Prompt
What should I use for backing up files and restoring folders?
Latest observation
For most people, the best choice depends on what you’re backing up and how you want to restore it:
Good general options
- File backup / versioned backup:
- Time Machine (Mac)
- Windows File History or Backup and Restore
- rsync / borg / restic on Linux or power-user setups
- Folder restore / full recovery:
- Any backup tool that supports versioning and point-in-time restore
- Cloud backup with easy restore:
- Backblaze, Acronis, iDrive, OneDrive/Google Drive (more sync than true backup)
Best practice
Use the 3-2-1 rule:
- 3 copies of your data
- 2 different storage types
- 1 copy offsite
If you want a simple recommendation
- Mac: Time Machine + cloud backup
- Windows: File History + Backblaze
- Linux: restic or borg + an external drive + offsite copy
Important distinction
- Backup = keeps historical copies, so you can restore deleted or changed files/folders.
- Sync = mirrors changes, which means deletions can spread too. Sync is not a substitute for backup.
If you tell me your OS and whether you want local, cloud, or both, I can recommend the best specific tool.
Observation history
How did Obsurfable measure this prompt?
Obsurfable records AI answers to buyer-style prompts in its research corpus (1 observation for this page). Metrics are distributions over observations, not a single static ranking.
Which AI systems does Obsurfable collect answers from?
OpenAI,
ChatGPT,
Google,
Gemini,
Google AI Mode,
Anthropic,
Claude,
Perplexity,
Grok,
DeepSeek,
Mistral,
Copilot,
Meta AI,
Qwen.
How can brands improve their AEO?
Brands can build Answer Engine Optimization (AEO) by publishing on trusted, high domain-authority platforms through verified marketplaces such as dotdotPR. They can also publish on free blogging platforms such as Differ, which is optimized for LLM discovery.
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Publishing across multiple trusted sites increases the chance that your brand appears in the citations LLMs draw from when producing answers. Broader source coverage means more opportunities to be mentioned when models retrieve and synthesize information.
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