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- 🎉 Hey Gate Square friends! Non-stop perks and endless excitement—our hottest posting reward events are ongoing now! The more you post, the more you win. Don’t miss your exclusive goodies! 🚀
🆘 #Gate 2025 Semi-Year Community Gala# | Square Content Creator TOP 10
Only 1 day left! Your favorite creator is one vote away from TOP 10. Interact on Square to earn Votes—boost them and enter the prize draw. Prizes: iPhone 16 Pro Max, Golden Bull sculpture, Futures Vouchers!
Details 👉 https://www.gate.com/activities/community-vote
1️⃣ #Show My Alpha Points# | Share your Alpha points & gains
Post your - 🚀 ETH jumped to $4,600 this morning, up 8.69% in 24h!
Just shy of the $4,891 ATH—think it breaks through?
📍 Follow Gate_Square, vote and drop your reason.
🎁 4 winners split $100 Futures Voucher! - 💙 Gate Square #Gate Blue Challenge# 💙
Show your limitless creativity with Gate Blue!
📅 Event Period
August 11 – 20, 2025
🎯 How to Participate
1. Post your original creation (image / video / hand-drawn art / digital work, etc.) on Gate Square, incorporating Gate’s brand blue or the Gate logo.
2. Include the hashtag #Gate Blue Challenge# in your post title or content.
3. Add a short blessing or message for Gate in your content (e.g., “Wishing Gate Exchange continued success — may the blue shine forever!”).
4. Submissions must be original and comply with community guidelines. Plagiarism or re
Microsoft Open Source New Version of Phi-4: Inference Efficiency Rises 10 Times, Can Run on Laptops
Jin10 data reported on July 10, this morning, Microsoft open sourced the latest version of the Phi-4 family, Phi-4-mini-flash-reasoning, on its official website. The mini-flash version continues the Phi-4 family’s characteristics of small parameters and strong performance, specifically designed for scenarios limited by Computing Power, memory, and latency, capable of running on a single GPU, suitable for edge devices like laptops and tablets. Compared to the previous version, mini-flash utilizes Microsoft’s self-developed innovative architecture, SambaY, resulting in a big pump in inference efficiency by 10 times, with average latency reduced by 2-3 times, achieving a significant improvement in overall inference performance.