1
407 votes
110 comments
HasData
Web scraping service for AI agents
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hasdata.comAI market research from Product Hunt launch data
Vercel Day on Product Hunt, mapped for founders.
We turned 60 Product Hunt launches into a founder-readable market map: who won attention, which product clusters were crowded, and what the data says before you build or launch.
5 winners
Five winners, one launch-day signal.
Read the winners left to right from #1 to #5. The useful part is not the ranking alone; it is what these products reveal about where Product Hunt attention concentrated.
2
345 votes
36 comments
PHBench
Predict the next Series A from a ProductHunt launch
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Product Hunt3
286 votes
56 comments
Lensmor
Turn exhibitor data into pre-booked sales meetings
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lensmor.com4
178 votes
46 comments
Agentic Website Builder 2.0 by Lokuma
Design, build, and run your site with a design agent harness
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lokuma.ai5
155 votes
24 comments
Gradient Bang
Massively multi-player game played by talking to an LLM
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Product HuntFounder takeaways
What the launch data says a founder can use.
This is the AI market research layer: not a raw topic list, but a read on visibility, buyer jobs, category language, and prompt-to-outcome promises.
Market signal
Market-signal products punched above their size.
8 products that turn public, market, web, or discovery signals into action were 13.3% of the field but captured 41.3% of votes. That is the clearest AI market research pattern in the dataset.
Founder lens
Founders pay attention to visibility.
The Go-to-market tools for founders cluster was only 13.3% of the field but captured 27.7% of votes. If a product helps founders see demand, buyers, or distribution channels sooner, make that promise unavoidable.
Category lens
Official tags can hide the real competitive landscape.
8 products carried the Product Hunt Developer Tools tag but captured only 3.4% of votes. The founder lesson is to map competitors by buyer job, not only by platform taxonomy.
Product story
Do not sell the prompt. Sell the finished outcome.
10 launches turned a prompt, description, or reference into an artifact or action, capturing 20.3% of votes. The founder lesson is to lead with the job completed, not the interface pattern.
Copy rule
The prompt is not the product story.
10 prompt-to-outcome launches captured 23.1% of votes. The founder lesson is simple: lead with the finished outcome, not the input box.
Product clusters
The five product clusters behind the map
Each cluster groups launches by the job they seemed to promise buyers, not by official Product Hunt tags. That makes the map useful for founder market research: you can see where products are crowded, where attention concentrated, and where category language hides the real buyer job.
Attention capture leader8 products
Go-to-market tools for founders
Launch, visibility, and lead-gen tools for founders trying to turn attention into pipeline: Product Hunt analytics, AI visibility tracking, event lead discovery, LinkedIn content, email verification, and commerce discovery.
13.3%
Product share
27%
Weighted traction share
2.03
Attention capture efficiency
Traction and conversation leader11 products
Agent/dev infrastructure
The strongest traction and conversation lane, led by HasData plus tools around agent infrastructure, shared AI context, coding agents, and developer control planes.
18.3%
Product share
29.4%
Weighted traction share
1.61
Attention capture efficiency
Most crowded20 products
Operator and vertical workflows
The largest lane by product count: concrete jobs, vertical workflows, productivity, finance, hiring, compliance, travel, food, and education workflows.
33.3%
Product share
19.8%
Weighted traction share
0.59
Attention capture efficiency
Creative/game lane10 products
Consumer, games, and creative experiments
Games, playful utilities, consumer apps, and creative experiments. This is where Gradient Bang, Cats Lock, Tiny World Builder, Loremill, and Fetch MTG belong.
16.7%
Product share
10.9%
Weighted traction share
0.65
Attention capture efficiency
Event-native stack11 products
Vercel web/design stack
The event-native web and design tooling lane: site builders, analytics, CMS, branded QR/web surfaces, indexing/SEO utilities, motion/design tooling, and content operations.
18.3%
Product share
12.9%
Weighted traction share
0.70
Attention capture efficiency
Crowded vs attention
Crowded lanes do not always win attention.
Product share shows where founders chose to build. Weighted traction share shows where the Product Hunt launch data concentrated attention.
Crowded vs attention
Most total traction
Agent/dev infrastructure
29.4%Weighted traction share
Most crowded
Operator and vertical workflows
20 products33.3%
Best at capturing attention
Go-to-market tools for founders
2.03Attention captured / product share
Weighted traction shareProduct share
GTMAttention capture leader
Go-to-market tools for founders
8 products / 27% Weighted traction share
DEVTraction and conversation leader
Agent/dev infrastructure
11 products / 29.4% Weighted traction share
OPSMost crowded
Operator and vertical workflows
20 products / 19.8% Weighted traction share
EXPCreative/game lane
Consumer, games, and creative experiments
10 products / 10.9% Weighted traction share
WEBEvent-native stack
Vercel web/design stack
11 products / 12.9% Weighted traction share
Cluster-level takeaways
Each product cluster changes the founder decision.
Read the clusters as competitive landscape analysis. They show where a founder faces category pressure, channel pressure, memorability pressure, or vertical specificity.
Vertical wedge
Vertical workflows need a sharper wedge.
13 explicit vertical or job-specific workflow products made up 21.7% of the field but captured 14.6% of votes. Vertical markets can work, but the buyer and use case need to be instantly legible.
Positioning pressure
Crowded verticals are a positioning test.
Operator and vertical workflows was the largest lane with 20 of 60 launches, but captured 19.8% of weighted traction. Vertical workflow can work, but the wedge has to be painfully specific.
Job-specific AI
Vertical AI is really job-specific software.
12 of 20 Operator and vertical workflow products used AI, agent, or automation language. The stronger founder takeaway is that AI won attention when it was attached to a recognizable job.
Distribution signal
Distribution intelligence became a product category.
5 of 8 Go-to-market tools for founders products used AI or agent language. The lane worked because it promised founders clearer demand, buyers, visibility, or sales motion.
Memorability
Creative products need a memorable hook.
10 consumer, game, and creative products represented 16.7% of launches, but three products captured 94.4% of the votes in that lane. In playful categories, memorability did most of the work.
Web/design stack
The web/design stack was real, but top-heavy.
11 web/design-stack products made up 18.3% of launches and 12.9% of weighted traction, with the top three carrying 93.3% of the votes in that lane. Founders should treat this as a real cluster, not a guaranteed distribution channel.
Positioning context
AI language was the default, not the differentiator.
35 of 60 startups used AI or agent language and captured 84.2% of votes. For a founder, the lesson is that AI helps explain the market, but it no longer makes the product stand out by itself.
Vote concentration
Where the Product Hunt vote curve bends.
Sort every startup by votes, split the list into five equal groups, and the L-shape is immediate: the first quintile captures almost all voting attention while the long tail contains most of the products.
Long-tail proof
72%
12 startups / 2,146 votes
Bottom 60% of startups
2.9%
36 startups / 86 votes
38 startups had 1-9 votes. That is 63.3% of participants, but only 3.4% of votes.
Votes by startup quintile
12 startups captured 72%.
Top 20% of startups
72%
Next 20%
25.1%
Bottom 60% of startups
2.9%
vote share
Quintile detail
60 startups / 2,979 votes
Q1
Vote rank: 1-12
Top 20% by votes
12 startups / 2,146 votes
LeadersHasDataPHBenchLensmorAgentic Website Builder 2.0 by Lokuma
72%
vote share
Q2
Vote rank: 13-24
Quintile 2 by votes
12 startups / 747 votes
LeadersPromptScoutAtlas NavigationJust The TipsNimbus
25.1%
vote share
Q3
Vote rank: 25-36
Quintile 3 by votes
12 startups / 41 votes
LeadersWinchePostrailHireCheckKiwotHire
1.4%
vote share
Q4
Vote rank: 37-48
Quintile 4 by votes
12 startups / 27 votes
LeadersAvocado Studio AI - Site Content OpsPingmapFragmentsStyleRef.io
0.9%
vote share
Q5
Vote rank: 49-60
Quintile 5 by votes
12 startups / 18 votes
LeadersLoremillMaitribAI, Re-imagining Family TravelApplications. Questions. Answers. AQUA
0.6%
vote share
Attention economics
Launch attention is the first market signal.
Before treating any category as a market signal, check the concentration curve. Then use comments to separate passive reach from deeper buyer or builder curiosity.
Attention pattern
Product Hunt attention is winner-take-most.
The top 20% of startups captured 72.0% of votes, while the bottom 60% captured only 2.9%. For founders, launch data is a reminder that distribution has to start before launch day.
Research signal
Comments are founder research.
Agent/dev infrastructure captured 27.8% of votes but 34.3% of comments. For technical products, the comment thread can reveal deeper buyer curiosity than the vote count alone.
About the author
Who is this guy?
Jorge is a software engineer who builds market research systems, launch analysis workflows, and tiny opinions about distribution.
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Last updated: May 16, 2026 refresh