Via mexc.com
The AI-driven DeFi platform rolls out customizable trading controls, USDC pair launches, and one-click liquidity zaps on Solana's largest AMM protocol
HeyAnon just dropped version 2.8 of its AI-powered DeFi platform, and the update, which went live on July 30, brings customizable price impact settings, the ability to launch PumpFun pairs denominated in USDC, and single-click zaps into Meteora liquidity positions.
What’s actually in the update #
The headline feature is customizable price impact settings. Users can now define acceptable price impact thresholds before executing swaps, giving them granular control over how much market movement they’re willing to absorb on entry or exit.
The 2.8 update also lets users create PumpFun pairs against USDC instead of SOL, which matters for traders who prefer stablecoin-denominated exposure and want to avoid the added variable of SOL price fluctuations.
The Meteora integration allows users to provide liquidity to the protocol’s pools without manually splitting tokens, approving multiple transactions, and navigating Meteora’s own interface. HeyAnon’s conversational AI handles the orchestration, turning what used to be a multi-step process into a single click.
Beyond the marquee features, the release includes eight improvements and three fixes spanning trading functionality, liquidity management, and portfolio tracking.
Why Meteora matters here #
Meteora is a Solana-native liquidity protocol with a total value locked of approximately $293 million. It has processed cumulative swap volumes exceeding $340 billion, and fees generated on the platform have surpassed $1.86 billion.
The bigger picture for agentic DeFi #
HeyAnon’s platform supports autonomous agents that can execute across both centralized and decentralized finance rails. Its integrations extend beyond Solana, including the Pandora prediction market on Ethereum. The ANON token serves as both the governance mechanism and utility token within this ecosystem.
The price impact settings in 2.8 give users protective guardrails rather than letting the AI run unsupervised, reflecting the tension between abstraction and user control in AI-driven trading interfaces.
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