67,292 featured launches over 7 years, linked to 528 verified Series A rounds via Crunchbase. Champion model: 4.7x lift over random. Team size × community engagement is the strongest signal; B2B (API, Payments, Fintech) converts at 3x baseline; Rank #1 raises at 2.2x unranked. Dataset, code, and baselines open. Submit at phbench.com and subscribe for weekly high-probability launches.
@rajiv_ayyangar, thank you so much for hunting us!
Hey PH Community 👋
We're Yagiz, a Senior Technical Product Manager at Amazon and an independent researcher and Yigit, co-founder and GP of Vela Partners. Today, we're launching PHBench in collaboration with the University of Oxford (Ben Griffin and Rick Chen) and Vela Partners, the leading quant VC.
And yes, the irony of launching a Product Hunt benchmark on Product Hunt is completely intentional 🙂
Here's the origin story. We kept asking a question nobody had answered: Can you predict which Product Hunt launches will raise Series A funding, based solely on what you see on launch day (votes, rank, team size, category, timing)?
So we built PHBench. We collected 67,292 featured PH launches going back to 2019, matched them to Crunchbase funding records, and identified 528 verified Series A raises within 18 months. Seven years of data. Every featured launch.
Three findings I think this community will find interesting:
→ The signals work. Our model is 4.7x better than random. Statistically significant.
→ The strongest predictor isn't votes alone. It's team size × community engagement together. A large coordinated team achieving high traction is more predictive than either signal alone.
→ B2B categories convert at 3x the baseline rate. API, Payments, Fintech. If you launch a developer tool on a Tuesday with a big team and high engagement, that's a strong signal.
We also tested three frontier Gemini models on the same task. The most capable model performed the worst. Better reasoning doesn't help with pure numbers.
The dataset is available on HuggingFace. The leaderboard is live. The code is public. Can you beat our baseline?
The paper is on arXiv and has been submitted to the NeurIPS 2026 Evaluations & Datasets Track.
Would love your feedback — especially from anyone who's launched on PH and gone on to raise Series A. You're in our dataset :)
About PHBench on Product Hunt
“Predict the next Series A from a ProductHunt launch”
PHBench launched on Product Hunt on May 15th, 2026 and earned 292 upvotes and 32 comments, earning #3 Product of the Day. PHBench: the first public benchmark predicting Series A funding from Product Hunt launch signals. We analyzed 67,292 featured launches over 7 years, linked to 528 verified Series A rounds via Crunchbase. Champion model: 4.7x lift over random. Team size × community engagement is the strongest signal; B2B (API, Payments, Fintech) converts at 3x baseline; Rank #1 raises at 2.2x unranked. Dataset, code, and baselines open. Submit at phbench.com and subscribe for weekly high-probability launches.
On the analytics side, PHBench competes within Venture Capital, Artificial Intelligence, GitHub, Data and Vercel Day — topics that collectively have 561.4k followers on Product Hunt. The dashboard above tracks how PHBench performed against the three products that launched closest to it on the same day.
Who hunted PHBench?
PHBench was hunted by Rajiv Ayyangar. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Rajiv Ayyangar
@rajiv_ayyangar, thank you so much for hunting us!
Hey PH Community 👋
We're Yagiz, a Senior Technical Product Manager at Amazon and an independent researcher and Yigit, co-founder and GP of Vela Partners. Today, we're launching PHBench in collaboration with the University of Oxford (Ben Griffin and Rick Chen) and Vela Partners, the leading quant VC.
And yes, the irony of launching a Product Hunt benchmark on Product Hunt is completely intentional 🙂
Here's the origin story. We kept asking a question nobody had answered: Can you predict which Product Hunt launches will raise Series A funding, based solely on what you see on launch day (votes, rank, team size, category, timing)?
So we built PHBench. We collected 67,292 featured PH launches going back to 2019, matched them to Crunchbase funding records, and identified 528 verified Series A raises within 18 months. Seven years of data. Every featured launch.
Three findings I think this community will find interesting:
→ The signals work. Our model is 4.7x better than random. Statistically significant.
→ The strongest predictor isn't votes alone. It's team size × community engagement together. A large coordinated team achieving high traction is more predictive than either signal alone.
→ B2B categories convert at 3x the baseline rate. API, Payments, Fintech. If you launch a developer tool on a Tuesday with a big team and high engagement, that's a strong signal.
We also tested three frontier Gemini models on the same task. The most capable model performed the worst. Better reasoning doesn't help with pure numbers.
The dataset is available on HuggingFace. The leaderboard is live. The code is public. Can you beat our baseline?
The paper is on arXiv and has been submitted to the NeurIPS 2026 Evaluations & Datasets Track.
Would love your feedback — especially from anyone who's launched on PH and gone on to raise Series A. You're in our dataset :)