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 :)
My best product hunt launches were driven by public curiosity and correlated with it. I was using those metric for A/B/C testing and it was way more making sense when you test yourself as founder or an idea of early prediction when same amount of effort is spent.
I will definitely try to benchmark using my past launches and give feedback!
Would be interesting to see a breakdown of false positives: high PH engagement but no Series A. That’s often where the real insight is.
After today's launch, we all expect to see PHBench's chances of hitting Series A based on its own model. Good luck!
Given the temporal performance decay you observed across funding regimes, how should users operationalize the score: do you recommend retraining/refreshing on a schedule, calibrating by year/sector, or using it mainly as a relative ranking signal—and why did you choose F0.5 as the primary leaderboard metric for that workflow?
Interesting, most people assume raw upvotes are the proxy for quality. So the finding about team size × community engagement being a stronger signal than votes alone is genuinely counterintuitive but very curious. Have you looked at whether solo founders who hit high engagement are penalized by this model? Do they show up as a distinct cluster? Would love to see how the signal degrades for truly first-time founders vs. repeat ones. Incredible dataset, congrats on getting years of data cleaned!
Been quietly working on this with Yagiz, Yigit and Rick for a while.
While I mostly focus on using founder profiles to predict raises, PHBench tries the same prediction but from the product side. A similar question but from the other side.
Have a go at the leaderboard if you fancy; the data's on HuggingFace.
So excited to see this live! This has been a labor of love, collecting data, running +100 experiments, and testing LLMs against good old gradient boosting.
The leaderboard is open. If you can beat us, you're the new champion. Who's in?
Are you using only launch day signals, or do you include post launch traction like follows and comments over the first week?
Really excited to bring PHBench to you guys! By extending the short-term productivity signals on Product Hunt to predict long-term funding materialization, we help to identify outlier products that are truly valuable in the VC environment. We think it will be greatly beneficial to the Product Hunt community.
Come to beat our baseline and get to the top of the leaderboard!
“Predict the next Series A from a ProductHunt launch”
PHBench launched on Product Hunt on May 15th, 2026 and earned 296 upvotes and 33 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.
PHBench was featured in Venture Capital (49.5k followers), Artificial Intelligence (468.4k followers), GitHub (41.2k followers), Data (2.3k followers) and Vercel Day (7 followers) on Product Hunt. Together, these topics include over 117.2k products, making this a competitive space to launch in.
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.
Want to see how PHBench stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
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 :)