How to use wut without tmux or screen?

Currently, wut requires tmux or screen to capture terminal output, as stated in the README; the roadmap mentions dropping this requirement, but for now, it's a limitation. You might need to switch to a session manager or wait for updates.

wut vs ChatGPT for explaining terminal errors

wut integrates directly into your terminal workflow by automatically capturing output, whereas ChatGPT requires manual copying and pasting. This makes wut faster for in-context debugging, but it depends on LLM setup and session management.

How to set up wut with local Ollama models?

Install Ollama and run a model locally, then export the OLLAMA_MODEL environment variable with the model name. After that, wut will use the local model for explanations without needing cloud API keys.

wut itself is open-source and free, but using it with cloud LLMs like OpenAI or Claude incurs API costs based on usage. Local models via Ollama are free but require computational resources and setup.

Can wut automatically fix commands?

No, wut only provides explanations and suggestions; the --fix option for automatic execution is listed in the roadmap but not yet implemented. Users must manually run any recommended commands.

What happens if wut can't capture my command output?

If you're not in a tmux or screen session, wut won't work, as it relies on these to capture output. Ensure you're in a compatible session, and check the README for future updates to remove this dependency.

Open-Awesome

StackExplain

MITPythonv0.1.0

A CLI tool that uses LLMs to explain the output of your last terminal command, helping you understand errors and logs.

GitHub

1.4k stars86 forks0 contributors

What is StackExplain?

wut is a command-line interface (CLI) tool that uses large language models (LLMs) to explain the output of your last terminal command. It helps developers understand error messages, stack traces, and logs by providing AI-generated explanations directly in the terminal, reducing confusion and speeding up debugging.

Target Audience

Developers and system administrators who frequently work in the terminal and need quick explanations for command output, errors, or logs without switching contexts.

Value Proposition

wut offers instant, AI-powered explanations for terminal output within your existing workflow, supporting multiple LLM providers and allowing specific queries, making it a versatile tool for troubleshooting and learning.

Overview

A terminal assistant for the hopelessly confused

Use Cases

Best For

Understanding complex error messages and stack traces in the terminal
Deciphering unfamiliar command output or error codes
Getting quick explanations for failed commands or scripts
Summarizing lengthy log files or command outputs
Learning new commands or debugging tools through AI assistance
Troubleshooting installation or configuration issues in CLI environments

Not Ideal For

Developers working in standard terminal sessions without tmux or screen installed
Projects with strict data privacy requirements that cannot send terminal output to external APIs
Teams on a tight budget, as using cloud LLM providers like OpenAI or Claude incurs API costs

Pros & Cons

Pros

Multi-LLM Flexibility

Supports OpenAI, Claude, and local models via Ollama, allowing users to choose providers based on cost, privacy, or performance needs, as shown in the installation instructions.

Contextual Query Support

Enables users to ask specific questions about command output, such as 'how do i add this to my PATH variable?', providing targeted assistance beyond generic explanations.

Error Deciphering Capability

Effectively explains stack traces, error codes, and incorrect commands, as demonstrated in the demo with git command errors, speeding up troubleshooting.

Log Summarization

Condenses lengthy logs into understandable summaries, saving time and effort when dealing with verbose terminal output, as highlighted in the key features.

Cons

Session Dependency

Requires being inside a tmux or screen session to capture command output, which limits usability for developers who prefer native terminal environments, as admitted in the roadmap.

LLM API Reliance

Depends on external LLM services or local setups like Ollama, which can incur costs, require internet access, or involve complex configuration for API keys and models.

Limited Automation Features

Lacks automatic command execution for fixes; the --fix option is only on the roadmap, so users must manually implement suggested solutions.

Frequently Asked Questions

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