tlnr 0.1.68

TLNR (Too Long; Not Reading) - Real-time terminal command education for Zsh

tlnr

too long; no need to read — Real-time terminal command education

tlnr (pronounced "tee-el-en-are") gives you instant command explanations as you type — no context switching, no waiting, no interruptions.

Why tlnr?

The name says it all: "too long; no need to read" → tlnntr → tlnr (the 'nt' is silent, like in "knight")

• tldr: "too long; didn't read" — you already stopped to look it up
• tlnr: "too long; no need to read" — you never stopped typing

What Makes tlnr Different?

Feature	tlnr	tldr	cheat.sh	man pages	explainshell
Real-time	✅ As you type	❌ Lookup	❌ Lookup	❌ Lookup	❌ Lookup
Speed	0.24ms avg	N/A	>100ms	>50ms	>500ms
Context switching	✅ Zero	❌ Yes	❌ Yes	❌ Yes	❌ Browser
Installation	pip install tlnr	pip install tldr	curl	Built-in	Web only
Coverage	32,000+ commands	1,000+	1,000+	System-dependent	Limited

The difference: Every other tool makes you stop typing. tlnr doesn't.

Installation

🐳 Try It Now (Docker - No Install Required)

Fastest way to try tlnr — works on any platform:

docker run -it --rm jatinmayekar/tlnr

You'll drop into a Zsh shell with tlnr pre-installed. Just start typing!

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Quick Start (Recommended)

# Install with pip
pip install tlnr

# Install shell integration (Zsh required for real-time)
tlnr install

# Restart shell
exec zsh

Alternative Methods

# Using pipx (isolated environment)
pipx install tlnr

# From source
git clone https://github.com/yourusername/tlnr.git
cd tlnr
pip install -e .

Usage

Once installed, just start typing. tlnr shows explanations automatically:

$ git sta█
🟢 SAFE git status - Show the working tree status

Manual Commands

# Get command explanation
tlnr predict "git status"

# Natural language search
tlnr ask "how to list files"

# Debug with performance metrics
tlnr debug "docker"

# Control real-time predictions
tlnr enable    # Turn on
tlnr disable   # Turn off
tlnr status    # Check current state

Features

⚡ Real-Time Predictions (Zsh)

• Keystroke-by-keystroke command education
• 0.24ms average response time (20,931 ops/sec)
• Zero latency — never notice it's running

🧠 Intelligent Fuzzy Matching

• State-of-the-art algorithm with exponential position decay
• Context-aware recommendations
• FZF-inspired smart scoring

🛡️ Safety First

• 🟢 SAFE / 🟡 CAUTION / 🔴 DANGEROUS risk levels
• Warns before destructive commands
• Explains what commands actually do

🗣️ Natural Language Search

$ tlnr ask "how to find large files"
find - 🟢 SAFE - Search for files in a directory hierarchy

📊 32,000+ Commands

• Git, Docker, Kubernetes, AWS, system utilities
• Updated from tldr-pages community database
• Add custom commands via ~/.tlnr_custom_commands.json

Performance

Unit Tests:        40/40 PASSED ✅
Edge Case Tests:   ALL PASSED ✅
Average Response:  0.24ms
Throughput:        20,931 ops/sec

Architecture

tlnr achieves sub-millisecond performance through:

1. Daemon Mode: Eliminates Python startup overhead (~50ms → ~0.2ms)
2. Unix Socket IPC: Fast local communication
3. Trie-Based Search: O(k) lookups where k = command length
4. Zero External Deps: No API calls, no network latency

Platform Support

Platform	Real-Time	Manual Commands
macOS (Zsh)	✅ Full support	✅
Linux (Zsh)	✅ Full support	✅
Other shells (Bash/Fish)	⚠️ Manual only	✅

Note: Windows/WSL2 support is planned but not yet tested.

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

Good First Issues:

• Add new command descriptions
• Improve fuzzy matching algorithm
• Port to other shells (Fish, Bash)
• Performance optimizations

License

AGPL-3.0 — Free for personal and open-source use.

For commercial/enterprise use, the AGPL requires you to open-source any modifications. If you need a different license, please contact me.

Why AGPL?

I'm on an H-1B visa and can't start a company or raise VC funding. Open source is my only path to building reputation and career leverage. The AGPL ensures:

1. Individuals can use tlnr freely
2. Open-source projects can integrate without restriction
3. Companies who want to use it internally must either:
   • Open-source their modifications (strengthens the project)
   • Contact me for dual licensing (creates career opportunities)

This isn't about making money — it's about building undeniable proof of product + technical skill to unlock opportunities at OpenAI, Anthropic, Google, or Meta.

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Built by Jatin Mayekar | GitHub | Email

⭐ Star on GitHub if tlnr saves you time!