Chainly
Attention Is Alpha
The problem Chainly solves
Chainly solves the fundamental problem of reactive trading in crypto markets. Currently, traders can only react to price movements after they happen, missing the massive profits that come from predicting trends early.
What Chainly enables:
🎯 Trade Attention, Not Price - Instead of buying PEPE at $0.01 after it pumps, trade PEPE's social attention when it first starts trending on Twitter
📊 Predictive Alpha - Our platform tracks 2.4M+ social mentions across Twitter, Reddit, Farcaster, and GitHub to identify viral trends before price reacts
âš¡ New Asset Class - Create entirely new financial instruments:
Attention Futures (24h, 48h, 7d contracts)
Leveraged Attention Tokens (PEPE3XATTN for 3x attention exposure)
Viral Trend Prediction Markets
Inverse Attention Tokens (short viral trends)
🔄 Cross-Chain Intelligence - Trade attention on Ethereum, settle on Avalanche using Chainlink CCIP
Real-world use cases:
Traders can profit from social buzz before price pumps
Influencers can hedge their content's viral potential
Marketing Teams can bet on campaign success metrics
Institutional Traders get early signals for social sentiment analysis
Why it matters: Attention precedes price in crypto. When DOGE starts trending, smart traders profit from the attention surge before retail FOMO drives price up 300%.
Chainly qualifies for the Onchain Finance by creating an entirely new asset class - attention derivatives - with novel financial instruments like attention futures and leveraged attention tokens that don't exist in traditional DeFi.
Cross-Chain Solutions Track through our Chainlink CCIP implementation that bridges attention scores between Ethereum Sepolia and Avalanche Fuji, enabling cross-chain attention derivatives trading.
Implemented Avalanche via deployment on Avalanche Fuji testnet, DeFi & Web3 Agents Track for automated viral trend prediction and oracle-based settlement, plus eligibility for the Grand Prize as an innovative platform that transforms how traders interact with social sentiment data.
Challenges I ran into
When i shot video , requirements guided 3 - 5 minute, so i wanted to show proper execution of trading but i couldnt give due to timing increase, tried my best 😊
for trading execution , refer my sepolia and fuji contracts over my github , for showing demo -
https://sepolia.etherscan.io/tx/0xbbe6f466627794611d5e2e3739d983f556e6e047cab3c6660484862446529269
Oracle Data Freshness Crisis -
The Problem: Encountered a persistent NotFreshData() error when trying to open positions. Users couldn't trade because the oracle was rejecting "stale" data.
Root Cause: The DataRefresher contract had maxAge set to 300 seconds, but our attention data was being marked as stale within minutes due to high social media volatility.
Fix: Implemented token-specific data refreshers since the generic refresher wasn't monitoring BTC attention scores properly.
Cross-Chain Message Delivery Complexity
The Problem: Implementing Chainlink CCIP for cross-chain attention score delivery between Sepolia and Avalanche-Fuji was more complex than expected. Messages were being sent but not properly decoded on the destination chain.
Root Cause: ABI encoding/decoding mismatches between source and destination contracts for complex attention score data structures.
Tracks Applied (3)
Onchain Finance
Cross-Chain Solutions
Avalanche Track
Avalanche
Technologies used

