Anurag Singh details seven small Python scripts designed to automate common tasks and improve productivity on both Mac and Windows machines:
**Script Name** | **Purpose** | **Key Features/Notes** |
|---|---|---|
| **Inbox Cleaner** | Cleans email inbox | Archives or deletes emails older than a specified number of days. Works with IMAP, compatible with most email providers. |
| **S3 Backup** | Backs up files to cloud storage | Uploads files to an S3-compatible storage provider. Minimalist, suitable for daily/weekly backups. |
| **Screenshot Sorter** | Organizes screenshots | Finds screenshots, sorts them by date into a tidy folder structure. Includes a dry-run mode. |
| **Bulk Rename** | Renames multiple files | Renames files using patterns or sequentially. Uses regular expressions for advanced renaming. |
| **Arrange It** | Organizes files into folders | Moves files into categorized folders based on their extension (Images, Docs, Spreadsheets, etc.). |
| **Clipboard Save** | Saves clipboard history | Saves every new clipboard item to a daily text file. Creates a personal history of copied text. |
**Script Name** | **Purpose** | **Key Features/Notes** |
|---|---|---|
| **Inbox Cleaner** | Cleans email inbox | Archives or deletes emails older than a specified number of days. Works with IMAP, compatible with most email providers. |
| **S3 Backup** | Backs up files to cloud storage | Uploads files to an S3-compatible storage provider. Minimalist, suitable for daily/weekly backups. |
| **Screenshot Sorter** | Organizes screenshots | Finds screenshots, sorts them by date into a tidy folder structure. Includes a dry-run mode. |
| **Bulk Rename** | Renames multiple files | Renames files using patterns or sequentially. Uses regular expressions for advanced renaming. |
| **Arrange It** | Organizes files into folders | Moves files into categorized folders based on their extension (Images, Docs, Spreadsheets, etc.). |
| **Clipboard Save** | Saves clipboard history | Saves every new clipboard item to a daily text file. Creates a personal history of copied text. |
This blog post details how to build a natural language Bash agent using NVIDIA Nemotron Nano v2, requiring roughly 200 lines of Python code. It covers the core components, safety considerations, and offers both a from-scratch implementation and a simplified approach using LangGraph.
An article detailing the author's experience replacing bash scripts with Python, outlining the improvements in readability, error handling, and testability, as well as the challenges encountered during the migration.
This GitHub repository contains a collection of example files demonstrating various use cases and configurations for the llamafiles tools, including examples:
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
* **System Administration:** Scripts and configurations for Ubuntu, Raspberry Pi 5, and macOS.
* **LLM Interaction:** Examples of prompts and interactions with LLMs like Mixtral and Dolphin.
* **Text Processing:** Scripts for summarizing text, extracting information, and formatting output.
* **Development Tools:** Examples related to Git, Emacs, and other development tools.
* **Hardware Monitoring:** Scripts for monitoring GPU and NVMe drive status.
This project provides an LLM Websearch Agent using a local SearXNG server for search functionality and includes Python scripts and a bash script for interacting with an LLM to summarize search results.
Simon Willison recently delivered a talk during the Mastering LLMs: A Conference For Developers & Data Scientists, which was a six-week long online event. The talk centered around Simon's LLM Python command-line utility and its plugins, emphasizing how they can be utilized to explore Large Language Models (LLMs) and perform various tasks. Last week, he discussed accessing LLMs from the command-line, sharing valuable insights and techniques with the audience.
