Users are able to buy “keys” of other users X (Twitter) accounts. By owning a “key” the user gets access to a private chat with the person whose “key” one owns. When a “key” of a users account is traded the user earns a commission (supply-side fees).

friend.tech is a crypto native social media application built on Base. friend.tech was founded by pseudonymous developers known as Racer and Shrimp and launched in 2023.

friend.tech team and investors own the protocol. They currently take a cut of the total trading volume (revenue). friend.tech’s investors include Paradigm.

twitter

website

Social

friend.tech

# friend.tech Active users (weekly) Methodology

## 1. Overview

Friend.tech Weekly Active Users (WAU) measures the number of unique users who engaged in trading activities on the Friend.tech social trading platform within any 7-day rolling period. This metric captures user engagement across both Friend.tech v1 (individual creator shares) and v2 (club trading) features. The calculation uses HyperLogLog probabilistic data structures to efficiently handle large-scale user counting while providing dimensional breakdowns by chain, user type (buyer/seller), and product features.

## 2. Data sources

- **Friend.tech v1 trading events**: Parsed `FriendtechSharesV1_Trade_event` logs from Base blockchain containing trader addresses, buy/sell indicators, and share amounts for individual creator trading
- **Friend.tech v2 club events**: Parsed `Clubs_Buy_event` and `Clubs_Sell_event` logs from Base blockchain capturing club trading activities with user addresses, points amounts, and protocol fees
- **Base transaction records**: Base blockchain transaction data used to map event logs to transaction initiators (`from_address`) to identify actual users performing trades
- **Token metadata**: Friend.tech token contract address (`0x0bd4887f7d41b35cd75dff9ffee2856106f86670`) for volume calculations

## 3. Step-by-step calculation

### Component A: User activity extraction
- **Purpose**: Identify and classify all users who performed trading activities on Friend.tech
- **Logic**: Extract user addresses from both v1 and v2 trading events on Base blockchain. For v1 events, parse `FriendtechSharesV1_Trade_event` logs to capture trader addresses, filtering for valid trades where share amounts are greater than zero. Classify users as buyers when `isBuy` flag is true, and sellers when false. For v2 events, process `Clubs_Buy_event` and `Clubs_Sell_event` logs to extract account addresses and join with Base transaction records to identify the actual transaction initiators (`from_address`) as the users. Classify v2 users as buyers or sellers based on the event type (buy vs sell events).
- **Key fields**: `user_address` (the trading participant), `user_type` (Buyer or Seller), `block_timestamp` (event timing), `chain` (Base network), `transaction_hash` (event identifier)

### Component B: Daily user aggregation  
- **Purpose**: Create daily snapshots of distinct active users across all dimensions
- **Logic**: Group all user activities by day using timestamp truncation to daily boundaries. Create distinct user counts per day while preserving dimensional attributes including chain, user type, market sector, business segment, and product identifiers. Apply chain availability filters to ensure data completeness. Generate daily active user records with all relevant dimensional breakdowns for downstream processing.
- **Key fields**: `timestamp` (daily boundary), `user_dau` (distinct user address), `user_type` (buyer/seller classification), `chain` (Base), `product_id` (feature identifier)

### Component C: HLL sketch creation and rolling windows
- **Purpose**: Convert daily user sets into memory-efficient probabilistic data structures and create 7-day rolling windows
- **Logic**: Transform daily user lists into HyperLogLog sketches using 15-bit precision for efficient approximate counting of large user sets. Create a datespine covering all possible days in the dataset range. For each target day, generate a 7-day window including the current day plus the previous 6 days. Use array generation and unnesting to create all day combinations within each 7-day window. Join the rolling window dates with the daily HLL sketches to capture user activity across the full 7-day period.
- **Key fields**: `hll` (HyperLogLog sketch of users), `timestamp` (target day), `joinday` (days within 7-day window)

### Component D: Dimensional WAU calculation
- **Purpose**: Calculate weekly active users across multiple dimensional breakdowns
- **Logic**: Create separate calculations for four dimensional views: overall project level, by chain, by user type, and by product. For each dimension, merge HLL sketches across the 7-day rolling window to get deduplicated user counts. The HLL merge operation handles users who were active on multiple days within the same 7-day period, ensuring they are counted only once. Extract final distinct counts from merged HLL sketches to get precise WAU numbers for each dimensional breakdown.
- **Key fields**: `dimensions` (breakdown type), `user_wau` (weekly active user count), dimensional identifiers (`chain_id`, `user_type`, `product_id`)

### Final aggregation
- **Combining components**: Union all dimensional breakdowns (project-level, chain-level, user type-level, product-level) into a unified output table with consistent schema
- **Currency conversion**: Not applicable for user count metrics
- **Time aggregation**: Uses 7-day rolling windows where each day represents the WAU for that day plus the previous 6 days, providing daily time series of weekly activity levels
- **Final calculation**: The final `user_wau` value represents the number of distinct users who performed at least one trading action (buy or sell) within the 7-day period ending on each timestamp, calculated using HyperLogLog probabilistic counting for scalability and accuracy

*This methodology was automatically generated by Token Terminal's methodology analyzer.*

Unique revenue-generating users over a rolling 7-day window.

friend.techFRIEND

Active users (weekly)

Active users (weekly)

Competitive landscape