The 66% ETH surge in Q3 2025 (closing at ~$3,887) drove nominal L1 gas fees up 6% to ~$0.37/tx, but ETH-denominated fees fell 11% post-Dencun, reducing L2 migration urgency. For Optimism (OP), Layer2 gas competition eased as fees stayed under $0.01/tx, enabling 20% QoQ tx growth. Rollup volume hit 2.5M daily by Sept (from 2M avg), with Superchain capturing 60% of L2 activity, though TVL dipped 12% to $643M amid Base's rise. Overall, surge amplified OP's scalability edge but intensified multi-L2 rivalry.
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Planck Network gained spotlight in TheStreet's September 2025 report, where CEO Diam Hamstra highlighted decentralized GPU infrastructure powering AI-crypto growth amid market volatility. Its AI Layer 0 protocol, Planck₀, offers modular blockchain for AI-native chains and DePIN, challenging Avalanche (AVAX)'s Layer 0 claims by providing native GPU access for scalable, cost-effective AI compute—potentially diverting developers from AVAX's high-performance L1 ecosystem. Ecosystem growth is accelerating: Raised $3.5M in funding plus $4M staking commitments; DevNet launch enables AI app building; GPU-as-a-service market projected at 23% CAGR to $XXB by 2030, fueling 100+ projects and partnerships like Arcanum Ventures. This positions Planck as a rising AI contender, pressuring AVAX's DeFi dominance.
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The OpenAI integration task in Assemble $ASM's NS3 news platform airdrop involves using OpenAI's o3-mini model to auto-tag crypto news for enhanced analysis and rewards (e.g., quizzes/sharing earn vesting $ASM over 5 years). Content Tag Placement Steps: Extract news text via API. Prompt OpenAI: "Generate 5-10 tags from taxonomy [list] for this article: [text]. Output JSON." Parse response; insert tags into Markdown frontmatter (e.g., tags: [tag1, tag2]) or database metadata. Validate consistency with schema. Multilingual Support Notes: Leverage o3's 16-language coverage (e.g., English, Chinese, Spanish); prepend language detection prompt, translate tags if needed to avoid bias. Developer feedback highlights tag variance—enforce strict taxonomy for accuracy; batch via LangChain for 100K+ items to cut costs.
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