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aipyapp

Sun, 21 Sep 2025 15:25:16 +0800

knownsec/aipyapp

Python use

AIPy is an implementation of the Python-use concept, demonstrating its practical value and potential.

Mission: unleash the full potential of large language models.
Vision: a future where LLMs can think independently and proactively leverage AIPy to solve complex problems.

What

Python use provides the entire Python execution environment to LLM. Imagine LLM sitting in front of a computer, typing various commands into the Python command-line interpreter, pressing Enter to execute, observing the results, and then typing and executing more code.

Unlike Agents, Python use does not define any tools interface. LLM can freely use all the features provided by the Python runtime environment.

Why

If you are a data engineer, you are likely familiar with the following scenarios:

Handling various data file formats: csv/excel, json, html, sqlite, parquet, etc.
Performing operations like data cleaning, transformation, computation, aggregation, sorting, grouping, filtering, analysis, and visualization.

This process often requires:

Starting Python, importing pandas as pd, and typing a bunch of commands to process data.
Generating a bunch of intermediate temporary files.
Describing your needs to ChatGPT/Claude, copying the generated data processing code, and running it manually.

So, why not start the Python command-line interpreter, directly describe your data processing needs, and let it be done automatically? The benefits are:

No need to manually input a bunch of Python commands temporarily.
No need to describe your needs to GPT, copy the program, and run it manually.

This is the problem Python use aims to solve!

How

Python use (aipython) is a Python command-line interpreter integrated with LLM. You can:

Enter and execute Python commands as usual.
Describe your needs in natural language, and aipython will automatically generate Python commands and execute them.

Moreover, the two modes can access data interchangeably. For example, after aipython processes your natural language commands, you can use standard Python commands to view various data.

Usage

AIPython has two running modes:

Task mode: Very simple and easy to use, just input your task, suitable for users unfamiliar with Python.
Python mode: Suitable for users familiar with Python, allowing both task input and Python commands, ideal for advanced users.

The default running mode is task mode, which can be switched to Python mode using the --python parameter.

Basic Config

~/.aipyapp/aipyapp.toml:

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[llm.deepseek]
type = "deepseek"
api_key = "Your DeepSeek API Key"

Task Mode

uv run aipy

>>> Get the latest posts from Reddit r/LocalLLaMA
......
......
>>> /done

pip install aipyapp and run with aipy

-> % aipy
🚀 Python use - AIPython (0.1.22) [https://aipy.app]
>> Get the latest posts from Reddit r/LocalLLaMA
......
>>

Python Mode

Basic Usage

Automatic task processing:

`1`	`>>> ai("Get the title of Google's homepage")`

Automatically Request to Install Third-Party Libraries

Python use - AIPython (Quit with 'exit()')
>>> ai("Use psutil to list all processes on MacOS")

📦 LLM requests to install third-party packages: ['psutil']
If you agree and have installed, please enter 'y [y/n] (n): y

Thanks

Hei Ge: Product manager/senior user/chief tester
Sonnet 3.7: Generated the first version of the code, which was almost ready to use without modification.
ChatGPT: Provided many suggestions and code snippets, especially for the command-line interface.
Codeium: Intelligent code completion
Copilot: Code improvement suggestions and README translation

cobra

Sat, 30 Aug 2025 15:25:30 +0800

spf13/cobra

Cobra is a library for creating powerful modern CLI applications.

Visit Cobra.dev for extensive documentation

Cobra is used in many Go projects such as Kubernetes, Hugo, and GitHub CLI to name a few. This list contains a more extensive list of projects using Cobra.

^{Supported by:}

Warp, the AI terminal for devs

Try Cobra in Warp today

Overview

Cobra is a library providing a simple interface to create powerful modern CLI interfaces similar to git & go tools.

Cobra provides:

Easy subcommand-based CLIs: app server, app fetch, etc.
Fully POSIX-compliant flags (including short & long versions)
Nested subcommands
Global, local and cascading flags
Intelligent suggestions (app srver… did you mean app server?)
Automatic help generation for commands and flags
Grouping help for subcommands
Automatic help flag recognition of -h, --help, etc.
Automatically generated shell autocomplete for your application (bash, zsh, fish, powershell)
Automatically generated man pages for your application
Command aliases so you can change things without breaking them
The flexibility to define your own help, usage, etc.
Optional seamless integration with viper for 12-factor apps

Concepts

Cobra is built on a structure of commands, arguments & flags.

Commands represent actions, Args are things and Flags are modifiers for those actions.

The best applications read like sentences when used, and as a result, users intuitively know how to interact with them.

The pattern to follow is APPNAME VERB NOUN --ADJECTIVE or APPNAME COMMAND ARG --FLAG.

A few good real world examples may better illustrate this point.

In the following example, ‘server’ is a command, and ‘port’ is a flag:

hugo server --port=1313

In this command we are telling Git to clone the url bare.

git clone URL --bare

Commands

Command is the central point of the application. Each interaction that the application supports will be contained in a Command. A command can have children commands and optionally run an action.

In the example above, ‘server’ is the command.

More about cobra.Command

Flags

A flag is a way to modify the behavior of a command. Cobra supports fully POSIX-compliant flags as well as the Go flag package. A Cobra command can define flags that persist through to children commands and flags that are only available to that command.

In the example above, ‘port’ is the flag.

Flag functionality is provided by the pflag library, a fork of the flag standard library which maintains the same interface while adding POSIX compliance.

Installing

Using Cobra is easy. First, use go get to install the latest version of the library.

`1`	`go get -u github.com/spf13/cobra@latest`

Next, include Cobra in your application:

`1`	`import "github.com/spf13/cobra"`

Usage

cobra-cli is a command line program to generate cobra applications and command files. It will bootstrap your application scaffolding to rapidly develop a Cobra-based application. It is the easiest way to incorporate Cobra into your application.

It can be installed by running:

`1`	`go install github.com/spf13/cobra-cli@latest`

For complete details on using the Cobra-CLI generator, please read The Cobra Generator README

For complete details on using the Cobra library, please read The Cobra User Guide.

License

Cobra is released under the Apache 2.0 license. See LICENSE.txt

trippy

Wed, 23 Jul 2025 15:35:45 +0800

fujiapple852/trippy

Trippy combines the functionality of traceroute and ping and is designed to assist with the analysis of networking issues.

Quick Start

See the getting started guide.

Install

Trippy runs on Linux, BSD, macOS, and Windows. It can be installed from most package managers, precompiled binaries, or source.

For example, to install Trippy from cargo:

`1`	`cargo install trippy --locked`

All package managers

Cargo

`1`	`cargo install trippy --locked`

APT (Debian)

`1`	`apt install trippy`

ⓘ Note:

Only available for Debian 13 (trixie) and later.

PPA (Ubuntu)

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add-apt-repository ppa:fujiapple/trippy
apt update && apt install trippy

ⓘ Note:

Only available for Ubuntu 24.04 (Noble) and 22.04 (Jammy).

Snap (Linux)

`1`	`snap install trippy`

Homebrew (macOS)

`1`	`brew install trippy`

WinGet (Windows)

`1`	`winget install trippy`

Scoop (Windows)

`1`	`scoop install trippy`

Chocolatey (Windows)

`1`	`choco install trippy`

NetBSD

`1`	`pkgin install trippy`

FreeBSD

`1`	`pkg install trippy`

OpenBSD

`1`	`pkg_add trippy`

Arch Linux

`1`	`pacman -S trippy`

Gentoo Linux

`1`	`emerge -av net-analyzer/trippy`

Void Linux

`1`	`xbps-install -S trippy`

ALT Sisyphus

`1`	`apt-get install trippy`

Chimera Linux

`1`	`apk add trippy`

Nix

`1`	`nix-env -iA trippy`

Docker

`1`	`docker run -it fujiapple/trippy`

All Repositories

See the installation guide for details of how to install Trippy on your system.

Run

To run a basic trace to example.com with default settings, use the following command:

`1`	`sudo trip example.com`

See the usage examples and CLI reference for details of how to use Trippy. To use Trippy without elevated privileges, see the privileges guide.

Documentation

Full documentation is available at trippy.rs.

documentation links

Getting Started

See the Getting Started guide.

Features

See the Features list.

Distributions

See the Distributions list.

Privileges

See the Privileges guide.

Usage Examples

See the Usage Examples.

Command Reference

See the Command Reference.

Theme Reference

See the Theme Reference.

Column Reference

See the Column Reference.

Configuration Reference

See the Configuration Reference.

Locale Reference

See the Locale Reference.

Versions

See the Version Reference.

Frequently Asked Questions

Why does Trippy show “Awaiting data…”?

See the Awaiting Data guide.

How do I allow incoming ICMP traffic in the Windows Defender firewall?

See the Windows Defender Firewall guide.

What are the recommended settings for Trippy?

See the Recommended Tracing Settings guide.

Acknowledgements

Trippy is made possible by ratatui ( formerly tui-rs), crossterm as well as several foundational Rust libraries.

Trippy draws heavily from mtr and also incorporates ideas from both libparistraceroute & Dublin Traceroute.

The Trippy networking code is inspired by pnet and some elements of that codebase are incorporated in Trippy.

The AS data is retrieved from the IP to ASN Mapping Service provided by Team Cymru.

The trippy.cli.rs CNAME hosting is provided by cli.rs.

The Trippy chat room is sponsored by Zulip.

Trippy logo designed by Harun Ocaksiz Design.

License

This project is distributed under the terms of the Apache License (Version 2.0).

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in time by you, as defined in the Apache-2.0 license, shall be licensed as above, without any additional terms or conditions.

See LICENSE for details.

kilo

Thu, 22 May 2025 15:29:05 +0800

antirez/kilo

Kilo

Kilo is a small text editor in less than 1K lines of code (counted with cloc).

A screencast is available here: https://asciinema.org/a/90r2i9bq8po03nazhqtsifksb

Usage: kilo <filename>

Keys:

CTRL-S: Save
CTRL-Q: Quit
CTRL-F: Find string in file (ESC to exit search, arrows to navigate)

Kilo does not depend on any library (not even curses). It uses fairly standard VT100 (and similar terminals) escape sequences. The project is in alpha stage and was written in just a few hours taking code from my other two projects, load81 and linenoise.

People are encouraged to use it as a starting point to write other editors or command line interfaces that are more advanced than the usual REPL style CLI.

Kilo was written by Salvatore Sanfilippo aka antirez and is released under the BSD 2 clause license.

BabelDOC

Tue, 15 Apr 2025 15:30:04 +0800

funstory-ai/BabelDOC

PDF scientific paper translation and bilingual comparison library.

Online Service: Beta version launched Immersive Translate - BabelDOC 1000 free pages per month.
Self-deployment: PDFMathTranslate 1.9.3+ Experimental support for BabelDOC, available for self-deployment + WebUI with more translation services.
Provides a simple command line interface.
Provides a Python API.
Mainly designed to be embedded into other programs, but can also be used directly for simple translation tasks.

Preview

We are hiring

See details: EN | ZH

Getting Started

Install from PyPI

We recommend using the Tool feature of uv to install yadt.

First, you need to refer to uv installation to install uv and set up the PATH environment variable as prompted.
Use the following command to install yadt:

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uv tool install --python 3.12 BabelDOC

babeldoc --help

Use the babeldoc command. For example:

babeldoc --bing  --files example.pdf

# multiple files
babeldoc --bing  --files example1.pdf --files example2.pdf

Install from Source

We still recommend using uv to manage virtual environments.

First, you need to refer to uv installation to install uv and set up the PATH environment variable as prompted.
Use the following command to install yadt:

# clone the project
git clone https://github.com/funstory-ai/BabelDOC

# enter the project directory
cd BabelDOC

# install dependencies and run babeldoc
uv run babeldoc --help

Use the uv run babeldoc command. For example:

uv run babeldoc --files example.pdf --openai --openai-model "gpt-4o-mini" --openai-base-url "https://api.openai.com/v1" --openai-api-key "your-api-key-here"

# multiple files
uv run babeldoc --files example.pdf --files example2.pdf --openai --openai-model "gpt-4o-mini" --openai-base-url "https://api.openai.com/v1" --openai-api-key "your-api-key-here"

[!TIP] The absolute path is recommended.

Advanced Options

[!NOTE] This CLI is mainly for debugging purposes. Although end users can use this CLI to translate files, we do not provide any technical support for this purpose.

End users should directly use Online Service: Beta version launched Immersive Translate - BabelDOC 1000 free pages per month.

End users who need self-deployment should use PDFMathTranslate

If you find that an option is not listed below, it means that this option is a debugging option for maintainers. Please do not use these options.

Language Options

--lang-in, -li: Source language code (default: en)
--lang-out, -lo: Target language code (default: zh)

[!TIP] Currently, this project mainly focuses on English-to-Chinese translation, and other scenarios have not been tested yet.

(2025.3.1 update): Basic English target language support has been added, primarily to minimize line breaks within words([0-9A-Za-z]+).

HELP WANTED: Collecting word regular expressions for more languages

PDF Processing Options

--files: One or more file paths to input PDF documents.
--pages, -p: Specify pages to translate (e.g., “1,2,1-,-3,3-5”). If not set, translate all pages
--split-short-lines: Force split short lines into different paragraphs (may cause poor typesetting & bugs)
--short-line-split-factor: Split threshold factor (default: 0.8). The actual threshold is the median length of all lines on the current page * this factor
--skip-clean: Skip PDF cleaning step
--dual-translate-first: Put translated pages first in dual PDF mode (default: original pages first)
--disable-rich-text-translate: Disable rich text translation (may help improve compatibility with some PDFs)
--enhance-compatibility: Enable all compatibility enhancement options (equivalent to –skip-clean –dual-translate-first –disable-rich-text-translate)
--use-alternating-pages-dual: Use alternating pages mode for dual PDF. When enabled, original and translated pages are arranged in alternate order. When disabled (default), original and translated pages are shown side by side on the same page.
--watermark-output-mode: Control watermark output mode: ‘watermarked’ (default) adds watermark to translated PDF, ’no_watermark’ doesn’t add watermark, ‘both’ outputs both versions.
--max-pages-per-part: Maximum number of pages per part for split translation. If not set, no splitting will be performed.
--no-watermark: [DEPRECATED] Use –watermark-output-mode=no_watermark instead.
--translate-table-text: Translate table text (experimental, default: False)
--skip-scanned-detection: Skip scanned document detection (default: False). When using split translation, only the first part performs detection if not skipped.

[!TIP]

Both --skip-clean and --dual-translate-first may help improve compatibility with some PDF readers

--disable-rich-text-translate can also help with compatibility by simplifying translation input

However, using --skip-clean will result in larger file sizes

If you encounter any compatibility issues, try using --enhance-compatibility first

Use --max-pages-per-part for large documents to split them into smaller parts for translation and automatically merge them back.

Use --skip-scanned-detection to speed up processing when you know your document is not a scanned PDF.

Translation Service Options

--qps: QPS (Queries Per Second) limit for translation service (default: 4)
--ignore-cache: Ignore translation cache and force retranslation
--no-dual: Do not output bilingual PDF files
--no-mono: Do not output monolingual PDF files
--min-text-length: Minimum text length to translate (default: 5)
--openai: Use OpenAI for translation (default: False)

[!TIP]

Currently, only OpenAI-compatible LLM is supported. For more translator support, please use PDFMathTranslate.

It is recommended to use models with strong compatibility with OpenAI, such as: glm-4-flash, deepseek-chat, etc.

Currently, it has not been optimized for traditional translation engines like Bing/Google, it is recommended to use LLMs.

You can use litellm to access multiple models.

OpenAI Specific Options

--openai-model: OpenAI model to use (default: gpt-4o-mini)
--openai-base-url: Base URL for OpenAI API
--openai-api-key: API key for OpenAI service

[!TIP]

This tool supports any OpenAI-compatible API endpoints. Just set the correct base URL and API key. (e.g. https://xxx.custom.xxx/v1)

For local models like Ollama, you can use any value as the API key (e.g. --openai-api-key a).

Output Control

--output, -o: Output directory for translated files. If not set, use current working directory.
--debug, -d: Enable debug logging level and export detailed intermediate results in ~/.cache/yadt/working.
--report-interval: Progress report interval in seconds (default: 0.1).

Offline Assets Management

--generate-offline-assets: Generate an offline assets package in the specified directory. This creates a zip file containing all required models and fonts.
--restore-offline-assets: Restore an offline assets package from the specified file. This extracts models and fonts from a previously generated package.

[!TIP]

Offline assets packages are useful for environments without internet access or to speed up installation on multiple machines.

Generate a package once with babeldoc --generate-offline-assets /path/to/output/dir and then distribute it.

Restore the package on target machines with babeldoc --restore-offline-assets /path/to/offline_assets_*.zip.

The offline assets package name cannot be modified because the file list hash is encoded in the name.

If you provide a directory path to --restore-offline-assets, the tool will automatically look for the correct offline assets package file in that directory.

The package contains all necessary fonts and models required for document processing, ensuring consistent results across different environments.

The integrity of all assets is verified using SHA3-256 hashes during both packaging and restoration.

If you’re deploying in an air-gapped environment, make sure to generate the package on a machine with internet access first.

Configuration File

--config, -c: Configuration file path. Use the TOML format.

Example Configuration:

[babeldoc]
# Basic settings
debug = true
lang-in = "en-US"
lang-out = "zh-CN"
qps = 10
output = "/path/to/output/dir"

# PDF processing options
split-short-lines = false
short-line-split-factor = 0.8
skip-clean = false
dual-translate-first = false
disable-rich-text-translate = false
use-alternating-pages-dual = false
watermark-output-mode = "watermarked"  # Choices: "watermarked", "no_watermark", "both"
max-pages-per-part = 50  # Automatically split the document for translation and merge it back.
# no-watermark = false  # DEPRECATED: Use watermark-output-mode instead
skip-scanned-detection = false  # Skip scanned document detection for faster processing

# Translation service
openai = true
openai-model = "gpt-4o-mini"
openai-base-url = "https://api.openai.com/v1"
openai-api-key = "your-api-key-here"

# Output control
no-dual = false
no-mono = false
min-text-length = 5
report-interval = 0.5

# Offline assets management
# Uncomment one of these options as needed:
# generate-offline-assets = "/path/to/output/dir"
# restore-offline-assets = "/path/to/offline_assets_package.zip"

Python API

[!TIP]

Before pdf2zh 2.0 is released, you can temporarily use BabelDOC’s Python API. However, after pdf2zh 2.0 is released, please directly use pdf2zh’s Python API.

This project’s Python API does not guarantee any compatibility. However, the Python API from pdf2zh will guarantee a certain level of compatibility.

You can refer to the example in main.py to use BabelDOC’s Python API.

Please note:

Make sure call babeldoc.high_level.init() before using the API
The current TranslationConfig does not fully validate input parameters, so you need to ensure the validity of input parameters

For offline assets management, you can use the following functions:

# Generate an offline assets package
from pathlib import Path
import babeldoc.assets.assets

# Generate package to a specific directory
# path is optional, default is ~/.cache/babeldoc/assets/offline_assets_{hash}.zip
babeldoc.assets.assets.generate_offline_assets_package(Path("/path/to/output/dir"))

# Restore from a package file
# path is optional, default is ~/.cache/babeldoc/assets/offline_assets_{hash}.zip
babeldoc.assets.assets.restore_offline_assets_package(Path("/path/to/offline_assets_package.zip"))

# You can also restore from a directory containing the offline assets package
# The tool will automatically find the correct package file based on the hash
babeldoc.assets.assets.restore_offline_assets_package(Path("/path/to/directory"))

[!TIP]

The offline assets package name cannot be modified because the file list hash is encoded in the name.

When using in production environments, it’s recommended to pre-generate the assets package and include it with your application distribution.

The package verification ensures that all required assets are intact and match their expected checksums.

Background

There are a lot projects and teams working on to make document editing and translating easier like:

There are also some solutions to solve specific parts of the problem like:

layoutreader: the read order of the text block in a pdf
Surya: the structure of the pdf

This project hopes to promote a standard pipeline and interface to solve the problem.

In fact, there are two main stages of a PDF parser or translator:

Parsing: A stage of parsing means to get the structure of the pdf such as text blocks, images, tables, etc.
Rendering: A stage of rendering means to render the structure into a new pdf or other format.

For a service like mathpix, it will parse the pdf into a structure may be in a XML format, and then render them using a single column reader order as layoutreader does. The bad news is that the original structure lost.

Some people will use Adobe PDF Parser because it will generate a Word document and it keeps the original structure. But it is somewhat expensive. And you know, a pdf or word document is not a good format for reading in mobile devices.

We offer an intermediate representation of the results from parser and can be rendered into a new pdf or other format. The pipeline is also a plugin-based system which everybody can add their new model, ocr, renderer, etc.

Roadmap

Add line support
Add table support
Add cross-page/cross-column paragraph support
More advanced typesetting features
Outline support
…

Our first 1.0 version goal is to finish a translation from PDF Reference, Version 1.7 to the following language version:

Simplified Chinese
Traditional Chinese
Japanese
Spanish

And meet the following requirements:

layout error less than 1%
content loss less than 1%

Known Issues

Parsing errors in the author and reference sections; they get merged into one paragraph after translation.
Lines are not supported.
Does not support drop caps.
Large pages will be skipped.

How to Contribute

We encourage you to contribute to YADT! Please check out the CONTRIBUTING guide.

Everyone interacting in YADT and its sub-projects’ codebases, issue trackers, chat rooms, and mailing lists is expected to follow the YADT Code of Conduct.

Immersive Translation sponsors monthly Pro membership redemption codes for active contributors to this project, see details at: CONTRIBUTOR_REWARD.md