Someone shipped a tiny tool this week and it’s quietly brilliant. It’s called Gibberish. It compresses prompts on the fly. And step two, the part where it fires system-wide across every app on your machine, is the bit worth stopping for.
Here’s the problem it solves: most people write prompts like emails. Polite. Padded. “Hi, can you please explain this in a simple way?” LLMs don’t need the manners. They need the intent. Gibberish strips everything else out automatically. No discipline required on your end. No mental overhead. You write however you write, hit a shortcut, and the tool handles the cleanup before the model ever sees it.
This matters more than it sounds. Bloated prompts introduce ambiguity. The model has to infer what’s filler and what’s actual instruction, and sometimes it gets that wrong. Tight prompts produce tighter outputs. Fewer hallucinations, fewer detours, fewer “here’s a fun fact you didn’t ask for” interjections. Every word you cut is a word that can’t confuse the model.
How It Works
- 🖱️ Select any text: browser, editor, terminal, anywhere
- ⌨️ Press Ctrl + ;
- ✨ The text gets replaced with a compressed, structured version
No copy-paste. No tab switching. No manual editing. The example from the repo is clean: “Hi, can you please explain neural networks in a simple way with examples?” becomes “Explain neural networks / Basics with simple examples.” Same intent. Half the words.
That transformation is doing real work. It’s not just removing filler words. It’s restructuring the sentence into a format that aligns with how language models parse instructions: verb first, parameters second, constraints last. This is roughly how most prompt engineering frameworks tell you to write prompts manually. Gibberish just does it for you, in under a second, without breaking your flow. You stay in the file, the editor, the chat window. The shortcut fires, the text updates in place, and you keep going.
The compression also stacks. If you’re building a longer prompt with multiple instructions, the tool works on the full selection. You can write a paragraph of loosely structured instructions, select all of it, and get back a clean, hierarchical version. That alone saves the kind of micro-editing sessions that eat ten minutes and feel like they took two.
The Twist
System-wide means system-wide. This isn’t a browser extension scoped to one chat window. It’s a keyboard-level intervention that works wherever you can select text. That’s a different category of tool.
Think about what that actually covers. You’re writing a prompt in VS Code? Works. You’re in a terminal running a local model? Works. You’re in Notion, Linear, Google Docs, your email client, a random web form? Works. Any surface where you can highlight text and type is now a surface where your prompts get cleaned up before they go anywhere. That’s not a productivity tweak. That’s infrastructure. You install it once and it quietly improves every AI interaction you have, regardless of what tool you’re using or which model is on the other end.
Most prompt tools are scoped to one product. They live inside ChatGPT, or inside a browser extension, or inside a specific editor plugin. When you switch context, you lose the tooling. Gibberish doesn’t care about context. It operates at the OS level, which means it follows you everywhere.
Pro Tips
- Customize what gets stripped. Compression level and removal rules are configurable in the settings
- Run your saved prompt templates through it before reusing them. Tighter input equals more predictable output across every model
- Works with Claude, Gemini, local models, any interface where you type
- Use it on your system prompts too, not just your in-conversation messages. If you’re configuring a custom assistant or writing an agent instruction set, compressing that input can meaningfully reduce token usage across every call that system prompt touches
- Test the before and after. Run the same expanded and compressed prompt through your model of choice and compare outputs side by side. Most people are surprised how often the compressed version wins, both on relevance and on length
The repo is open source. Fork it, break it, make it yours. The compression logic is exposed, which means you can tune it toward your specific use case: more aggressive stripping for quick one-liners, lighter touch for multi-step technical instructions. That configurability is what makes this worth building into a workflow rather than just testing once and forgetting.
📎 github.com/Hundred-Trillion/gibberish
If you’ve been manually trimming prompts by hand, or if you’ve been shipping padded prompts and wondering why your outputs feel slightly off, this is the shortcut you didn’t know you needed. Install it, run a few prompts through it, and pay attention to what changes. The results tend to speak for themselves. Try it and let me know what breaks 🚀
I built a tool that automatically optimizes prompts with a shortcut
by u/adithyasrivatsa in PromptEngineering