Keynote VS Cloud GPU

• User-friendly Interface
  Keynote has an intuitive, easy-to-navigate interface that makes it accessible for users of all skill levels.
• Seamless Integration with Apple Ecosystem
  Keynote integrates well with other Apple software and devices, allowing for easy sharing and collaboration across iOS and macOS platforms.
• High-Quality Templates and Design Tools
  The application offers a wide variety of professionally designed templates and powerful design tools, enabling users to create visually appealing presentations.
• Real-time Collaboration
  Keynote supports real-time collaboration, enabling multiple users to work on the same presentation simultaneously.
• iCloud Support
  With iCloud support, Keynote presentations are automatically synced across devices, making it easy to access and edit them from anywhere.

Possible disadvantages

• Limited Compatibility with Windows
  Keynote is primarily designed for macOS and iOS, which can create compatibility issues when sharing or collaborating with Windows users.
• Limited Export Options
  While Keynote allows exporting to PowerPoint and PDF formats, some advanced features and animations may not be preserved during the export process.
• Steeper Learning Curve for Non-Mac Users
  Users who are not familiar with Apple's ecosystem may find it challenging to get accustomed to Keynote compared to more universally used software like PowerPoint.
• Less Popular in Business Settings
  Due to its limited cross-platform compatibility, Keynote is less commonly used in corporate environments compared to alternatives like PowerPoint.
• Fewer Third-Party Integrations
  Keynote has fewer integrations with third-party tools and plugins compared to competitors, potentially limiting its functionality for advanced users.

• Scalability
  Cloud GPUs offer scalable resources, allowing users to easily adjust the amount of GPU power they need depending on their workloads without investing in physical hardware.
• Cost-Effectiveness
  Pay-as-you-go pricing models and the absence of upfront costs for hardware make cloud GPUs a cost-effective solution for organizations that require flexibility in processing power.
• Accessibility
  Cloud GPUs provide remote access to powerful computational resources, enabling users to perform graphic-intensive tasks from any location with an internet connection.
• Integration and Ecosystem
  Cloud GPUs integrate seamlessly with other cloud services within the Google Cloud ecosystem, enhancing productivity and operational efficiency.
• Maintenance-Free
  By using cloud GPUs, users are relieved of the responsibility of maintaining and upgrading hardware, which is handled by the cloud provider.

Possible disadvantages

• Latency
  Cloud-based solutions can sometimes suffer from latency issues, especially if the user is geographically distant from the data center.
• Data Security and Privacy
  Using cloud-based GPUs involves transferring data to and from the cloud, which may raise concerns about data security and privacy depending on the sensitivity of the information.
• Dependency on Internet Connection
  The performance and reliability of cloud GPUs are heavily dependent on a stable and fast internet connection.
• Potential Costs for High Usage
  While flexible pricing is a benefit, costs can escalate quickly with extensive GPU usage, potentially becoming more expensive than maintaining on-premises hardware for prolonged workloads.
• Learning Curve
  Adopting cloud GPUs requires technical knowledge and training, which may involve a learning curve for teams unfamiliar with cloud technologies.

Keynote for iPad Tutorial 2019

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• Jack Dorsey says Block employees now bring prototypes, not slides, to meetings

  > "I hate the way people use slide presentations instead of thinking," Jobs once said, according to a book published last month by David Pogue. "People who know what they're talking about don't need PowerPoint. I guess they need Keynote,... - Source: Hacker News / 7 months ago

• Does Google Cloud GPU use physical GPUS or are they emulated

  Per https://cloud.google.com/gpu, they use NVIDIA L4, P100, P4, T4, V100, and A100 GPUs. These are physical units loaded into servers and then shared to the OS by the hypervisor. Source: over 3 years ago

• Fine-tuning?

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• Home Server - Student

  Uh, you ask these questions before you buy the hardware. There are various tools you could have used for free, or for cheap instead of spending $2500 on equipment, and not even seemingly the right equipment. You would know more than me,... Source: almost 4 years ago

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