#软件评测

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🙋硬盘告急/意外断电，哪款「录屏软件」还能保住你的内容？ 为了验证录屏软件在存储空间紧张时的真实表现，我在仅剩 10GB 硬盘空间的 Mac 上测试了多款录屏软件，结果还是挺意外的：6 分钟内几乎全部被系统强制停止录制。 所以在面对突发中断时，软件的以下表现显得非常重要： ✅ 能否及时提醒用户 ✅ 能否保证录制内容正常播放 ✅ 意外退出时内容能否找回 我测试了十多款软件，完整结果已整理到 飞书文档（将来持续更新）： 👉 --- 这次测试表现 👇 1️⃣📊 系统中断录制流后（App 未退出）的表现分类 🟢 优秀表现（及时提示 + 可恢复播放） - 系统自带录屏 - Screen Studio - ScreenSage Pro - CleanShot X - FocuSee - Bandicam 🟡 中等表现（不提示 + 假装继续录制 + 可恢复播放） - OBS - Rec - Kite 🔴 糟糕表现（崩溃/不提示/无法恢复播放） 避嫌不点名字 --- 2️⃣🆘 意外退出数据恢复能力 在极端情况下（断电、程序被杀）App 退出，能够找回录制内容的软件： - ScreenSage Pro - OBS - Rec - Kite --- 💬 你还想让我测试哪款录屏软件？ 留言告诉我，我会更新到
#录屏软件 #硬盘告急 #意外断电 #数据恢复 #软件评测
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🍵录屏软件哪家强，ScreenSage so strong？
#录屏软件 #ScreenSage #软件评测 #工具推荐 #中性
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Susan/STEM MOM
8个月前
Dave's Garage对于Deepseek 的评价，10个小时破百万。他还是不错的，算是我的硬核博主单。 Title: Deepseek R1 Explained by a Retired Microsoft Engineer Author: Dave's Garage Upload Date: 2025-01-28T02:47:25Z URL: Let me help break this detailed technical presentation into time blocks, providing context for each section to help build a comprehensive understanding: 0:00-2:30 Introduction and Context Setting Dave Plumber, a retired Microsoft engineer from the MS-DOS and Windows 95 era, introduces Deep Seek R1 as a "Sputnik moment" in AI development. He frames this Chinese open-source AI model as a significant technological milestone that's challenging Western assumptions about AI dominance. 2:30-5:00 Economic Impact and Market Significance A critical discussion of how Deep Seek R1's reported $6 million development cost has rattled the tech industry, particularly affecting Nvidia and Microsoft stock prices. The presenter draws an apt analogy: it's like building a Ferrari in your garage using Chevy parts, which challenges the entire premium AI development ecosystem. 5:00-8:30 Technical Architecture Explanation Details Deep Seek R1's fundamental architecture as a distilled language model. Dave explains how it leverages larger AI models like GPT-4 or Meta's Llama as scaffolding, using an insightful apprenticeship analogy to explain model distillation - where a smaller model learns from larger ones without needing to replicate their entire knowledge base. 8:30-12:00 Training Methodology Deep Dive Explores how Deep Seek R1 combines insights from multiple AI architectures, comparing it to assembling a panel of experts to train one exceptional student. This section includes practical demonstrations of the model's capabilities, including its handling of sensitive topics like Tiananmen Square. 12:00-15:30 Hardware Requirements and Accessibility Detailed discussion of running Deep Seek R1 on various hardware configurations, from high-end AMD Threadrippers to consumer-grade MacBooks and even $249 Ora Nano systems. This section emphasizes the model's accessibility compared to traditional AI infrastructure requirements. 15:30-19:00 Limitations and Trade-offs Thoughtful analysis of the model's potential drawbacks, including increased likelihood of hallucinations and limitations in specialized knowledge domains. Dave draws parallels to the early personal computing era, suggesting Deep Seek R1 might represent a similar democratizing force in AI. 19:00-22:30 Global Implications and Competition Examines how Deep Seek R1's release affects the global AI landscape, particularly its impact on American tech companies and their business models. Discusses the potential democratization of AI access worldwide. 22:30-25:00 Critical Analysis and Skepticism Addresses skepticism about Deep Seek's development claims, including the possibility of undisclosed state-level support and strategic implications for global AI competition. 25:00-27:00 Conclusion and Channel Information Wraps up with final thoughts on Deep Seek R1's significance and includes standard YouTube engagement requests and information about Dave's other content, including his book on autism spectrum experiences. This timeline breakdown reveals how Dave skillfully builds from basic concepts to complex implications, helping viewers understand both the technical and strategic significance of Deep Seek R1 in the evolving AI landscape.
#DeepSeek #Dave's Garage #硬核博主 #科技评论 #软件评测