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- 🚨 Gate Alpha Ambassador Recruitment is Now Open!
📣 We’re looking for passionate Web3 creators and community promoters
🚀 Join us as a Gate Alpha Ambassador to help build our brand and promote high-potential early-stage on-chain assets
🎁 Earn up to 100U per task
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- 🔥 Gate Square #Gate Alpha Third Points Carnival# Trading Sharing Event - 5 Days Left!
Share Alpha trading screenshots with #Gate Alpha Trading Share# to split $100!
🎁 10 lucky users * 10 USDT each
📅 July 4, 4:00 – July 20, 16:00 UTC+8
Gate Alpha 3rd Points Carnival Issue 10 is in full swing!
Trade and post for double the chances to win!
Learn more: https://www.gate.com/campaigns/1522alpha?pid=KOL&ch=5J261cdf
- 🎉 [Gate 30 Million Milestone] Share Your Gate Moment & Win Exclusive Gifts!
Gate has surpassed 30M users worldwide — not just a number, but a journey we've built together.
Remember the thrill of opening your first account, or the Gate merch that’s been part of your daily life?
📸 Join the #MyGateMoment# campaign!
Share your story on Gate Square, and embrace the next 30 million together!
✅ How to Participate:
1️⃣ Post a photo or video with Gate elements
2️⃣ Add #MyGateMoment# and share your story, wishes, or thoughts
3️⃣ Share your post on Twitter (X) — top 10 views will get extra rewards!
👉
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.