Chatting with LLM is a great way to learn new languages. You can practice, get feedback on mistakes, and pick up the flow of the language. Even if the model sometimes makes things up or misses details, the overall structure is still there, so you learn how the language works. It can also understand what you mean, even if your skills are bad. So, the remarkable strength of LLMs is their ability to decode and reshape language structures. It’s impressive how advanced this has become.
But if agents can understand and fix poorly written texts, why do we need to write perfectly at all, even in our native languages? Why put in so much mental effort to polish our words? This focus on form can actually get in the way - our self-imposed linguistic effort is really an unnecessary barrier. We spend energy on formulation instead of voicing raw thoughts, prioritizing immediate precision over expression.
This isn’t about intellectual laziness - it’s about building better tools for the modern world. The goal is a channel with throughput far beyond natural language, one that lets you type at the speed of thought, and, by its structure, has minimal latency, so that thoughts are expressed instantly, even during complex reasoning. Our brains don’t work in neat, straight lines - we jump between ideas, revisit points, and mix fragments together. Forcing these thoughts into linear speech makes us pause and buffer, waiting for ideas to come together before we speak. If we could express ideas in real time, memory limits wouldn’t hold us back and we could focus on the concepts, not the language. In the past, one could achieve similar results delegating interpretation to their future self, but now it can be seamlessly automated.
I suggest you try the following experiment. By analogy with “vibe coding”, I would call it vibe phrasing.
Next time you use an LLM, don’t bother about phrasing. Just type whatever comes to mind, as fast as you can. Make mistakes, skip proper grammar, jump between ideas, mix languages, and even include random associations or wrong assumptions. Drain that mental buffer as soon as possible. Never go back to edit - just keep adding new thoughts. Think of it as a journal, not a finished snapshot. Don’t worry - no one will judge you, because no one else will see your prompt.
The text will look messy, but both reasoning and creative models will still understand it similarly to a well-phrased one. Think about how much time that saves. You can stop here or take this idea even further.
Abstract out paraphrasing and make it a linguistic middleware. At this point, your interface becomes a language, naturally shaped by your personal context and optimized for maximum performance. Come up with the rules and shape its built-in memory.
Practically, this can be achieved via system prompts and fine-tuning. Share your story, your subject, and any other details it needs. Provide examples of your input and output, explain the principles. When something self-contained feels too long to say - invent new words or abbreviations. To express more, create new linguistic structures such as markers for tone and emotions. Always try to simplify, just write, don’t care too much - at the point LLM stops to understand, if your writing is not really that bad, extend the global context.
Use this model as a flexible encoder for any target - for instance, it:
- Nicely fits into a prompting pipeline
- Gets you around unnecessary stylistic rules designed to enforce conformity or elitism
- Acts as a notebook that lets you think bigger, providing unlimited and effortless memory with power to generalize
- Enables communication in any language (instant translation tool)
- Delivers your message to people with different backgrounds - does context alignment by filling in gaps and explaining concepts you consider obvious
By the way, the kind of text corpus it gets as an input, with raw reasoning processes exposed, could be incredibly valuable for training better reasoning models.
But why Middleware? Because it also works the other way around - now you have a personalized decoder and summarization tool. Give it a document that’s either dense and complex or overly wordy, do some prompting, and the LLM will generate an output which uses your own words, style, and rules, aimed at fast and easy reading.
Ultimately, this idea encourages personal languages for more efficient communication, reasoning, and data analysis, pushing traditional languages into the role of a stable and unified transport layer.