PeerPush Signals
The major AI assistants read PeerPush product data around the clock, and a community of real builders votes the best tools up. Here is the scale of both.
Live · last 30 days
Product reads by AI assistants
all time
Upvotes from real builders
Live · last 30 days
AI systems reading PeerPush
Read by the AI systems people actually use
Where attention is concentrated
The product categories drawing the most discovery from people and AI right now, and how broadly that attention holds.
The shape of the market
How the catalog clusters by use case, and how products are priced for each audience.
Trust & quality
The tools earning the most credibility, and where peer endorsements are accelerating.
Community pulse
Where genuine human discussion is concentrating right now, per product in the category.
Where the openings are
Underserved categories, intensifying demand, and the openings the crowd hasn't noticed yet.
Rising use cases
Rising audiences
Frequently asked questions
PeerPush Signals is a free market-intelligence layer that shows what is rising, getting discovered, and most trusted across the software market, measured from both human and AI activity on PeerPush.
It tracks which AI systems are reading PeerPush, the most-watched product categories, how broadly attention is holding across the market, the most-trusted products, underserved category whitespace, and overall demand intensity.
Signals are derived from real traffic and engagement on PeerPush: AI crawlers, AI agents browsing on behalf of users, MCP tool calls, and human visitors. Only privacy-safe, aggregated shape is published.
Aggregations refresh on rolling schedules: short windows update every few minutes and longer windows on slower schedules. Each page shows a "Data fresh as of" timestamp.
AI traffic combines AI crawlers, AI agents, and MCP clients, classified by user-agent and entry point. The methodology page explains exactly what is counted and what is excluded.
Yes. There is a free public tier with a Pro depth layer for deeper history and detail. Public surfaces apply a k-anonymity floor so no small audience can be identified.
Data fresh as of