开启时会模糊预览图，关闭后正常显示
原文：I analyzed 180M jobs to see what jobs AI is actually replacing today https://t.co/YxkpbggBmt 翻译：https://t.co/wTw73mpXKl
Prompt Engineer, dedicated to learning and disseminating knowledge about AI, software engineering, and engineering management.
RT @0xMert_: @VitalikButerin @ilblackdragon new judge: @eddylazzarin CTO of a16z crypto! this might be the most stacked lineup of judges…
CTO @a16zcrypto
一筐大模型揉一起做篮球识别！ 给大家看这个识别效果，投篮位置，是否进球，球衣编号，球在哪里(甚至裁判手里拿的另外一个球都能捕捉到)，篮筐，运动员都能识别。 总计用了这些模型： F-DETR （检测球员）—— 这是个类 DETR 的实时目标检测器。微调后用来检测球员、球衣号码、裁判、篮球，甚至投篮类型。 SAM2 （跟踪球员）—— 用于分割与跟踪。它在球员被遮挡后重新识别，并在身体接触中保持目标 ID 稳定。 SigLIP + UMAP + K-means （无监督球队聚类）— 结合视觉-语言嵌入与无监督聚类，通过统一的颜色和纹理将球员自动分组，无需人工标注 SmolVLM2 （识别球员号码）— 这个比较猛，今年2月份发布的，有256M, 500M, 2.2B 三个版本。一般用在OCR场景，是个VLM，经过 NBA 球衣裁剪图像微调后，识别队服和编号准确率从 56%提升至 86% ResNet-32 — （号码分类）一种经典 CNN，经过微调用于球衣号码分类，测试准确率达到 93%，优于微调后的 SmolVLM2 原文写的非常棒，作为学习资料足够了，推荐给大家：
A coder, road bike rider, server fortune teller, electronic waste collector, co-founder of KCORES, ex-director at IllaSoft, KingsoftOffice, Juejin.
now I really want a good ai first email client with strong CRM integration (that’s not a sales tool)
VC by day @untappedvc, builder by night: @babyagi_, @pippinlovesyou @pixelbeastsnft. Build-in-public log: https://t.co/UdHHGbZba5
6 dads vs. 1 hotel provided stroller
Marketer, self-taught developer, and founder of @Bento and https://t.co/lcsIohchEv. Designing a quiet family life in 福岡, Japan. DMs open if you need email help 🌿
RT @jonsommet: @shl You have to choose safe or affordable.
Founder/CEO @Gumroad