A GPU-accelerated cross-platform terminal emulator and multiplexer written by @wez and implemented in Rust

A tool-use-focused LLM plugin for neovim.

Inference Llama 2 in one file of pure C

A tiling window manager for macOS based on binary space partitioning

Bootstrap Kubernetes the hard way. No scripts.

Observability for your Supabase project, using Prometheus/Grafana

PgQueuer is a Python library leveraging PostgreSQL for efficient job queuing.

TypeScript-centric app development platform: notebook and AI app builder

A launch point for your personal nvim configuration

An implementation of Shazam's song recognition algorithm.

List of libraries, tools and APIs for web scraping and data processing.

Simple LRU cache for asyncio

Get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 2, and other large language models.

Postgres CLI with autocompletion and syntax highlighting

Deep inspection of Python objects

kaskade is a text user interface for kafka, which allows you to interact and consume topics from your terminal in style!

A curated list of code and resources for computer-controlled drawing machines and other visual art robots.

FastAPI pagination 📖

BASIC environment with a REPL, a web interface, a graphical console, and RPi support written in Rust

Personal Wiki for Vim

🤖 CDN assets - The #1 free and open source CDN built to make life easier for developers.

A cross-platform command-line utility that creates projects from cookiecutters (project templates), e.g. Python package projects, C projects.

📚 Freely available programming books

AeroSpace is an i3-like tiling window manager for macOS

Python framework for building efficient data pipelines. It promotes modularity and collaboration, enabling the creation of complex pipelines from simple, reusable components.

Learn how to design systems at scale and prepare for system design interviews

🌳 Web Development in Pure Python with Type-Guided Components.

search for files (even inside tar/zip/7z/rar) using a SQL-WHERE filter

Dispatch and distribute your ML training to "serverless" clusters in Python, like PyTorch for ML infra. Iterable, debuggable, multi-cloud/on-prem, identical across research and production.