A DeepSeek-native terminal coding agent in Rust with OS-level sandboxing, persistent goal mode, background tasks, and verification gates.
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CLI to Chat with your AWS Cloud from Terminal using generative AI.
AI coding agent that works in your terminal, IDE, desktop app, or browser.
✨ Fully autonomous AI Agent that can perform complicated tasks and projects using terminal, browser, and editor.
An open-source terminal agent with sandbox and approval controls.
Apply AI to everyday challenges in the comfort of your terminal. Help’s to get better results with tried and tested library of prompt pattern’s.
An open-source AI agent that brings the power of Gemini directly into your terminal. opensource.
Let AI agents message, watch, and spawn each other across terminals. Claude Code, Gemini CLI, Codex, OpenCode.
The AI coding agent built for the terminal.
Open source, terminal-based AI programming engine for complex tasks.
Lightweight Bash scripts that enhance your terminal coding workflow with web-based AI assistants like Claude or Grok.
Coding agents help developers plan, implement, review, test, and debug software. For independent capability comparisons, see SWE-bench and.
PyTorch Implementation of No Token Left Behind: Explainability-Aided Image Classification and Generation.
Tokenizers for Natural Language Processing in Julia.
Claws execute, Pilots oversee. Deploy AI agents, run missions, and earn $OPENWORK tokens on Base.
Smart code context extractor for AI assistants with accurate token counting and budget management.
An open dataset with 30 trillion tokens for training Large Language Models.
A C++ library for unsupervised text tokenization and detokenization, widely used in modern NLP models.
Golang implementation of Punkt sentence tokenizer.
Pure-Rust tokenizer for GGUF models, compatible with llama.cpp tokenization.
StreamingLLM is a technique that can enable language models like Llama-2 to have conversations that span across millions of tokens.
Versatile Multi-concept Personalization in Token Modulation Space.
Minimize LLM token complexity to save API costs and model computations.