开启时会模糊预览图，关闭后正常显示
有些平台对于上传的视频大小有一定限制，制作完的视频还得额外进行压缩，手动调整相关参数控制大小颇为麻烦。 最近发现了 8mb .local 这款开源工具，正好解决了这个痛点，能精准将视频压缩到我们指定的目标大小。 只需拖放视频并选择目标大小（如 8MB、25MB），它会自动用显卡加速处理，实时显示进度，如果文件超标还会自动重新压缩。 GitHub：https://t.co/VQeFreCWjS 主要特性： - 自动识别显卡进行硬件加速，处理速度快； - 拖放式操作界面，简单易用； - 实时显示压缩进度和处理日志； - 支持多任务并发，可同时处理多个视频； - 智能优化文件大小，超标自动调整重压； - 本地部署运行，保护视频内容隐私。 通过 Docker 一键部署即可使用，适合需要频繁处理视频压缩以及注重数据安全的创作者。
💡 挖掘开源的价值 🧑🏻💻 坚持分享 GitHub 上高质量、有趣、实用的教程、AI工具、前沿 AI 技术 🧐 A list cool, interesting projects of GitHub. ✏️ 公众号：GitHubDaily
Gumroad revenue is now verified on TrustMRR! 🤩 (I can't believe I just wrote this.) Thank you @shl for paving the way in the small bets world. And for allowing me to compute the 50M transactions. 😮💨 Here are 5 interesting facts: 1. Sahil built Gumroad in a single weekend. 2. He was only 19 years old and had just left Pinterest. 3. He ran Gumroad alone for nearly two years. 4. In 2021, Gumroad became one of the first startups to crowdfund equity, allowing its users to invest, raising $5M in a day. 5. The platform has now paid out around $1B to creators. Which startup do you want to see next on TrustMRR?
Ha, I almost forgot. Here's Gumroad revenue over the last 15 years: https://t.co/QqBMCIIddL
I took a look at the social media profiles of the founders of the top 15 startups with more revenue on this chart and 80% of them have less than 10k followers. I don’t want to draw any conclusion from this but it’s interesting because it goes against the common narrative that you need a huge following to be successful.
Marketer
I took a look at the social media profiles of the founders of the top 15 startups with more revenue on this chart and 80% of them have less than 10k followers. I don’t want to draw any conclusion from this but it’s interesting because it goes against the common narrative that you need a huge following to be successful.
Marketer
can a big lab plss train a massive model exclusively on synthetic data, including only and only correct raw type theory statements and proofs no English NO LEAN no metavars no tactics just core, complete terms in raw CoIC or similar then publish it pls thank you 🫶😍
Kind / Bend / HVM / INets / λCalculus
can a big lab plss train a massive model exclusively on synthetic data, including only and only correct raw type theory statements and proofs no English NO LEAN no metavars no tactics just core, complete terms in raw CoIC or similar then publish it pls thank you 🫶😍
Kind / Bend / HVM / INets / λCalculus