A Tool to Easily Change Key Behavior for Linux Users. This project is a tool specifically designed for the Linux operating system, capable of easily altering the behavior of input devices. It supports a variety of input devices including keyboards, mice, game controllers, and joysticks.

GPU-Accelerated Terminal Emulator. This is a cross-platform terminal emulator based on GPU acceleration, designed to provide developers with a smooth command-line operation experience. It significantly improves rendering speed using OpenGL rendering technology, faster than traditional terminals, and supports custom fonts, multi-window, multi-tab, and high-resolution display features.

A Tool for One-Click Translation of Text in Images. This is a Python project designed to recognize and translate text within images, supporting multiple languages such as Japanese, Chinese, English, and Korean. It is suitable for translating the text content in comics and illustrations.

Minimalist PaaS Tool. This is a lightweight Platform as a Service (PaaS) tool that allows users to deploy their applications to servers with just a simple git push command. It consists of only 1500 lines of Python code, runs on devices with low specifications such as Raspberry Pi, and supports multiple programming languages including Python, Node.js, and Java. Additionally, it offers multi-application deployment and free SSL certificate functionalities.

Cool Terminal API Debugger. This is a modern terminal HTTP client, which can be considered as the command-line version of Postman and Insomnia. It provides developers with a convenient TUI experience, optimizes keyboard operation processes, supports auto-completion, syntax highlighting, Vim key binding, and storing requests locally in YAML files.

Lightweight Text Chunking Python Library. This is a lightweight text chunking library designed specifically for RAG applications. It is easy to use and fast, capable of splitting text into fixed sizes, supporting various tokenizers, vector models, and flexible chunking strategies, suitable for long text processing and constructing RAG applications.

from chonkie import TokenChunker
from tokenizers import Tokenizer

tokenizer = Tokenizer.from_pretrained("gpt2")
chunker = TokenChunker(tokenizer)

chunks = chunker("HelloGitHub! Chonkie, the chunking library is so cool! I love the tiny hippo hehe.")

for chunk in chunks:
    print(f"Chunk: {chunk.text}")
    print(f"Tokens: {chunk.token_count}")

Python Library for Manipulating Font Files. This is a Python library for editing and converting font files, supporting the interconversion between TrueType and OpenType fonts and XML format (TTX), compatible with a variety of font formats, suitable for scenarios such as font editing, debugging, and optimization.

from fontTools.afmLib import AFM

f = AFM("Tests/afmLib/data/TestAFM.afm")
print(f["A"])
# (65, 668, (8, -25, 660, 666))

f.FontName = "TestFont HelloGitHub"
f.write("testfont-hellogithub.afm")

A Tool for Easily Capturing HTTP(S) Requests in Python Programs. This project is a tool designed to assist developers in debugging HTTP(S) requests within Python programs. By running the program with the pyhttpdbg command, one can view the outgoing HTTP requests in a browser, supporting various operation modes including script execution, interactive console, and unit testing.

GDB/LLDB Plugin Specifically for Reverse Engineering. This is a plugin designed specifically for GDB and LLDB debuggers, supporting features such as register state display, memory search, memory leak detection, and more. It is suitable for scenarios like low-level software development, hardware debugging, and reverse engineering.

Power System Analysis Python Library. This is a Python library for power system analysis, focusing on modeling and optimization of electric and multi-energy systems. It is based on libraries such as Pandas, NumPy, GLPK, Cbc, and can efficiently calculate optimal power flow (OPF), linear and non-linear electric power flows, and supports simulation of various electric and energy system components.

import pypsa

# create a new network
n = pypsa.Network()
n.add("Bus", "mybus")
n.add("Load", "myload", bus="mybus", p_set=100)
n.add("Generator", "mygen", bus="mybus", p_nom=100, marginal_cost=20)

# load an example network
n = pypsa.examples.ac_dc_meshed()

# run the optimisation
n.optimize()

# plot results
n.generators_t.p.plot()
n.plot()

# get statistics
n.statistics()
n.statistics.energy_balance()