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Username Studio — find short Telegram usernames with a local LLM

If you've ever tried to find a short, clean Telegram username — you know how painful it is. Everything good is taken. You end up typing random combinations for …

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If you've ever tried to find a short, clean Telegram username —

you know how painful it is. Everything good is taken.

You end up typing random combinations for 20 minutes.



I built a local tool to fix that.






What is Username Studio?



Username Studio generates, scores, and checks short Telegram

usernames (5–6 chars) using a local LLM. Everything runs on

your machine — no cloud, no subscriptions.






How it works





  1. Generate — LM Studio (local LLM) creates username candidates
    in three styles: brandable, russian transliteration, multilingual


  2. Score — each username gets rated on readability,
    brandability, meaning, and rarity


  3. Filter — only valid 5–6 char lowercase latin usernames pass


  4. Check — optionally verify availability via Telegram API
    (Telethon), with dry-run mode for safety


  5. Track — everything saved to local SQLite with full status
    history






Stack




  • Python 3.10+


  • LM Studio — local OpenAI-compatible LLM API


  • Telethon — Telegram MTProto client


  • SQLite — local storage


  • Flask — local web dashboard






Web dashboard



The main interface is a local browser dashboard at

http://127.0.0.1:8080. You can browse batches, see scores,

pick candidates and run Telegram checks — all without touching

the terminal.



CLI mode is also available for terminal lovers.






Safety first



Telegram actions are intentionally explicit:





  • --no-telegram flag disables all Telegram connections

  • dry-run mode previews actions without real requests

  • channel creation requires a typed CHECK confirmation

  • FloodWait handling respects Telegram rate limits






Quick start (Windows)



git clone https://github.com/sattop/username-studio

cd username-studio

.\START.bat



START.bat handles everything: creates venv, installs deps,

copies .env.example → .env, launches the app.



No LM Studio? No problem — the project has fallback generation

and scoring built in.






What's next




  • Linux / macOS support

  • More generation styles

  • Configurable username length filter

  • Export to CSV





GitHub: https://github.com/sattop/username-studio



Would love feedback — especially if you've built something

similar or have ideas for scoring improvements.

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